جست‌وجوی استاد و پژوهشگر

Anupam Guptaمشاهده پروفایل

استاد مدعو

• Algorithms
• Complexity
• Artificial Intelligence
• +۴ مورد دیگر

Anupam Gupta is an Adjunct Professor in the Computer Science Department at New York University's Courant Institute of Mathematical Sciences. Previously, he held a faculty position at Carnegie Mellon University. His research focuses on algorithms, complexity, and theoretical computer science, with an emphasis on network design, metric embeddings, and approximation algorithms. He has received notable awards, including the ACM Fellowship (2021) and the Herbert A. Simon Award for Teaching Excellence (2019). Education: Ph.D., University of California, Berkeley (2000); B.Tech., Indian Institute of Technology Kanpur (1996) Teaching: Taught courses on algorithms, theoretical computer science, and real-world applications at both undergraduate and graduate levels. Research: Explores algorithms for uncertain environments, stochastic optimization, and online algorithms. His work bridges theoretical foundations with practical applications in networking and combinatorial optimization. Awards: ACM Fellow, Sloan Fellowship, and multiple teaching awards. Students: Advised over 15 Ph.D. students, including notable researchers in algorithms and theoretical computer science. Grants: Supported by NSF grants, including a CAREER Award, and collaborates on projects like the Indo-US Algorithms under Uncertainty initiative. His work on metric embeddings and graph algorithms has had significant impacts, with contributions to approximation algorithms and competitive analysis. He actively participates in academic conferences and serves as an organizer for programs like the Simons Institute's Algorithms and Uncertainty initiative.

Nilanjan Chakrabortyمشاهده پروفایل

دانشیار

• Robotics
• Artificial Intelligence
• Dynamical Systems
• +۵ مورد دیگر

Nilanjan Chakraborty is an Associate Professor in the Department of Mechanical Engineering at Stony Brook University, where he has been since 2014. His research focuses on robotics, artificial intelligence, and dynamical systems, with particular emphasis on developing robust autonomy for robots operating in collaboration with humans or independently. He directs the Interacting Robotic Systems Laboratory, exploring algorithmic foundations for multi-robot systems, manipulation planning, and human-swarm interaction. Chakraborty holds a Ph.D. from Rensselaer Polytechnic Institute (2008) and previously served as a Postdoc/Project Scientist at Carnegie Mellon University’s Robotics Institute. His work includes pioneering contributions to contact dynamics-based algorithms, multi-robot coordination, and human-robot interaction. He is an IEEE member and has reviewed for top-tier robotics conferences/journals. Research interests span: (1) Manipulation planning with environmental interactions, (2) Safe whole-body robot control using complementarity constraints, (3) Human-guided motion planning via screw geometry, (4) Swarm robotics coordination under uncertainty, and (5) Agricultural automation with cobots. Recent trends in his publications emphasize real-time obstacle avoidance, deep learning for mechanism design, and provable search methods with imperfect sensors. He has received the 2013 Best Paper Award (HFES) and 2007 Best Student Paper (RSS). His lab investigates topics like LiDAR-based tripping hazard detection, time-optimized trajectory planning, and connectivity-resilient multirobot teams. Ongoing work addresses challenges in human-swarm interfaces and adaptive grasping systems.

Eric Pauwelsمشاهده پروفایل

پژوهشگر

• Operations Research
• Transportation Systems
• Machine Learning
• +۳ مورد دیگر

Eric Pauwels is a Scientific Staff Member at Centrum Wiskunde & Informatica (CWI) in Amsterdam, The Netherlands, affiliated with the Intelligent and Autonomous Systems department. His research spans interdisciplinary domains including transportation systems, energy markets, and behavioral economics. Key Research Areas: Transportation demand modeling, machine learning applications in mobility, reinforcement learning for cooperative systems, and energy systems optimization. Active Projects: ALIGN4energy (aligning citizen behavior with systems), Energy Intranets (NEAT project), and industrial collaborations like Impactstudie Noord/Zuidlijn. Scientific Contributions: 2005 ERCIM Best Working Group Prize recipient, with expertise in discrete choice modeling, wavelet analysis, and production scheduling optimization. Publications: Recent work focuses on bicycle-train integration, electric vehicle bidding frameworks, and CKLS financial process analysis. Contact: Email Eric.Pauwels@cwi.nl , Phone +31 20 592 4225 (Room M358).

