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Joshua S AgterbergView profile

Assistant Professor

• Statistical Network Analysis
• High-dimensional Statistics
• Spectral Methods
• +6 more

Joshua S Agterberg is an Assistant Professor in the Department of Statistics at the University of Illinois Urbana-Champaign (UIUC), affiliated with the College of Liberal Arts & Sciences. He holds a PhD in Applied Mathematics and Statistics from Johns Hopkins University (2023) and a BBA in Actuarial Science and Mathematics from the University of Wisconsin-Madison (2017). His research focuses on statistical network analysis, high-dimensional statistics, spectral methods, and mathematical data science, with notable contributions to tensor analysis, mixed-membership models, and nonparametric hypothesis testing. Education: PhD in Applied Mathematics and Statistics, Johns Hopkins University, 2023 Bachelor of Business Administration (BBA) in Actuarial Science and Mathematics, University of Wisconsin-Madison, 2017 Research Interests: Statistical Network Analysis (e.g., community detection, graph clustering) High-dimensional statistics (e.g., singular vector estimation, sparse PCA) Spectral methods (e.g., tensor perturbation, matrix decomposition) Applications in data science, neuroscience, and connectomics Awards: Summer 2022 Acheson J. Duncan Travel Award Multiple MINDS Data Science Fellowships Best Presentation Award, JSM Student Competition (Nonparametric Statistics, 2021) Advising & Grants: Supervises PhD students at UIUC in statistics and data science Collaborator on grants related to statistical network analysis and tensor models Member of the Math Alliance to diversify STEM participation Labs & Teams: Active in collaborative research groups at UIUC, Johns Hopkins University, and the University of Pennsylvania, focusing on statistical theory and applications in networks and structured data.

Matthias HeinkenschlossView profile

Professor

• Nonlinear Optimization
• PDE-Constrained Optimization
• Optimal Control
• +3 more

Matthias Heinkenschloss is the Noah G. Harding Chair and Professor of Computational Applied Mathematics and Operations Research at Rice University. He joined Rice in 1996 after serving as an assistant professor at Virginia Tech and earlier at the University of Trier in Germany. His research focuses on large-scale nonlinear optimization, PDE-constrained optimization, and optimal control, with applications in engineering and scientific domains such as flow control, reservoir management, and structural acoustics. He holds a Ph.D. (Dr. rer.nat.) and M.S. in Applied Mathematics from the University of Trier, Germany. His academic leadership includes six years as department chair at Rice and advisory roles on the Scientific Advisory Board of the Weierstrass Institute for Applied Analysis and Stochastics, among others. Research interests span optimization under uncertainty, model reduction, iterative solvers for KKT systems, and domain decomposition in optimization. His work emphasizes integrating optimization algorithms with underlying differential equations to enhance computational efficiency and solution fidelity. Notable contributions include methods for PDE-constrained optimization, reduced-order modeling, and parallel-in-time algorithms for optimal control. Key publications include advancements in nonlinear manifold reduced order models, domain decomposition techniques, and parallel-in-time solutions for linear-quadratic optimal control problems. His work has been recognized with awards such as the Mercator Fellowship (2016). He actively contributes to academic societies like the Society for Industrial and Applied Mathematics (SIAM) and teaches courses in numerical optimization and PDE-constrained optimization.

Cong MaView profile

Assistant Professor

• Mathematics of Data Science
• Reinforcement Learning
• Transfer Learning
• +3 more

Cong Ma is an Assistant Professor in the Department of Statistics at the University of Chicago. He holds a PhD from Princeton University (2020), advised by Yuxin Chen and Jianqing Fan, and a BEng from Tsinghua University (2015). Previously, he was a postdoctoral researcher at UC Berkeley under Martin Wainwright. His research focuses on the mathematics of data science, emphasizing reinforcement learning, transfer learning, multi-modal learning, high-dimensional statistics, and nonconvex optimization. Key areas include developing computationally and statistically efficient methods for large-scale data problems, with recent work on contrastive learning, contextual bandits, and robust matrix completion. Education: PhD in Operations Research & Financial Engineering, Princeton University (2020) BEng in Electrical Engineering, Tsinghua University (2015) Awards: SIAM Activity Group on Imaging Science Best Paper Prize (2024) Teaching: STAT 253/317: Introduction to Probability Models (Winter 2025) STAT 28000: Optimization (Winter 2025) Machine Learning (Autumn 2022) His work bridges theoretical insights with practical applications, often addressing challenges in statistical guarantees for optimization algorithms and distributionally robust learning. Recent highlights include contributions to multi-modal contrastive learning and batched contextual bandits.

