Michigan Institute for Computational Discovery and Engineering

Ph.D. in Scientific Computing Seminar Series
2025-11-18 11:45 - 12:45 (America/Detroit) | 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.
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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.

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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.

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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.

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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.

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Ph.D. in Scientific Computing Student Seminar
2026-04-22 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.
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Thomas Coons (Mechanical Engineering and Scientific Computing)

Celia Kelly (Mechanical Engineering and Scientific Computing)

Liliang Wang (Aerospace Engineering and Scientific Computing)

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Ph.D. in Scientific Computing Student Seminar
2026-04-29 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

Profit-Driven Polarization: The Algorithmic Market for Partisan Attention
[Removed]
Jun Fang (Political Science and Scientific Computing)
Jun Fang is a PhD candidate at the University of Michigan, where he is pursuing a joint degree in Political Science and Scientific Computing.

Ganlin Chen (Materials Science and Engineering and Scientific Computing)

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Ph.D. in Scientific Computing Student Seminar
2026-06-03 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

Persona-Based Modeling of Human Opinion from Social Media at Population Scale
What does it take to simulate a specific human being rather than a demographic stereotype? While large language models (LLMs) generate plausible human-like text, existing simulations rely heavily on demographic correlations, which strip away individual heterogeneity and yield concentrated, homogenous responses. We introduce SPIRIT (Semi-structured Persona Inference and Reasoning for Individualized Trajectories), a framework designed explicitly for simulation rather than prediction. SPIRIT infers psychologically grounded, semi-structured personas from public social-media traces, integrating structured attributes (e.g., personality traits and world beliefs) with unstructured narrative signals reflecting values and lived experience. These personas condition LLM-based agents to act as specific individuals when answering survey questions or responding to events. Using the Ipsos KnowledgePanel, a nationally representative probability sample of U.S. adults, we show that SPIRIT-conditioned simulations recover self-reported responses more faithfully than demographic baselines and reproduce human-like heterogeneity in response patterns. We further demonstrate that persona banks can function as virtual respondent panels for studying both stable attitudes and time-sensitive public opinion.
Mao Li (Survey and Data Science and Scientific Computing)
Mao Li is a Ph.D. candidate in Survey and Data Science and Scientific Computing at the University of Michigan. His research develops and applies large language models and other computational methods to study public opinion, social media discourse, and survey-related questions.

Numerical Study of Bidirectional Shallow-Water Wave Kinetics
The traditional view is that one-dimensional shallow-water waves do not admit a wave kinetic description, as their dynamics can be described by integrable systems. We revisit this problem by studying bidirectional shallow-water waves using the integrable Kaup-Boussinesq (KB) equation and a related non-integrable variant. For both systems, a normal-form transformation yields interaction coefficients with the same general structure, differing only through the dispersion relation. We numerically confirm that the coefficient vanishes exactly on the resonant manifold for the KB equation, consistent with integrability, while the non-integrable model admits a non-zero resonant coefficient and thus a non-trivial wave kinetic equation (WKE).
The WKE is derived in the infinite-domain, weak-nonlinearity limit, where the dynamics are dominated by exact resonances. In numerical simulations, we no longer operate in this regime as computations are performed on a discrete grid at finite nonlinearity. Consequently, exact resonances may be sparse or absent, allowing for quasi-resonant interactions to play a significant role. We perform a set of numerical experiments demonstrating that these quasi-resonant interactions govern the observed spectral evolution. Despite differing on the exact resonant manifold, the integrable KB and non-integrable models exhibit nearly identical stationary spectra, revealing the dominant role of near-resonant interactions and elucidating the wave-kinetic picture in shallow-water and integrable systems.
Ashleigh Simonis (Naval Architecture & Marine Engineering and Scientific Computing)
Ashleigh is a Ph.D. candidate in the Department of Naval Architecture and Marine Engineering, advised by Dr. Yulin Pan. Her research focuses on theoretical and numerical studies of wave turbulence and coherent structures in dispersive wave systems.

