Michigan Institute for Computational Discovery and Engineering

MICDE Ph.D. Student Seminars: Jamie Holber
2023-11-14 12:30 - 13:00 (America/Detroit) | 2022 South Thayer Building
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.
Active Learning for Physics Informed Data Sampling and Construction of Free Energy Representations
Using 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.
Jamie Holber, Ph.D. candidate in Applied Physics and Scientific Computing
Jamie Holber is a PhD Candidate in Applied Physics and Scientific Computing working in the Computational Physics Group in the Mechanical Engineering Department.
Advisor: Krishna Garikipati
Register to attend this seminar

MICDE Ph.D. Student Seminars 2023-2024: Guoer Liu
2023-11-28 12:00 - 13:00 | 2022 South Thayer Building
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.
Register to attend this seminar
When Is Big Data Biased?
Using 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.
Guoer Liu, Ph.D. candidate in Political Science and Scientific Computing
Guoer 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.
Advisors: Mary Gallagher, Charles Shipan
Register to attend this seminar

MICDE Ph.D. Student Seminars 2023-2024: Jeffrey Hatch
2023-12-05 12:00 - 13:00 | 2022 South Thayer Building
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.
Computational Methods in Chemistry
Abstract coming soon…
Jeffrey Hatch, Ph.D. candidate in Chemistry and Scientific Computing
Bio coming soon…
Register to attend this seminar

MICDE Ph.D. Student Seminar: Lingfeng Luo
2024-03-05 12:00 - 12:30 | Duderstadt Center – 1180
Please register to attend
Lingfeng Luo will present: Gradient Boosting-Based Discrete Failure Time Model for Selecting Time-Varying Effects and Interactions as a part of the MICDE PhD Student Seminar Series.
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 all attendees register in advance so that we can be prepared with lunch for all attendees.
If you have any questions, please email micde-events@umich.edu.
Please register to attend

Workshop / Seminar:MICDE Ph.D. Student Seminar: Hannah Van Wyk and Musawer Ahmad Saqif
2024-03-12 12:00 - 13:00 | Duderstadt Center – 1180
Hannah Van Wyk will present her talk: Using a hidden markov model to pinpoint the date of the primary case in a dengue outbreak in rural Ecuador.
An infectious disease outbreak investigation typically consists of retrospectively determining information regarding the outbreak such as the timing of the primary case (the first case of the outbreak, whether detected or not) and transmission dynamics that occurred prior to the outbreak. However, information on the primary case is often hard to obtain, especially in scenarios where the disease has a high asymptomatic ratio. Using a hidden Markov model, we estimate the most likely date of the primary case of a dengue outbreak in a small community in northern coastal Ecuador.
*Bio*:
Hannah is a doctoral student in the Department of Epidemiology. Her research involves using mathematical models of infectious diseases to understand transmission dynamics. She has a background in mathematics and computer science at her undergraduate institution and a Master of Public Health in Epidemiology.
Musawer Ahmad Saqif will present his talk: Fracture and Collapse Simulation of UHPC Structures.
This presentation delves into the advanced simulation techniques for understanding the fracture and collapse behaviors of ultra-high performance concrete (UHPC) structures. Leveraging finite element modeling and experimental validation, the study elucidates the critical mechanical properties and failure mechanisms of UHPC under various conditions. Through a detailed investigation of microstructural characteristics and their impact on macroscopic performance, the research provides valuable insights into optimizing the design and resilience of UHPC structures against extreme loading scenarios. The findings contribute significantly to the field of civil engineering by enhancing the predictive capabilities for the structural integrity and durability of UHPC infrastructures.
*Bio*:
M. A. Saqif is a fifth year PhD student in Civil Engineering and Scientific Computing at the Computational Structural Simulation Lab. His research interest lies in fracture and collapse simulation of concrete structures.
*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.
If you have any questions, please email micde-events@umich.edu.*

SciFM24 Conference
2024-04-02 09:00 - 17:00, 2024-04-03 09:00 - 17:00 | Rackham Graduate School (Horace H.) – Amphitheater
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.
This 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.

