جست‌وجوی استاد و پژوهشگر

Daniel E. Koditschekمشاهده پروفایل

استاد

• Dynamical Systems and Control
• Hybrid Control Systems
• Legged Locomotion
• +۷ مورد دیگر

Daniel E. Koditschek is the Alfred Fitler Moore Professor in the Department of Computer and Information Science at the University of Pennsylvania’s School of Engineering and Applied Science. He also holds primary appointments in the Department of Electrical and Systems Engineering and a research affiliation with the Department of Mechanical Engineering and Applied Mechanics. He is a leading figure in the GRASP Lab, where he leads the Kod*lab, a specialized group focused on physical interaction and locomotion in autonomous robots. His research lies at the intersection of dynamical systems theory and robotics, emphasizing legged locomotion, hybrid control systems, and bio-inspired design. Koditschek's work integrates formal mathematical modeling with empirical testing of physical robots that run, jump, climb, and manipulate objects. He actively explores how biological insights into animal mobility can inform robotic autonomy and control. His group maintains strong collaborations with biologists and emphasizes embodied intelligence in machine behavior. The recent publications reflect a strong trend in applying theoretical control frameworks—such as hybrid dynamical systems, averaging methods, and navigation functions—to practical robotic challenges in unstructured environments. Topics include terrain adaptation, energy-efficient locomotion, reactive planning, and affordance-based interaction. There is a clear focus on bridging abstract mathematical models with real-world robotic performance, particularly in legged and mobile manipulation systems. IEEE RAS Pioneer Award Heilmeier Research Award AFOSR MURI Award (2010) Daniel Koditschek has advised numerous PhD students and postdoctoral researchers, many of whom now hold faculty positions or leadership roles in robotics companies like Ghost Robotics and Boston Dynamics. His research is supported by major grants from the NSF and AFOSR, including the MURI award and REU/RET programs that engage K-12 and undergraduate educators. He has also been involved in international outreach, including activities at the Penn Wharton China Center. Koditschek leads the Kod*lab within the GRASP Lab’s PERCH facility, which houses advanced legged robots such as the Ghost Minitaur, XRHhex, Inu, Delta Hopper, and Jerboa platforms. The lab emphasizes experimental validation of control theories using custom hardware and real-world terrain challenges.

Sylvia Richardsonمشاهده پروفایل

استاد پژوهشی

• Biostatistics
• Bayesian Statistics
• Spatial Modeling
• +۷ مورد دیگر

Sylvia Richardson is an MRC Investigator at the MRC Biostatistics Unit and holds a Research Professorship at the University of Cambridge, where she served as Director of the Biostatistics Unit from 2012 to 2021. She is affiliated with the Cambridge Mathematics of Information in Healthcare Hub (CMIH) at the Centre for Mathematical Sciences. Her work bridges advanced statistical methodology with critical healthcare applications, particularly in the analysis of complex biomedical data. Richardson's research spans multiple domains of biostatistics with a strong emphasis on Bayesian approaches. Her work has significantly advanced spatial modeling and disease mapping techniques, developed sophisticated methods for handling measurement error in epidemiological studies, and pioneered mixture and clustering models for integrative analysis of heterogeneous data sources. Her research addresses fundamental challenges in analyzing longitudinal health data, multimorbidity patterns, and complex disease trajectories. Her publication record demonstrates consistent methodological innovation applied to pressing healthcare challenges. Recent work focuses on traumatic brain injury outcomes, multimorbidity progression, genomic analysis, and statistical approaches to pandemic data. The articles reveal a strong pattern of methodological development driven by real-world healthcare challenges, with particular attention to longitudinal analysis, Bayesian computation, and integrative modeling approaches that can handle diverse and complex data structures. While specific awards are not detailed in the available information, Richardson's leadership as Director of the MRC Biostatistics Unit for nearly a decade and her continued Research Professorship reflect significant recognition of her contributions to the field. Her work with major international consortia like CENTER-TBI demonstrates her role in large-scale collaborative research efforts addressing critical health challenges. Richardson's research has substantial implications for healthcare policy and practice, particularly in understanding disease progression, developing predictive models for patient outcomes, and creating methodological frameworks that can integrate diverse data sources to generate meaningful clinical insights. Her work continues to influence both statistical methodology and healthcare applications through ongoing research and leadership in the field.

