Prof. David Bortz

Prof. Bortz earned his PhD in 2002 with H.T. Banks at North Carolina State University. After a postdoc in Mathematics at the University of Michigan, he joined the faculty in Applied Math at the University of Colorado in 2006. The core of his research interest is in scientific computation methodologies for data-driven modeling and inverse problems at the intersection of applied math and statistics. His group has been developing a Weak-form Scientific Machine Learning framework with a wide range of applications to biology and medicine (wound healing, microbiology, epidemiology, ecology, etc.) and more recently to computational plasma physics in the context of fusion. His research has received support from NSF, NIH, DOE, and DOD.

The creation and inference of mathematical models is central to modern scientific discovery in the life sciences. As more realism is demanded of models, however, the conventional framework of biology-guided model proposal, discretization, parameter estimation, and model refinement becomes unwieldy, expensive, and computationally daunting. Recent advances in Weak form-based Scientific Machine Learning (WSciML) allow for the creation and inference of interpretable models directly from data via advanced numerical functional analysis, computational statistics, and numerical linear algebra techniques. This class of methods completely bypasses the need for forward-solve numerical discretizations and yields both parsimonious mathematical models and efficient parameter estimates. These methods are orders of magnitude faster and more accurate than traditional approaches and far more robust to the high noise levels common to data in the biological sciences. The combination of these features in a single framework provides a compelling alternative to both traditional modeling approaches as well as modern black-box neural networks. In this talk, I will present our weak form approach, describing our equation learning (WSINDy) and parameter estimation (WENDy) algorithms. I will demonstrate these performance properties via applications to several canonical problems in structured population modeling, cell migration, and mathematical epidemiology.

Seminar date and time: November 20 (Thursday), 11 AM ET.

This seminar has been rescheduled.

Prof. Ameya Jagtap

I am an Assistant Professor (tenure-track) in the Department of Aerospace Engineering at Worcester Polytechnic Institute (WPI), USA. Prior to joining WPI, I served as an Assistant Professor of Applied Mathematics (Research) at Brown University for three and a half years. My academic journey includes earning both my PhD and Master's degrees in Aerospace Engineering from the esteemed Indian Institute of Science (IISc) in India. Following this, I engaged in postdoctoral research at the Tata Institute of Fundamental Research—Center for Applicable Mathematics (TIFR-CAM) in India. Subsequently, I transitioned to Brown University to continue my postdoctoral research within the Division of Applied Mathematics.

My research is uniquely positioned at the intersection of mechanical/aerospace engineering, applied mathematics, and computation. I am particularly dedicated to advancing scientific machine learning algorithms that seamlessly integrate data and physics, offering versatile applications across computational physics. My areas of expertise encompass scientific machine learning, deep learning, data- and physics-driven deep learning techniques, uncertainty quantification, and propagation, as well as multi-scale/multi-physics simulations (solids, fluids, and acoustics). I bring proficiency in spectral/finite element methods, WENO/DG schemes, and domain decomposition techniques, among others. Beyond these, I am actively engaged in more traditional machine learning algorithms such as deep generative models, and novel artificial neural network architectures, such as quantum and graph neural networks. To this end, my interests also extend to spiking neural networks and other bio-inspired computing techniques. 

TBD

Seminar date and time: November 24 (Monday), 10 AM ET.

Zoom information here.

Prof. Manmeet Singh

Dr. Manmeet Singh is an Assistant Professor in the Department of Earth, Environmental, and Atmospheric Sciences at Western Kentucky University. His interdisciplinary research lies at the intersection of climate science, artificial intelligence, and Earth system modeling. Before joining WKU, Dr. Singh was a Distinguished Postdoctoral Fellow at The University of Texas at Austin, where he worked on AI-augmented weather and climate prediction systems to improve forecasts related to extreme weather, health impacts, and disasters. He also served as a Scientist at the Indian Institute of Tropical Meteorology (IITM), contributing to national climate modeling efforts and leading several projects on aerosol-monsoon dynamics and climate data downscaling. Dr. Singh holds a Ph.D. in Climate Studies from the Indian Institute of Technology Bombay. His research has been recognized globally, including participation in high-impact international collaborations such as the Paris Olympics 2024 Research Demonstration Project and the Earth Virtualization Engines. Dr. Singh also maintains an active interest in mentoring, open science tools, and advancing geoscience education through digital platforms. For more information about his research and teaching, visit https://manmeet3591.github.io or contact him at manmeet.singh@wku.edu.

TBD

Seminar date and time: December 1 (Monday), 10 AM ET.

Zoom information here.

Dr. Saad Qadeer

Saad is a staff scientist at the Pacific Northwest National Laboratory interested in high-order numerical methods, scientific machine learning, and their applications in physical sciences and engineering. More specifically, his work seeks to combine insights from analysis and machine learning to multiscale problems on varied domains, as well as to understand the approximation capabilities and limitations of machine learning architectures. Prior to joining PNNL, he worked as a postdoc at UNC Chapel Hill on developing high-order analogs of the immersed boundary method for complex fluid models. He did his graduate work at UC Berkeley, where he wrote his thesis on the computation of nonlinear Faraday waves on a three-dimensional cylinder.  

A long-standing obstacle in the use of machine-learnt surrogates with larger PDE systems is the onset of instabilities when solved numerically. Efforts towards ameliorating these have mostly concentrated on improving the accuracy of the surrogates or imbuing them with additional structure, and have garnered limited success. In this talk, we shall present some insights obtained from studying a prototype problem and how they can help with more complex systems. In particular, we shall focus on a viscous Burgers'-ML system and, after identifying the cause of the instabilities, prescribe strategies to stabilize the coupled system. Next, we will discuss methods based on the Mori--Zwanzig formalism to improve the accuracy of the stabilized system. We shall also draw analogies with more complex systems and how these prescriptions generalize to those settings.

Seminar date and time: December 5 (Friday), 10 AM ET.

Zoom information here.

You?

Please reach out to rmaulik@psu.edu if you are interested in presenting in the ISCL Seminar Series! Graduate students and postdocs are particularly encouraged to present their work.

Seminar date and time: TBA. 10 AM ET.

Zoom link here.