BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Michigan Institute for Computational Discovery and Engineering - ECPv6.17.1//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:Michigan Institute for Computational Discovery and Engineering X-ORIGINAL-URL:https://micde.umich.edu X-WR-CALDESC:Events for Michigan Institute for Computational Discovery and Engineering REFRESH-INTERVAL;VALUE=DURATION:PT1H X-Robots-Tag:noindex X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:America/Detroit BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20220313T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20221106T060000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20230312T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20231105T060000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20240310T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20241103T060000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20250309T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20251102T060000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20260308T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20261101T060000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;TZID=America/Detroit:20251209T114500 DTEND;TZID=America/Detroit:20251209T124500 DTSTAMP:20251208T171351Z CREATED:20250926T143954Z LAST-MODIFIED:20251208T171351Z UID:10000841-1765280700-1765284300@micde.umich.edu SUMMARY:Ph.D. in Scientific Computing Seminar Series DESCRIPTION:The MICDE PhD Student Seminar Series showcases the research of students in the Ph.D. in Scientific Computing. Lunch will be served. These events are open to the public\, but we request that all who plan to attend register in advance. Planned sessions will be canceled if no one signs up to present\, and registered attendees will be notified. \nIf you have any questions\, please email micde-phd@umich.edu. \nRegister to attend \n  \n\nImproving Slater Orbital Integration Accuracy through Prolate Spheroidal Coordinates\nThe core of electronic structure calculations is the integration of forces exerted on and by\nelectrons and nuclei in a system. Some of these interactions have forms which manifest in such a way that makes integration challenging depending on the choice of basis (specifically Slater Type Orbitals (STOs)). This difficulty lies in the fact that not all integrals have a known analytically integrable form when Slater functions are used as a basis. The Prolate Spheroidal coordinate system has only been applied to diatomic systems\, but offers an advantage in numerical integration accuracy over more generally applicable schemes. A third center is added in the PS coordinate grid in this work\, where we will note the challenges and steps taken to handle a third center. It is important to note that the addition of a third center is sufficient to solve all integrals required by the Hamiltonian under the Resolution of the Identity(RI) approximation. Analysis was performed using metrics which test the scheme directly (error values for integral matrix elements) and indirectly(applying integrals to Hartree-Fock(HF) and post-HF methods to get observables). The methods ability to accurately calculate 2-center properties allows for the use of larger basis sets which were previously unserviceable. \nAlexander Stark (Chemistry and Scientific Computing)\nThis is Alexander Stark\, he is in the Zimmerman group in the chemistry department\, his research involves refining different levels of wave-function theory as to improve the accuracy of predictions. \n\nMultiscale Modeling of Radical and Vibrational Pathways in Plasma-Assisted Ammonia Synthesis on Fe(110) and Ni(111)\nLow-temperature plasma (LTP)-assisted ammonia synthesis is a promising alternative to the Haber-Bosch process for decentralized\, renewable energy-driven production. Progress has been limited by an incomplete mechanistic understanding\, particularly the debated roles of vibrationally excited N2(g)\,ν and plasma-generated N · /H · radicals\, which may explain the unexpected insensitivity of catalyst performance across metals. We apply first-principles multiscale modeling—combining density functional theory (DFT) calculations and a packed-bed reactor microkinetic model—to disentangle these contributions to LTP-assisted NH3(g) synthesis over Fe(110) and Ni(111) catalysts. The model incorporates an experimentally derived vibrationally excited N2(g)\,ν distribution from a radiofrequency (RF) plasma source and accounts for their vibrational surface quenching. The model predicts that vibrational excitation enhances the dissociation of N2(g)\,ν on Ni but its impact on Fe is limited. Quenching of vibrationally excited N2(g)\,ν\ndue to collisions with the reactor walls and the catalyst surface does not significantly affect ammonia yields on either catalyst\, with less an an order of magnitude increase. In contrast\, Eley-Rideal reactions involving N · and H · radicals dominate ammonia formation\, bypassing the conventional rate-controlling steps of thermal catalysis on Fe and Ni materials. This mechanistic picture explains the experimentally observed insensitivity of ammonia production rates to metal catalyst identity and highlights the central role of radical chemistry in plasma-assisted ammonia synthesis. \nOluwatosin Ohiro (Chemical Engineering and Scientific Computing)\nOluwatosin earned his primary degree in petroleum and gas engineering and worked for several years as a reservoir engineer and oil asset planner. He is currently pursuing his PhD in the Chemical Engineering Department under the supervision of Prof. Bryan Goldsmith. His research focuses on the interface of computational materials science and heterogeneous catalysis. \n\nQuantifying the state of inflammation in invasive lobular breast cancer using a one-class logistic regression algorithm\nAfter invasive ductal cancer (IDC)\, invasive lobular cancer (ILC) is the second most diagnosed type of breast cancer. Given complexities with detection\, patients with ILC may be diagnosed at an advanced stage of disease\, presenting larger tumors and a higher metastasis incidence when compared to IDC. It is increasingly appreciated that the immune system plays a crucial role in both primary tumor and metastatic progression and is a complex balance of both innate and adaptive immune interactions. Critically\, the success of modern immunotherapies\, such as immune checkpoint blockade\, depends not only on the T cells on which they directly act\, but also the complicated and often contradictory influence of innate myeloid cells on the lymphoid compartment. Innate myeloid cells in the tumor microenvironment (TME) have the potential to be both pro- and anti-cancer and often present in a spectrum within the TME. The dynamic nature of these immune components makes understanding and interpreting the state of the immune system in the TME very difficult. Simple methods\, like quantifying tumor infiltrating lymphocytes (TILs) or tumor-associated macrophages (TAMs) do not account for the function of these cells\, which may be pro- or anti-tumor. We investigated the role of the immune system in the tumor microenvironment (TME) of ILC by developing a machine learning-based inflammation score (IS) that can quantify the complex state of the immune system within a primary tumor on a numerical scale from pro- to anti-inflammatory. We correlate the IS with overall survival and disease-free survival to set prognostic thresholds for immune dysregulation. \nKate Griffin (Biomedical Engineering and Scientific Computing)\nKate is a PhD Candidate in Biomedical Engineering in the Shea Lab. Her research involves engineering nanoparticles to reverse immunosuppression in metastatic breast cancer\, and using computational methods to understand immune dysregulation in the metastatic niche. URL:https://micde.umich.edu/event/workshop-seminarph-d-in-scientific-computing-seminar-series-7/ LOCATION:North Quad – 2185 CATEGORIES:Astronomy,Chemical Engineering,Chemistry,College Of Engineering,Computational Science,computing,Electrical And Computer Engineering,Electrical Engineering and Computer Science,Engineering,Free,Graduate,Graduate and Professional Students,Graduate School,Graduate Students,In Person,Interdisciplinary,Mechanical Engineering,Micde,Michigan Engineering,Networking,Phd Seminar,Political Science,Prospective Graduate Students,Rackham,Research,Science,Scientific Computing,Seminar,Talk ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/09/2025-12-09-Fang-Ohiro-Griffin.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20251118T114500 DTEND;TZID=America/Detroit:20251118T124500 DTSTAMP:20251023T021817Z CREATED:20250926T143953Z LAST-MODIFIED:20251023T021817Z UID:10000840-1763466300-1763469900@micde.umich.edu SUMMARY:Ph.D. in Scientific Computing Seminar Series DESCRIPTION:The MICDE PhD Student Seminar Series showcases the research of students in the Ph.D. in Scientific Computing. Lunch will be served. These events are open to the public\, but we request that all who plan to attend register in advance. Planned sessions will be canceled if no one signs up to present\, and registered attendees will be notified. \nIf you have any questions\, please email micde-phd@umich.edu. \nRegister to attend \n\nTailored Ultrashort Pulse Bursts in a Gain-Managed Nonlinear Fiber Amplifier for Coherent 50fs Pulse Stacking at mJ Energies\nWe show a method of scaling gain-managed nonlinear amplifiers (GMNA) to mJ energies using feedback-driven scaling of pulse bursts that can be time-combined into a single 50fs output pulse using coherent pulse stacking. \nLauren Cooper (Electrical Engineering and Scientific Computing)\nLauren Cooper is working on coherent pulse stacking of gain-managed nonlinear amplified pulse bursts for high power applications. She is being advised by Professor Almantas Galvanauskas in the Electrical Engineering department at the University of Michigan. \n\nLeveraging multipole models to measure rotation in time-dependent potentials\nMultipole expansion models are efficient and flexible methods by which to encode aspherical and time-dependent fluctuations in 3D functions of galactic densities and potentials. Historically these techniques have been used primary to perform orbit integration and N-body simulations. However\, it is becoming increasingly clear that the expansion series coefficients encode useful physical information that may be used to discover novel dynamics. In this talk\, I will outline my recent work using multipole expansion coefficient series\, including methods I have developed for measuring rotation in the quadrupole component and the discoveries multipole expansion has facilitated. \nNeil Ash (Astronomy and Scientific Computing)\nNeil is a 5th year graduate student in the Astronomy Department working with Professor Monica Valluri. His research interests include hydrodynamical simulations of cosmic structure formation and galactic dynamics\, with a special focus on the dark matter haloes and their interactions with the baryonic (stellar) galactic component. \n\nTracing Refractory Material in the Inner 10 AU of Protoplanetary Disks\nPlanets form in protoplanetary disks by building their cores from rocky/refractory material that drifts inward toward the central star\, establishing this material as the fundamental building blocks of all planets. Identifying the physical processes that regulate rocky material within the inner 10 AU during disk evolution is essential for understanding the formation of the observed diversity of planetary systems\, particularly for all rocky planets. In my PhD dissertation\, I