Steven A. Millerمشاهده پروفایل

استاد

• Personality characteristics
• Individual differences
• Emotional experiences
• +۵ مورد دیگر

Dr. Steven A. Miller is a Professor of Psychology in the Department of Psychology at Rosalind Franklin University of Medicine and Science, within the College of Health Professions. He joined RFUMS in 2013 and serves as a statistics consultant for the university. His academic background includes a PhD in Social Psychology from Loyola University Chicago, an M.S. in Psychology from Illinois State University with specialization in Clinical Psychology, and an M.S. in Mathematics from Loyola University Chicago with specialization in Probability and Statistics. PhD in Social Psychology, Loyola University Chicago M.S. in Psychology, Illinois State University (Clinical Psychology specialization) M.S. in Mathematics, Loyola University Chicago (Probability and Statistics specialization) Dr. Miller's research focuses on the intricate relationship between personality characteristics/individual differences and emotional experiences. He investigates anxiety and emotional disorders, social cognitive models of personality, and applies quantitative methodology to psychological questions. His work examines intra-individual variability in emotional responses and how situational factors interact with personality to shape emotional experiences. He employs diverse methodologies including experience sampling studies and laboratory experiments to explore these complex dynamics. His recent publications demonstrate a strong focus on psychopathy, emotion regulation, network analysis of personality, and the tripartite model of anxiety and depression. His work spans clinical, forensic, and general populations, often employing sophisticated statistical techniques. There's a clear trajectory toward more complex modeling approaches including network analysis, longitudinal modeling, and advanced psychometric techniques across his publication history. Accredited Professional Statistician (PStat®) with the American Statistical Association Chartered Statistician (CStat) with the Royal Statistical Society Dr. Miller actively mentors graduate students, with numerous student co-authors appearing in his publications. He teaches advanced statistical courses including multivariate statistics, longitudinal models, and categorical data analysis. He is currently accepting doctoral students for the 2026/2027 academic year. His collaborative research spans multiple institutions including DePaul University and Texas A&M, focusing on emerging adults, romantic relationships, and chronic illness. His research laboratory examines the fundamental relationship between personality and emotion, exploring how situational contingencies and individual expectancies shape emotional responses. Current collaborative projects investigate daily experiences of emerging adults, psychopathy in romantic relationships, and social media use among individuals with chronic illness using diverse methodological approaches.

Subash Jonnalagaddaمشاهده پروفایل

استاد

• Medicinal Chemistry
• Organic Chemistry
• Drug Development
• +۴ مورد دیگر

Subash Jonnalagadda, Ph.D., is a Professor and Department Head of Chemistry & Biochemistry at Rowan University's College of Science & Mathematics, also affiliated with the Biological & Biomedical Sciences program. He holds a B.S. from Pondicherry University, M.S. from University of Hyderabad, and Ph.D. in Organic Chemistry from Purdue University, with postdoctoral training at University of Pennsylvania and University of Minnesota. Recipient of Rowan University's Wall of Fame Teaching Award (2013, 2016) Eli Lilly International Graduate Scholar (2000-2005) Research focuses on: Medicinal Chemistry: Developing boron-based small molecules (e.g., benzoboroxoles) and betulinic acid derivatives as anti-cancer agents Biomass Valorization: Converting cellulose into chemicals like hydroxymethylfurfural for bio-based polymers Publications emphasize anti-cancer drug design, nanocarrier systems, and enzyme inhibition strategies. Advised over 50 graduate/undergraduate students, many progressing to academic and pharmaceutical careers. Collaborates with Rowan School of Osteopathic Medicine on Alzheimer's drug candidates.

