Asaf Ferber is Associate Professor in Mathematics at University of California, Irvine, School of Physical Sciences. His research spans discrete mathematics including combinatorial games, random graphs, extremal hypergraph theory, and quantum computation. Research explores Hamiltonian cycles in random graphs, structural properties of pseudorandom graphs, and quantum algorithms for combinatorial problems. Recent work develops quantum approaches to graph learning and sparse recovery in random matrices. Awards: NSF CAREER Award Sloan Fellowship Distinguished Early Career Faculty Award for Research Air Force Research Grant NSF-BSF Grant Organizes conferences including SoCalDM Symposium and Desert Discrete Math Workshop, mentoring graduate students through UCI's Probability and Combinatorics Seminar.
Howard Elman is a Professor in the Department of Computer Science at the University of Maryland, with affiliations to the Institute for Advanced Computer Studies (UMIACS) and as an Affiliate Professor in the Department of Mathematics. His research spans numerical analysis, computational fluid dynamics, and uncertainty quantification, focusing on iterative solvers for partial differential equations. Education: PhD in Computer Science, Yale University (1982); BA in Mathematics, Columbia University (1975); Stuyvesant High School (1971) Elman's research integrates Scientific Computing with Numerical Linear Algebra , Computational Fluid Dynamics , and Uncertainty Quantification . His work addresses Stochastic Galerkin Methods , Reduced-Order Modeling , and Low-Rank Approximations for PDEs with random data. Recent publications emphasize Surrogate Models and Deep Learning in Bayesian inverse problems. His scientific awards include SIAM Fellowship (2009) and roles as Associate Editor for journals like Mathematics of Computation and SIAM Journal on Scientific Computing . He served as SIAM Editor-in-Chief (1998-2004) and Vice President for Publications. Contact: helman@umd.edu | Office: 4210 Iribe Center | Courses: AMSC/CMSC 460 Computational Methods
Florian Schäfer is an Assistant Professor at the School of Computational Science and Engineering at Georgia Tech. His research spans numerical computation, statistical inference, and competitive games, with applications in materials science, turbulence modeling, computer graphics, and computational geometry. He will join the Courant Institute at NYU in September 2025. PhD in Applied and Computational Mathematics, Caltech Bachelor’s and Master’s in Mathematics, University of Bonn His work focuses on information geometric mechanics to design structure-preserving numerical methods for continuum mechanics. This includes: State-of-the-art solvers for elliptic PDEs via Gaussian elimination and conditional independence Efficient multi-agent optimization algorithms Information geometric regularization for compressible fluid dynamics Enabling the first compressible fluid simulation exceeding 100 trillion grid cells His recent research trends integrate: Machine learning for materials science (e.g., active learning, Bayesian approaches) Stochastic modeling of microstructures and phase-field problems Neural operators for super-resolution fluid dynamics Generative models for polycrystalline material datasets Optimal transport and diffusion models for conditional density transformations High-performance computing at extreme scales Florian collaborates with researchers including Houman Owhadi, Jessie Liu, Spencer Bryngelson, Tamer Zaki, and Ali Mani. He actively presents at conferences like SIAM and UCLA seminars, and is recruiting PhD students for work at the Courant Institute starting 2025.
Assoc Prof Ng Teng Yong is an Associate Professor at the School of Mechanical & Aerospace Engineering (NTU), specializing in numerical modeling and simulation. With a background as Research Manager at A*STAR Institute of High Performance Computing, his work spans materials science, nanotechnology, and aerospace engineering. Current focus on graphene-based desalination membranes Expertise in molecular dynamics simulations Investigates nanoscale fluid mechanics and structural dynamics Recent publications highlight advancements in energy-efficient electrodialysis, smart robotics, and nonlinear vibration analysis. His interdisciplinary approach integrates computational methods with experimental validation in additive manufacturing and soft material mechanics.