MICDE Ph.D. Student Seminar: Jeffrey Hatch and Jiadong Chen
2024-04-09 12:00 - 13:00 | Duderstadt Center – 1180
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 all attendees register so that we can prepare properly. If you have any questions, please email micde-events@umich.edu.
Register to attend this seminar

Jeffrey Hatch: An introduction to the many-body basis set amelioration method
Modeling the interaction of subatomic particles in chemical species has become a routine part of modern chemical research as this analysis can be used to find a system’s energy, thermodynamic properties and more. The algorithms that can accomplish this vary in their accuracy and computational cost, with more accurate methods generally incurring more cost. Additionally, the more subatomic particles present, the more costly the calculation will be. A computationally intensive family of methods known as select configuration interaction methods are generally thought of as the most accurate and most computationally expensive family of methods. However, due to their high cost, it is requisite that we investigate mechanisms of diminishing the cost while maintaining accuracy. The nature of one such method, incremental full configuration interaction (iFCI) lends itself to accurate extrapolations based on much cheaper levels of theory in smaller basis sets. This is known as the many-body basis set amelioration (MBBSA) method. The MBSSA method has be shown to reduce the cost of a given calculation by as much as 80% without loss in the accuracy of the calculation.

Jeffrey Hatch, Ph.D. candidate in Chemistry and Scientific Computing
Jeffrey is in the Zimmerman group in the chemistry department. He is interested in using HPC methods to improve quantum chemical calculations especially in the configuration interaction realm of electronic structure calculations.

Register to attend this seminar

Jiadong Chen: High dimensional phase diagrams: Engineering relative stability in 4-dimensions
Sequential learning algorithms based on Bayesian optimization are routinely being deployed for materials stability optimization in high-parameter spaces. We anticipate these optimization methods would perform better if they were built upon stronger priors, for example, as derived from the fundamental thermodynamics underlying the equilibrium behavior of materials. Here, we present a thermodynamics-based technique to optimize the relative stability of a materials in high-dimensional thermodynamic space, based on a new derivation of a generalized high-dimensional Clausius Clapeyron relation. Using this thermodynamic infrastructure, we design several pathways to enhance the relative acid stability of Mn-oxides versus its dissolved states for potential electrochemical catalyst application. We construct a 4-D Pourbaix diagram with pH, redox potential E, particle radius 1/R and a chemical potential μK as axis. By exploring the gradients of the high-dimensional phase boundaries, we derive first-principles insights that nano-sizing (1/R) and certain doping ions (μK) can stabilize some metastable Mn-oxides polymorphs, where 1/R decreases acid stability and μK increases it. Our high-dimensional thermodynamic framework is a general method to engineer relative stability in parameter spaces that leverage multiple forms of thermodynamic work.

Jiadong Chen, Ph.D. candidate in Materials Science and Engineering and Scientific Computing
Jiadong Chen is a 5th year PhD in materials science and engineering department, Wenhao Sun group, focusing on use computational and data-driven methods to predict materials stability and synthesis recipes.

Register to attend this seminar

Workshop / Seminar:MICDE Ph.D. Student Seminar: Anna Halstenbach and Katie Nissen
2024-04-16 12:00 - 13:00 | Duderstadt Center – 1180
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 all attendees register so that we can prepare properly. If you have any questions, please email micde-events@umich.edu.
Register to attend this seminar

Anna Halstenbach: “Uncovering the network of political parties’ event sponsors in Germany”
Gray money in politics – those contributions that don’t fall under transparency regulations but cannot be made completely anonymous either, for example sponsorships of political parties’ events – could change our understanding of money as a signal. However, the fact that neither political parties nor firms are obliged to disclose such transactions makes studying them difficult. In the talk, I will present one approach to measure gray money on the basis of tweets from party conferences.

Anna Halstenbach, Ph.D. candidate in Political Science and Scientific Computing
Anna earned her Bachelor’s degree from the University of Mannheim and her Master’s degree from Heinrich Heine University Düsseldorf. Her current research analyzes the distinct incentives within the regulatory frameworks governing political party finances in Germany and the United States. Additionally, she explores the impact of consumer behavior on firms’ decisions to be vocal about their political donations and take controversial political stances.

