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Moo Chung is a Professor of Biostatistics and Medical Informatics at the University of Wisconsin-Madison, affiliated with the School of Medicine and Public Health. He holds additional appointments in the Department of Statistics and the College of Letters and Science. Chung earned his Ph.D. in Mathematics and Statistics from McGill University under Professors Keith J. Worsley and James O. Ramsay. His research focuses on computational neuroimaging, leveraging MRI, fMRI, and DTI to study brain dynamics through topological and geometric methods. Key areas include persistent homology, brain network analysis, and statistical modeling of high-dimensional imaging data. He has pioneered methods like hyper-network construction and exact topological inference for paired brain networks, addressing computational challenges in analyzing large-scale neuroimaging datasets. Chung’s work integrates advanced mathematical techniques such as Hodge Laplacian, spectral graph theory, and Wasserstein distances to analyze brain connectivity. He has secured NIH funding for projects like the Brain Initiative (2017-2020) and current grant MH133614 (2023), focusing on geometric data analysis and topological dynamics. His contributions include three books on brain imaging and network analysis, with ongoing research on topological data analysis applications. Awards: Vilas Associate Award (2013-2014), NIH Brain Initiative Award, Editor's Award (2011) Labs/Teams: Leads brain imaging workshops globally, including Seoul National University (2024) and POSTECH (2024), and organizes conferences like ISBI and MICCAI special sessions. Grants: NIH EB022856 (Brain Initiative), MH133614 (2023), and collaborations with institutions like Vanderbilt University and University of Chicago. Chung actively mentors students and postdocs in biomedical data science, offering fellowships through the CIBM program. His group maintains a Google mailing list for brain image analysis discussions and hosts regular seminars on methodological advancements.

• Computational Neuroimaging
• Topological Data Analysis
• Brain Networks
• +5 more

Alfred Hero 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. 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 affiliated with multiple research centers including 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). Hero's research focuses on data science, developing theory and algorithms for multimodality data collection, fusion, analysis and visualization using 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 research group has produced numerous PhD students who have gone on to prominent academic and industry positions. His recent publications show a strong focus on high-dimensional statistical methods, machine learning theory, network analysis, and applications in biomedical domains. The research trends indicate increasing emphasis on multimodal data fusion, robust learning algorithms, and applications to complex systems in biology and security domains. His work bridges theoretical foundations with practical implementations across diverse application areas. Fellow of the Institute of Electrical and Electronics Engineers (IEEE) Fellow of the Society for Industrial and Applied Mathematics (SIAM) Fourier Award in Signal Processing from the IEEE Hero has advised numerous PhD, MS, and undergraduate students who have gone on to successful careers in academia and industry. His research has been supported by various grants, though specific grant details are not provided in the source material. His lab collaborates extensively across disciplines with researchers in statistics, biomedical engineering, and computational medicine. The Hero Research Group maintains active collaborations with institutions worldwide and participates in major conferences in machine learning, signal processing, and data science.

• Data Science
• Statistical Machine Learning
• Distributed Optimization
• +10 more

Xuebin Wei is an Associate Professor of Geography at the College of Integrated Science & Engineering (CISE) at James Madison University (JMU). He holds a Ph.D. in Geography from the University of Georgia, complemented by advanced degrees in GIS and Urban Planning from Wuhan University and ITC at the University of Twente. Dr. Wei specializes in integrating geospatial technologies with social media analysis, machine learning, and cloud computing to address urban and environmental challenges. His research focuses on spatial-social networks, 3D urban modeling using LiDAR and aerial imagery, and applying social media data for disaster response and public health. Notable projects include the JMU Virtual 3D Campus and LBSocial initiative exploring geosocial data frameworks. Wei teaches courses in GIS, cloud computing (AWS), programming for data science, and machine learning. His publications span topics like election prediction using Twitter data, spatial analysis of climate phenomena (El Niño), and optimizing urban transit infrastructure. While no specific awards are listed, his work reflects interdisciplinary innovation at the intersection of geography, technology, and society. He advises students in CISE's data science and geospatial programs through courses like IA340 Data Mining and GEOG215 Cartography.