Yves-Alexandre de Montjoyeمشاهده پروفایل

دانشیار

• Computational Privacy
• AI Safety
• Machine Learning
• +۳ مورد دیگر

Yves-Alexandre de Montjoye is an Associate Professor of Applied Mathematics and Computer Science at Imperial College London, where he leads the Computational Privacy Group. He holds a joint affiliation between the Department of Computing and the Data Science Institute. His roles include serving as a Special Adviser on AI and Data Protection to the EC Justice Commissioner Didier Reynders, a Parliament-appointed Commissioner for the Belgian Data Protection Agency, and a Special Adviser to EC Competition Commissioner Margrethe Vestager, co-authoring the 'Competition Policy for the Digital Era' report. He earned his PhD from MIT in 2015 under Alex 'Sandy' Pentland. His master's degrees include an M.Sc. in Applied Mathematics from UCLouvain, an M.Sc. (Centralien) from École Centrale Paris, and an M.Sc. in Mathematical Engineering from KU Leuven. He also holds a B.Sc. in Engineering from UCLouvain. His research interests focus on computational privacy, anonymization techniques, AI safety, and machine learning attacks. He develops methods to 'red team' AI systems and create privacy-preserving mechanisms. His work addresses vulnerabilities such as membership inference, attribute inference, and re-identification risks in datasets, with applications to location tracking, synthetic data, and LLMs. His articles analyze adversarial attacks against privacy systems, emphasizing robustness and practical guarantees. He advocates for privacy-by-design approaches in big data analytics and has explored ethical AI, competition policy in digital markets, and humanitarian uses of mobile data. While no scientific awards are explicitly listed, his contributions have been widely covered in media. He is currently recruiting motivated PhD students for his group at Imperial College. His advising and grants narrative includes work on privacy-preserving technologies and policy implications of AI, with collaborations across academia and public institutions. He is affiliated with the Computational Privacy Group and contributes to platforms like OPAL for privacy analytics. His office is in the ACE Extension building (ACEX 259), accessible via Exhibition Road.

Parviz Moinمشاهده پروفایل

استاد

• Computational Physics
• Turbulent Boundary Layers
• Hypersonic Flows
• +۴ مورد دیگر

Parviz Moin holds the Franklin P. and Caroline M. Johnson Professorship in Stanford University's School of Engineering. As founding director of the Center for Turbulence Research (CTR)—a NASA-Stanford consortium established in 1987—he has pioneered computational methods for turbulence physics, including direct numerical simulation and Large Eddy Simulation (LES) techniques. CTR serves as an international hub for turbulence studies across engineering, mathematics, and physics disciplines. Moin's research encompasses computational physics of turbulent flows, with emphasis on boundary layer control, hypersonic aerodynamics, propulsion systems, and aircraft icing. His recent work advances high-fidelity simulations for aerospace applications, particularly developing wall models for LES that accurately capture separation phenomena under complex pressure gradients and Reynolds number effects. Recent publications demonstrate extensive applications of LES to aircraft design challenges, including transonic buffet prediction, high-lift configuration analysis, and icing aerodynamics. Investigations consistently address fundamental turbulence physics while developing practical computational tools for aerospace engineering, with particular focus on hypersonic boundary layers, flow separation mechanisms, and conjugate heat transfer in iced environments.

Nikita Zhivotovskiyمشاهده پروفایل

استادیار

• Mathematical Statistics
• Applied Probability
• Statistical Learning Theory
• +۵ مورد دیگر

Nikita Zhivotovskiy is an Assistant Professor in the Department of Statistics at the University of California Berkeley within the College of Letters and Science. His research spans the intersection of mathematical statistics, probability theory, and statistical learning theory with particular focus on high-dimensional data analysis and non-parametric inference. His research interests include mathematical statistics, applied probability, statistical learning theory, high-dimensional data analysis, non-parametric inference, and artificial intelligence/machine learning. Zhivotovskiy's work addresses fundamental questions in statistical learning theory, including risk bounds, algorithmic stability, and convergence rates, with applications spanning multiple domains including robust statistics and private learning. His recent publications (2021-2025) demonstrate significant contributions to theoretical machine learning, particularly in statistical learning theory, risk bounds, high-dimensional statistics, and algorithmic stability. These works appear in top venues including NeurIPS, COLT, and FOCS, reflecting the theoretical depth and importance of his contributions to the field. Among his notable achievements is a Best Paper Award at the Conference on Learning Theory (COLT) in 2020 for his work on 'Proper Learning, Helly Number, and an Optimal SVM Bound.' Zhivotovskiy has also contributed to the theoretical foundations of PAC learning, risk minimization, and statistical aggregation. Prior to his current position, Zhivotovskiy was a postdoctoral researcher at ETH Zürich (2021-2022) and Google Research (2019-2020). He completed his PhD at Moscow Institute of Physics and Technology in 2018 under the supervision of Vladimir Spokoiny and Konstantin Vorontsov.