study the content of rocky material in the inner regions of protoplanetary disks. I utilize spectroscopic observations across the entire electromagnetic spectrum\, using both ground-based and space telescopes\, to disclose how much rocky material reaches the inner disk and what its composition is. I have found (1) evidence for refractory depletion in the inner gas disk\, 2) connections between age and dust-trapping/planet-forming mechanisms with higher depletion values\, and 3) estimates of the impact of sublimation temperature and dust drifts on the composition of rocky material in the inner disk. Overall\, my work probes dust trapping and dust drift theories. \nMarbely Micolta (Astronomy and Scientific Computing)\nI’m a fifth-year Ph.D. student in Astronomy\, working with Prof. Nuria Calvet. I’m from Venezuela. My research aims to constrain the physical and chemical processes that regulate rocky (refractory) material\, the building blocks of planets\, in the inner 10AU of protoplanetary disks. I have developed a broad expertise in disk characterization\, using observations across the electromagnetic spectrum\, both from the ground and space telescopes. URL:https://micde.umich.edu/event/workshop-seminarph-d-in-scientific-computing-seminar-series-6/ LOCATION:North Quad – 2185 CATEGORIES:Astronomy,Chemical Engineering,Chemistry,College Of Engineering,Computational Science,computing,Electrical And Computer Engineering,Electrical Engineering and Computer Science,Engineering,Free,Graduate,Graduate and Professional Students,Graduate School,Graduate Students,In Person,Interdisciplinary,Mechanical Engineering,Micde,Michigan Engineering,Networking,Phd Seminar,Political Science,Prospective Graduate Students,Rackham,Research,Science,Scientific Computing,Seminar,Talk ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/09/2025-11-18-Cooper-Ash-Micolta.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20251111T114500 DTEND;TZID=America/Detroit:20251111T124500 DTSTAMP:20251105T194338Z CREATED:20250926T143952Z LAST-MODIFIED:20251105T194338Z UID:10000839-1762861500-1762865100@micde.umich.edu SUMMARY:Ph.D. in Scientific Computing Seminar Series DESCRIPTION:The MICDE PhD Student Seminar Series showcases the research of students in the Ph.D. in Scientific Computing. Lunch will be served. These events are open to the public\, but we request that all who plan to attend register in advance. Planned sessions will be canceled if no one signs up to present\, and registered attendees will be notified. \nIf you have any questions\, please email micde-phd@umich.edu. \nRegister to attend \n\nPy-Conformational-Sampling: Towards Predicting Stereoselectivity\nStereoselective reactions are an integral part of organic synthesis due to the abundance of chiral centers in natural products and drug molecules. The design of these reactions remains challenging due to specific substrate requirements\, delicate reaction conditions and more importantly\, multiple competing product-forming transition states (TSs). These TSs often arise from a range of conformers present within the reactant complex. Thus\, predicting stereoselectivity requires detailed insights into favorable interactions amidst the conformational ensemble. This work introduces Py-Conformational-Sampling (PyCoSa) as a methodical approach to sample transition-metal-catalyzed stereoselective reactions. This technique\, when devoted to atroposelective Suzuki-Miyaura coupling to generate axially chiral biaryl products\, shows a variety of mechanistic possibilities through which C(sp2)–C(sp2) bond formation takes place. \nSoumik Das (Chemistry and Scientific Computing)\nSoumik is currently pursuing Ph.D. in Chemistry and Scientific Computing under the supervision of Dr. Paul Zimmerman. His research interests involve developing and applying automated and predictive computational tools using quantum chemistry for reaction design and discovery. Among other things\, he’s also a recipient of MICDE Graduate Fellowship for the academic year 2023-2024 and presented his research in MICDE conference SciFM ’24. \n\nDensity Functional Theory Simulations of Icosahedral Quasicrystals\nQuasicrystals (QCs) are fascinating materials with their long-range aperiodicity and forbidden rotational symmetry\, which opened a new type of classification in crystallography and attracted much attention to its potential applications to condensed matter\, statistical and solid-state physics. The characterization and identification of QCs after the first discovery is widely undertaken\, but thermodynamic stability and kinetics of nucleation are ongoing questions to answer the synthesizability and design novel structures. The quantum mechanical simulation including the density functional theory (DFT) is a widely used method for atomic-scale simulation\, however\, aperiodicity of QC structure makes it challenging to apply a computational model for periodic boundary frameworks. In this present work\, atomistic simulation of Tsai-type ScZn and YbCd icosahedral quasicrystals (iQCs)\, which is one of recently discovered iQCs types\, were performed using density functional theory – finite element (DFT-FE) method to study the thermodynamic stability\, role of surface energy to the stability\, and driving force of QC formation. The size-dependent and mixed-thermodynamic-and-kinetic phase diagram from quantitative theoretical calculations can provide fundamental insights into the origin of QC formation. \nWoohyeon Baek (Materials Science and Engineering and Scientific Computing)\nWoohyeon Baek is a PhD student in Materials Science and Engineering and Scientific Computing under the supervision of Dr. Wenhao Sun. He is working on the thermodynamics and kinetics of non-traditional materials formation from computational simulations including quasicrystals\, minerals\, functional materials\, and organic crystals. \n\nData-Driven Development of Constitutive Equations for Thixotropic Waxy Oil Rheology for Flow Assurance Using Symbolic Regression and PINNs\nWaxy crude oils crystallize below the wax appearance temperature\, forming networks that make rheology strongly dependent on temperature and prior shear history\, complicating pipeline restart operations. We develop a compact\, predictive modeling framework that combines data-driven and mechanistic approaches\, with all methods using differential scanning calorimetry crystallinity measurements to encode temperature effects. Symbolic regression (PySR) trained on two temperatures accurately predicts steady-state flow curves at remaining temperatures. A Fractal Isotropic-Kinematic Hardening (FIKH) model\, fitted at two temperatures for steady response\, predicts steady behavior at other temperatures; for transients\, parameters identified at 5°C reproduce rejuvenation and recovery dynamics at additional temperatures. We introduce LFP-IKH (Liquid Free-Path IKH)\, a novel approach that defines the structural state as liquid-network connectivity bounded by crystallinity. When calibrated only on steady-state data\, LFP-IKH predicts both steady and transient responses across all temperatures without refitting. This yields a mechanism-based framework that requires no parameter adjustment across temperature ranges\, making it suitable for flow-assurance prediction and restart design applications. \nSamuel Ogunwale (Chemical Engineering and Scientific Computing)\nSamuel Ogunwale is a sixth-year PhD student in Chemical Engineering working in the Larson group. His research focuses on developing predictive models for complex fluid systems\, combining mechanistic understanding with experimental validation to address industrial flow assurance challenges. URL:https://micde.umich.edu/event/workshop-seminarph-d-in-scientific-computing-seminar-series-5/ LOCATION:North Quad – 2185 CATEGORIES:Astronomy,Chemical Engineering,Chemistry,College Of Engineering,Computational Science,computing,Electrical And Computer Engineering,Electrical Engineering and Computer Science,Engineering,Free,Graduate,Graduate and Professional Students,Graduate School,Graduate Students,In Person,Interdisciplinary,Mechanical Engineering,Micde,Michigan Engineering,Networking,Phd Seminar,Political Science,Prospective Graduate Students,Rackham,Research,Science,Scientific Computing,Seminar,Talk ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/09/2025-11-11-Das-Baek-Ogunwale.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20251104T114500 DTEND;TZID=America/Detroit:20251104T124500 DTSTAMP:20251009T184957Z CREATED:20250926T143951Z LAST-MODIFIED:20251009T184957Z UID:10000838-1762256700-1762260300@micde.umich.edu SUMMARY:Ph.D. in Scientific Computing Seminar Series DESCRIPTION:The MICDE PhD Student Seminar Series showcases the research of students in the Ph.D. in Scientific Computing. Lunch will be served. These events are open to the public\, but we request that all who plan to attend register in advance. Planned sessions will be canceled if no one signs up to present\, and registered attendees will be notified. \nIf you have any questions\, please email micde-phd@umich.edu. \nRegister to attend \n\nEmbodying mechano-intelligence in mechanical metastructures for in-memory phononic learning\nMechano-intelligence (MI)—intelligence embodied within the mechanical domain of materials and structures—promises autonomous systems with higher effectiveness\, efficiency\, and resilience. Rather than outsourcing information processing entirely to electronics\, MI envisions materials that store\, process\, and adapt to environmental inputs through intrinsic mechanical responses\, reducing latency and energy while improving robustness in extreme and cyber-contested conditions. Realizing MI requires three elements: a memory module to retain knowledge from inputs\, a computing module to interpret and act on information\, and a physical communication interface linking storage and computation. In this talk\, I will introduce a new approach to realizing MI in and through a reconfigurable phononic metastructures via the concept of in-memory phononic learning\, where mechanical states are programmed to encode and store information and the elastic-wave physics is harnessed to carry out computation and decision—a framework that unifies the full information chain in the mechanical domain and provides efficient\, physically interpretable processing by using elastic waves as the natural communication and processing medium. \nYuning Zhang (Mechanical Engineering and Scientific Computing)\nYuning is a Ph.D. candidate in Mechanical Engineering under Prof. Kon-Well Wang. His research focuses on wave propagation in phononic metastructures\, and the development of physical computing and mechanical intelligence. \n\nGlobal Probabilistic Geomagnetic Perturbation Forecasting \nAccurately predicting the horizontal component of the ground magnetic field perturbation (dBH)\, as a proxy for Geomagnetically Induced Currents (GICs)\, is crucial for estimating the impact of geomagnetic storms and remains a topic under active investigation. The current operational Geospace model is computationally expensive for fine-grid global simulations\, while existing machine learning methods consistently tend to underestimate dBH. Additionally\, these models either lack uncertainty quantification (UQ)\, which is either overlooked or treated as secondary. In this work\, as part of the NextGen SWMF project funded by NSF\, we develop a data-driven\, grid-free global model using deep Gaussian process (DGP)\, a Bayesian non-parametric approach that forecasts the dBH for the full surface of Earth with calibrated uncertainty. The model uses solar wind measurements and the Dst index as input\, and it is trained based on ground magnetometer station data provided by SuperMAG over the period 1995-2022. The model’s predictions are evaluated based on the Heidke skill score (HSS) for a total of 23 storms in 2015. We further test the model on the 2024 Gannon superstorm. The results demonstrate that our model outperforms the state-of-the-art model\, with predictions exhibiting high accuracy in mid-latitudes and high-latitude regions in the northern hemisphere. \nHongfan Chen (Mechanical Engineering and Scientific Computing)\nHongfan Chen is a fourth-year PhD student in Mechanical Engineering and the Michigan Institute for Computational Discovery and Engineering (MICDE) Scientific Computing program. His research develops computational methods for uncertainty quantification (UQ) and machine learning (ML) in complex scientific and engineering systems\, with emphases on data assimilation (DA)\, knowledge-guided machine learning\, and optimal experimental design (OED). \n\n  URL:https://micde.umich.edu/event/workshop-seminarph-d-in-scientific-computing-seminar-series-4/ LOCATION:North Quad – 2185 CATEGORIES:Astronomy,Chemical Engineering,Chemistry,College Of Engineering,Computational Science,computing,Electrical And Computer Engineering,Electrical Engineering and Computer Science,Engineering,Free,Graduate,Graduate and Professional Students,Graduate School,Graduate Students,In Person,Interdisciplinary,Mechanical Engineering,Micde,Michigan Engineering,Networking,Phd Seminar,Political Science,Prospective Graduate Students,Rackham,Research,Science,Scientific Computing,Seminar,Talk ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/09/2025-11-4-Zhang-Chen.