جست‌وجوی استاد و پژوهشگر

Ajmal Mianمشاهده پروفایل

استاد

• Artificial Intelligence
• Computer Vision
• Machine Learning
• +۸ مورد دیگر

Ajmal Mian is a Professor of Computer Science at the University of Western Australia (UWA), affiliated with the School of Physics, Maths and Computing. He holds an Australian Research Council Future Fellowship (2022) and leads research in Artificial Intelligence, Computer Vision, and Machine Learning. His work focuses on 3D computer vision, adversarial AI defense, and explainable AI. His research interests include 3D point cloud analysis, face recognition, human action recognition, and remote sensing. He has published over 300 papers and secured major grants from ARC, NHMRC, and DARPA, totaling millions in funding. He has supervised 29 PhD students and mentored 12 postdoctoral researchers. Key projects include 3D diffusion models for scene generation, robust 3D vision systems, and defense against AI deception attacks. He serves as a fellow of IAPR, an ACM Distinguished Speaker, and has editorial roles at IEEE Transactions on Neural Networks and Pattern Recognition. Research Awards: HBF Mid-Career Scientist of the Year, West Australian Early Career Scientist of the Year, IAPR Best Scientific Paper Award. Grants: ARC Discovery Projects, National Intelligence & Security Discovery grants, DARPA grants for AI security. His teaching spans computer vision, machine learning, and programming courses. Collaborations include defense, medical, and agricultural applications.

Jiliang Tangمشاهده پروفایل

استاد

• Graph Machine Learning
• Trustworthy AI
• Deep Learning
• +۳ مورد دیگر

Jiliang Tang is an MSU Foundation Professor in the Department of Computer Science and Engineering at Michigan State University (MSU), part of the College of Engineering. He holds a PhD from Arizona State University (2015) and previously worked as a research scientist at Yahoo Research. His research focuses on graph machine learning, trustworthy AI, and applications in education and biology. He has received numerous awards, including the 2022 AI's 10 to Watch, IAPR J.K. Aggarwal Award, and NSF CAREER Award. Education: PhD in Computer Science, Arizona State University, 2015 (Advisor: Huan Liu) Research Interests: Graph Neural Networks (GNNs) and Deep Learning on Graphs Trustworthy AI: Safety, Robustness, and Fairness AI+X Applications: Education Technology and Biological Data Analysis His work bridges theoretical advancements and practical applications, with contributions to graph representation learning, privacy in generative models, and educational AI systems. Awards & Recognition: Over 8 best paper awards (or runner-ups) Rock Star Award from Association of Chinese Scholars in Computing Extensive media coverage for innovations in AI education and biology Grants & Projects: NSF CAREER Award (2019) for research on signed networks Co-PI on a $1.7M grant for 5G research Leadership in projects like DSE Lab and Data Science initiatives Labs & Teams: Directs the Data Science and Engineering (DSE) Lab at MSU, focusing on advancing AI for real-world challenges. The lab collaborates with industry leaders and publishes widely in top conferences (e.g., KDD, SIGIR, ACL).

Svitlana Rogovchenkoمشاهده پروفایل

استاد

• Differential Equations
• Impulsive Differential Equations
• Asymptotic Methods in Nonlinear Mechanics
• +۳ مورد دیگر

Svitlana Rogovchenko is a Professor at the Department of Engineering Sciences, University of Agder, Norway. Her academic work bridges pure mathematical research in differential equations with innovative pedagogical approaches in engineering and interdisciplinary education. Educational Background: Doctor of Philosophy in Differential Equations (1988), Institute of Mathematics, National Academy of Sciences, Kyiv, Ukraine Master of Science in Mathematics (1983), Kyiv State University, Ukraine Research Interests: Qualitative theory of ordinary and impulsive differential equations, asymptotic methods in nonlinear mechanics, mathematical modeling, and the intersection of mathematics education with engineering and biology curricula. She focuses on conceptual understanding, pedagogical challenges, and commognitive conflicts in learning differential equations. Recent Publications Trends: Her work spans mathematical modeling of vehicle crashworthiness, interdisciplinary education for biologists and engineers, and pedagogical innovations in ordinary differential equations. Articles emphasize assessment design, group work tensions, and bridging mathematical theory with applied contexts.