Rahul MazumderView profile

Associate Professor

• Statistics
• Machine Learning
• Mathematical Optimization
• +7 more

Rahul Mazumder is the NTU Associate Professor and Robert G. James Career Development Associate Professor of Operations Research and Statistics at MIT Sloan School of Management, affiliated with LIDS, the MIT Institute for Data, Systems, and Society, and the MIT Center for Statistics. Prior roles include Assistant Professor at Columbia University and Postdoctoral Associate at MIT. His research bridges statistics and optimization, focusing on computational statistics, machine learning, and large-scale algorithms with applications in finance, healthcare, and AI. Education: BStat and MStat from Indian Statistical Institute (2007), PhD in Statistics from Stanford University (2012). Awards include the 2024 Leo Breiman Junior Award, IISA Early Career Award, and ONR Young Investigator Award. Research Interests: Statistical machine learning and mathematical optimization High-dimensional statistics and sparsity Applications in recommender systems, computational finance, and computational biology Neural network pruning and efficient AI systems Key Article Trends: Focus on optimization frameworks for LLMs, sparse learning, and scalable algorithms for high-dimensional problems. Recent work addresses privacy-preserving fine-tuning, efficient neural network deployment, and statistical methodologies in genomics. Awards: Extensive recognition for contributions to optimization, machine learning, and statistics, including student paper awards as advisor. Funded by NSF, ONR, IBM, and Google Research. Advising & Grants: Supervised over 40 graduate and undergraduate students. Research supported by grants from NSF, ONR, and industry partners. Active in editorial roles for top journals like Annals of Statistics and Operations Research. Labs/Teams: Mazumder Lab develops software like L0Learn and COMET, emphasizing open-source tools for sparse learning and optimization.

Robert M. FreundView profile

Professor

• Nonlinear optimization theory
• Computational complexity
• Interior-point methods
• +5 more

Robert M. Freund is the Theresa Seley Professor in Management Science at the MIT Sloan School of Management, specializing in Operations Research. His research focuses on nonlinear optimization, computational complexity, and first-order methods with applications in management and engineering. He holds a PhD in Operations Research from Stanford University and has authored influential textbooks like Data, Models, and Decisions: The Fundamentals of Management Science . Education: B.A. in Mathematics (Princeton, 1975), M.S. and Ph.D. in Operations Research (Stanford, 1979–1980). Roles: Faculty Director of MIT Sloan MBA Program, Deputy Dean for Faculty (2008–2011), Co-Director of MIT Program in Computation for Design and Optimization (2004–2008). His research interests span optimization theory, computational science, and applications in machine learning. Notable contributions include advancements in Frank-Wolfe algorithms, interior-point methods, and photonic crystal design. He has received the Longuet-Higgins Prize (2007) and multiple teaching awards. Recent work includes the development of restarted PDHG algorithms, Taylor-approximated gradients for empirical risk minimization, and analysis of first-order methods' computational guarantees. His collaborative efforts with students and researchers like Zikai Xiong and Haihao Lu have produced impactful publications in Mathematical Programming and SIAM Journal on Optimization . Freund has also contributed to educational initiatives, including MIT's Professional Certificate in Data Science and Analytics. His work bridges theoretical optimization advancements with practical applications in engineering and data-driven decision-making.