Song Mei is an Assistant Professor in the Department of Statistics and Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. She received her Ph.D. from Stanford University in 2020 under Andrea Montanari and maintains active research collaborations with institutions including Amazon (as a 2023 Amazon Research Award recipient) and OpenAI (where she is currently on leave). Her research spans the intersection of statistics, machine learning, information theory, and computer science, with particular emphasis on foundational theories for modern AI systems. Key interests include language models, diffusion models, quantum algorithms, and high-dimensional statistics, often leveraging insights from statistical physics literature. Analysis of her recent publications reveals a strong focus on theoretical underpinnings of generative AI, with significant contributions to understanding contrastive pre-training (CLIP), attention mechanisms in LLMs, and mathematical foundations of diffusion models. Her work demonstrates consistent interdisciplinary connections between statistical theory and practical AI development. Sloan Research Fellowship (2025) Noether Early Career Scholar Award (2025) Google Research Scholar Award (2024) Amazon Research Award (2024) She actively advises graduate students through MA programs and has secured significant research grants including Amazon Research Awards. Her work with AGI Labs at Amazon demonstrates applied impact of theoretical research. Current projects focus on mechanistic interpretability of large language models and mathematical frameworks for generative AI. Professor Mei leads research on the statistical principles behind frontier AI models, with particular focus on developing rigorous theoretical frameworks for understanding emergent behaviors in large-scale systems.
Alfred O. Hero, III is the John H. Holland Distinguished University Professor of Electrical Engineering and Computer Science and the R. Jamison and Betty Williams Professor of Engineering at the University of Michigan, Ann Arbor. He is currently on leave from the University of Michigan as a Program Director in the CISE Directorate at the National Science Foundation. His primary appointment is in the Department of Electrical Engineering and Computer Science (EECS), with secondary appointments in the Department of Biomedical Engineering and the Department of Statistics. He is also affiliated with the UM Center for Computational Medicine and Bioinformatics (CCMB), the UM Graduate Program in Applied and Interdisciplinary Mathematics (AIM), the UM Applied Physics Program, and the Michigan Institute for Data Science (MIDAS), which he co-founded from 2015-2018. Hero's research focuses on data science, developing theory and algorithms for multimodality data collection, fusion, analysis and visualization that use statistical machine learning and distributed optimization. His work has applications in wearable technologies for personalized health and predictive medicine, spatio-temporal networks in biology, climate, and social discourse, anomaly detection, and data analysis for international security. His recent research interests include high dimensional spatio-temporal data analysis, multimodal data integration, statistical signal processing, and machine learning, with particular emphasis on predictive mathematical models for biological and physical sciences, social networks, network security and forensics, and personalized health and disease. His recent publications demonstrate a strong focus on high-dimensional statistical methods, contrastive learning, neural network optimization, change detection in temporal graphs, and applications in microbiome analysis and epidemic modeling. The research spans theoretical foundations in information theory and statistical learning while addressing practical applications across multiple domains. Scientific Awards: IEEE Signal Processing Society Best Paper Award (1998) Best Original Paper Award from Journal of Flow Cytometry (2008) Best Magazine Paper Award from IEEE Signal Processing Society (2010) SPIE Best Student Paper Award (2011) IEEE ICASSP Best Student Paper Award (2011) IEEE Signal Processing Society Technical Achievement Award (2014) IEEE Signal Processing Society Society Award (2015) IEEE Fourier Award (2020) University of Michigan Distinguished Faculty Achievement Award (2011) Hero has advised over 60 PhD students and 30 postdocs in areas including modeling, computation, and inference for large scale time varying data in the biosciences. He has received significant research funding from the Department of Energy, Army Research Office, Air Force Office of Scientific Research, and National Science Foundation. He has held leadership positions including President of the IEEE Signal Processing Society (2006-2007), Director of Division IX (Signals and Applications) on the IEEE Board of Directors (2009-2011), and Chair of the Committee on Applied and Theoretical Statistics of the US National Academies (2018-2020).