Alexander Vladimirskyمشاهده پروفایل

استاد

• Numerical Analysis
• Applied Mathematics
• Nonlinear PDEs
• +۵ مورد دیگر

Roles & Affiliations: Professor of Mathematics at Cornell University, Department of Mathematics. Member of graduate programs in Mathematics, Applied Mathematics, Operations Research and Information Engineering, Theoretical & Applied Mechanics, and Computational Science & Engineering. Co-organizer of the Scientific Computing and Numerics (SCAN) Seminar and founder of the Cornell Mathematical Contest in Modeling. Education: Ph.D. in Applied Mathematics from University of California, Berkeley (2001); B.A. in Applied Mathematics (with high honors) from University of California, Berkeley (1995). Research Interests: Focuses on numerical analysis, nonlinear PDEs, control theory, and dynamical systems. Explores applications in optimal control, front propagation, anisotropy, bifurcation theory, and mathematical biology. Develops methods for invariant manifold approximation, Eikonal equations, and stochastic systems. Recent work includes studies on cancer therapy optimization, surveillance evasion, and pedestrian flow modeling. Teaching: Teaches courses like Introduction to Partial Differential Equations, Differential Games, Numerical Analysis, and Mathematical Modeling. Recent courses include Math 4280 (Spring 2025) and Math 3610 (Fall 2024). Labs/Teams: Active in interdisciplinary collaborations, including work on computational biology, robotics path planning, and mathematical contest problem-solving initiatives.

Feng Fuمشاهده پروفایل

دانشیار

• Evolutionary Game Theory
• Computational Social Science
• Biomedical Data Science
• +۷ مورد دیگر

Feng Fu is an Associate Professor of Mathematics at Dartmouth College, with an adjunct appointment in Biomedical Data Science. He leads the Fu Lab, focusing on interdisciplinary research at the intersection of evolutionary game theory, computational social science, and biomedical data science. His academic roles include teaching courses such as Evolutionary Game Theory, Stochastic Processes, and Game Theory and Artificial Intelligence. Education: Senior Postdoc, ETH Zurich (2012-2015); Postdoc, Harvard University (2010-2012); PhD, Peking University (2010); B.S., Fudan University (2004). Research interests span evolutionary dynamics of cooperation, computational models of human behavior and social networks, cancer evolution, and behavioral epidemiology. Notable work includes studies on vaccine hesitancy, misinformation dynamics, and the hysteresis effect in vaccination uptake. His lab has received prestigious funding, including a Bill & Melinda Gates Foundation Grant (2019). Teaching and mentoring: Advised numerous graduate and undergraduate researchers, many of whom have received awards and advanced to academic or industry roles. Courses taught include QSS/MATH 30.04 (Evolutionary Game Theory) and MATH 146 (Game Theory and AI). Labs/Teams: Fu Lab at Dartmouth collaborates across disciplines, with projects in cancer immunotherapy modeling, network-based interventions, and computational social science. Recent lab highlights include advancements in understanding polarization and the development of targeted public health strategies.