Andreea BobuView profile

Assistant Professor

• Human-Robot Interaction
• Autonomous Agents
• Inverse Reinforcement Learning
• +7 more

Andreea Bobu is an Assistant Professor at the Department of Aeronautics and Astronautics and CSAIL in the School of Engineering at Massachusetts Institute of Technology (MIT) . She leads the Collaborative Learning and Autonomy Research Lab (CLEAR Lab) , focusing on developing autonomous agents that align their behavior with human expectations through interactive learning. Ph.D. in Electrical Engineering and Computer Science, UC Berkeley B.S. in Computer Science, MIT Her research integrates deep learning , mathematical human modeling , inverse reinforcement learning , and Bayesian inference to address key challenges in human-robot collaboration. Recent work explores language-guided state abstractions and preference-conditioned learning for better task generalization. Selected scientific awards include: Emerging Research Award, International Symposium on Mathematics of Neuroscience (2023) Apple Scholar in AI/ML (2021) Best Paper Award, HRI 2020 IEEE T-RO Honorable Mention (2020) BEST PAPER MICCAI-PATCHMI 2016 Robotics: Science and Systems Pioneer (2022) The CLEAR Lab has produced 15 recent publications (2022-2025) spanning topics like: Language-guided abstraction Representational alignment Adaptive computation allocation Motion expressivity modeling Misspecification diagnosis Human-in-the-loop frameworks with applications to assistive robots, large language models, and motion planning systems.

Haizhao YangView profile

Associate Professor

• AI for Science
• Machine Learning
• High-Performance Computing
• +4 more

Haizhao Yang is an Associate Professor of Mathematics and Computer Science at the University of Maryland College Park (UMCP). He holds affiliate appointments in the University of Maryland Institute for Advanced Computer Studies (UMIACS) and the Applied Mathematics and Scientific Computation (AMSC) program. Previously, he served as an Assistant Professor at Purdue University and the National University of Singapore, and as a Visiting Assistant Professor at Duke University (2015–2017). His education includes a B.Sc. from Shanghai Jiao Tong University (2010), M.Sc. from The University of Texas at Austin (2012), and Ph.D. from Stanford University (2015). Yang's research focuses on machine learning theory, scientific computing, and applied mathematics. Key areas include AI for scientific discovery, high-performance computing, and algorithm development for differential equations. His work bridges mathematical rigor with practical applications, such as developing neural network-based solvers for high-dimensional PDEs and quantum computing integration for uncertainty quantification. He leads a dynamic research group with over 30 students and postdocs across PhD, master's, and undergraduate levels. Notable achievements include the NSF CAREER Award (2020), ONR Young Investigator Award (2022), and DARPA Young Faculty Award (2024). His laboratory collaborates with institutions like Lawrence Berkeley National Lab and Duke University, emphasizing interdisciplinary projects in data science and computational physics. Recent publications highlight innovations in operator learning for PDEs, quantum algorithm integration, and adversarial reinforcement learning. His group actively explores AI-driven approaches to overcome computational bottlenecks in scientific simulations, with techniques like the Finite Expression Method and OptimAI framework for automated problem-solving.

• Pharmaceutical Crystallization
• Bioprocess Engineering
• Process Modeling
• +7 more

Professor Brian Glennon is a distinguished academic at the University College Dublin (UCD), affiliated with the School of Chemical and Bioprocess Engineering. He holds the rank of Professor and has served in various roles, including Lecturer (1995–2004), Senior Lecturer (2004–2007), and Associate Professor (2007–2014) at UCD. His research focuses on bioprocess scale-up, pharmaceutical crystallization, process modeling, and continuous manufacturing. Glennon has extensive experience in academic leadership, including contributions to modularization and semesterization initiatives at UCD. Education: Glennon earned his BE and PhD degrees from University College Dublin. Research Interests: His work emphasizes optimizing pharmaceutical manufacturing processes, particularly crystallization, continuous flow chemistry, and computational modeling. He has pioneered methods for solvent swap systems, bubble column evaporators, and hybrid dynamic models for bioprocesses. Over 160 publications, including seminal studies on AAV purification and diastereomeric salt resolution, highlight his expertise in biopharmaceutical and chemical engineering. Grants & Collaborations: Glennon led projects such as the 'Design and Synthesis of Nanoparticles with Novel Cell Targeting Functionalities' (2009–2011) and an internal funding scheme (2016–2019). He collaborates with industry partners like Merck Sharp & Dohme and contributes to professional organizations including AIChE and Engineers Ireland. Teaching & Leadership: A champion of pedagogical innovation, Glennon has restructured curricula and promoted hands-on learning. He supervises PhD students and actively participates in academic governance, holding roles in Engineers Ireland and the European Federation of Chemical Engineering.