Register to attend this seminar

Katie Nissen: “Climate Change Protests and Gender: The Influence of Role Incongruence on Support for Social Movements”
In recent years, we have seen an increase in the incidences of radical protests about climate change in response to lackluster mitigation efforts worldwide. Historically, the environmental movement as a whole has long been an issue space where women have held prominent leadership positions. However, role congruity theory posits that women – due to being perceived as more passive and docile than men – may not be regarded as “proper” leaders of social movements, especially when such movements use radical tactics. How does the interaction of protest tactics and leadership gendering affect support for the current climate movement? In this paper, we report the results of a pilot survey experiment to address this question. Respondents were exposed to mock news stories about climate protests where they received one of four treatment conditions that varied the protest characteristics between combinations of radical/traditional tactics and women leadership/gender of leadership not specified. Respondents were then asked to report on their level of support for the movement, level of support for the protest, and the likelihood of the movement’s success. We also asked respondents to write in their most important reason for supporting the movement or not, to which we applied basic topic modelling to extract key themes. Ultimately, our pilot results show support for notion that radical protest tactics harm both support and perceptions of the movement, but that gender of the protest leadership does not make a significant difference. We believe that the latter result is most likely due to weakness of the gender treatment in the survey experiment, so future iterations of this work will seek to develop a stronger prime for this component of our analysis.

Katie Nissen, Ph.D. candidate in Political Science and Scientific Computing
Katie is a current fourth year Joint PhD Candidate in Political Science and Scientific Computing. Her research is primarily about factors that influence climate change discourse and public opinion, and her dissertation will focus on the effects of optimism and pessimism on climate attitudes.

Register to attend this seminar

MICDE Fellowships Information Session
2024-04-19 11:00 - 12:00 | Zoom Event
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!
These fellowships are sponsored by the Michigan Institute for Computational Discovery & Engineering. For more information, see https://live-umor-micde.pantheonsite.io/academic-programs/fellowships/.

SciFM24 Summer School
2024-07-08 09:00 - 2024-07-26 16:00
The SciFM Summer School runs from July 8 to July 26. The first instance is going to be run in parallel at the University of Michigan (UM) and at Argonne National Laboratory (ANL). The focus will be on developing technical plans for specific instances for SciFMs, a hackathon to jumpstart the construction of SciFMs, as an incubator for ideas, a training ground for researchers and students, and further a vision for a national SciFM ecosystem.

MICDE / MIDAS Graduate Information Session
2024-09-30 15:00 - 16:00 | 1100 North University Building – 1528
The educational programs represented are:

PhD in Scientific Computing (MICDE)
Graduate Certificate in Computational Discovery & Engineering (MICDE)
Graduate Certificate in Computational Neuroscience (MICDE)
Graduate Certificate in Data Science (MIDAS)

These 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.
If 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).

MICDE / MIDAS Graduate Information Session
2024-10-07 14:00 - 15:00 | Johnson Rooms, Lurie Engineering Center, 3rd Floor LEC 3213ABC, 1221 Beal Ave., Ann Arbor, MI, United States

Conference / Symposium:MICDE ACES Mini-Symposium 2024
2024-10-22 13:20 - 17:00 | Lurie Robert H. Engin. Ctr – Johnson Rooms, 3rd floor
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.
ACES 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!

Taking the Next Step: Graduate Studies in Computation/AI for Science and Engineering at U-M
2024-11-07 18:00 - 19:00 | GG Brown Laboratory – 2147
PhD in Scientific Computing director Eric Johnsen will speak about opportunities for undergraduate or master’s students seeking a graduate education in Computation and Artificial Intelligence for Science and Engineering at the University of Michigan. Food will be provided. Please register to attend.
Please register via the link: https://sessions.studentlife.umich.edu/p/track/12857
Zoom option available after registering.