• Decision and Control
• Game Theory
• Multi-Agent Systems
• +۷ مورد دیگر

Jeff Shamma is the Department Head and Professor of Industrial and Enterprise Systems Engineering (ISE) at the University of Illinois at Urbana-Champaign, holding the Jerry S. Dobrovolny Chair. He is also courtesy Professor in Aerospace Engineering and Mechanical Science and Engineering. Formerly, he held the Julian T. Hightower Chair at Georgia Institute of Technology and faculty positions at KAUST. Dr. Shamma earned his PhD in Systems Science and Engineering from MIT (1988) and a BS in Mechanical Engineering from Georgia Tech (1983). He is a Fellow of IEEE and IFAC, recipient of the IFAC High Impact Paper Award, AACC Donald P. Eckman Award, and NSF Young Investigator Award. His research spans Decision and Control , Game Theory , and Multi-Agent Systems , focusing on human-machine networks, distributed autonomy, and adaptive robotic systems. Recent work examines crowd dynamics, risk-sensitive control, and feedback linearization for constrained optimization. Jeff has served as Editor-in-Chief of IEEE Transactions on Control of Network Systems (2020–2024) and held editorial roles in journals like Annual Reviews in Control and IEEE Transactions on Robotics . His 15 most recent publications (2024–2025) analyze learning dynamics, multi-agent optimization, and UAV-crawler systems, reflecting trends in autonomous systems, game-theoretic modeling, and industrial inspection technologies. Scientific distinctions include: Fellow of IEEE and IFAC IFAC High Impact Paper Award (2020) AACC Donald P. Eckman Award (1996) NSF Young Investigator Award (1992) Mohammed Dahleh Distinguished Lecture Award (2013) Dr. Shamma advises current PhD students Hassan Abdelraouf, Aya Hamed, and Nawaf Otaibi, with former advisees including Sarah Toonsi (2025) and Fat-hy Rajab (2025). His lab integrates theoretical research with applied projects like FalconScan, a UAV-crawler system for industrial inspection, and develops magnetic legs for curved surface UAV landing.

Themistoklis Sapsisمشاهده پروفایل

استاد

• Probabilistic Prediction
• Nonlinear Systems Analysis
• Machine Learning in Fluid Dynamics
• +۳ مورد دیگر

Themistoklis Sapsis is a Professor in the Department of Mechanical Engineering at the Massachusetts Institute of Technology (MIT), where he also holds an affiliation with the MIT Institute for Data, Systems, and Society. He earned his Ph.D. in Mechanical Engineering from MIT in 2011 and previously served as an Assistant Research Scientist at NYU’s Courant Institute of Mathematical Sciences. His research focuses on developing analytical, computational, and data-driven methods to predict and quantify extreme events in high-dimensional nonlinear systems, such as turbulent fluid flows and mechanical systems. Key areas include probabilistic modeling of climate extremes, machine learning for climate simulation corrections, and uncertainty quantification in complex dynamical systems. Recent work emphasizes applications in ocean engineering (e.g., vortex-induced vibrations, wave energy systems) and environmental science (e.g., spatially resolved climate extremes, bias correction in Earth system models). His methodologies combine stochastic emulators, Bayesian experimental design, and neural networks to address challenges in data sparsity and model fidelity. Notable contributions include frameworks for correcting coarse-scale climate simulations using machine learning, real-time ocean temperature reconstruction from satellite data, and data-driven modeling of hydrodynamic interactions in marine risers. His research bridges theoretical developments with practical applications in energy systems, structural monitoring, and autonomous systems. Prof. Sapsis collaborates with interdisciplinary teams and has contributed to initiatives such as FIRSTLING-DIGIMAR (a marine riser digital twin) and multi-fidelity frameworks for autonomous seakeeping. His work is supported by grants focused on advancing machine learning in scientific modeling and extreme event prediction.