• Computational Mathematics
• Manifold Learning
• Data Compression
• +1 more

Dr. Keaton Hamm is an Assistant Professor in the Department of Mathematics and Division of Data Science at The University of Texas at Arlington. His research bridges theoretical mathematics and computational data science, with postdoctoral experience at the University of Arizona and Vanderbilt University. Primary research domains include computational mathematics, manifold learning techniques, optimization algorithms, and tensor decompositions. His work develops novel methods for high-dimensional data analysis with applications in machine learning and scientific computing. Recent publications demonstrate strong focus on Wasserstein space methodologies (2023-2025), advancing techniques in optimal transport, dimensionality reduction, and geometric learning. Machine learning research explores adversarial robustness and federated learning systems, while mathematical contributions include innovations in matrix decompositions and approximation theory. No awards or student information was available in the provided profile.

• Algebraic Geometry
• Numerical Analysis
• Homotopy Methods
• +3 more

Kisun Lee is a Research Fellow in Mathematical and Statistical Sciences at Clemson University's College of Science. His research develops computational methods for solving polynomial systems using homotopy continuation, certification algorithms, and tropical geometry approaches. Research focuses on: Certified numerical methods for algebraic systems Homotopy continuation and monodromy techniques Tropical geometry applications Implementation of computational algebra tools Real solution finding for polynomial equations Publications demonstrate innovations in certified computation methods, including alpha theory certification using interval arithmetic and Krawczyk-based homotopy tracking. Developed the NumericalCertification package for Macaulay2 and contributes to computational algebra toolkits for polynomial system solving.

Peimeng YinView profile

Assistant Professor

• Computational Mathematics
• Applied Mathematics
• Numerical Analysis
• +7 more

Peimeng Yin is an Assistant Professor in the Department of Mathematical Sciences at the University of Texas at El Paso (UTEP). His research focuses on computational mathematics and applied mathematics, emphasizing numerical analysis, partial differential equations, scientific computing, and data science. He designs and implements numerical algorithms for PDEs with applications in physics, astrophysics, engineering, biology, energy, and oncology. Key interests include discontinuous Galerkin methods, structure-preserving numerical techniques, dynamical low-rank approximation, and PDE-based data-driven modeling. His work bridges rigorous mathematical theory with practical computation, addressing challenges like high derivatives, singularities, and multiscale phenomena. Recent research involves adaptive finite element methods, positivity-preserving schemes, and machine learning-enhanced algorithms for parabolic equations. Yin's studies often emphasize energy stability and error estimation in gradient flow systems, with applications ranging from fluid dynamics to biomedical decision support systems. Notable contributions include advancements in biharmonic problem solvers, neutrino kinetic equations, and surgical outcome prediction models. He maintains an active publication record in top-tier computational mathematics journals, with a focus on developing robust numerical frameworks for complex physical systems.

Miao YinView profile

Assistant Professor

• Spatial Intelligence Systems
• Multimodal Intelligence Systems
• Large-Scale Neural Model Optimization
• +3 more

Dr. Miao Yin is an Assistant Professor in the Department of Computer Science and Engineering at the University of Texas at Arlington (UTA), part of the College of Engineering. He holds a PhD in Computer Engineering from Rutgers University and has industry experience at Google, Amazon Web Services, and Samsung Research America. His research focuses on high-performance and energy-efficient spatial/multimodal intelligence systems, leveraging higher-order tensor decomposition and algorithm-hardware co-optimization. Education: PhD in Computer Engineering, Rutgers University (2023). Research Interests include Spatial Intelligence Systems, Large-Scale Neural Model Optimization, and High-Performance Computing. His lab is equipped with advanced computing resources, including H100 GPU nodes and Ada6000/4090 servers. Awards include the UT System Rising STARs Award ($100k), and multiple travel grants from top conferences. He serves on committees for MLSys, PPoPP, DAC, and ICCD, and reviews for CVPR, NeurIPS, and IEEE journals. Advises PhD students including Sunny Shree and Wei Lin, and collaborates on projects funded by UTA and NSF grants. His lab emphasizes cross-disciplinary collaboration in AI and scientific computing.