Scientific Computing in the Physical Sciences information session
2025-04-01 16:00 - 17:00 | Weiser Hall – 170
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.
This session will be offered in-person and on Zoom. Please indicate how you plan to attend when you register.
Register to attend

Scientific Computing in the Biological and Health Sciences information session
2025-04-03 16:00 - 17:00 | Weiser Hall – 170
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.
This session will be offered in-person and on Zoom. Please indicate how you plan to attend when you register.
Register to attend

Frontiers in Scientific Machine Learning Seminar 17: SALSA-RL: Stability Analysis in the Latent Space of Actions for Reinforcement Learning
2025-07-11 12:00 - 13:00 | Walter E Lay Auto Lab – 2004
Date: June 6, 2025, 12pm – 1pm
This is a hybrid event. To join via Zoom: Meeting ID: 978 2352 7756, Passcode: [REDACTED] last year in format YYYY
To join in person: 2004 Lay Auto Lab. Refreshments will be available!
Abstract: Modern deep reinforcement learning (DRL) methods have made significant advances in handling continuous action spaces. However, real-world control systems–especially those requiring precise and reliable performance–often demand interpretability in the sense of a-priori assessments of agent behavior to identify safe or failure-prone interactions with environments. To address this limitation, we propose SALSA-RL (Stability Analysis in the Latent Space of Actions), a novel RL framework that models control actions as dynamic, time-dependent variables evolving within a latent space. By employing a pre-trained encoder-decoder and a state-dependent linear system, our approach enables interpretability through local stability analysis, where instantaneous growth in action-norms can be predicted before their execution. We demonstrate that SALSA-RL can be deployed in a non-invasive manner for assessing the local stability of actions from pretrained RL agents without compromising on performance across diverse benchmark environments. By enabling a more interpretable analysis of action generation, SALSA-RL provides a powerful tool for advancing the design, analysis, and theoretical understanding of RL systems.
Bio: Romit Maulik is an Assistant Professor in the College of Information Sciences and Technology at Pennsylvania State University (Penn State). He is also a co-hire in the Institute for Computational and Data Sciences at Penn State and a Joint Appointment Faculty at Argonne National Laboratory. He obtained his PhD in Mechanical and Aerospace Engineering at Oklahoma State University (in 2019) and was the Margaret Butler Postdoctoral Fellow (from 2019-2021) before becoming an Assistant Computational Scientist at Argonne National Laboratory (from 2021-2023). His group studies high-performance multifidelity scientific machine learning algorithm development with applications to various multiphysical nonlinear dynamical systems such as those that arise in fluid dynamics, geophysical modeling, nuclear fusion, and beyond. He is an Early Career Awardee of the Army Research Office.

Ph.D. in Scientific Computing Seminar Series
2025-10-07 11:45 - 12:45 | North Quad – 2185
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.
If you have any questions, please email micde-phd@umich.edu.
Register to attend

Bridging Wavefunctions and Density Functionals: Unlocking Accurate Data for Functional Development
Density 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.
Vaibhav Khanna (Chemistry and Scientific Computing)
Vaibhav 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.

Turbulence transport and size segregation of shock-driven multiphase flows
The 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.
Archana Sridhar (Aerospace Engineering and Scientific Computing)
Archana 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.

Ph.D. in Scientific Computing Seminar Series
2025-10-28 11:45 - 12:45 | North Quad – 2185
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.
If you have any questions, please email micde-phd@umich.edu.
Register to attend

Automated removal of artifactual false positive High Frequency Oscillations in intracranial EEG
High 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.
Ashley Tan (Mechanical Engineering and Scientific Computing)
Her 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.

Emergence of three-dimensional structures from vortex pair instabilities in shocked interfacial flows
The 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.
William White (Mechanical Engineering and Scientific Computing)
William 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.

Ph.D. in Scientific Computing Seminar Series
2025-11-04 11:45 - 12:45 | North Quad – 2185
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.
If you have any questions, please email micde-phd@umich.edu.
Register to attend

Embodying mechano-intelligence in mechanical metastructures for in-memory phononic learning
Mechano-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.
Yuning Zhang (Mechanical Engineering and Scientific Computing)
Yuning 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.