Raegan Higginsمشاهده پروفایل

استاد

• Time Scales
• Mathematical Biology
• Mathematics Education
• +۳ مورد دیگر

Raegan Higgins is a Professor and Associate Vice Provost for Faculty Success at Texas Tech University's Department of Mathematics & Statistics. She earned her BS from Xavier University of Louisiana and MS/PhD from the University of Nebraska, focusing on time scales calculus. Her research spans oscillation criteria for dynamic equations, mathematical biology, and STEM education equity. She has led initiatives like the Bridges Across Texas LS-AMP program to support underrepresented students in mathematics. Higgins has received awards including the Gweneth Humphreys Award for Mentoring (2021) and was recognized as a Top 20 Under 40 by the Lubbock Chamber of Commerce (2019). She teaches advanced mathematics courses and has mentored numerous graduate students in areas like prostate cancer modeling and calculus curriculum reform. Her educational history includes a Bachelor of Science in Mathematics (Xavier University) and advanced degrees from the University of Nebraska, with an emphasis on time scales. Higgins actively engages in outreach through organizations like the Association for Women in Mathematics and the Infinite Possibilities Conference, promoting diversity in STEM. She has secured NSF grants totaling millions to support teacher training, minority participation, and undergraduate research experiences. Key research themes include time scales applications in biological models and improving mathematics teaching practices. Higgins' work bridges academia and community through programs like the South Plains Mathematics Fellows and the West Texas Middle School Math Partnership. Her articles address oscillation theory, STEM retention strategies, and interdisciplinary approaches to mathematics education.

Ravid Shwartz-Zivمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Neural Networks
• +۷ مورد دیگر

Ravid Shwartz-Ziv is an Assistant Professor and Faculty Fellow at NYU's Center for Data Science, with a dual role as Senior Research Scientist at Wand AI. His research bridges theoretical foundations and practical applications in artificial intelligence, focusing on Large Language Models (LLMs), information theory, and neural network interpretability. Ph.D. in Computational Neuroscience, Hebrew University of Jerusalem (2021) B.Sc. in Computer Science and Computational Biology, Hebrew University of Jerusalem (2014) His research spans: Developing min-p sampling for LLM text generation Preventing representation collapse in Transformers Creating contamination-free LLM benchmarks like LiveBench Advancing information-theoretic frameworks for neural networks Exploring representation learning and model adaptation Recent publications demonstrate expertise in LLM efficiency, self-supervised learning, and multi-agent systems. Notable awards include the Google PhD Fellowship, Moore-Sloan Fellowship, and multiple best paper recognitions. He has led research initiatives at Intel and Google AI, focusing on neural network compression, XGBoost comparisons for tabular data, and innovative benchmarking frameworks.

Mary Lou Zeemanمشاهده پروفایل

استاد

• Geometric Dynamical Systems
• Mathematical Biology
• Population Dynamics
• +۳ مورد دیگر

Mary Lou Zeeman is the R. Wells Johnson Professor of Mathematics at Bowdoin College, specializing in geometric dynamical systems, mathematical biology, and climate modeling. Her work bridges theoretical mathematics with real-world applications in ecology, sustainability, and neuroendocrinology. Education: PhD in Mathematics from the University of California, Berkeley; MA and BA in Mathematics from the University of Oxford. Research focuses on population dynamics, resilience in ecosystems, and interdisciplinary approaches to sustainability. She co-leads initiatives like the Mathematics and Climate Research Network (MCRN) and contributed to the Mathematics of Planet Earth (MPE) 2013 initiative. Her teaching includes Biomathematics (MATH 1758/BIOL 1175) and Multivariate Calculus (MATH 1800). Key contributions include modeling hormone oscillations in the menstrual cycle, climate change impacts, and fisheries management policies. Her work emphasizes decision-support frameworks for environmental challenges. Grants and collaborations span NSF-funded projects on computational sustainability and climate change research, reflecting her commitment to applied interdisciplinary science.