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20251028T114500 DTEND;TZID=America/Detroit:20251028T124500 DTSTAMP:20251027T214532Z CREATED:20250926T143950Z LAST-MODIFIED:20251027T214532Z UID:10000837-1761651900-1761655500@micde.umich.edu SUMMARY:Ph.D. in Scientific Computing Seminar Series DESCRIPTION:The MICDE PhD Student Seminar Series showcases the research of students in the Ph.D. in Scientific Computing. Lunch will be served. These events are open to the public\, but we request that all who plan to attend register in advance. Planned sessions will be canceled if no one signs up to present\, and registered attendees will be notified. \nIf you have any questions\, please email micde-phd@umich.edu. \nRegister to attend \n\nAutomated removal of artifactual false positive High Frequency Oscillations in intracranial EEG\nHigh frequency oscillations (HFOs) are a promising biomarker of the epileptogenic zone. Automated HFO detectors alleviate manual labeling but false positives\, artifacts\, remain. Clinicians recognize artifacts readily while viewing the EEG at standard resolution across channels\, and observing artifacts at the times of HFO events leads to a loss of trust in the detections. In this work\, we collect a new gold standard of HFO labeling using clinician expertise\, train several machine learning algorithms\, and develop an artifact filter compatible with any HFO detector to distinguish between true and false positives. \nAshley Tan (Mechanical Engineering and Scientific Computing)\nHer research involves developing engineering tools to control epilepsy. She is currently developing machine learning methods for artifact detection of a potential biomarker and investigating the effects of electrical brain stimulation on pathological activity. \n\nEmergence of three-dimensional structures from vortex pair instabilities in shocked interfacial flows\nThe Crow instability is a vortex-line instability that leads to the three-dimensional growth of perturbations in counter-rotating vortices\, with pinch-off leading to the generation of vortex rings at late time. Classically\, two incompressible\, inviscid vortices are studied in this context; in the present work\, we use numerical simulations to demonstrate that the cores which are generated from the compressible multi-material Richtmyer-Meshkov instability are subject to the Crow instability. Thus\, the onset of the Crow instability from the Richtmyer-Meshkov-induced cores can act as a mechanism for transitioning a nominally two-dimensional Richtmyer-Meshkov flow to three dimensions. \nWilliam White (Mechanical Engineering and Scientific Computing)\nWilliam is a PhD student in the Scientific Computing and Flow Physics Lab working on high-order numerical methods for compressible interfacial flows\, as well as interfacial and vortex-line hydrodynamic instabilities. \n\n  URL:https://micde.umich.edu/event/workshop-seminarph-d-in-scientific-computing-seminar-series-3/ LOCATION:North Quad – 2185 CATEGORIES:Astronomy,Chemical Engineering,Chemistry,College Of Engineering,Computational Science,computing,Electrical And Computer Engineering,Electrical Engineering and Computer Science,Engineering,Free,Graduate,Graduate and Professional Students,Graduate School,Graduate Students,In Person,Interdisciplinary,Mechanical Engineering,Micde,Michigan Engineering,Networking,Phd Seminar,Political Science,Prospective Graduate Students,Rackham,Research,Science,Scientific Computing,Seminar,Talk ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/09/2025-10-28-Tan-White.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20251007T114500 DTEND;TZID=America/Detroit:20251007T124500 DTSTAMP:20251008T041229Z CREATED:20250926T143945Z LAST-MODIFIED:20251008T041229Z UID:10000833-1759837500-1759841100@micde.umich.edu SUMMARY:Ph.D. in Scientific Computing Seminar Series DESCRIPTION:The MICDE PhD Student Seminar Series showcases the research of students in the Ph.D. in Scientific Computing. Lunch will be served. These events are open to the public\, but we request that all who plan to attend register in advance. Planned sessions will be canceled if no one signs up to present\, and registered attendees will be notified. \nIf you have any questions\, please email micde-phd@umich.edu. \nRegister to attend \n\nBridging Wavefunctions and Density Functionals: Unlocking Accurate Data for Functional Development\nDensity Functional Theory (DFT) is one of the most widely used electronic structure methods in chemistry\, physics\, and materials science\, striking a balance between accuracy and computational efficiency. However\, its accuracy is fundamentally limited by the choice of the exchange-correlation (XC) functional\, which remains an approximation in all practical applications. A key shortcoming of existing functionals is their failure to reproduce critical features of the exact XC potential\, such as the asymptotic -1/r decay and the step at integer electron transitions—features essential for correctly describing ionization energies\, band gaps\, and dissociation limits. In this work\, we take a data-driven approach to improving DFT by generating XC potentials from full configuration interaction (FCI) calculations. Using a large Slater basis\, we systematically recover key features of the exact XC potential across atomic systems and analyze their behavior. Additionally\, we compute exchange-correlation energy densities via an aufbau path integral\, ensuring consistency with total XC energy values from FCI. These highly accurate DFT quantities establish a benchmark for diagnosing errors in existing functionals and guiding the development of new approximations that incorporate wavefunction-level accuracy while retaining DFT’s efficiency. \nVaibhav Khanna (Chemistry and Scientific Computing)\nVaibhav Khanna is a Ph.D. candidate in Chemistry and Scientific Computing at the University of Michigan\, where he works under the supervision of Prof. Paul Zimmerman. His research focuses on developing improved density functionals that bridge the gap between highly accurate but computationally expensive wavefunction methods and the efficiency of the popular Density Functional Theory (DFT). By incorporating wavefunction-level accuracy\, his work aims to significantly improve the predictive power of DFT\, a widely used computational method in chemistry\, physics\, and materials science. \n\nTurbulence transport and size segregation of shock-driven multiphase flows\nThe phenomena of a shock-wave interacting with a particle suspension is observed in applications such as pulse detonation engines\, volcanic eruptions\, coal dust explosions and plume-surface interactions during spacecraft landings. Compressibility effects during these interactions give rise to complicated dynamics in the suspensions. While there has been a lot of effort and progress in modeling incompressible flows\, much less work has been done in modeling the microscale physics in turbulent flows at finite Mach numbers. Particle-resolved numerical simulations of shock passing through monodisperse suspensions are used to guide the development of subgrid-scale models for turbulence transport. Turbulent kinetic energy (TKE) is found to contribute to a significant portion of the resolved kinetic energy. A two-equation model is proposed and implemented within a hyperbolic Eulerian-based two-fluid model. The model is found to be accurate across a wide range of volume fractions and Mach numbers. Additionally\, to analyse particle dispersion and segregation in bidisperse suspensions with extreme diameter size ratios\, a hybrid numerical framework is developed\, combining an immersed boundary method for large particles with Lagrangian particle tracking of small particles. \nArchana Sridhar (Aerospace Engineering and Scientific Computing)\nArchana is a 5th year PhD student in the Aerospace Engineering department. She is a MICDE Fellow working with Dr. Jesse Capecelatro. Her focus is on computational fluid dynamics of multiphase compressible flows. \n\n  URL:https://micde.umich.edu/event/workshop-seminarph-d-in-scientific-computing-seminar-series/ LOCATION:North Quad – 2185 CATEGORIES:Astronomy,Chemical Engineering,Chemistry,College Of Engineering,Computational Science,computing,Electrical And Computer Engineering,Electrical Engineering and Computer Science,Engineering,Free,Graduate,Graduate and Professional Students,Graduate School,Graduate Students,In Person,Interdisciplinary,Mechanical Engineering,Micde,Michigan Engineering,Networking,Phd Seminar,Political Science,Prospective Graduate Students,Rackham,Research,Science,Scientific Computing,Seminar,Talk ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/09/2025-10-07-Khanna-Sridhar.