Global Probabilistic Geomagnetic Perturbation Forecasting
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 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.
Hongfan Chen (Mechanical Engineering and Scientific Computing)
Hongfan 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).

Ph.D. in Scientific Computing Seminar Series
2025-11-11 11:45 - 12:45 | North Quad – 2185
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.
If you have any questions, please email micde-phd@umich.edu.
Register to attend

Py-Conformational-Sampling: Towards Predicting Stereoselectivity
Stereoselective 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.
Soumik Das (Chemistry and Scientific Computing)
Soumik 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.

Density Functional Theory Simulations of Icosahedral Quasicrystals
Quasicrystals (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.
Woohyeon Baek (Materials Science and Engineering and Scientific Computing)
Woohyeon 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.

Data-Driven Development of Constitutive Equations for Thixotropic Waxy Oil Rheology for Flow Assurance Using Symbolic Regression and PINNs
Waxy 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.
Samuel Ogunwale (Chemical Engineering and Scientific Computing)
Samuel 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.

Ph.D. in Scientific Computing Seminar Series
2025-11-18 11:45 - 12:45 | North Quad – 2185
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.
If you have any questions, please email micde-phd@umich.edu.
Register to attend

Tailored Ultrashort Pulse Bursts in a Gain-Managed Nonlinear Fiber Amplifier for Coherent 50fs Pulse Stacking at mJ Energies
We 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.
Lauren Cooper (Electrical Engineering and Scientific Computing)
Lauren 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.

Leveraging multipole models to measure rotation in time-dependent potentials
Multipole 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.
Neil Ash (Astronomy and Scientific Computing)
Neil 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.

Tracing Refractory Material in the Inner 10 AU of Protoplanetary Disks
Planets 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.
Marbely Micolta (Astronomy and Scientific Computing)
I’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.

Ph.D. in Scientific Computing Seminar Series
2025-12-09 11:45 - 12:45 | North Quad – 2185
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.
If you have any questions, please email micde-phd@umich.edu.
Register to attend

Improving Slater Orbital Integration Accuracy through Prolate Spheroidal Coordinates
The core of electronic structure calculations is the integration of forces exerted on and by
electrons 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.
Alexander Stark (Chemistry and Scientific Computing)
This 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.

Multiscale Modeling of Radical and Vibrational Pathways in Plasma-Assisted Ammonia Synthesis on Fe(110) and Ni(111)
Low-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
due 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.
Oluwatosin Ohiro (Chemical Engineering and Scientific Computing)
Oluwatosin 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.

Quantifying the state of inflammation in invasive lobular breast cancer using a one-class logistic regression algorithm
After 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.
Kate Griffin (Biomedical Engineering and Scientific Computing)
Kate 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.

Ph.D. in Scientific Computing Student Seminars
2026-02-18 12:00 - 13:00 | Room 4425, Green Court Building
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.
If you have any questions, please email micde-phd@umich.edu.
Register to attend

Hidden Relics: The Past and Present Lives of Satellites Around MW-Mass Galaxies
Mergers are one of the most important drivers of galaxy evolution, as present-day galaxies have been built up over time through hierarchical evolution. The main bodies of galaxies have a diversity of structural properties that can be highly influenced by mergers; unfortunately, the response of a galaxy to a merger largely erases observational markers that allow us to infer the characteristics of the merger. But simulations have shown that material deposited into a galaxy through merger is retained by its stellar halo, thereby leaving a “fossil record” we can trace. My PhD thesis takes a multi-faceted approach to uncover this historical record and learn what processes govern how galaxies form and evolve, from massive Milky Way-like galaxies to their small, ultra-faint companions. I have harnessed the power of resolved-star photometry and spectroscopy to 1) create the deepest stellar halo map of the nearby galaxy M94, revealing that it underwent one of the quietest merger histories among galaxies of similar stellar mass, 2) illuminate the structural diversity of faint satellite galaxies around M81, improving ground-based characterizations and finding one of the most concentrated satellites we know of, and 3) make the first-ever measurement of the kinematics of NGC 253’s stellar halo, finding that it has slight prograde bulk motion and pioneering fiber-fed spectroscopy in a low S/N regime. With the techniques I have developed, I am laying the foundation for doing resolved stellar population science with next-generation observing facilities such as the Rubin Observatory, Roman Space Telescope, and the ELT.
Katya Gozman (Astronomy and Scientific Computing)
Katya is a 6th year PhD student in the Astronomy Department working with Prof. Eric Bell. She uses ground- and space-based observations of resolved stars in the outskirts of nearby galaxies to understand their merger histories and satellite populations.