Stephen Ferrignoمشاهده پروفایل

استادیار

• Cognitive Development
• Comparative Cognition
• Number Cognition
• +۴ مورد دیگر

Stephen Ferrigno is an Assistant Professor and Chair of the Biology of Brain and Behavior Area Group at the University of Wisconsin-Madison's Department of Psychology. His research investigates the origins of uniquely human cognition through developmental, comparative, and cross-cultural methodologies. He focuses on foundational cognitive abilities such as number sense, recursive grammar, logical reasoning, and metacognition, exploring their evolutionary and developmental bases. Ferrigno's work bridges disciplines including cognitive neuroscience, developmental psychology, and cultural anthropology, with practical applications in early childhood education and curriculum design. His research employs cross-cultural studies with indigenous populations (e.g., the Tsimane) to assess cultural variability in cognitive development. Key themes include spatial mappings of numerical concepts, recursive sequence processing in non-human primates, and the metacognitive capacities shared between humans and monkeys. The Cognitive Origins Lab, led by Ferrigno, seeks to uncover universal cognitive principles while identifying culturally mediated differences. No scientific awards or grants are explicitly listed in the provided materials. While no advisees are mentioned, his research program suggests involvement in mentoring graduate students through the Department of Psychology's PhD program. Current projects include investigating the developmental trajectories of logical reasoning, the neural correlates of recursive sequence processing, and the design of culturally adaptive educational interventions targeting early math and language skills in preschool settings.

Michael J. Shelleyمشاهده پروفایل

استاد

• Applied Mathematics
• Biophysics
• Complex Fluids
• +۴ مورد دیگر

Michael J. Shelley is the Lilian and George Lyttle Professor of Applied Mathematics and holds joint appointments in Mathematics, Neural Science, and Mechanical Engineering at New York University's Courant Institute of Mathematical Sciences. He also serves as Co-Director of the Applied Mathematics Laboratory and Director of the Center for Computational Biology at the Flatiron Institute. Education: PhD (Applied Mathematics) from the University of Arizona (1985), MS (Applied Mathematics) from the University of Arizona (1984), BA (Mathematics) from the University of Colorado (1981). Research: Focuses on complex phenomena in active matter, biophysics, and complex fluids. Key areas include fluid-structure interactions (e.g., swimming/flying mechanics), cytoskeletal dynamics, and collective behavior in biological systems. Collaborates closely with experimentalists through the Applied Math Lab and Flatiron Institute. Labs & Affiliations: Co-Director, Applied Mathematics Laboratory; Director, Center for Computational Biology (Simons Foundation); affiliated with NYU’s Courant Institute and Department of Mathematics. Notable Work: Models for microtubule-motor assemblies, active suspensions, and fluid-structure interactions. Pioneered computational frameworks for Stokes suspensions and fiber dynamics in viscous fluids.

Tamon Stephenمشاهده پروفایل

استاد

• Operations Research
• Combinatorial Optimization
• Algorithms
• +۲ مورد دیگر

Tamon Stephen is a Professor in the Department of Mathematics at Simon Fraser University (SFU), part of the Faculty of Science. His research focuses on operations research, with an emphasis on combinatorial optimization, algorithms, discrete geometry, and computational biology. He holds a Ph.D. in Mathematics from the University of Michigan (2002). His work often bridges theoretical and computational aspects, addressing interdisciplinary applications. Stephen is affiliated with the Centre for Operations Research and Decision Sciences (CORDS) and has contributed to software tools for hypergraph transversals and colorful linear programming. He has taught courses such as Math 208W (Introduction to Operations Research) and has advised projects in metabolic network analysis and scheduling optimization. His office is located at the Surrey campus (SRYC 2886). Key research collaborations include studies on firefighter scheduling, nurse rostering, and metabolic pathway analysis. His methodologies often leverage algorithm design, polytope theory, and discrete mathematics. Stephen actively participates in academic service, organizing seminars and contributing to conferences such as the West Coast Optimization Meeting. His work emphasizes practical applications of theoretical results, with a focus on solving real-world optimization challenges.

