Michigan Institute for Computational Discovery and Engineering

MICDE - Mechanical Engineering seminar: Phani Motamarri, Indian Institute of Science, Bangalore
2026-05-29 11:00 - 12:00 (America/Detroit) | 1311 EECS, 1301 Beal Ave., Ann Arbor, MI, 48109, United States
Bio: Phani Motamarri is an Assistant Professor in the Department of Computational and Data Sciences at the Indian Institute of Science, Bengaluru, where he leads the MATRIX Lab. He is an alumnus of the University of Michigan–Ann Arbor, where he earned his PhD in Mechanical Engineering.
His research lies at the intersection of computational mechanics, materials science, numerical analysis, and high-performance computing. His work focuses on developing mathematical techniques and hardware-aware algorithms for quantum modeling of materials, with applications to structural and functional materials and multiscale modeling methodologies. He is also interested in machine learning frameworks for accelerating materials discovery and quantum computing, particularly in the context of quantum-centric supercomputing.
Prof. Motamarri’s research contributions include advances in finite-element methods, numerical analysis, and large-scale scientific software development. He is one of the lead developers of DFT-FE, an open-source, massively parallel finite-element code for density functional theory calculations. He received the ACM Gordon Bell Prize in 2023 and was a finalist for the ACM Gordon Bell Prize in 2019.
Inexact yet Accurate: Unlocking Quantum Modeling of Materials at Scale through Approximation-Tolerant Algorithms
Abstract: Modern computing architectures increasingly rely on iterative solvers that employ reduced-precision computation and communication-reduction techniques to lower time-to-solution and improve scalability. However, eigensolvers in scientific simulations have struggled to exploit such approximations without compromising accuracy. We present an eigensolver R-ChFSI, a residual-based reformulation of Chebyshev Filtered Subspace Iteration (ChFSI) provably tolerant to inexact matrix–vector products. By expressing the Chebyshev recurrence in terms of residuals rather than eigenvector estimates, R-ChFSI naturally accommodates multiple sources of approximation, including reduced-precision arithmetic (FP32 and TF32) in the filtering step, lossy compression with compression ratios exceeding 4x for inter-process communication, and approximate inverses for generalized eigenproblems, while preserving eigensolver robustness. Large-scale experiments on GPU accelerators are conducted using finite-element discretized generalized eigenproblems arising in Kohn–Sham density functional theory for quantum modeling of materials. The results demonstrate that R-ChFSI achieves eigen-residual norms orders of magnitude smaller than standard ChFSI under comparable inexactness, while delivering substantial performance gains. This work provides a practical pathway toward approximation-tolerant eigensolvers enabling accurate and scalable simulations on modern computing architectures.

The MICDE 2025-26 Seminar Series is open to all.
Graduate Certificate in Computational Discovery and Engineering, and MICDE fellows, please use this form to record your attendance.
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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.
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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
Hardik Patil (Civil & Environmental Engineering and Scientific Computing)

Ziqi Wang (Mechanical Engineering and Scientific Computing)

Topic Modeling of Firearm-Related Social Media Content for Survey Development
Esther Lee (Health Behavior & Health Equity and Scientific Computing)