• Geographic Information Systems (GIS)
• Social Media Analysis
• Machine Learning
• +4 more

Laura Tipton is an Assistant Professor in the Department of Mathematics & Statistics at James Madison University (JMU), affiliated with the College of Science and Mathematics. Her research focuses on microbiome dynamics across diverse habitats such as human lungs, aerobiota, invertebrate guts, and fermented foods, emphasizing bacterial-fungal interactions and robust statistical methodologies. She integrates techniques from ecology, machine learning, and graph theory, with side projects in digital humanities, data visualization, and data feminism. Education: PhD in Computational Biology (2016), University of Pittsburgh/Carnegie Mellon University MS in Statistics (2011), George Washington University BA in Biostatistics (2007), University of Virginia Her research interests span microbiome analysis, microbial interactions, and interdisciplinary data science. Recent work includes studies on airborne fungal DNA dynamics, fungal contributions to neonatal lung diseases, and psychobiotic field developments. She emphasizes methodological innovation in microbiome studies and advocates for inclusive data practices. While no formal awards or grants are listed, her work reflects significant contributions to collaborative projects like the Global Spore Sampling Project and Pacific Innovations, Knowledge, and Opportunities (PIKO) Program. Laura’s advising focuses on mentoring students in interdisciplinary projects at the intersection of mathematics, biology, and computational science.

• Microbiome Analysis
• Bacterial-Fungal Interactions
• Statistical Ecology
• +4 more

Rajmohan Rajaraman is a Professor and Associate Dean of Faculty Affairs at the College of Computer and Information Science (CCIS) at Northeastern University. His research focuses on algorithms, theoretical computer science, and distributed systems, with a particular emphasis on network optimization, scheduling, and peer-to-peer systems. He has held leadership roles in academic administration and has contributed to significant advancements in algorithmic theory and applied computing. His research interests span a wide range of topics including online algorithms, graph theory, resource allocation, and dynamic network analysis. Recent work explores foundational AI infrastructure, communication complexity in graph problems, and stability in peer-to-peer networks. Rajaraman has advised numerous PhD students, including current candidates Andisheh Ghasemi, Amanuel Tesfaye, and Omer Wasim. Rajaraman has been recognized with awards such as the Google Faculty Research Award (2015). His publications reflect a deep engagement with theoretical challenges and practical applications, often bridging the gap between algorithm design and real-world distributed systems. As an Associate Dean, Rajaraman oversees faculty affairs and academic policies, contributing to Northeastern’s strategic initiatives in computing education and research. His work intersects academia and policy, emphasizing the importance of federal research funding and institutional collaboration.

• Algorithms
• Theoretical Computer Science
• Networks
• +4 more

Blake Jackson is an Assistant Research Professor at the University of Connecticut's Department of Mathematics. His research focuses on algebraic and enumerative combinatorics, representation theory, and applications of machine learning to mathematical problems. He earned his Ph.D. from the University of Alabama under Kyungyong Lee and is currently mentored by Ralf Schiffler and Kyu-Hwan Lee at UConn. Education and Background: Bachelor's degree from Jacksonville State University, Alabama. Ph.D. in Mathematics from the University of Alabama (Tuscaloosa). Research Interests: Blake’s work spans cluster algebras, symmetric functions, quiver representations, and machine learning. Recent projects include geometric modeling of modules over path algebras, studying Banff/Louise quivers, c-vector geometry for mutation-infinite quivers, and using machine learning to explore quiver mutation-acyclicity. He is actively developing algorithms to find combinatorial bijections on Dyck paths related to q,t-Catalan numbers. Publications: His recent work combines theoretical mathematics with computational methods, focusing on geometric and algebraic structures in combinatorics. Machine learning techniques are increasingly central to his research, driven by promising results in quiver mutation analysis. Awards: No specific awards mentioned in the provided texts. Grants and Advising: While no grants are listed, Blake collaborates with leading researchers in algebraic combinatorics. He mentors students through his research projects, though no formal advisees are noted. Labs/Teams: His current work involves interdisciplinary collaborations, including machine learning applications in mathematical research.