Jonathan A. Kelner is a Professor of Applied Mathematics in the Department of Mathematics at the Massachusetts Institute of Technology (MIT) and a member of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). His research focuses on applying techniques from pure mathematics to solve fundamental problems in algorithms and complexity theory, with the goal of developing practical algorithms for real-world questions. Dr. Kelner received his undergraduate degree from Harvard University and his Ph.D. in Computer Science from MIT in 2006. Before joining the MIT faculty, he spent a year as a member of the Institute for Advanced Study. His educational background has provided a strong foundation for his interdisciplinary research spanning mathematics and computer science. His research interests include combinatorial optimization, mathematical programming, spectral graph theory, distributed computing, machine learning, computational geometry and topology, computational biology, signal processing, and random matrix theory. Kelner's work demonstrates how deep theoretical insights can lead to practical algorithmic improvements, particularly in graph algorithms and optimization problems. His approach often involves connecting seemingly disparate areas of mathematics to create novel algorithmic techniques. Analysis of his recent publications reveals a strong focus on spectral graph theory, optimization algorithms, and the Sum-of-Squares method. His work frequently addresses fundamental questions in theoretical computer science with practical implications for algorithm design. A notable trend in his research is the development of nearly-linear-time algorithms for various graph problems, which represents significant improvements over previous approaches. NSF CAREER Award Alfred P. Sloan Research Fellowship NEC Award for Research in Computers and Communication Sprowls Doctoral Dissertation Award Best Student Paper Award at STOC 2004 Best Paper Award at STOC 2011 Kokusai Denshin Denwa Junior Faculty Chair 2008 Harold E. Edgerton Faculty Achievement Award 2011 School of Science Award for Excellence in Undergraduate Education 2012 Professor Kelner has been actively involved in mentoring students and has received recognition for his teaching excellence, including the School of Science Award for Excellence in Undergraduate Education in 2012. His research has been supported by prestigious grants including the NSF CAREER Award. He has collaborated extensively with researchers across multiple institutions, often working with other leading figures in theoretical computer science to produce groundbreaking results in algorithm design. At MIT, Kelner is part of both the Mathematics Department and CSAIL, positioning him at the intersection of theoretical mathematics and practical computer science. This dual affiliation reflects the interdisciplinary nature of his work, which bridges pure mathematical theory with concrete algorithmic applications.
Vijay Kumar is the Nemirovsky Family Dean of Penn Engineering at the University of Pennsylvania, with faculty appointments in the Departments of Mechanical Engineering, Computer and Information Science, and Electrical and Systems Engineering. He is a leading figure in robotics and computer architecture research. Research Interests include robotics, particularly multi-robot systems and micro aerial vehicles (MAVs), as well as computer architecture innovations for machine learning, GPU acceleration, and datacenter efficiency. His work spans theoretical foundations and practical applications in autonomous systems and hardware optimization. Scientific Awards include: 1991 NSF Presidential Young Investigator Award 1996 Lindback Award for Distinguished Teaching 2012 ASME Mechanisms and Robotics Award 2014 Engelberger Robotics Award 2017 IEEE George Saridis Leadership Award Multiple best paper awards at DARS, ICRA, and RSS conferences Editorial Leadership includes serving as Editor of the ASME Journal of Mechanisms and Robotics and Advisory Board Member of AAAS Science Robotics Journal . His GRASP Lab team developed foundational frameworks for micro UAV testbeds and swarm robotics.
Ivan Dokmanic is an Assistant Professor at the Coordinated Science Laboratory (CSL) within the University of Illinois . His research bridges signal processing , machine learning , and applied inverse problems , with a focus on acoustics, biomedical imaging, and distance geometry. Current Role : Assistant Professor, CSL Email : dokmanic@illinois.edu Research Interests : Dokmanic explores machine learning applications in inverse problems , particularly distance geometry for molecular imaging and acoustics . His work includes unlabeled sensing , where distances between points are known but their arrangement is not. This has implications for powder diffraction , indoor localization , and echo modeling . Article Trends : His recent publications emphasize distance geometry in machine learning , acoustic signal processing , and inverse problem theory . Key areas include molecular imaging , audio encryption , and sensor positioning . Collaborative work spans medical imaging , cyberphysical systems , and geometric invariants . 2016 Google Faculty Award NSF Grant (1 year, $157,079) Students and Grants : Dokmanic mentors PhD students like Puoya, Shuai, and Anadi. His research is funded by the National Science Foundation , Google , VISA , and nVidia .