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20250515T090000 DTEND;TZID=America/Detroit:20250515T103000 DTSTAMP:20250501T205802Z CREATED:20250501T205802Z LAST-MODIFIED:20250501T205802Z UID:10000820-1747299600-1747305000@micde.umich.edu SUMMARY:Bioinformatics PhD Dissertation Defense - Yueyang Shen: Complex Time Representation and Observability of Repeated Measurement Processes with Applications to Spacekime Analytics DESCRIPTION:Zoom link \nBio: Yueyang Shen is a PhD student in bioinformatics at the University of Michigan. His current research interests include spacetime analytics\, geometric deep learning\, applied neuroimaging studies\, and physics-inspired ML. I am broadly interested in mathematical\, statistical\, and physical modeling and its biological applications. Some of my past projects involve spatial analytics and studying symmetry effects on neural networks. I am currently working on decoding the music pathway in the brain using machine learning. \nComplex Time Representation and Observability of Repeated Measurement Processes with Applications to Spacekime Analytics\nAbstract: \nThis work develops and validates mathematical\, computational\, statistical\, and algorithmic techniques to represent observable processes as computable data objects\, which are amenable to subsequent modeling\, scientific inference\, AI prediction\, classification\, forecasting\, and spacekime analytics. Chapter 1 provides study motivation\, an overview of current knowledge\, and lays the foundation of complex-time (kime) representation of repeated measurement processes. \nThe core of this dissertation is organized in four integrated chapters with an overarching theme of observable process representation\, computational modeling\, scientific inference\, AI prediction\, classification\, and statistical forecasting using high-dimensional spatiotemporal data and (spacekime) analytics. In Chapter 2 we introduce non-local constraints to solve ultrahyperbolic equations. In Chapter 3\, we address a particular numerical strategy to convert repeated timeseries observations into richer mathematical objects\, kime-surfaces\, that can be used for novel statistical learning\, computational inference\, and artificial intelligence predictions. We show examples using neuroscience data to examine regional brain activation via tensor linear regression on kime-surfaces. We also develop a framework to analyze time-varying distribution modeling on differential equations using reproducing kernel Hilbert spaces (RKHS). \nIn Chapter 4\, we develop a theoretical statistical foundation for building robust and generalizable neural networks (NN). Specifically\, we use a string theory dataset to benchmark different NN architectures and discuss their group invariance. In Chapter 5\, we develop a brain tumor segmentation method with attention and fractal encoding NN architecture. We also study spatiotemporal analytics using an fMRI music genre dataset. The final\, Chapter 6 synthesizes the content of the whole dissertation\, draws overall conclusions\, and sets directions for future work. URL:https://micde.umich.edu/event/bioinformatics-phd-defense-shen/ LOCATION:2903 Taubman Health Sciences Library\, 1135 CATHERINE ST\, Ann Arbor\, MI\, 48109 CATEGORIES:Biosciences,Computational Medicine,Graduate School,Graduate Students,Micde,Science ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/05/MICDE-Seminar-Series-Speaker-Portraits-2.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20250403T160000 DTEND;TZID=America/Detroit:20250403T170000 DTSTAMP:20250310T172122Z CREATED:20250308T043518Z LAST-MODIFIED:20250310T172122Z UID:10000813-1743696000-1743699600@micde.umich.edu SUMMARY:Scientific Computing in the Biological and Health Sciences information session DESCRIPTION:Learn about academic opportunities and fellowships for graduate students who combine Scientific Computing with Biology\, Kinesiology\, Medicine\, Pharmacy\, Public Health\, or any other biological or health-related science. \nThis session will be offered in-person and on Zoom. Please indicate how you plan to attend when you register. \nRegister to attend URL:https://micde.umich.edu/event/presentationscientific-computing-in-the-biological-and-health-sciences/ LOCATION:Weiser Hall – 170 CATEGORIES:Basic Science,Biology,Biomedical Engineering,Biosciences,Computation,Computational Modeling,Computational Science,Computational Social Science,computing,Ecology And Evolutionary Biology,Epidemiology,Evolutionary Biology,Generative Ai,Graduate,Graduate and Professional Students,Graduate School,Graduate Students,Health Data,High Performance Computing,In Person,Interdisciplinary,Kinesiology,Life Science,Machine Learning,Medicine,Micde,Natural Sciences,Neuroscience,Pharmacy,Prospective Graduate Students,Psychology,Public Health,Rackham,Research,Science,Scientific Computing,Virtual ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/03/2025-04-Scientific-Computing-in-the-Biological-and-Health-Sciences.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20250401T160000 DTEND;TZID=America/Detroit:20250401T170000 DTSTAMP:20250310T172100Z CREATED:20250308T043515Z LAST-MODIFIED:20250310T172100Z UID:10000812-1743523200-1743526800@micde.umich.edu SUMMARY:Scientific Computing in the Physical Sciences information session DESCRIPTION:Learn about academic opportunities and fellowships for graduate students who combine Scientific Computing with Applied Physics\, Astronomy\, Biophysics\, Chemistry\, Earth and Environmental Sciences\, Math\, Physics\, or any other physical science. \nThis session will be offered in-person and on Zoom. Please indicate how you plan to attend when you register. \nRegister to attend URL:https://micde.umich.edu/event/presentationscientific-computing-in-the-physical-sciences/ LOCATION:Weiser Hall – 170 CATEGORIES:Applied Physics,Astronomy,Biophysics,Chemistry,Computation,Computational Modeling,Computational Science,computing,Earth And Environmental Sciences,Environment,Generative Ai,Graduate,Graduate and Professional Students,Graduate School,Graduate Students,In Person,Interdisciplinary,Life Science,Machine Learning,Mathematics,Micde,Natural Sciences,Physics,Prospective Graduate Students,Rackham,Research,Science,Scientific Computing,Virtual ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/03/2025-04-Scientific-Computing-in-the-Physical-Sciences.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20250328T120000 DTEND;TZID=America/Detroit:20250328T130000 DTSTAMP:20250324T152136Z CREATED:20250324T152136Z LAST-MODIFIED:20250324T152136Z UID:10000816-1743163200-1743166800@micde.umich.edu SUMMARY:FSML Lecture Series - Lianghao Cao (Caltech): Derivative-Informed Operator Learning with Applications to Cost-Efficient Bayesian Inversion DESCRIPTION:Zoom link \nBio: Dr. Lianghao Cao is a Postdoctoral Scholar Research Associate from the Department of Computing and Mathematical Sciences at the California Institute of Technology. He obtained a B.S. in Engineering Mechanics from the University of Illinois at Urbana-Champaign and a Ph.D. in Computational Science\, Engineering\, and Mathematics from The University of Texas at Austin. His research blends mechanistic modeling\, uncertainty quantification\, and scientific machine learning to understand\, enhance\, and control the quality\, validity\, and reliability of simulation-based predictions of complex physical systems. \nDerivative-Informed Operator Learning with Applications to Cost-Efficient Bayesian Inversion\nAbstract: This talk focuses on a derivative-informed supervised learning method for efficiently building machine learning surrogates of high-fidelity computational models\, particularly those governed by parametric partial differential equations. Unlike the conventional supervised learning method that treats the model as a black box\, our approach leverages additional model sensitivity information\, extracted via solving forward or adjoint sensitivity equations. This sensitivity information is integrated into the surrogate’s architecture and training process based on rigorous error analysis. We refer to such a surrogate construction as DINO (derivative-informed neural operator). \nDINO offers two key advantages over conventional surrogate construction. First\, it significantly improves the cost-accuracy trade-off for a wide range of models\, often by one to two orders of magnitude. Second\, it directly controls the surrogate Jacobian (Fréchet derivative) errors\, thus enhancing performance in surrogate-driven outer-loop problems that use gradient- and Hessian-based optimization algorithms. We demonstrate DINO’s capability to accelerate infinite-dimensional Bayesian inversion. First\, we show that geometric MCMC driven by DINO achieves a 2–9x speed up in asymptotically exact posterior sampling. Second\, we introduce LazyDINO\, a DINO-driven measure transport method for amortized Bayesian inversion\, which is one to two orders of magnitude more cost-efficient than competing methods.\nThis talk is based on joint work with Michael Brennan\, Joshua Chen\, Omar Ghattas\, Youssef Marzouk\, and Thomas O’Leary-Roseberry. URL:https://micde.umich.edu/event/fsml-lecture-series-lianghao-cao-caltech-derivative-informed-operator-learning-with-applications-to-cost-efficient-bayesian-inversion/ LOCATION:2636 GGBA\, 2350 Hayward St\, Ann Arbor\, MI\, United States CATEGORIES:Engineering,FSML,Science ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/03/Lianghao-Cao-Caltech.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20250314T120000 DTEND;TZID=America/Detroit:20250314T130000 DTSTAMP:20250311T130831Z CREATED:20250311T130516Z LAST-MODIFIED:20250311T130831Z UID:10000814-1741953600-1741957200@micde.umich.edu SUMMARY:FSML Lecture Series - Alexander Tong (Mila - Quebec AI Institute): Flow matching in cell trajectories and protein design DESCRIPTION:Zoom link \nBio: Alexander Tong is a postdoctoral fellow at Mila with Yoshua Bengio\, visiting researcher at Oxford with Michael Bronstein\, cofounder of Dreamfold—a protein design startup\, and incoming assistant professor at Duke University starting July 2025. Alex completed his Ph.D. in Computer Science at Yale University in 2021 with Smita Krishnaswamy. His research interests span generative modeling\, graph signal processing\, and optimal transport to understand biological systems with a focus on cells and proteins. \nFlow matching in cell trajectories and protein design\nAbstract: Generative flow models learn a (possibly stochastic) mapping between source and target distributions. Common paradigms include diffusion models\, score matching models\, and continuous normalizing flows. In this talk I will first present methods for improved training of flow models using flow matching objectives using ideas from optimal transport. I will then show how these improved methods can be applied to the tasks of (1) modelling cell dynamics\, which allow us to better understand disease programs –leading to a new potential therapeutic pathway for triple-negative breast cancer and (2) generative protein design\, with applications to biologic drug discovery. URL:https://micde.umich.edu/event/fsml-lecture-series-alexander-tong-mila/ LOCATION:1642 GGBL\, 2350 HAYWARD ST\, Ann Arbor\, 48109\, United States CATEGORIES:Engineering,FSML,Science ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/03/Alexander-Tong.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20250228T120000 DTEND;TZID=America/Detroit:20250228T130000 DTSTAMP:20250227T225237Z CREATED:20250225T171723Z LAST-MODIFIED:20250227T225237Z UID:10000809-1740744000-1740747600@micde.umich.edu SUMMARY:FSML Lecture Series - Doruk Aksoy: From Turbulent Flows to Video Games: Managing Large-Scale Data with Tensor Decomposition DESCRIPTION:Zoom link \nBio:Doruk Aksoy is a 5th year PhD candidate in Aerospace Engineering and Scientific computing at the University of Michigan\, working under the supervision of Prof. Alex Gorodetsky. Prior to joining UM\, he studied Mechanical Engineering at Bogazici University in Istanbul Turkey. During his PhD\, he worked on developing incremental tensor decomposition algorithms to accelerate scientific machine learning through data reduction. \nFrom Turbulent Flows to Video Games: Managing Large-Scale Data with Tensor Decomposition\nAbstract: The rapid advancement of large-scale parallel computing created a surge of interest in developing high-fidelity digital twins for complex systems. However\, the computational demands for training these models are immense\, requiring vast amounts of data. As the spatial and temporal resolution of simulations increases\, even data storage becomes a critical bottleneck. This talk presents how low-rank tensor decomposition methods can be used to exploit the structure in large-scale data. We showcase a diverse array of applications\, from 3D turbulent Navier-Stokes simulations to Minecraft gameplay videos\, demonstrating the versatility and power of these techniques. URL:https://micde.umich.edu/event/fsml-lecture-series-doruk-aksoy/ LOCATION:2636 GGBA\, 2350 Hayward St\, Ann Arbor\, MI\, United States CATEGORIES:Engineering,FSML,Science ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2023/09/Aksoy-Doruk.