• Cluster Algebras
• Enumerative Combinatorics
• Representation Theory
• +3 more

Prasad Tadepalli is a Professor in the School of Electrical Engineering and Computer Science at Oregon State University, serving as the AI Graduate Program Director. He is affiliated with the Collaborative Robotics and Intelligent Systems Institute. His expertise spans artificial intelligence, machine learning, reinforcement learning, and automated planning, with impactful contributions to explainable AI and natural language processing. Tadepalli holds a Ph.D. from Rutgers University and M.Tech/B.Tech degrees from Indian institutions. He has authored over 100 papers, organized international conferences, and received awards such as the AAAI Outstanding Paper Award (2013) and ICAPS Best Student Paper (2009). Education: Ph.D. (Rutgers University, 1990), M.Tech (IIT Madras, 1981), B.Tech (Regional Engineering College, 1979) His research focuses on advancing AI through techniques like relational planning, reinforcement learning, and interpretable models. Recent work includes integrating planning and RL for multiagent systems and developing explainable models via tree ensemble compression. His articles highlight contributions to time-series imputation, adversarial attacks on bandits, and chess rating estimation using CNN-LSTM networks. Awards: AAAI Outstanding Paper Award (2013), ICAPS Best Student Paper (2009) Tadepalli emphasizes independent thinking in students and has advised numerous researchers. His work bridges theoretical AI with practical applications, such as robotics and data-driven decision-making.

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +5 more

Wynne Chin is a Professor of Decision & Information Sciences at the C.T. Bauer College of Business, University of Houston. He holds a Ph.D. from the University of Michigan, MBA from the University of Michigan, and degrees from Northwestern University and UC Berkeley. His research focuses on IT adoption, structural equation modeling (SEM), and predictive analytics. He developed the PLS-Graph software, widely used in PLS-based SEM, and has contributed to cross-cultural analysis of IT sourcing and workplace assessment. Education: Ph.D. (Computers & Information Systems, U. Michigan), M.S. (Chemical Engineering, Northwestern), MBA (U. Michigan), B.S. (Biophysics, UC Berkeley). Research interests include IT decision-making, end-user satisfaction, group process measures, and methodological innovations in SEM. He has been recognized globally, ranking 3rd in first-authored articles in MISQ/ISR. Awards include the AIS Fellow, Esther Farfel Award, and AIS Technology Legacy Award. His work bridges IS and statistics, influencing both academic and applied fields. His articles emphasize technology acceptance models, causal modeling, and PLS techniques. Grants and advising contributions are significant but not explicitly detailed here. He has held visiting roles at institutions like the Australian School of Business and is a leader in the PLS research community.

• Information Systems
• Structural Equation Modeling
• Technology Acceptance
• +5 more

Adam Lowrance is an Associate Professor of Mathematics and Statistics at Vassar College, where he has served since 2012. He holds a BA in Mathematics and Computer Science from Amherst College and a PhD in Mathematics from Louisiana State University. His research focuses on low-dimensional topology, particularly knot theory and 3-manifold theory. Recent work has explored properties of 2-bridge knots, Turaev genus, Khovanov homology, and geometric invariants. His publications frequently intersect algebraic and geometric approaches to topological problems. Teaching responsibilities include Linear Algebra (MATH 221) and Advanced Linear Algebra (MATH 364). He maintains an active research program with a focus on undergraduate research, as evidenced by grants such as the NSF RUI initiative. Professional activities include service on departmental committees and contributions to topological research communities. Contact details: adlowrance@vassar.edu , Rockefeller Hall Box 302.