Benjamin Steinbergمشاهده پروفایل

استاد

• Finite Semigroup Theory
• Geometric Group Theory
• Algebraic Combinatorics
• +۵ مورد دیگر

Benjamin Steinberg is a Professor in the Mathematics Department at the City College of New York (CCNY) and the CUNY Graduate Center. He holds a Ph.D. from the University of California, Berkeley (1998) under John Rhodes and has held positions at the University of Porto (Portugal) and Carleton University (Canada). His research focuses on algebra, including semigroups, geometric group theory, algebraic combinatorics, representation theory, and automata theory, with notable work on etale groupoids, inverse semigroups, and ring theory. He is the author of several books, including *The q-theory of Finite Semigroups* and *Representation Theory of Finite Monoids*. Steinberg serves as Managing Editor of the *International Journal of Algebra and Computation* and has organized conferences such as the International Conference on Semigroups and Groups in Honor of John Rhodes. Research interests include the interplay between algebraic structures and their applications, such as in automata theory and Markov chains. His work bridges pure mathematics with combinatorial and geometric approaches, often involving categorical and topological methods. Recent articles explore topics like Nekrashevych algebras, twisted Steinberg algebras, and Lyndon's identity theorem for monoids. He has contributed to the study of profinite groups and their connections to symbolic dynamics. Steinberg’s editorial roles and conference organization reflect his leadership in the mathematical community. Despite his defunct blog, his academic contributions remain prolific, with ongoing editorial work and research in algebraic combinatorics and representation theory.

• Experimental Economics
• Game Theory
• Behavioral Economics
• +۷ مورد دیگر

Nicholas Feltovich is a Professor of Economics at Monash University, specializing in experimental economics, game theory, and behavioral economics. His research focuses on understanding strategic behavior in markets, bargaining institutions, and decision-making under uncertainty, with notable contributions to schizophrenia-related loss aversion studies and experimental political science. He holds a PhD in Economics from the University of Pittsburgh (1997) and a BS in Mathematics from Virginia Tech (1991). Education PhD in Economics, University of Pittsburgh, 1997 BS in Mathematics, Virginia Polytechnic Institute and State University, 1991 Research Interests His work explores experimental investigations of market institutions, bargaining power dynamics, and social norms. Key topics include the effects of communication in strategic interactions, the role of fairness perceptions in pricing, and the application of neuroeconomic methods to study decision-making impairments in schizophrenia. He has conducted influential studies on corporate social responsibility, leadership in public goods provision, and the impact of leniency programs on collusion. Grant Activities Posted prices, bargaining and auctions : ARC-funded project (2014–2019) examining experimental economics of market mechanisms. Effect of bargaining power : ARC project (2013–2018) analyzing institutional and normative influences on bargaining outcomes. Collaborations He collaborates internationally on projects spanning behavioral ethics, experimental game theory, and schizophrenia-related economic behavior. His work contributes to the UN Sustainable Development Goal on Quality Education through applied research in decision-making frameworks.

Max Alekseyevمشاهده پروفایل

دانشیار

• Computational Graph Theory
• Enumerative Combinatorics
• Computational/Algorithmic Biology
• +۱ مورد دیگر

Max Alekseyev is an Associate Professor in the Mathematics Department and Computational Biology Institute. His research spans computational graph theory, enumerative combinatorics, computational/algorithmic biology, and comparative genomics. He focuses on interdisciplinary problems, blending mathematics with biological applications, particularly in genome assembly and analysis. His work includes advancements in genome scaffolding algorithms, combinatorial sequence analysis, and mathematical biology. Notable contributions involve genome assembly tools like CAMSA and studies on ancestral genome reconstruction. He also explores theoretical topics such as Bernoulli series generalizations and modular data classification. His research trends highlight a blend of pure mathematics (e.g., number theory, graph theory) and applied computational methods, addressing challenges in genomics and evolutionary biology. He secured an NSF Student Travel Grant in 2018 for computational molecular biology.

• Mathematical Statistics
• Model Selection
• Robust Estimation
• +۳ مورد دیگر

Yannick BARAUD is a Full Professor in Mathematics at the University of Luxembourg, leading the group 'Developing Contemporary Mathematical Statistics' within the Department of Mathematics. He holds the ERA-Chair 'SanDAL' in Mathematical Statistics and Data Science, funded by the European Commission. His research focuses on robust estimation, model selection, hypothesis testing, and nonparametric methods. He serves as the Study Programme Director for the Master in Data Science and has held academic positions at the University of Nice Sophia Antipolis and CNRS. His career includes roles as a researcher at École Normale Supérieure (Paris) and a lecturer at the same institution. He earned his PhD from Université Paris-Sud and studied at École Normale Supérieure de Cachan. His work emphasizes rigorous statistical methodologies, including rho-estimation and robust Bayes-like approaches. Notable contributions include advancements in density estimation under shape constraints and robust regression techniques. He has published extensively on topics such as loss functions, empirical processes, and statistical inference, with applications in epidemiology and data science. His leadership in the SanDAL initiative underscores his commitment to bridging mathematical statistics and practical data science challenges. Collaborations and grants further highlight his role in advancing interdisciplinary research.