Gregory OngieView profile

Assistant Professor

• Machine Learning
• Medical Imaging
• Computational Imaging
• +5 more

Dr. Gregory Ongie is an Assistant Professor in the Department of Mathematical and Statistical Sciences at Marquette University. He holds a Ph.D. in Applied Mathematical and Computational Sciences from the University of Iowa (2016), an M.S. in Mathematics (2011), and a B.S. in Mathematics and Physics from Coe College (2008). His research focuses on machine learning, optimization, and compressed sensing applied to medical imaging, particularly in MRI and CT reconstruction. He has developed algorithms for inverse problems in imaging, including deep equilibrium models and Neumann networks. Research Interests: Mathematics of deep learning, applied algebraic geometry in data science, machine learning for medical image reconstruction, and optimization in computational imaging. His work bridges theoretical foundations of neural networks with practical applications in medical imaging, including model observer analysis and compressed sensing techniques. Grants and Awards: NSF CISE Research Initiation Initiative Grant (2022), Way Klinger Early Career Award (2023), D. C. Spriestersbach Dissertation Prize (2018), and Best Student Paper Award at ISBI 2015 for MRI super-resolution work. Teaching: Courses include Applied Linear Algebra, Differential Equations, and Mathematics of Medical Imaging. He has mentored multiple graduate and undergraduate students in imaging-related projects. Active in service roles, including Marquette's Faculty Hiring Committee and seminar coordination. Labs & Collaborations: Engaged in collaborative research with institutions like the University of Chicago and University of Michigan, focusing on computational imaging and machine learning. GitHub repositories include tools for matrix completion and MRI reconstruction.

Austin NapierView profile

Professor

• Experimental Particle Physics
• High Energy Physics
• Computational Physics
• +5 more

Austin Napier is a Professor of Physics & Astronomy at Tufts University, affiliated with the School of Arts and Sciences. He holds a PhD (1979) and BS (1969) in Physics from MIT. His research focuses on Experimental Particle Physics, High Energy Physics, and Computational Physics, with emphases on heavy quark studies, Standard Model tests, and collider data analysis. He has been at Tufts since 1980, progressing from Assistant to Associate Professor before achieving his current rank. Education: PhD Physics, MIT, 1979 BS Physics, MIT, 1969 Research Interests: Dr. Napier’s work explores experimental particle physics at colliders like the Tevatron, focusing on Higgs boson searches, top quark dynamics, and electroweak interactions. His computational physics research includes advanced data analysis techniques and simulation methodologies for high-energy experiments. Publications: His recent work includes studies of Higgs-like particles, top quark asymmetries, and exotic meson discovery. These contributions highlight his role in advancing precision measurements and collider-based physics. Teaching: Teaches introductory physics courses and lab modules, emphasizing hands-on experimental methods and computational techniques.

Hsin-Hsiung HuangView profile

Associate Professor

• Bayesian Statistics
• Machine Learning
• Neuroimaging
• +3 more

Dr. Hsin-Hsiung Huang is an Associate Professor in the Department of Statistics and Data Science at the University of Central Florida (UCF). His research focuses on Bayesian modeling, dimension reduction, neuroimaging, and threat detection. He holds a Ph.D. in Statistics from the University of Illinois at Chicago and multiple advanced degrees in Mathematics and Statistics from Georgia Tech and National Taiwan University. Dr. Huang has led projects funded by NSF's ATD program (e.g., NSF DMS-1924792 and DMS-2318925) and NIH's NINDS R01 award. His team achieved top placements in the ATD Challenge competitions from 2021 to 2023. Awards include the UCF Research Incentive Award (2021), College of Sciences Seed Grant (2023–24), and the 2025 OSCaR Award for Outstanding Scholarship. His research interests include Bayesian ultrahigh-dimensional variable selection, matrix/tensor regression, and spatiotemporal modeling. Key areas of application include neuroimaging reconstruction, threat detection algorithms, and medical data analysis. Publications span statistical methodologies, neuroimaging algorithms, and applications in healthcare and environmental risk modeling. He actively collaborates across disciplines, integrating machine learning with biomedical and ecological data challenges.