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Ph.D. in Scientific Computing Student Seminar
2026-06-25 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
Transforming Static Trusses into Functional Shape-morphing Systems
Deployable and reconfigurable infrastructure is crucial for applications such as post-disaster recovery response, temporary shelters, and aerospace systems. However, most civil structures, especially trusses, are characterized by a slow, member-by-member assembly and are locked into a single configuration post-construction. These properties make trusses ill-suited for applications requiring rapid deployment, compact storage, and controlled shape change.
To address this gap, my PhD research develops a framework that transforms static trusses into reconfigurable systems while preserving the structure’s load paths and load-bearing behavior. The framework uses quadrilateral linkage principles and member force information from a design load case to introduce additional joints in the triangular units of a truss. These joints convert triangles into flat-foldable quadrilateral linkages, in turn, adding global reconfigurability to the truss structure.
In this framework, fabrication-aware design strategies account for member thickness and joint clearances to prevent unintended overlap and interlocking during deployment of physical prototypes. Complementary kinematic simulation tools help visualize deployment trajectories and evaluate the packing efficiency during the reconfiguration of these systems.
To explore functional applications beyond civil structures, the reconfigurable trusses are augmented with torsional springs that introduce rotational resistance at the reconfiguring joints. Our work also develops computational formulations for rotational hinges and integrates them into nonlinear structural solvers to evaluate actuation force demands and system-level mechanical response. This approach enables controlled deployment and absorption of energy from impact loads through elastic buckling and multistable mechanical behavior.
Together, the tools developed in this work provide practical methods for making existing static structural designs reconfigurable and enabling programmable mechanical responses in adaptable systems beyond civil engineering.
Hardik Patil (Civil & Environmental Engineering and Scientific Computing)
Our speaker today is Hardik Patil, a fifth-year PhD student in Civil Engineering at the University of Michigan. He works with Professor Evgueni Filipov in the Regenerative, Architected, and Reconfigurable Structures Lab, where his research focuses on the design and analysis of deployable and reconfigurable bar-linked structures. Hardik earned a bachelor’s degree in Civil Engineering from the Indian Institute of Technology Bombay and a master’s degree in Structural Engineering from the University of Michigan. Today, he will presenting his work on “Transforming Static Trusses into Functional Shape-Morphing Systems”.

Trust-worthy LLM Agent for science
Density functional theory (DFT) underpins computational materials discovery, but building high-fidelity workflows demands expertise that bottlenecks exploration. Large language model (LLM) agents promise automation, yet their use in rigorous science is undermined by a trust gap: they hallucinate or misattribute numerical values, rarely support the parameter-sensitive periodic simulations central to materials work, and diagnose failures only superficially. We introduce DREAMS (DFT-based Research Engine for Agentic Materials Simulation), a hierarchical multi-agent framework for periodic solid-state DFT built around auditable results. A planning supervisor coordinates worker agents for DFT setup, HPC execution, and convergence diagnosis through the canvas, a shared memory that tracks every value’s provenance rather than passing raw text. Trust is enforced at two layers: deterministic safety guards block fabrication and value mismatch at the moment of tool use, and a report-judge agent audits each report against rules for provenance, parameter sensitivity, and rationale quality. When results still surprise, a debug tool walks the value-flow graph parameter by parameter, ruling out hidden errors before any finding is claimed as genuine. DREAMS reaches human-expert-level accuracy on Sol27LC lattice constants, resolves the contested “CO/Pt(111) puzzle” in agreement with the literature, and quantifies functional uncertainty via Bayesian statistics. These results mark a step toward L3-level automation, and the provenance, verification, and debugging mechanisms generalize to any agentic scientific workflow where numerical claims must be trusted.
Ziqi Wang (Mechanical Engineering and Scientific Computing)
Ziqi Wang is a Ph.D. candidate in Mechanical Engineering at the University of Michigan, where he is advised by Prof. Venkat Viswanathan. His research focuses on trustworthy agentic AI for computational materials science, combining large language models with high-fidelity physics-based simulations to enable autonomous scientific discovery. He leads the development of DREAMS, a hierarchical multi-agent framework for autonomous density functional theory workflows, and has applied these ideas to catalyst screening, materials discovery, and solid-state battery problems. His broader interests include first-principles thermodynamics, phase stability, interfacial materials design, and machine learning for accelerated materials screening.