Stephen A. Vavasis is a Professor in the Department of Combinatorics and Optimization at the University of Waterloo, part of the Faculty of Mathematics. He holds a PhD in Computer Science from Stanford University (1989) and has held academic positions at Cornell University (1989–2006) before joining Waterloo. His research focuses on continuous optimization, data science, first-order methods, scientific computing, and computational mechanics. Current teaching includes courses on convex optimization and portfolio optimization methods. He has served as Associate Dean of Computing (2017–2020) and Interim Director of Data Science graduate programs. His work is supported by NSERC grants. Notable awards include the Hertz Fellowship, Churchill Scholarship, and Guggenheim Fellowship. Vavasis's research emphasizes applications of optimization to clustering, machine learning, and fracture mechanics. His publications span convex optimization frameworks, algorithmic analysis of gradient methods, and numerical methods in mechanics. Recent work explores unifying analyses of first-order optimization algorithms and robust optimization techniques for high-dimensional data problems.
Anna Levina is an Assistant Professor for Computational Neuroscience at the University of Tübingen , affiliated with the Department of Computer Science under the Faculty of Science. Her research focuses on the self-organization of neuronal activity, critical dynamics in neural networks, and the excitation/inhibition balance in cortical circuits. Current positions: Assistant Professor (since 2018), Group Leader (2017-2018), Equality Officer (Computer Science) Previous roles: IST Fellow (2015-2017), Associated Researcher (2011-2015), Postdoc/PI (2011-2015), Postdoc (2008-2011) Her research integrates mathematical modeling , statistical physics , and computational neuroscience to study criticality phenomena, neural avalanches, and adaptive network dynamics. Key interests include: Self-organized criticality in neural systems Excitation/Inhibition balance mechanisms Network topology and dynamics Timescale analysis in neural processing Stochastic modeling of neural activity Recent publications reveal trends in understanding critical dynamics across biological and artificial networks, with applications to memory systems, sensorimotor integration, and disease modeling. She has received recognition as an IST Fellow .
Andrea Montanari is a Professor of Mathematics and Statistics at Stanford University, affiliated with the Department of Mathematics and Statistics. His research focuses on high-dimensional statistics, machine learning theory, optimization algorithms, and statistical physics, with applications to neural networks and complex systems. He has contributed extensively to understanding generalization in overparametrized models, spin glass theory, and algorithmic methods like approximate message passing. His work bridges theoretical computer science and mathematical physics, addressing challenges in data analysis and learning from high-dimensional datasets. Notable themes include analyzing neural network dynamics, optimizing high-dimensional landscapes, and developing efficient algorithms for sparse and low-rank matrix estimation. Montanari’s publications explore topics such as the interplay between statistical and computational limits, the behavior of gradient-based methods, and the theoretical foundations of modern machine learning. His recent research demonstrates a focus on fundamental questions in learning theory, including the study of phase transitions in statistical estimation, the role of overparametrization in generalization, and the mathematical underpinnings of contemporary algorithms. While no specific awards are listed here, his contributions reflect significant impact in interdisciplinary fields.
Tengyao Wang is a Professor in the Department of Statistics at the London School of Economics and Political Science (LSE), serving as the MSc Statistics (Financial Statistics) Programme Director. Prior to LSE, he held positions as a Lecturer at University College London and a Research Fellow at the Cantab Capital Institute for the Mathematics of Information, University of Cambridge. His research focuses on high-dimensional statistics, computational efficiency, and statistical limitations imposed by computational constraints. Education: PhD in Statistics under Prof Richard Samworth at the University of Cambridge, with earlier studies including a Part III Essay in Empirical Process Theory. Research interests include sparse signal detection, change-point analysis, dimension reduction, robust statistics, and applications in medical statistics, financial data analysis, and material discovery. Key contributions include methodologies for handling missing data, high-dimensional change-point detection algorithms, and statistical learning techniques. Publications span theoretical advancements and applied innovations, with recent work emphasizing deep learning with missing data, residual permutation tests, and semi-supervised learning via random projections. His work has been recognized with awards such as the Royal Statistical Society Research Prize (2019) and the Guy Medal in Bronze (2023). He is an Associate Editor of the Journal of the Royal Statistical Society, Series B (JRSS B), and actively contributes to open-source tools like the 'ocd' and 'MissInspect' R packages for changepoint detection and missing data analysis.