Fracture Criterion for Ultra-Low Cycle Fatigue Based on Measured Void Characteristics
Commonly used ultra-low cycle fatigue (ULCF) fracture models rely on idealized void shapes and sizes. However, the void shapes generated by real fracture processes are irregular, forming non-uniform half-dimples and voids on the fracture surface. Therefore, a gap remains in validating the link between simulated void behavior and fracture initiation with actual fracture surface data. To address this, monotonic tensile and ULCF tests were performed on axisymmetric circumferential tensile (CNT) specimens with medium to high stress triaxiality, and dimple-voids were examined using scanning electron microscope (SEM) fractographs. For the first time, a correlation between simulated and actual void formation under ULCF was established, leading to a new fracture criterion based on measured void features.
Min-Chun Han (Civil and Environmental Engineering and Scientific Computing)
Min-Chun is a Ph.D. candidate in Civil and Environmental Engineering. Her research focuses on the behavior of structures and structural materials under extreme loading and environmental conditions.

A Holistic Performance-Based Framework for Assessing Coupled Building Envelope–Structural System Performance under Extreme Winds
High-rise building envelopes are vulnerable to extreme winds, requiring robust performance assessment to ensure safety. Existing models often assume linear or simplified post-elastic structural behavior, overlooking strong nonlinearities that can arise near collapse. This study presents a performance-based computational framework combining high-fidelity nonlinear structural modeling with a progressive damage model for envelope assessment. Localized damage mechanisms, including yielding, buckling, low-cycle fatigue, and fracture, are simulated, and envelope vulnerability is quantified via component-level sequential fragility functions. Dynamic wind pressures are captured using wind tunnel-informed stochastic models, while internal pressures at damage-induced openings are estimated via Bernoulli’s equation and mass conservation. A case study of a 45-story reinforced concrete building in New York City providing insights into the global probabilistic performance and the local coupled progression of envelope and structural damage under extreme wind events.
Jieling Jiang (Civil and Environmental Engineering and Scientific Computing)
She is currently a phd candidate in the civil engineering department, working on developing next-generation probabilistic modeling frameworks for high-rise building systems under extreme natural hazards. Her research involves high fidelity simulation and stochastic simulation methods.

Register to attend

Ph.D. in Scientific Computing Student Seminars
2026-02-25 12:00 - 13:00 | Room 4425, Green Court Building
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.
If you have any questions, please email micde-phd@umich.edu.
Register to attend

Estimating potential-dependent physicochemical properties at metal–electrolyte interfaces using machine learning interatomic potentials
Metal–electrolyte interfaces play a central role in electrocatalysis, energy storage, and environmental remediation. Understanding the structure and properties of these interfaces is therefore essential to designing efficient electrochemical systems. Density functional theory (DFT)-based molecular dynamics (MD) can accurately capture interfacial structure but is restricted to short timescales and small system sizes. To overcome these limitations, we develop machine learning interatomic potentials (MLIPs) using the MACE architecture within an active learning workflow to model aqueous NaCl electrolytes in contact with Au, Cu, and Rh (111) electrodes. The resulting committee of MLIPs achieves DFT-level accuracy across 21 electrolyte–metal systems spanning a wide range of surface charge densities. MACE–MD simulations reproduce key interfacial properties obtained from ab initio MD, including water density profiles, water orientation, and chemisorbed water coverage.
Our simulations reveal a universal trend across all metals: the total coverage of water and ions decreases with increasing surface charge density or potential, reaches a minimum at or slightly below the pzc, and increases thereafter. Overall, this work demonstrates that MLIPs based on the MACE architecture enable long-timescale, first-principles-accurate simulations of metal electrolyte interfaces and provide detailed mechanistic insight into their potential-dependent physicochemical properties.
Ankit Mathanker (Chemical Engineering and Scientific Computing)
Ankit Mathanker is a Ph.D. researcher in Chemical Engineering in the Goldsmith Lab. His work leverages DFT, AIMD, and machine-learning interatomic potentials to understand and predict electrochemical interfacial phenomena relevant to catalysis and energy conversion.