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20250214T093000 DTEND;TZID=America/Detroit:20250214T103000 DTSTAMP:20250212T173805Z CREATED:20250212T173700Z LAST-MODIFIED:20250212T173805Z UID:10000808-1739525400-1739529000@micde.umich.edu SUMMARY:FSML Lecture Series - Ricardo Vinuesa: Identifying coherent structures and controlling turbulent flows through deep learning DESCRIPTION:Zoom link \nBio: Dr. Ricardo Vinuesa is joining the Department of Aerospace Engineering at the University of Michigan in the Fall of 2025. He is currently an Associate Professor at the Department of Engineering Mechanics\, KTH Royal Institute of Technology in Stockholm. He studied Mechanical Engineering at the Polytechnic University of Valencia (Spain)\, and he received his PhD in Mechanical and Aerospace Engineering from the Illinois Institute of Technology in Chicago. His research combines numerical simulations and data-driven methods to understand\, control and predict complex wall-bounded turbulent flows\, such as the boundary layers developing around wings and urban environments. Dr. Vinuesa has received\, among others\, an ERC Consolidator Grant\, the TSFP Kasagi Award\, the MST Emerging Leaders Award\, the Goran Gustafsson Award for Young Researchers\, the IIT Outstanding Young Alumnus Award\, the SARES Young Researcher Award and he leads several large Horizon Europe projects. He is also a member of the Young Academy of Science of Spain. \nIdentifying coherent structures and controlling turbulent flows through deep learning\nAbstract: In this work we first use explainable deep learning based on Shapley explanations to identify the most important regions for predicting the future states of a turbulent channel flow. The explainability framework (based on gradient SHAP) is applied to each grid point in the domain\, and through percolation analysis we identify coherent flow regions of high importance. These regions have around 70% overlap with the intense Reynolds-stress (Q) events in two-dimensional vertical planes. Interestingly\, these importance-based structures have high overlap with classical turbulence structures (Q events\, streaks and vortex clusters) in different wall-normal locations\, suggesting that this new framework provides a more comprehensive way to study turbulence. We also discuss the application of deep reinforcement learning (DRL) to discover active-flow-control strategies for turbulent flows\, including turbulent channels\, three-dimensional cylinders and turbulent separation bubbles. In all the cases\, the discovered DRL-based strategies significantly outperform classical flow-control approaches. We conclude that DRL has tremendous potential for drag reduction in a wide range of complex turbulent-flow configurations. \n  URL:https://micde.umich.edu/event/fsml-lecture-series-ricardo-vinuesa-identifying-coherent-structures-and-controlling-turbulent-flows-through-deep-learning/ LOCATION:2210 Lurie Engineering Center\, 1221 Beal Ave\, Ann Arbor\, MI\, 48105 CATEGORIES:Engineering,FSML,Science ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/02/Ricardo-Vinuesa.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20250131T120000 DTEND;TZID=America/Detroit:20250131T130000 DTSTAMP:20250127T193042Z CREATED:20250127T173918Z LAST-MODIFIED:20250127T193042Z UID:10000799-1738324800-1738328400@micde.umich.edu SUMMARY:FSML Lecture Series - Panos Stinis: When big neural networks are not enough: physics\, multi-fidelity and kernels DESCRIPTION:Zoom link \nBio: Panos Stinis specializes in scientific computing with application interests in model reduction of complex systems\, multiscale modeling\, uncertainty quantification\, and machine learning. He studied aeronautical engineering at the Technical University of Athens\, Greece. He earned his PhD in applied mathematics in 2003\, from Columbia University in New York and began his career as a postdoctoral fellow at Lawrence Berkeley National Laboratory and the Stanford Center for Turbulence Research. In 2008\, he became a faculty member at the Mathematics Department at the University of Minnesota. He moved to the Pacific Northwest National Laboratory in 2014\, where he is currently leading the Computational Mathematics group. \nWhen big neural networks are not enough: physics\, multi-fidelity and kernels\nAbstract: Modern machine learning has shown remarkable promise in multiple applications. However\, brute force use of neural networks\, even when they have huge numbers of trainable parameters\, can fail to provide highly accurate predictions for problems in the physical sciences. We present a collection of ideas about how enforcing physics\, exploiting multi-fidelity knowledge\, and the kernel representation of neural networks can lead to a significant increase in efficiency and/or accuracy. Various examples are used to illustrate the ideas. \n  URL:https://micde.umich.edu/event/fsml-lecture-series-panos-stinis-when-big-neural-networks-are-not-enough-physics-multi-fidelity-and-kernels/ LOCATION:2004 Lay Auto Lab CATEGORIES:Engineering,FSML,Science ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/01/Panos-Stinis.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20241206T120000 DTEND;TZID=America/Detroit:20241206T130000 DTSTAMP:20241204T144214Z CREATED:20241011T181202Z LAST-MODIFIED:20241204T144214Z UID:10000779-1733486400-1733490000@micde.umich.edu SUMMARY:FSML Lecture Series - Anoushka Bhutani: Foundation Model for Molecular Design DESCRIPTION:Zoom link \nBio: Anoushka is a third-year PhD student in Prof. Venkat Viswanathan’s group at the University of Michigan. Her research interests include machine learning for materials design and electrochemical battery modeling. \nFoundation Model for Molecular Design\nAbstract: The paradigm of molecular machine learning for material screening has accelerated material development cycles\, improved efficiency\, and reduced costs. However\, current state-of-the-art molecular property prediction models still require labeled training data generated using wet-lab experiments or Density Functional Theory (DFT) calculations. Their utility is limited by the scarcity and heterogeneity of labeled materials datasets. Foundation models (FMs) offer a solution to this: these models use self-supervised pre-training strategies to leverage unlabeled datasets and learn representations of data that can be applied to downstream tasks. Large unlabeled datasets of billions of synthesizable molecules are readily available. Prior attempts to train FMs for molecular property prediction demonstrate promise; however\, equivariant geometric models trained using supervised learning are still more accurate. This can be attributed to the fact that foundation models are extremely expensive to train and can be difficult to interpret; they require huge computing budgets\, complex distributed computing techniques\, and extensive hyperparameter searches. Our work addresses these challenges on three fronts: (1) we have prototyped a scalable workflow for distributed training of molecular foundation models (2) we have trained large foundation models using this workflow which demonstrates state-of-the-art molecular property prediction capabilities across several benchmarks\, and (3) we have applied model interpretability strategies such as the attention visualization to shed insight on molecular structure relationships learn by the transformer. \n  URL:https://micde.umich.edu/event/workshop-seminaranoushka-bhutani-foundation-model-for-molecular-design/ LOCATION:2636 GGBA\, 2350 Hayward St\, Ann Arbor\, MI\, United States CATEGORIES:Computational Science,Engineering,FSML,Graduate School,Graduate Students,Michigan Engineering,Rackham,Research,Science ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2024/10/Copy-of-MICDE-2022-2023-Fellowship-Portraits.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20241115T120000 DTEND;TZID=America/Detroit:20241115T130000 DTSTAMP:20260522T154425Z CREATED:20241012T182154Z LAST-MODIFIED:20260522T154425Z UID:10000785-1731672000-1731675600@micde.umich.edu SUMMARY:FSML Lecture Series - Hongfan Chen: Global Geomagnetic Perturbation Forecasting with Quantified Uncertainty using Deep Gaussian Process DESCRIPTION:Zoom link \nBio: Hongfan Chen is a third-year PhD student in the Department of Mechanical Engineering at the University of Michigan. His research interests include data assimilation\, uncertainty quantification\, and machine learning applications in space weather. \nGlobal Geomagnetic Perturbation Forecasting with Quantified Uncertainty Using Deep Gaussian Process\nAbstract: Accurately predicting the horizontal component of the ground magnetic field perturbation (dBH)\, as a proxy for Geomagnetically Induced Currents (GICs)\, is crucial for estimating the impact of geomagnetic storms and remains a topic under active investigation. The current state-of-the-practice Geospace model is computationally expensive for fine-grid global simulations while existing machine learning methods consistently tend to underestimate dBH. Additionally\, these models either lack uncertainty quantification (UQ) or provide UQ that lacks calibration. In this work\, as part of the NextGen SWMF project funded by NSF\, we develop a data-driven\, grid-free global model using deep Gaussian process (DGP)\, a Bayesian non-parametric approach that forecasts the dBH for the full surface of Earth with calibrated uncertainty. The model uses solar wind measurements and the Dst index as input\, and it is trained based on ground magnetometer station data provided by SuperMAG over the period 1995-2022. The model’s predictions are evaluated based on the Heidke skill score (HSS) for a total of 22 geomagnetic storms in 2015. We further test the model on the 2024 May 10-12 storm. The results demonstrate that our model outperforms the state-of-the-art model\, with predictions exhibiting high accuracy in mid-latitudes and high-latitude regions in the northern hemisphere. \n  URL:https://micde.umich.edu/event/lecture-discussionsciml-lecture-series-8/ LOCATION:2636 GGBA\, 2350 Hayward St\, Ann Arbor\, MI\, United States CATEGORIES:Engineering,FSML,Science ATTACH;FMTTYPE=image/jpeg:https://micde.umich.edu/wp-content/uploads/2024/10/Hongfan_Chen.jpg END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20241101T120000 DTEND;TZID=America/Detroit:20241101T130000 DTSTAMP:20241106T181820Z CREATED:20241012T182153Z LAST-MODIFIED:20241106T181820Z UID:10000784-1730462400-1730466000@micde.umich.edu SUMMARY:FSML Lecture Series - Nicholas Galioto: Discovery of Cellular Reprogramming Methodology Through Single-cell Foundation Models DESCRIPTION:Zoom link \nBio: Nick Galioto is a second-year postdoctoral research fellow in the Department of Computational Medicine and Bioinformatics at the University of Michigan (UM). He received his PhD at UM in aerospace engineering in 2023 under the advising of Alex Gorodetsky and remained in the lab for an additional year as a postdoc. In the Gorodetsky lab\, Nick researched how to use stochastic models of dynamical systems to improve system identification. Now\, Nick works in the Rajapakse lab researching how to create data-driven models of the dynamics of cell reprogramming. \n  \nDiscovery of Cellular Reprogramming Methodology Through Single-cell Foundation Models\n  \nAbstract: Cell reprogramming\, the transformation of a cell from one cell type to another through the introduction of exogenous transcription factors (TFs)\, is a rapidly developing research area that could lead to groundbreaking therapeutic technologies in areas such as tissue regeneration\, disease modeling\, and personalized medicine. However\, many challenges still exist that obstruct its practical viability. Discovering which TFs induce reprogramming requires a combinatorial search\, and testing a single candidate set of TFs experimentally can cost tens of thousands of dollars and take multiple months. Moreover\, even when an effective set of TFs is known\, cell conversion efficiency lies only around 5%. Faced with these challenges\, researchers have developed computational surrogate models to rapidly explore the TF search space at a fraction of the cost of wet lab experimentation. Unfortunately\, these models have seen limited success in practice due to the difficulty of capturing the complex gene-gene interactions within the cell\, most of which are still not well understood. With the recent high-profile rise of transformer-based foundation models for natural language\, researchers are now turning to the transformer to push past\, current performance limitations in a wide range of digital biology tasks\, including cell reprogramming. Of particular interest in these models is the attention mechanism\, which is potentially well-suited for capturing long-range gene-gene interactions at a higher fidelity than previously possible. In this talk\, I will describe how the transformer architecture has been adapted for cellular biology and analyze the utility of one such model\, Geneformer\, in identifying TFs for cell reprogramming. Specifically\, I will present the results of an in silico perturbation experiment for reprogramming fibroblast cells to hematopoietic stem cells and compare the outcomes to experimental results found in the literature. I will conclude the talk with a discussion of the drawbacks and limitations of the Geneformer model and provide an assessment of what will be needed in the future for digital biology to fully reap the benefits of large-scale foundation models. URL:https://micde.umich.edu/event/lecture-discussionsciml-lecture-series-7/ LOCATION:Walter E Lay Auto Lab – 2052 CATEGORIES:Engineering,FSML,Science END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20241022T132000 DTEND;TZID=America/Detroit:20241022T170000 DTSTAMP:20241018T223652Z CREATED:20241018T223652Z LAST-MODIFIED:20241018T223652Z UID:10000786-1729603200-1729616400@micde.umich.edu SUMMARY:Conference / Symposium:MICDE ACES Mini-Symposium 2024 DESCRIPTION:This year’s focus of the Advanced Computational Science & Engineering Showcase (ACES) mini-symposium is connecting advanced algorithms\, artificial intelligence (AI)\, and high-performance computing (HPC) architectures to advance scientific discovery. The event showcases the work of the University of Michigan faculty members at the intersection of AI\, HPC\, and advanced algorithms. It also includes a panel discussion on the current trends in AI\, novel algorithms\, and computer architectures for efficient scientific applications.\nACES is an event that promotes state-of-the-art research at the University of Michigan and the current trends and hot topics in computational science and engineering. Furthermore\, it is the nucleus for increasing collaboration at the University of Michigan by offering an opportunity for faculty members to share their ideas and network during a reception. Take advantage of this exciting opportunity to connect\, learn\, and be inspired! URL:https://micde.umich.edu/event/conference-symposiummicde-aces-mini-symposium-2024/ LOCATION:Lurie Robert H. Engin. Ctr – Johnson Rooms\, 3rd floor CATEGORIES:Aces,Ai In Science And Engineering,Artificial Intelligence,Computation,Computational Science,Engineering,Free,High Performance Computing,In Person,Interdisciplinary,Micde,Michigan Engineering,parallel computing,Research,Science,Scientific Computing,symposium END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20241018T120000 DTEND;TZID=America/Detroit:20241018T130000 DTSTAMP:20241104T135643Z CREATED:20241012T182153Z LAST-MODIFIED:20241104T135643Z UID:10000783-1729252800-1729256400@micde.umich.edu SUMMARY:FSML Lecture Series: Domain decomposition and coupling data-driven models of fluid flows by Christopher Wentland\, Sandia National Labs DESCRIPTION:Zoom link \n \nAbstract: Simulating complex physical systems often requires joining non-uniform subsystems\, which may be characterized by different geometries or mesh topologies. Coupling these separate subsystems often relies on time-intensive meshing workflows or empirical coupling models\, which may not generalize well across all operational regimes. The Schwarz alternating method proposes to overcome these issues\, establishing an effective domain decomposition framework that allows for the coupling of arbitrary geometries. This talk presents a brief history of Schwarz-based coupling work at Sandia National Laboratories\, along with recent work on combining the Schwarz alternating method with data-driven modeling approaches\, namely projection-based reduced order models (PROMs) and operator inference. This approach can generate surrogates that are capable of simulating advection-dominated fluid flows with higher accuracy and lower cost than comparable monolithic models\, aiding analysis in many-query applications such as uncertainty quantification and engineering design. Several nuances of the Schwarz algorithm and their impacts on model performance are explored\, specifically non-overlapping decompositions and PROM hyper-reduction under domain decomposition. A look into ongoing Schwarz coupling work at Sandia discusses existing challenges and efforts to apply this approach to relevant engineering problems. URL:https://micde.umich.edu/event/lecture-discussionsciml-lecture-series-6/ LOCATION:2636 GGBA\, 2350 Hayward St\, Ann Arbor\, MI\, United States CATEGORIES:Engineering,FSML,Science END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20241007T140000 DTEND;TZID=America/Detroit:20241007T150000 DTSTAMP:20240920T130536Z CREATED:20240920T130536Z LAST-MODIFIED:20240920T130536Z UID:10000751-1728309600-1728313200@micde.umich.edu SUMMARY:MICDE / MIDAS Graduate Information Session DESCRIPTION:The educational programs represented are: \n\nPhD in Scientific Computing (MICDE)\nGraduate Certificate in Computational Discovery & Engineering (MICDE)\nGraduate Certificate in Computational Neuroscience (MICDE)\nGraduate Certificate in Data Science (MIDAS)\n\nThese programs are open to all U-M graduate students with an interest in scientific computing or data science. These methodologies can have a wide range of applications – current and past students have come from a variety of home departments including Aerospace Engineering\, Applied Physics\, Biostatistics\, Biomedical Engineering\, Civil & Environmental Engineering\, Chemistry\, Chemical Engineering\, Climate and Space Sciences and Engineering\, Computational Medicine and Bioinformatics\, Ecology and Evolutionary Biology\, Earth and Environmental Sciences\, Epidemiology\, Health Behavior and Health Education\, Health Infrastructures & Learning Systems\, Information\, Industrial & Operations Engineering\, Kinesiology\, Linguistics\, Macromolecular Science & Engineering\, Math\, Molecular\, Cellular\, and Developmental Biology\, Mechanical Engineering\, Materials Science & Engineering\, Naval Architecture & Marine Engineering\, Nuclear Engineering & Radiological Sciences\, Neuroscience\, Pharmaceutical Sciences\, Physics\, Political Science\, Psychology\, Environment and Sustainability\, Sociology and Statistics.\nIf you have any questions about these programs or about the information session\, please reach out to MICDE (micde-contact@umich.edu) or MIDAS (midas-contact@umich.edu). URL:https://micde.umich.edu/event/micde-midas-graduate-information-session-2/ LOCATION:Johnson Rooms\, Lurie Engineering Center\, 3rd Floor LEC 3213ABC\, 1221 Beal Ave.\, Ann Arbor\, MI\, United States CATEGORIES:Computation,Computational Modeling,Computational Science,Computational Social Science,data,Data Science,Deep Learning,Engineering,Free,Graduate,Graduate and Professional Students,Graduate School,Graduate Students,In Person,Interdisciplinary,Machine Learning,Micde,Michigan Engineering,Midas,Neuroscience,Prospective Graduate Students,Rackham,Research,Science,Scientific Computing ATTACH;FMTTYPE=image/gif:https://micde.umich.edu/wp-content/uploads/2023/08/Info-session.gif GEO:42.2914823;-83.7138452 X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Johnson Rooms Lurie Engineering Center 3rd Floor LEC 3213ABC 1221 Beal Ave. Ann Arbor MI United States;X-APPLE-RADIUS=500;X-TITLE=1221 Beal Ave.:geo:-83.7138452,42.2914823 END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20240930T150000 DTEND;TZID=America/Detroit:20240930T160000 DTSTAMP:20240920T130342Z CREATED:20240920T130342Z LAST-MODIFIED:20240920T130342Z UID:10000750-1727708400-1727712000@micde.umich.edu SUMMARY:MICDE / MIDAS Graduate Information Session DESCRIPTION:The educational programs represented are: \n\nPhD in Scientific Computing (MICDE)\nGraduate Certificate in Computational Discovery & Engineering (MICDE)\nGraduate Certificate in Computational Neuroscience (MICDE)\nGraduate Certificate in Data Science (MIDAS)\n\nThese programs are open to all U-M graduate students with an interest in scientific computing or data science. These methodologies can have a wide range of applications – current and past students have come from a variety of home departments including Aerospace Engineering\, Applied Physics\, Biostatistics\, Biomedical Engineering\, Civil & Environmental Engineering\, Chemistry\, Chemical Engineering\, Climate and Space Sciences and Engineering\, Computational Medicine and Bioinformatics\, Ecology and Evolutionary Biology\, Earth and Environmental Sciences\, Epidemiology\, Health Behavior and Health Education\, Health Infrastructures & Learning Systems\, Information\, Industrial & Operations Engineering\, Kinesiology\, Linguistics\, Macromolecular Science & Engineering\, Math\, Molecular\, Cellular\, and Developmental Biology\, Mechanical Engineering\, Materials Science & Engineering\, Naval Architecture & Marine Engineering\, Nuclear Engineering & Radiological Sciences\, Neuroscience\, Pharmaceutical Sciences\, Physics\, Political Science\, Psychology\, Environment and Sustainability\, Sociology and Statistics.