• Low-dimensional Topology
• Knot Theory
• 3-Manifold Theory
• +3 more

Qianqian Tong is an Assistant Professor in the Computer Science department at the University of North Carolina at Greensboro. Her research focuses on stochastic optimizations, sparse learning, federated learning, and privacy-preserving machine learning algorithms. She has developed novel methods for efficient optimization in deep learning and federated learning frameworks, including communication-efficient distributed algorithms and decentralized systems. Education: Ph.D. Computer Science and Engineering, University of Connecticut M.S. Computational Mathematics, Zhengzhou University B.S. Mathematics, Zhengzhou University Research interests include designing algorithms for sparse learning, federated learning with privacy guarantees, and applying deep graph learning to drug discovery. Recent projects involve tensor-based models for multidimensional data analysis and improving convergence in ADAM optimization. Her publications span optimization theory, federated learning systems, and molecular modeling applications. Though no awards are listed, her work demonstrates significant contributions to efficient machine learning methodologies. Teaching includes advanced courses on data science and computer science foundations. No lab affiliations or grant details are explicitly mentioned in the provided text.

• Stochastic Optimization
• Sparse Learning
• Federated Learning
• +4 more

Gagan Agrawal is the UGA Foundation Professorship in Computing and a Professor at the School of Computing, University of Georgia. He serves as Director of the School and is affiliated with the Franklin College of Arts & Sciences. Agrawal holds a PhD and MS in Computer Science from the University of Maryland (1994-1996). His research focuses on high-performance computing, parallel algorithms, compiler optimization for deep learning, GPU acceleration, and interdisciplinary applications in health informatics. Notable contributions include frameworks like ForensiBlock (blockchain for data forensics) and DELITE (tensorized instruction compilation). Agrawal has secured over $2.5M in NSF grants for projects addressing extreme-scale computing challenges. Recent grants include: SHF: Small: Memory Hierarchy Optimizations Meet Transformers (MITTEN) ($600K, 2024-2027) DELITE compilation system for deep learning models ($600K, 2023-2026) Publications span parallel computing methodologies, cybersecurity frameworks, and health outcomes analysis. His work on social determinants of health in cancer survival has been systematically reviewed in top-tier medical journals. Agrawal leads UGA's computing initiatives, emphasizing interdisciplinary research and student mentorship in HPC and AI domains.

• Parallel Computing
• High-Performance Computing (HPC)
• Data Analytics
• +5 more

Luke Olson is a Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC), part of the College of Engineering. He holds an affiliate appointment in the Department of Mechanical Science and Engineering. His research focuses on numerical methods, high-performance computing, and parallel algorithms, particularly algebraic multigrid (AMG) solvers and sparse matrix computations. He leads the development of open-source libraries like PyAMG and RAPtor, advancing computational tools for scientific and engineering applications. Education: Ph.D. in Applied Mathematics from the University of Colorado Boulder (2003), M.S. in Mathematics from the University of Iowa (1999), and B.A. in Mathematics and Physics from Luther College (1997). Research Interests: Algebraic multigrid methods and preconditioners High-performance computing and parallel algorithms Numerical solutions to partial differential equations Scientific computing and software development Machine learning integration in numerical simulations Recent Articles Trends: Recent work merges machine learning with traditional numerical methods, such as neural network closures for turbulent combustion and reduced basis approximations using neural networks. Ongoing contributions include optimizing multigrid methods for exascale architectures and enhancing communication efficiency in distributed systems. Awards: Recognized with the NSF CAREER Award (2007), UIUC Campus Award for Excellence in Teaching (2024), and the Donald Biggar Willett Faculty Scholar distinction (2016). Active in conference organization, including the Copper Mountain Conference on Multigrid Methods. Teaching & Grants: Teaches courses like Numerical Methods for PDEs and Scientific Machine Learning. Leads projects funded by NSF and industry collaborations, emphasizing education innovation through the AE3 fellowship (2014–2016). Labs/Teams: Directs the Scientific Computing Group at UIUC and contributes to interdisciplinary initiatives like the Center for Exascale-enabled Scramjet Design (CEESD).

• Numerical Analysis
• High-Performance Computing
• Scientific Computing
• +4 more

Xiuyuan Cheng is an Associate Professor of Mathematics at Duke University, affiliated with the Trinity College of Arts & Sciences. Her expertise lies in applied analysis, focusing on developing theoretical and computational techniques for high-dimensional data analysis, signal processing, and machine learning. She holds a Ph.D. from Princeton University (2013). Her research emphasizes graph-based methods, kernel techniques, and deep learning applications in manifold data analysis. Notable contributions include work on graph Laplacian convergence, optimal transport for single-cell data, and rotation-equivariant neural networks. She has received grants from the National Science Foundation, including a CAREER award (2023–2028) for learning graph diffusion from high-dimensional data. Recent publications explore topics such as bi-stochastic graph normalization, neural tangent kernels, and adversarial defense using basis transformations. Teaching includes advanced courses like Measure and Integration and High-Dimensional Data Analysis. She collaborates extensively in interdisciplinary projects, integrating mathematical theory with computational tools for real-world applications.