Charles A. Bouman is the Showalter Professor of Electrical and Computer Engineering and Biomedical Engineering at Purdue University, with a courtesy appointment in Mathematics. He is a leading researcher in computational imaging, integrating statistical signal processing, physics, and computation for applications in healthcare, scientific, and industrial imaging. Education: B.S.E.E., University of Pennsylvania, 1981 M.S., University of California at Berkeley, 1982 Ph.D. in Electrical Engineering, Princeton University, 1989 His research focuses on computational imaging , including statistical image models, multiscale techniques, tomographic reconstruction, and fast algorithms. Key areas include Model-Based Iterative Reconstruction (MBIR), Plug-and-Play priors, document processing, and multiscale segmentation. His work has led to foundational contributions in total variation regularization and sparse-view reconstruction. The recent publications highlight a strong trend in integrating machine learning with physical models for image reconstruction, particularly through Plug-and-Play methods. His work spans optical tomography, halftoning, image scaling, and document compression, demonstrating consistent innovation in both theory and practical software implementation. Scientific Awards and Honors: Member, National Academy of Inventors Life Fellow, IEEE Fellow, IS&T; Honorary Member (2022); Service Award (2023) Fellow, SPIE and AIMBE IEEE Signal Processing Society Claude Shannon-Harry Nyquist Award (2021) Electronic Imaging Scientist of the Year (2014) SIAM Imaging Science Best Paper Prize (2020) Founder, IS&T Computational Imaging Conference (2003) Co-Founder, IEEE Transactions on Computational Imaging Vice President, IS&T; Former VP of Publications (2000–2004) Bouman has advised numerous graduate students and leads a vibrant research group developing open-source tools like MBIRJAX , SVMBIR , and OpenMBIR . His research has been supported by the National Science Foundation, General Electric, Intel, Xerox, Hewlett-Packard, and the State of Indiana 21st Century Fund. He maintains an active presence through tutorials, conference leadership, and educational resources including video lectures and a textbook on Foundations of Computational Imaging. Labs and Research Teams: His group develops cutting-edge software for tomographic reconstruction, clustering, segmentation, and dynamic sampling. Projects include Gaussian Mixture modeling (GMCluster), Plug-and-Play implementations, Sparse Matrix Transforms, and UAV sensing datasets. The research is highly interdisciplinary, bridging engineering, mathematics, and biomedical applications.
Mithuna S. Thottethodi is a Professor and Interim Associate Head of Teaching and Learning at the Elmore Family School of Electrical and Computer Engineering at Purdue University. He holds a B.Tech. from the Indian Institute of Technology, Kharagpur (1996), and a Ph.D. in Computer Science from Duke University (2002). His research focuses on computer architecture, interconnection networks, distributed systems, and security, with notable work on sparse tensor accelerators and processing-near-memory architectures. His work has been funded by the NSF, AT&T, and SK Hynix. Research interests include security, interconnection networks in multicores, storage performance optimization, and memory hierarchies. Notable contributions span hardware accelerators for machine learning, secure speculative execution, and datacenter network congestion control. He has advised over 20 graduate students, many of whom have joined top tech firms like Google, Microsoft, and Intel. Awards: NSF CAREER Award (2007) Wilfred Hesselberth Teaching Excellence Award (2021) Multiple Eta Kappa Nu Outstanding Professor Awards Teaching includes undergraduate courses like EE 437 (Computer Design) and graduate courses such as ECE 666 (Advanced Computer Architecture). He also advises VIP and EPICS student teams.