Topic Modeling of Firearm-Related Social Media Content for Survey Development
Firearm violence increasingly reaches emerging adults through social media and online news, yet validated measures of online firearm violence exposure remain limited. This study applied natural language processing to firearm-related posts from Reddit and Twitter/X to inform the development of an online firearm exposure measure. After filtering irrelevant content with a fine-tuned BERT spam classifier, three topic modeling methods, Latent Dirichlet Allocation, Non-negative Matrix Factorization, and BERTopic, were applied across a large corpus of social media posts and comment threads. Topics were consolidated into nine themes that guided concrete measurement decisions, showing how topic modeling can support instrument development on sensitive topics.
Esther Lee (Health Behavior & Health Equity and Scientific Computing)
Esther Lee is a PhD candidate in Health Behavior and Health Equity at the University of Michigan School of Public Health. Her research involves examining the multilevel correlates and consequences of interpersonal gun violence, with particular attention to how firearm violence exposure affect mental health among adolescents and emerging adults.

Ph.D. in Scientific Computing Student Seminar
2026-08-11 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
Improve influenza burden estimation through multiplier prediction and true case estimation
Influenza infection burden estimation in the US currently relies on multipliers that are not timely updated. This study aims to utilize Michigan Medicine patient records and local longitudinal cohorts to describe and predict next season values for influenza case-to-hospitalization ratios and percent of cases seeking outpatient care using machine learning classification models. We also estimated true number of inpatient and outpatient influenza cases in the capture population of five Michigan Medicine sites, which was validated through scenario simulations using an agent-based model.
Troy Zirui Zhou (Epidemiology and Scientific Computing)
Zhou research involves influenza burden estimation, phylodynamics, and modeling.

Fluttering and Tumbling in Falling Plates
Falling thin bodies are ubiquitous in nature: gliding birds and falling leaves are familiar examples. In this talk, we discuss how fast, low order numerical models can help explain the fundamental physics behind such motions.
Yu Jun Loo (Mathematics and Scientific Computing)
Loo Yu Jun is a PhD candidate in pure mathematics and scientific computing. He works with Professor Silas Alben on developing fast computational methods for bio-locomotion.

From the Method of Averaging to a Numerical Method to Simulate Neuronal Populations
We present a novel numerical algorithm that incorporates phase reduction techniques into Lagrangian particle methods to study population-level behaviors of noisy coupled oscillators. We use elliptical Gaussian basis functions to solve the Fokker-Planck equation that describes the evolution of the probability density function using a score-based approach. We propose the shrinkage of particles based on phase reduction, which takes advantage of the traditional approach that simplifies periodic dynamical systems. At the same time, we preserve the structure of the possibly high-dimensional state space and effectively avoid oversimplification. Our method reduces the number of particles and accelerates the particle updating process, thereby achieving higher computational efficiency.
Alexandra Du (Applied and Interdisciplinary Mathematics and Scientific Computing)
Alexandra obtained her bachelor’s degree from Oberlin College. She works with Professor Daniel Forger in the math department. Her research focuses on applied dynamical systems and computational neuroscience.

Ph.D. in Scientific Computing Student Seminar
2026-08-18 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
Accelerating Molecular Simulations with Multilayer Machine-Learned Interatomic Potentials
Machine-learned interatomic potentials (ML-IAPs) enable near-quantum-accurate molecular simulations at significantly lower computational cost than first-principles methods. However, accurately modeling covalently bonded systems requires resolving both highly featured short-range interactions and smoother long-range interactions, creating a trade-off between model complexity and computational efficiency. We address this challenge with a multilayer representation that decomposes the potential energy surface into separate short-range and long-range models, implemented within the ChIMES ML-IAP framework. Evaluated on propane, water, and reactive C/O systems spanning diverse thermodynamic conditions, the multilayer approach matches the accuracy of conventional single-layer models while reducing computational cost by more than an order of magnitude. A hyperparameter sensitivity analysis further provides practical guidelines for model design. These results establish multilayer ChIMES as a general framework for substantially improving ML-IAP efficiency without compromising predictive accuracy.
Awwal Oladipupo (Chemical Engineering and Scientific Computing)
Awwal Oladipupo is a Ph.D. candidate in Chemical Engineering and Scientific Computing at the University of Michigan. His research focuses on developing machine learning-enabled atomistic simulation methods, multiscale modeling frameworks, and scientific software to accelerate the discovery and design of materials for energy, environmental, and chemical applications, with an emphasis on improving the accuracy, scalability, and computational efficiency of molecular simulations.