Gheorghe Craciunمشاهده پروفایل

استاد

• Mathematical Biology
• Dynamical Systems
• Chemical Reaction Networks
• +۳ مورد دیگر

Gheorghe Craciun is a Professor in the Department of Mathematics and the Department of Biomolecular Chemistry at the University of Wisconsin-Madison. His research focuses on mathematical and computational models in biology and medicine, particularly dynamical systems models of biological interaction networks. He has been a visiting researcher at the Max Planck Institute for Mathematics in the Sciences during the 2019-2020 academic year and has organized the Madison Workshops on Mathematics of Reaction Networks. Craciun's primary research interests include Mathematical Biology, Dynamical Systems, Chemical Reaction Networks, Computational Biology, Systems Biology, and Algebraic Geometry. He investigates systems of differential equations with polynomial right-hand sides, which are common in biochemical reaction networks, ecological interactions, and epidemiological models. His work often involves proving global stability, analyzing multistability, and characterizing steady states using tools from algebraic geometry and combinatorics. Recent publications demonstrate his focus on toric differential inclusions, endotactic networks, and the global attractor conjecture, extending to applications in biochemical networks and discrete Boltzmann equations. His extensive publication record reveals a strong trend toward algebraic and geometric methods for analyzing complex biological networks, with significant contributions to reaction network theory, stability analysis, and parameter characterization. Craciun's work bridges abstract mathematical concepts with practical applications in biochemistry, ecology, and medicine, including modeling vitellogenin production in trout and peptide mass distributions. He has collaborated extensively with international researchers including Alicia Dickenstein, Anne Shiu, Bernd Sturmfels, Casian Pantea, and Miruna-Stefana Sorea. In education, Craciun teaches graduate courses such as Math 703 and mentors students through the Madison Math Circle and Putnam Club, while organizing specialized workshops that foster collaboration in reaction network theory.

Roummel F. Marciaمشاهده پروفایل

استاد

• Optimization
• Signal Processing
• Data Science
• +۵ مورد دیگر

Roummel F. Marcia is a Professor and current Chair of the Department of Applied Mathematics at the University of California, Merced, within the School of Natural Sciences. He received his Ph.D. from UC San Diego under Professor Philip Gill and previously held postdoctoral positions at the San Diego Supercomputer Center and University of Wisconsin-Madison, as well as a research scientist position in electrical engineering at Duke University. His research spans multiple areas in optimization and its applications, with a focus on signal processing, data science, machine learning, linear algebra, and mathematical biology. Dr. Marcia's work has significant interdisciplinary impact, particularly in biomedical imaging, computational biology, and quantum computing applications. His research methodology often combines theoretical optimization approaches with practical applications in data-intensive fields. Dr. Marcia's recent publications demonstrate a strong trend toward integrating optimization theory with deep learning architectures, particularly in applications requiring sparse data handling, biomedical imaging, and quantum computing. His work shows increasing focus on developing novel optimization algorithms specifically designed for machine learning contexts, including quasi-Newton methods adapted for deep learning and specialized techniques for handling non-convex optimization problems. School of Natural Sciences Faculty Award for 'Developing or Improving Academic Programs and Tracks' (2021-22) Leadership roles in SIAM Activity Group on Applied Mathematics Education Recognition as a Math Alliance Mentor for supporting underrepresented students Dr. Marcia has successfully mentored numerous doctoral students to completion, with graduates moving to positions at Meta, Johns Hopkins University Applied Physics Laboratory, Lawrence Livermore National Laboratory, and other prestigious institutions. His research has been consistently funded by major agencies including NSF (with grants IIS 1741490, DMS 1840265, DMS 2229495, CCF 2343610), DARPA, and ARPA-E. As the current graduate chair of the Applied Math Graduate Program, he plays a key role in shaping the next generation of mathematical scientists. His work with the SMaRT (Scientific Mathematics Research and Training) team demonstrates his commitment to collaborative, interdisciplinary research.