\nIf you have any questions about these programs or about the information session\, please reach out to MICDE (micde-contact@umich.edu) or MIDAS (midas-contact@umich.edu). URL:https://micde.umich.edu/event/micde-midas-graduate-information-session/ LOCATION:1100 North University Building – 1528 CATEGORIES:Computation,Computational Modeling,Computational Science,Computational Social Science,data,Data Science,Deep Learning,Engineering,Free,Graduate,Graduate and Professional Students,Graduate School,Graduate Students,In Person,Interdisciplinary,Machine Learning,Micde,Michigan Engineering,Midas,Neuroscience,Prospective Graduate Students,Rackham,Research,Science,Scientific Computing ATTACH;FMTTYPE=image/gif:https://micde.umich.edu/wp-content/uploads/2023/08/Info-session.gif END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20240419T110000 DTEND;TZID=America/Detroit:20240419T120000 DTSTAMP:20240412T140752Z CREATED:20240412T011911Z LAST-MODIFIED:20240412T140752Z UID:10000680-1713524400-1713528000@micde.umich.edu SUMMARY:MICDE Fellowships Information Session DESCRIPTION:Applications for the $4\,500 2024-2025 MICDE Graduate Fellowships will open May 1\, 2024. Please join this Zoom session to learn more about the fellowships and how to submit a great application! \nThese fellowships are sponsored by the Michigan Institute for Computational Discovery & Engineering. For more information\, see https://live-umor-micde.pantheonsite.io/academic-programs/fellowships/. URL:https://micde.umich.edu/event/livestream-virtualmicde-fellowships-information-session/ LOCATION:Zoom Event CATEGORIES:Aerospace Engineering,Ai In Science And Engineering,Astronomy,big data,Biomedical Engineering,Biosciences,Biostatistics,Chemical Engineering,Chemistry,Civil and Environmental Engineering,Climate and Space Sciences and Engineering,College Of Engineering,Complex Systems,Computation,Computational Modeling,Computational Science,Computational Social Science,computer science,Data Science,Earth And Environmental Sciences,Ecology,Ecology And Evolutionary Biology,Economics,Education,Electrical And Computer Engineering,Electrical Engineering and Computer Science,Engineering,Engineering Academic Calendar,Epidemiology,Evolutionary Biology,Fellowship,Free,Funding,Generative Ai,Graduate,Graduate and Professional Students,Graduate School,Graduate Students,Health Data,High Performance Computing,Industrial and Operations Engineering,Interdisciplinary,Kinesiology,Life Science,Lsaresearch,Machine Learning,Materials Science,Mathematics,Mechanical Engineering,Medicine,Micde,Natural Language Processing,Natural Sciences,Naval Architecture and Marine Engineering,Neuroscience,Nuclear Engineering and Radiological Sciences,parallel computing,Pharmacy,Physics,Politics,Prospective Graduate Students,Psychology,Public Health,Public Policy,Rackham,Research,Robotics,Scholarship,Science,Scientific Computing,Sciml,Sociology,Statistics,Virtual,Webcast,Workshops ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2024/04/Fellowships-2024-info-session.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20240403T090000 DTEND;TZID=America/Detroit:20240403T170000 DTSTAMP:20240226T163136Z CREATED:20240115T212036Z LAST-MODIFIED:20240226T163136Z UID:10000667-1712134800-1712163600@micde.umich.edu SUMMARY:SciFM24 Conference DESCRIPTION:This event is the first of its kind\, dedicated to scientific foundation models (SciFM)\, that are set to revolutionize science in the same way Generative AI has transformed natural language.\nThis two-day conference will bring together some of the most renowned experts from the field of scientific foundation models who will share their insights and knowledge on various topics related to this field. The event will also feature engaging panel discussions\, informative workshops\, and a poster competition\, providing attendees\, with ample opportunities to learn\, network\, and engage. URL:https://micde.umich.edu/event/conference-symposiumscifm24-conference-2/ LOCATION:Rackham Graduate School (Horace H.) – Amphitheater CATEGORIES:Astronomy,Basic Science,Biology,Biomedical Engineering,Biosciences,Biostatistics,Chemistry,College Of Engineering,Complex Systems,Computational Science,Engineering Academic Calendar,Epidemiology,Evolutionary Biology,Faculty,Free,Information and Technology,Kinesiology,Lsaresearch,Mathematics,Medicine,Micde,Michigan Engineering,Michigan Robotics,Midas,Physics,Public Health,Rackham,Research,Science,Scientific Computing,Workshops END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20240402T090000 DTEND;TZID=America/Detroit:20240402T170000 DTSTAMP:20240226T163019Z CREATED:20240115T212035Z LAST-MODIFIED:20240226T163019Z UID:10000666-1712048400-1712077200@micde.umich.edu SUMMARY:SciFM24 Conference DESCRIPTION:This event is the first of its kind\, dedicated to scientific foundation models (SciFM)\, that are set to revolutionize science in the same way Generative AI has transformed natural language.\nThis two-day conference will bring together some of the most renowned experts from the field of scientific foundation models who will share their insights and knowledge on various topics related to this field. The event will also feature engaging panel discussions\, informative workshops\, and a poster competition\, providing attendees\, with ample opportunities to learn\, network\, and engage. URL:https://micde.umich.edu/event/conference-symposiumscifm24-conference/ LOCATION:Rackham Graduate School (Horace H.) – Amphitheater CATEGORIES:Astronomy,Basic Science,Biology,Biomedical Engineering,Biosciences,Biostatistics,Chemistry,College Of Engineering,Complex Systems,Computational Science,Engineering Academic Calendar,Epidemiology,Evolutionary Biology,Faculty,Free,Information and Technology,Kinesiology,Lsaresearch,Mathematics,Medicine,Micde,Michigan Engineering,Michigan Robotics,Midas,Physics,Public Health,Rackham,Research,Science,Scientific Computing,Workshops END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20231205T120000 DTEND;TZID=America/Detroit:20231205T130000 DTSTAMP:20231204T192114Z CREATED:20230926T191303Z LAST-MODIFIED:20231204T192114Z UID:10000654-1701777600-1701781200@micde.umich.edu SUMMARY:MICDE Ph.D. Student Seminars 2023-2024: Jeffrey Hatch DESCRIPTION:The MICDE PhD Student Seminar Series showcases the research of students in the Ph.D. in Scientific Computing. These events are open to the public\, but we ask that you register to attend the seminar. If you have any questions\, please email micde-events@umich.edu. \nComputational Methods in Chemistry\nAbstract coming soon… \nJeffrey Hatch\, Ph.D. candidate in Chemistry and Scientific Computing \nBio coming soon… \nRegister to attend this seminar URL:https://micde.umich.edu/event/phd-seminar-jeffrey-hatch/ LOCATION:2022 South Thayer Building CATEGORIES:Computation,Computational Modeling,Computational Science,Engineering,Free,Graduate,Graduate and Professional Students,Graduate School,Graduate Students,In Person,Interdisciplinary,Michigan Engineering,Phd Seminar,Prospective Graduate Students,Rackham,Science,Scientific Computing,Seminar,Sessions,Talk ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2023/09/Template-Speaker-3.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20231128T120000 DTEND;TZID=America/Detroit:20231128T130000 DTSTAMP:20260522T152930Z CREATED:20230914T150100Z LAST-MODIFIED:20260522T152930Z UID:10000641-1701172800-1701176400@micde.umich.edu SUMMARY:MICDE Ph.D. Student Seminars 2023-2024: Guoer Liu DESCRIPTION:The MICDE PhD Student Seminar Series showcases the research of students in the Ph.D. in Scientific Computing. These events are open to the public\, but we ask that you register to attend the seminar. If you have any questions\, please email micde-events@umich.edu. \nRegister to attend this seminar \nWhen Is Big Data Biased?\nUsing automation technology to gather and disseminate information to the public is commonly viewed as a government-led effort to enhance oversight and address the principal-agent problem in bureaucracy. However\, focusing on the expansion of China’s automatic ambient air quality monitoring network in the last decade (2012-2022)\, I argue that technology is being utilized as a tool to emphasize optics but overlook the substantive problems. I illustrate the idea with multiple original georeferenced data sets on the automatic monitoring network\, pollution sources\, and satellite-derived vegetation density across time and space. I show that\, while the automation initiative has improved the data quality in some ways\, the undersupply of automatic monitoring stations\, over-represented clean locations\, and non-random missing pollution records continue to contribute to inaccurate air pollution information. As long as political incentives to manipulate information persist\, actors can mold technology that operates without human intervention to serve their own interests. \nGuoer Liu\, Ph.D. candidate in Political Science and Scientific Computing\nGuoer Liu is a Ph.D. candidate in Political Science. Her dissertation project\, ‘‘From Oversight to Overlook’’ investigates how political determinants distort the technology infrastructure and create seemingly credible but inaccurate information to the public. \nAdvisors: Mary Gallagher\, Charles Shipan \nRegister to attend this seminar URL:https://micde.umich.edu/event/phd-seminar-guoer-liu/ LOCATION:2022 South Thayer Building CATEGORIES:Computation,Computational Modeling,Computational Science,Engineering,Free,Graduate,Graduate and Professional Students,Graduate School,Graduate Students,In Person,Interdisciplinary,Michigan Engineering,Phd Seminar,Prospective Graduate Students,Rackham,Science,Scientific Computing,Seminar,Sessions,Talk ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2023/09/Template-Speaker-2.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20231114T123000 DTEND;TZID=America/Detroit:20231114T130000 DTSTAMP:20231103T200940Z CREATED:20230914T150100Z LAST-MODIFIED:20231103T200940Z UID:10000640-1699965000-1699966800@micde.umich.edu SUMMARY:MICDE Ph.D. Student Seminars: Jamie Holber DESCRIPTION:The MICDE PhD Student Seminar Series showcases the research of students in the Ph.D. in Scientific Computing. These events are open to the public\, but we ask that you register to attend the seminar. If you have any questions\, please email micde-events@umich.edu. \nActive Learning for Physics Informed Data Sampling and Construction of Free Energy Representations\nUsing automation technology to gather and disseminate information to the public is commonly viewed as a government-led effort to enhance oversight and address the principal-agent problem in bureaucracy. However\, focusing on the expansion of China’s automatic ambient air quality monitoring network in the last decade (2012-2022)\, I argue that technology is being utilized as a tool to emphasize optics but overlook the substantive problems. I illustrate the idea with multiple original georeferenced data sets on the automatic monitoring network\, pollution sources\, and satellite-derived vegetation density across time and space. I show that\, while the automation initiative has improved the data quality in some ways\, the undersupply of automatic monitoring stations\, over-represented clean locations\, and non-random missing pollution records continue to contribute to inaccurate air pollution information. As long as political incentives to manipulate information persist\, actors can mold technology that operates without human intervention to serve their own interests. \nJamie Holber\, Ph.D. candidate in Applied Physics and Scientific Computing \nJamie Holber is a PhD Candidate in Applied Physics and Scientific Computing working in the Computational Physics Group in the Mechanical Engineering Department. \nAdvisor: Krishna Garikipati \nRegister to attend this seminar URL:https://micde.umich.edu/event/phd-seminar-jamie-holber/ LOCATION:2022 South Thayer Building CATEGORIES:Computation,Computational Modeling,Computational Science,Engineering,Free,Graduate,Graduate and Professional Students,Graduate School,Graduate Students,In Person,Interdisciplinary,Michigan Engineering,Phd Seminar,Prospective Graduate Students,Rackham,Science,Scientific Computing,Seminar,Sessions,Talk ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2023/09/20231114-Jamie-Holber.