Katrin Tentمشاهده پروفایل

استاد

• Model theory
• Group theory
• Geometry
• +۴ مورد دیگر

Professor Katrin Tent is a distinguished mathematician specializing in mathematical logic at the University of Münster, where she holds a professorship in the Faculty of Mathematics and Computer Science within the Institute for Mathematical Logic and Foundations Research. She is an active researcher in Mathematics Münster, an investigator in CRC 1442 Geometry: Deformations and Rigidity, and contributes to multiple research projects including Topics in Mathematics Münster T3: Models and universes, T4: Groups and actions, and T8: Random discrete structures and their limits. PhD in Linguistics, Christian-Albrechts-Universität zu Kiel (1988) Diplom in Mathematics, Christian-Albrechts-Universität zu Kiel (1989) PhD in Mathematics, University of Notre Dame (1994) Habilitation, "Model theory of groups and BN-pairs" (2000) Professor Tent's research bridges model theory, group theory, and geometry, with particular focus on finite Morley rank structures, BN-pairs, and sharply multiply transitive groups. Her work often combines methods from these areas to prove unexpected results, either constructing groups or incidence geometries with surprising model theoretic properties or using model theory to construct new and interesting geometries or groups. Her recent publications reveal a consistent trajectory connecting model theory with group-theoretic structures. She has made significant contributions to understanding sharply 2- and 3-transitive groups, finite Morley rank geometries, and the model theory of generalized polygons. Her work demonstrates how model-theoretic techniques can solve deep problems in group theory and geometry, particularly through the study of BN-pairs and incidence structures. DFG Research Fellowship (1996-1998) Bayerischer Habilitationsförderpreis (1998-2001) Heisenberg-Stipendium (2001-2004) ERC Consolidator Grants expert panel (2016, 2018) Elected to DFG Senate (2019) Professor Tent leads an active research group with current members including Marco Amelio, Dr. Benjamin Brück, Anna Cascioli, Lukas Jonuska, Silke Meissner, Zahra Mohammadi Khangheshlaghi, and Dr. Sam Shepherd. Her former research group members include Dr. Simon Andre, Dr. Isabel Müller, and Dr. Tim Clausen, among others. Her supervisory work spans both theoretical foundations and specific applications in geometric group theory and model theory. Her research group operates within the Institute for Mathematical Logic and Foundations Research at the University of Münster, collaborating closely with other researchers in the Mathematics Münster cluster. The group participates in various projects including CRC 1442 - C04: Group theoretic aspects of negative curvature, contributing to the vibrant mathematical research environment at one of Germany's leading mathematics institutions.

Steven M. LaValleمشاهده پروفایل

استاد

• Robotics
• Motion Planning
• Artificial Intelligence
• +۷ مورد دیگر

Steven M. LaValle is a Professor at the University of Oulu's Faculty of Information Technology and Electrical Engineering since 2018. Previously, he held tenured positions at the University of Illinois Urbana-Champaign (UIUC) and was a Principal Scientist at Oculus VR. His research spans robotics, motion planning (notably pioneering RRT algorithms), virtual reality, and sensor fusion. He has authored influential textbooks like Planning Algorithms and Virtual Reality . Education: PhD (1995), MS (1993), and BS (1990) in Electrical Engineering from UIUC. Research Interests: Focuses on minimal information requirements for robots, perception engineering, and foundational VR/AR systems. His work integrates control theory, computational geometry, and human perception. Achievements: Recipient of the IEEE ICRA Milestone Award (2019), University Scholar (UIUC, 2012), and XTIC Award 2024 for Innovation. Leads the Perception Engineering Group at Oulu, advancing VR/AR and telepresence technologies. Grants & Industry: ERC Advanced Grant (2021–2026), former VP of Huawei's VR/AR division, and collaborator with institutions like IIT Madras. Advises startups in robotics and virtual reality.