Xiaoning Qianمشاهده پروفایل

استاد

• machine learning
• Bayesian methods
• optimal experimental design
• +۵ مورد دیگر

Xiaoning Qian is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University, where he also serves on the Faculty Advisory Committee for the Texas A&M Institute of Data Science (TAMIDS) and the Executive Committee for the Texas A&M TRIPODS Research Institute for Foundations of Interdisciplinary Data Science (FIDS). He holds a joint appointment in the Applied Math group within the Computational Science Initiative at Brookhaven National Laboratory (BNL). Previously, he was an Associate Professor (2018-2022) and Assistant Professor (2013-2018) at Texas A&M, and an Assistant Professor in the Department of Computer Science and Engineering at the University of South Florida (2009-2013). Dr. Qian received his B.S.E. and M.S.E. degrees from Shanghai Jiaotong University, China, and his M.Ph. and Ph.D. degrees in Electrical Engineering from Yale University. Dr. Qian's research focuses on developing mathematical models and computational algorithms in signal processing, machine learning, and Bayesian methods, particularly in learning, uncertainty quantification, and experimental design. His work spans multiple disciplines, with applications in life sciences and materials science. His research group, the Biomedical Imaging, Sensing, and Genomic Signal Processing Group, actively applies probabilistic models and optimization algorithms to solve complex problems in interdisciplinary domains. His research has evolved from foundational work in bioinformatics and biomedical image processing to more recent applications in materials science and broader AI for science initiatives. Dr. Qian has received numerous scientific awards and recognitions including: National Science Foundation (NSF) CAREER Award Segers Family Dean's Excellence Professorship II in the College of Engineering TEES (Texas A&M Engineering Experiment Station) Senior Faculty Fellow Montague-Center for Teaching Excellence Scholar J. T. Oden Faculty Fellow at the University of Texas, Austin Finalist of the 2023 INFORMS QSR Best Paper Faculty Impact Fellow from the Department of Electrical & Computer Engineering As an advisor , Dr. Qian has mentored numerous graduate students through their PhD and MS programs, with many of his alumni securing positions at prestigious institutions and companies including NIH/NCBI, Microsoft, Baidu Research Lab, and Qualcomm. His research has been supported by multiple grants, including an NSF CAREER award and collaborative research funding from the Information Integration and Informatics program. He is actively recruiting postdoc and graduate student research assistants for projects in machine learning and optimization methods with applications in bioinformatics and materials science. Dr. Qian is involved with several research initiatives including the Objective-Based Uncertainty Quantification (ObjectiveUQ) project, which provides a mathematical framework for integrating prior knowledge and data while enabling effective operational and experimental design under uncertainty. He also co-organizes the Bio-Seminar series for the Biomedical Imaging, Sensing & Genomic Signal Processing group at Texas A&M.

Tim Conradمشاهده پروفایل

پژوهشگر

• Bioinformatics
• Computational Biology
• Machine Learning
• +۵ مورد دیگر

Dr. Tim Conrad is a researcher at the Zuse Institute Berlin in the Visual and data-centric computing department under the Mathematics of Complex Systems division. He leads projects at the intersection of computational biology, AI, and medical data analysis. Projects: Geometric Learning for Single-Cell RNA Velocity Modeling, MODAL MedLab, Sparse Compressed Sensing in -Omics Data, BIFOLD (Big Data and Machine Learning) Research Networks: Affiliated with MATH+ and MODAL Research Campus His research focuses on applying machine learning , network optimization , and sparse data analysis to biological and medical challenges including disease modeling, microbiome dynamics, and ECG classification. Recent work explores hybrid PDE-ODE epidemic models and federated learning in healthcare. 2023-2025 publications highlight trends in AI for biological networks , temporal community detection , and medical signal processing . He co-authored studies on SARS-CoV-2 simulations, proteomics feature selection, and multi-label ECG analysis. His 2004 doctoral thesis at Monash University laid foundations for later work in metabolic pathway analysis. Awarded as a Zuse Fellow , he contributes to open science initiatives like FAIR data sharing . Collaborations span institutions including Freie Universität Berlin and Charité in medical informatics and clinical applications.