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20231114T080000 DTEND;TZID=America/Detroit:20231114T190000 DTSTAMP:20231020T163041Z CREATED:20231020T163041Z LAST-MODIFIED:20231020T163041Z UID:10000660-1699948800-1699988400@micde.umich.edu SUMMARY:Conference / Symposium:U-M Data Science & AI Summit 2023 DESCRIPTION:The U-M Data Science and AI Summit is the largest annual data science and AI event on campus. This event brings together the U-M data science and AI research community and their external collaborators to build research vision and collaboration. It also showcases the breadth and depth of U-M data science and AI research\, from theory and methodology development to the transformative use of data and AI to address scientific and societal challenges in all domains. The event is free for all attendees (U-M faculty\, staff\, and trainees\, as well as industry\, government and community members).\nTo view full Summit schedule\, please visit the event webpage at https://midas.umich.edu/midas-summit-2023/.\nKeynotes:\nSuresh Venkatasubramanian\, Director\, Center for Technological Responsibility\, Reimagination\, and Redesign\, Data Science Institute at Brown University; Professor of Data Science and Computer Science\, Brown University – Key player for the White House Blueprint of an AI Bill of Rights\nJulianne Dalcanton\, Director\, Center for Computational Astrophysics\, Flatiron Institute – The origina and evolution of galaxies\nEmre Kiciman\, Senior Principal Researcher\, Microsoft Research – A New Frontier at the Intersection of Causality and LLMs\nSummit Sessions:\nA panel discussion on: Federal priorities and opportunities in data science and AI\nPanelists:\n– Laura Biven\, Data Science Technical Lead\, Office of Data Science Strategy\, National Institutes of Health\n– Michael Molnar\, Director\, Advanced Manufacturing National Program Office\, National Institute of Standards and Technology\n– Hector Muñoz-Avila\, Program Director and Cluster Lead\, the Information Integration and Informatics Program\, National Science Foundation\n– Alvaro Velasquez\, Program Manager\, Information Innovation Office\, Defense Advanced Research Projects Agency\nResearch vision talks by University of Michigan faculty researchers\nThe Propelling Original Data Science grant awardees showcase\nPoster session\, lightning talks\, and awards\nUniversity of Michigan data science and AI organizations showcase URL:https://micde.umich.edu/event/conference-symposiumu-m-data-science-ai-summit-2023-2/ LOCATION:Rackham Graduate School (Horace H.) CATEGORIES:Ai In Science And Engineering,Applications,Artificial Intelligence,big data,Biostatistics,Climate and Space Sciences and Engineering,Computational Modeling,Computational Science,Computational Social Science,computing,Data Curation,Data Science,data visualization,Deep Learning,Electrical And Computer Engineering,Electrical Engineering and Computer Science,Engineering,Faculty,Free,Graduate,Graduate Students,Industrial and Operations Engineering,Information and Technology,Interdisciplinary,Lecture,Machine Learning,Michigan Engineering,Midas,Natural Language Processing,Networking,Science,Social Impact,symposium END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20231113T080000 DTEND;TZID=America/Detroit:20231113T190000 DTSTAMP:20231020T163040Z CREATED:20231020T163040Z LAST-MODIFIED:20231020T163040Z UID:10000659-1699862400-1699902000@micde.umich.edu SUMMARY:Conference / Symposium:U-M Data Science & AI Summit 2023 DESCRIPTION:The U-M Data Science and AI Summit is the largest annual data science and AI event on campus. This event brings together the U-M data science and AI research community and their external collaborators to build research vision and collaboration. It also showcases the breadth and depth of U-M data science and AI research\, from theory and methodology development to the transformative use of data and AI to address scientific and societal challenges in all domains. The event is free for all attendees (U-M faculty\, staff\, and trainees\, as well as industry\, government and community members).\nTo view full Summit schedule\, please visit the event webpage at https://midas.umich.edu/midas-summit-2023/.\nKeynotes:\nSuresh Venkatasubramanian\, Director\, Center for Technological Responsibility\, Reimagination\, and Redesign\, Data Science Institute at Brown University; Professor of Data Science and Computer Science\, Brown University – Key player for the White House Blueprint of an AI Bill of Rights\nJulianne Dalcanton\, Director\, Center for Computational Astrophysics\, Flatiron Institute – The origina and evolution of galaxies\nEmre Kiciman\, Senior Principal Researcher\, Microsoft Research – A New Frontier at the Intersection of Causality and LLMs\nSummit Sessions:\nA panel discussion on: Federal priorities and opportunities in data science and AI\nPanelists:\n– Laura Biven\, Data Science Technical Lead\, Office of Data Science Strategy\, National Institutes of Health\n– Michael Molnar\, Director\, Advanced Manufacturing National Program Office\, National Institute of Standards and Technology\n– Hector Muñoz-Avila\, Program Director and Cluster Lead\, the Information Integration and Informatics Program\, National Science Foundation\n– Alvaro Velasquez\, Program Manager\, Information Innovation Office\, Defense Advanced Research Projects Agency\nResearch vision talks by University of Michigan faculty researchers\nThe Propelling Original Data Science grant awardees showcase\nPoster session\, lightning talks\, and awards\nUniversity of Michigan data science and AI organizations showcase URL:https://micde.umich.edu/event/conference-symposiumu-m-data-science-ai-summit-2023/ LOCATION:Rackham Graduate School (Horace H.) CATEGORIES:Ai In Science And Engineering,Applications,Artificial Intelligence,big data,Biostatistics,Climate and Space Sciences and Engineering,Computational Modeling,Computational Science,Computational Social Science,computing,Data Curation,Data Science,data visualization,Deep Learning,Electrical And Computer Engineering,Electrical Engineering and Computer Science,Engineering,Faculty,Free,Graduate,Graduate Students,Industrial and Operations Engineering,Information and Technology,Interdisciplinary,Lecture,Machine Learning,Michigan Engineering,Midas,Natural Language Processing,Networking,Science,Social Impact,symposium END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20231107T120000 DTEND;TZID=America/Detroit:20231107T130000 DTSTAMP:20231019T200916Z CREATED:20230914T150100Z LAST-MODIFIED:20231019T200916Z UID:10000639-1699358400-1699362000@micde.umich.edu SUMMARY:MICDE Ph.D. Student Seminars: Bernardo Pacini & Srinivasan Arunachalam DESCRIPTION:The MICDE PhD Student Seminar Series showcases the research of students in the Ph.D. in Scientific Computing. These events are open to the public\, but we ask that you register to attend the seminar. If you have any questions\, please email micde-events@umich.edu. \nRegister to attend this seminar \nGradient-Based Multidisciplinary Design Optimization for Propeller Design\nUrban air mobility (UAM) vehicles have taken form as advanced rotorcraft with sets of wings\, rotors\, canards\, and other appendages. Noise generation is an important technical barrier that must be addressed to prevent these vehicles from causing excessive disturbance to the communities they are intended to service. To understand the noise these vehicles generate\, and to develop designs that can minimize disturbance\, there is a need for analysis and optimization tools specifically for the conceptual design and sizing phase of urban air mobility vehicle development. Such tools must be computationally efficient to allow for the repeated analyses needed for design optimization. This presentation will review the work being carried out at the University of Michigan\, coupling aerodynamic\, structural\, and aeroacoustic disciplines within the multidisciplinary gradient-based design optimization framework OpenMDAO. While aerostructural optimization has been performed previously\, coupling with aeroacoustics is challenging given the requirement for time accurate simulations and the associated computational cost of such analyses. By leveraging multiple model fidelities and utilizing efficient gradient calculation techniques\, such as the adjoint method and algorithmic differentiation\, these disciplines can be formulated into an optimization framework that can be applied to UAM vehicle designs. This presentation will review the work completed to date\, including preliminary results\, and expand on the future goals of the project\, working towards a broader optimization framework for rotorcraft vehicle design optimization. \nBernardo Pacini\, Ph.D. candidate in Mechanical Engineering and Scientific Computing \nBernardo Pacini is a Ph.D. Candidate at the University of Michigan focusing his research on aerodynamic\, structural\, and aeroacoustic modeling for multidisciplinary design optimization of urban air mobility vehicles. He is a member of the Multidisciplinary Design Optimization Laboratory led by Professor Joaquim R. R. A. Martins and of the Computational Aerosciences Laboratory led by Professor Karthik Duraisamy. Bernardo’s work to date is on developing an aero-structural-acoustic analysis framework that can be implemented within the multidisciplinary design optimization process for rotorcraft and urban air mobility vehicle design. \nRegister to attend this seminar \nValidation of a multivariate non-Gaussian\, non-stationary wind pressure simulation model for performance-based wind engineering\nWith a growing interest in probabilistic performance assessments of building systems subjected to wind loads\, there is a demand for accurately representing building-specific wind loads\, considering their non-Gaussian and non-stationary features. While typical wind tunnel data collected for a set of discrete wind directions provide a single realization of stationary pressures\, there is currently no experimentally validated model for the stochastic simulation of non-Gaussian and non-stationary wind pressures that can be calibrated to wind tunnel datasets. Such a model is essential for simulating building aerodynamics\, especially the stochastic\, path-dependent responses associated with time-varying wind speed and direction experienced by a building during hurricane wind events. This talk will review a recently developed theoretical formulation for generating these stochastic pressures. Through carefully designed tests conducted at the University of Florida wind tunnel facility\, the formulation was extensively validated with respect to its ability to capture trends over time\, occurrences of peaks\, and time-varying frequency content. \nSrinivasan Arunachalam\, Ph.D. candidate in Civil and Environmental Engineering and Scientific Computing \nSrinivasan Arunachalam is a PhD candidate in Civil and Environmental Engineering. His research interests lie in uncertainty quantification and understanding the inelastic behavior of wind-excited structures. He is excited about the algorithmic developments that enable efficient reliability assessments\, as well as the evolving insights into the physics of extreme responses and their implications for structural design. \nRegister to attend this seminar URL:https://micde.umich.edu/event/phd-seminar-bernardo-pacini-srinivasan-arunachalam/ LOCATION:2022 South Thayer Building CATEGORIES:Computation,Computational Modeling,Computational Science,Engineering,Free,Graduate,Graduate and Professional Students,Graduate School,Graduate Students,In Person,Interdisciplinary,Michigan Engineering,Phd Seminar,Prospective Graduate Students,Rackham,Science,Scientific Computing,Seminar,Sessions,Talk ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2023/09/2023117-Bernardo-Pacini-and-SrinivasanArunachalam-1.png END:VEVENT END:VCALENDAR