• Database Systems
• Data Management
• Recommender Systems
• +۴ مورد دیگر

Sihem Amer-Yahia is a distinguished Research Professor at the University of Grenoble Alpes (affiliated with Grenoble Informatics Laboratory ), with significant contributions to database systems , data exploration , and fairness in AI . Her work bridges human-computer interaction and machine learning to create systems that enhance data-driven decision-making. Research Pillars : Algorithmic fairness, interactive data mining, recommender systems, and human-AI collaboration Recent Advances : 2023-2025 publications focus on statistically sound hypothesis testing , multi-objective recommendation , and conversational analytics Leadership : Co-organized major conferences (DASFAA 2024) and led DEI initiatives in database communities Her 15 most recent articles (2020-2025) span topics like producer fairness in recommendation , statistical hypothesis frameworks , and AI-powered education systems , with keywords covering database optimization , reinforcement learning , and ethical data mining . She actively contributes to ACM/IEEE journals and VLDB/SIGMOD conferences.

Ryan K. Cosnerمشاهده پروفایل

استادیار

• Robotics
• Control Theory
• Machine Learning
• +۳ مورد دیگر

Ryan K. Cosner is an Assistant Professor in the Department of Mechanical Engineering at Tufts University's School of Engineering, commencing his appointment in January 2026. His research bridges robotics, machine learning, and control theory to develop provably safe autonomous systems through risk-aware control frameworks and deployable algorithms validated across diverse robot platforms. His academic background includes a Ph.D. from the California Institute of Technology (Caltech) and a B.S. from the University of California, Berkeley. Prior to joining Tufts, he contributed to autonomous vehicle research at Nvidia's Autonomous Vehicle Research Group. Dr. Cosner's work centers on robotics safety using Control Barrier Functions (CBFs) to ensure set-invariance under uncertainty. His research spans stochastic safety guarantees , risk-aware control synthesis , and safe learning frameworks for real-world applications. Key methodologies include discrete-time CBFs, probabilistic safety bounds, and integration of machine learning with formal verification. Analysis of his 2022-2025 publications reveals dominant themes in safety-critical control for uncertain systems, with significant contributions to humanoid robotics, autonomous vehicles, and stochastic safety frameworks. His work consistently advances CBF theory while addressing practical deployment challenges through validation on physical systems. No scientific awards were documented in the provided materials. Dr. Cosner is establishing his research group at Tufts and actively recruiting graduate students for projects in safety-critical autonomy. While specific grants aren't detailed, his research direction aligns with funding priorities in trustworthy AI and robotic systems safety. He leads a robotics safety laboratory focusing on humanoid platforms and autonomous vehicles, maintaining collaborations with industry partners like Nvidia and academic institutions including Caltech.

Alexandra Carpentierمشاهده پروفایل

استاد

• Machine Learning
• Mathematical Statistics
• High-Dimensional Statistical Inference
• +۷ مورد دیگر

Prof. Dr. Alexandra Carpentier chairs the Mathematical Statistics and Machine Learning group at the University of Potsdam , affiliated with the Institute of Mathematics. She serves as an associate editor for multiple journals, including the Journal of the European Mathematical Society and Annals of Statistics , and will co-edit the Electronic Journal of Statistics (EJS) starting in 2025. Her research focuses on high-dimensional statistical inference, unsupervised learning, and statistical-computational trade-offs, with applications in digital engineering and neuroscience.

Yongyi Guoمشاهده پروفایل

استادیار

• Statistical Learning
• Decision-Making Methods
• Digital Health Interventions
• +۳ مورد دیگر

Yongyi Guo serves as an Assistant Professor in the Department of Statistics at the University of Wisconsin-Madison, following a postdoctoral fellowship at Harvard University under Susan A. Murphy. Her academic foundation includes a Ph.D. in Statistics from Princeton University (2022) advised by Jianqing Fan and a B.S. in Mathematical Sciences from Peking University (2016). Dr. Guo's research integrates statistical learning with digital health applications, specializing in reinforcement learning for personalized mobile interventions and causal inference methodologies. Her work demonstrates particular expertise in micro-randomized trials for cannabis use reduction, where she developed the MiWaves and reBandit algorithms to optimize just-in-time adaptive interventions through online decision-making frameworks. Analysis of her publication trends reveals consistent focus on bridging theoretical statistics with health implementation challenges, featuring innovations in anytime-valid inference for N-of-1 trials, communication-efficient distributed estimation, and robust regression techniques for dependent data structures.