Predictive Modeling and Inverse Design of High-Entropy Semiconductor Alloys
The vast compositional space of high-entropy semiconductors offers unprecedented tunability but presents a significant challenge for traditional screening methods. This talk outlines a multi-tiered computational strategy designed to navigate this complexity, applied specifically to ferroelectric high-entropy III-nitrides (AlGaInScY-N). We detail a comprehensive workflow that begins with high-throughput first-principles calculations to generate accurate stability and property datasets. We then demonstrate how this data fuels a dual-pronged AI approach, which uses generative machine learning (symbolic regression) to discover interpretable governing equations for phase stability, and black-box machine learning models to rapidly predict structural properties and band gaps beyond the training set. This synergistic framework not only accelerates materials discovery but also reveals the physical descriptors driving entropy stabilization and ferroelectric performance.
Yujie Liu (Materials Science and Engineering and Scientific Computing)
Yujie is a Ph.D. student from materials science and engineering. He is working on semiconductor materials design, combineing high-throughput first-principles workflows with surrogate machine-learning models.

Rapid 3D Localization of Cavitation Events for Histotripsy Monitoring
Histotripsy is a noninvasive ultrasound therapy that relies on controlled cavitation to mechanically fractionate tissue, but accurately localizing cavitation events in real time remains a challenge, particularly in the presence of acoustic aberrations and attenutation. This talk presents computational and experimental methods for rapid three-dimensional localization of inertial cavitation events using a large-aperture, receive-capable focused ultrasound array. By combining narrowband signal processing with passive acoustic mapping techniques, these methods enable high-accuracy cavitation localization at clinically relevant treatment rates. Experimental validation using optical imaging and rib-mimicking phantoms demonstrates the potential of these approaches for treatment monitoring and feedback control in therapeutic ultrasound.
Mikey Komaiha (Biomedical Engineering and Scientific Computing)
Mikey is a Ph.D. candidate in the Department of Biomedical Engineering at the University of Michigan and a member of the Histosonics research group. His research focuses on computational signal processing and experimental methods for cavitation localization and monitoring in therapeutic ultrasound applications.

Register to attend

Ph.D. in Scientific Computing Student Seminars
2026-03-25 12:00 - 13:00 | Room 4425, Green Court Building
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.
If you have any questions, please email micde-phd@umich.edu.
Register to attend

A Parametric Approach for Solving Convex Quadratic Optimization with Indicators Over Trees
This talk investigates convex quadratic optimization problems involving n indicator variables, each associated with a continuous variable, particularly focusing on scenarios where the matrix Q defining the quadratic term is positive definite and its sparsity pattern corresponds to the adjacency matrix of a tree graph. We introduce a graph-based dynamic programming algorithm that solves this problem in time and memory complexity of O(n2). Central to our algorithm is a precise parametric characterization of the cost function across various nodes of the graph corresponding to distinct variables. Our computational experiments conducted on both synthetic and real-world datasets demonstrate the superior performance of our proposed algorithm compared to existing algorithms and state-of-the-art mixed-integer optimization solvers.
Aaresh Bhathena (Industrial and Operational Engineering and Scientific Computing)
Aaresh Bhathena is a PhD student in Industrial and Operations Engineering at the University of Michigan, advised by Professor Salar Fattahi. His research focuses on solving optimization problems that arise in machine learning and operations research.