• Applied Mathematics
• High-Dimensional Data Analysis
• Signal Processing
• +4 more

Madhur Tulsiani is a Professor at the University of Chicago's Department of Computer Science and a researcher at the Toyota Technological Institute at Chicago (TTIC). His research focuses on theoretical computer science, particularly complexity theory and algorithm design, with applications in coding theory and information theory. He has been supported by NSF grants 1254044, 1816372, and 2326685. Education: Bachelor’s in Computer Science, IIT Kanpur (2001-2005) Ph.D. in Computer Science, UC Berkeley (2005-2009), advised by Luca Trevisan Postdoctoral fellowships at the Institute for Advanced Study (IAS) and Princeton University Research Interests: Mathematical foundations of computation Complexity theory and algorithm design Coding theory and error-correcting codes Sum-of-Squares hierarchies and approximation algorithms Recent Contributions: Pioneering work on list decodable codes and expander-based constructions Advances in approximation algorithms for high-dimensional expanders Lower bounds for Sum-of-Squares algorithms using high-dimensional expanders Teaching: Information and Coding Theory Mathematical Toolkit (linear algebra/probability) Summer REU programs in theoretical computer science Students: Advised PhD students including Fernando Granha Jeronimo, Goutham Rajendran, and Shashank Srivastava Co-advised students with Sasha Razborov, Janos Simon, and others Labs/Groups: Member of the Theoretical Computer Science Group at TTIC and UChicago, contributing to cross-disciplinary research in algorithms and complexity.

• Theoretical Computer Science
• Complexity Theory
• Algorithm Design
• +3 more

Anuran Makur is an active Assistant Professor at Purdue University with dual appointments in the Department of Computer Science (College of Science) and the Elmore Family School of Electrical and Computer Engineering (College of Engineering). He is affiliated with the Institute for Control, Optimization and Networks (ICON) and teaches foundational courses in machine learning and data science. His educational background includes a B.S. in Electrical Engineering and Computer Sciences from UC Berkeley (2013, summa cum laude), an S.M. in Electrical Engineering and Computer Science from MIT (2015), and a Sc.D. from MIT (2019). B.S., UC Berkeley, 2013 S.M., MIT, 2015 Sc.D., MIT, 2019 Makur's research bridges theoretical machine learning, information theory, and applied probability. Key interests include ranking/preference learning, optimization for ML, non-parametric inference, information measures, permutation channel limits, broadcasting on graphs, and reliable computation. His work emphasizes fundamental theoretical limits and mathematical rigor in complex systems. Recent publications reveal strong trends in statistical learning theory (40%), information-theoretic methods (35%), and networked systems (25%), with growing emphasis on privacy-aware inference and high-dimensional statistics. His scientific achievements are recognized by prestigious awards: Arthur M. Hopkin Award (UC Berkeley, 2013) Ernst A. Guillemin Master's Thesis Award (MIT, 2015) Jin Au Kong Doctoral Thesis Award (MIT, 2020) Thomas M. Cover Dissertation Award (IEEE, 2021) NSF CAREER Award (2023) While specific advising details aren't public, his research leadership is evident through ICON affiliation and collaborations with MIT's LIDS/IDSS groups. The NSF CAREER grant supports his work on information-theoretic foundations of machine learning. He maintains active roles in theoretical computer science and information theory communities through conference organization and editorial work. Makur leads research within ICON, focusing on control-theoretic approaches to networked learning systems. His work integrates probabilistic modeling with optimization theory, particularly for distributed inference and networked decision-making under uncertainty.

• Machine Learning Theory
• Information Theory
• Applied Probability
• +4 more