Reconstruction of 3D Bacterial Genome Structures from Hi-C Data Using Diffusion Model
In this talk, I will present a generative framework for reconstructing three-dimensional bacterial genome structures from Hi-C data. Existing methods predominantly yield a single deterministic structure, overlooking the inherent heterogeneity and dynamic nature of chromosome organization. To address this limitation, I applied a conditional latent diffusion model that generates ensembles of genome conformations conditioned on contact frequencies. This project aims to deliver a diffusion-based reconstruction method that provides uncertainty-aware, population-level representations of bacterial genome organization.
Xiaofeng Dai (Chemistry and Scientific Computing)
Xiaofeng’s research focuses on bacterial genome organization. His work integrates quantitative microscopy and data-driven analysis to understand how chromosomes are structured and regulated in bacteria cells.

Incorporating Logic in Online Preference Learning for Safe Personalization of Autonomous Vehicles
Customizing autonomous vehicles to align with user preferences while ensuring safety may significantly impact their adoption. Collecting user preference data by asking a large number of comparison questions can be demanding. In this work, we use active learning along with temporal logic descriptions of constraints to enable safe learning of preferences with a reduced number of questions. We take a Bayesian inference approach combined with Weighted Signal Temporal Logic (WSTL), resulting in a WSTL formula that can rank signals based on user preferences and be used for correct-and-custom-by-construction control synthesis. Our method is practical for formulas and signals with various complexity since we compute STL-related values offline. We provide an upper bound for the number of answers in disagreement with user answers. We demonstrate the performance of our method both on synthetic data and by human subject experiments in an immersive driving simulator. We consider two driving scenarios, one involving a vehicle approaching a pedestrian crossing and the other with an overtake maneuver. Our results over synthetic experiments with ground truth weight valuation show that our query selection algorithm converges faster than random query selection. Human subject study results show an average agreement of 94% with user answers during training, and 79% during validation (which increases to 86% when restricted to high confidence results).
Ruya Karagulle (Electrical and Computer Engineering and Scientific Computing)
Ruya Karagulle is a PhD candidate in the Ozay Group whose research focuses on integrating formal methods and human feedback for safe and personalized control synthesis. Her work has been recognized through multiple fellowship awards, including Rackham Predoctoral Fellowship.

Register to attend

Ph.D. in Scientific Computing Student Seminars
2026-04-08 12:00 - 13:00 | Room 4425, Green Court Building
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.
If you have any questions, please email micde-phd@umich.edu.
Register to attend

Physics-based Simulation of Solar Energetic Particles Using the Solar Wind with Field Lines and Energetic Particles Model
Solar energetic particles (SEPs) can pose hazardous radiation risks to both humans and spacecraft electronics in space. In this talk, we present recent advances in physics-based simulation of solar energetic particles (SEPs) using the Solar Wind with Field Lines and Energetic Particles (SOFIE) model within the Space Weather Modeling Framework. We describe the development of a particle-conserving numerical scheme for particle acceleration and transport, together with a shock-capturing tool for coronal mass ejection-driven shocks, and show their application to one of the historical SEP events with multi-spacecraft comparison. We also discuss SOFIE’s recent evaluation unnder a simulated operational condition at NOAA’s Space Weather Prediction Center, where we demonstrated its ability to deliver SEP forecasts significantly faster than real time, supporting future space weather forecasting and human space exploration.
Weihao Liu (Climate and Space Sciences and Engineering and Scientific Computing)
Weihao Liu is a Ph.D. student in Climate and Space Sciences and Engineering at the University of Michigan. His research focuses on physics-based modeling of solar coronal mass ejections and solar energetic particles, and space weather forecasting, with an emphasis on developing and applying numerical models to understand particle acceleration and transport in the heliosphere.

Register to attend

2026 MICDE Predictive Science Conference
2026-04-14 08:00 - 17:00, 2026-04-15 08:00 - 17:00 | Palmer Commons – Forum Hall
This conference will center around predictive science. Fueled by advances in artificial intelligence and high-performance computing, predictive science is poised to evolve dramatically over the next few years. Featuring presentations and panel discussions from leading voices across academia, national laboratories, industry, and the government, the conference will bring together researchers in high-performance computing, verification and validation, uncertainty quantification, and artificial intelligence to discuss the state of the field of predictive science and its future outlook.