Alan Zameمشاهده پروفایل

استاد

• Mathematics
• Statistics
• Probability Theory
• +۴ مورد دیگر

Alan Zame is a Professor in the Department of Mathematics at the University of Miami , College of Arts and Sciences. His research spans diverse areas in mathematics, statistics, and probability theory. Education & Training: (Details not explicitly provided in the text) Research Interests: Dr. Zame's work includes combinatorics , probability theory , game theory , and number theory . He has contributed to understanding stochastic processes, tournament planning, urn schemes, and subgroup chains. Publication Trends: His publications from 1984–2010 focus on probabilistic modeling, strategic games, and mathematical structures. Key topics include random sequences, gambling strategies, string matching, and combinatorial fairness in tournaments. Advising & Grants: No specific students or grants are listed in the provided text.

James A. Grantمشاهده پروفایل

مدرس

• Bandit Learning
• Reinforcement Learning
• Algorithm Design
• +۴ مورد دیگر

Dr James A. Grant is a Lecturer in Statistics at the School of Mathematical Sciences, Lancaster University, with affiliations at Fylde College. He earned his PhD through the STOR-i Centre for Doctoral Training in 2019. Current Postgraduate Supervision Active Industry Collaborations (BT, Tesco, Datasparq) Research Specializations: Bandit Learning, Reinforcement Learning, Algorithm Design His research focuses on bandit learning and reinforcement learning algorithms that optimize decision-making through iterative model updates. Key themes include: Handling complex data and models Multi-fidelity simulation networks Safety constraints in reinforcement learning Streaming data applications Phylogenetic tree analysis Federated learning architectures Recent work trends show emphasis on: Contextual bandit frameworks Interdisciplinary applications (biology, digital infrastructure) Industry-partnered projects Personalization in algorithm design Statistical convergence guarantees Simulation optimization techniques Grant currently supervises 5 PhD students across diverse topics including: Triggered bandits in streaming data Multi-fidelity modeling Safety mechanisms in reinforcement learning

Mao Xiaojieمشاهده پروفایل

دانشیار

• Causal inference
• Data-driven optimization decision-making
• Machine Learning
• +۳ مورد دیگر

Mao Xiaojie is an Associate Professor at the Department of Management Science and Engineering, Tsinghua University's School of Economics and Management . Holding a PhD in Statistics and Data Science from Cornell University (2021) and a bachelor's in Mathematical Economics and Finance from Wuhan University (2016), Mao specializes in causal inference and data-driven optimization decision-making . PhD: Cornell University (2016-2021) Bachelor: Wuhan University (2012-2016) Mao's research bridges machine learning , statistics , and operations research to address challenges in contextual optimization, algorithmic fairness, and robust causal inference. Recent work focuses on data combination , surrogate variables , and minimax methods for handling unobserved confounding and limited outcome data. Key trends in Mao's publications include: Advancing bandit algorithms for efficient contextual decision-making Developing debiased machine learning frameworks for quantile treatment effects Designing robust optimization models under noisy and incomplete covariates Scientific recognition includes: Applied Probability Society Best Student Paper Competition Finalist (2020) Multiple teaching excellence awards at Tsinghua University (2022-2024) Research grants from the National Natural Science Foundation of China Mao currently teaches Empirical Methods in Management Science (PhD), Data Analysis: Inference and Decision Making (Master), and Probability Theory and Mathematical Statistics (Undergraduate). Research collaborations span institutions like Cornell and MIT, with publications in top venues including NeurIPS , ICML , and Operations Research .

Gergely Neuمشاهده پروفایل

عضو هیئت علمی

• Theoretical Machine Learning
• Reinforcement Learning Theory
• Online Optimization
• +۳ مورد دیگر

Gergely Neu is a Research Assistant Professor in the Department of Information and Communication Technologies (DTIC) at Universitat Pompeu Fabra in Barcelona, Spain. He leads a research group focused on theoretical aspects of sequential decision making and machine learning. Dr. Neu's research primarily centers on theoretical machine learning with a focus on: Reinforcement Learning Theory Online Optimization Bandit Problems Statistical Learning Theory Generalization Bounds Algorithms with provable performance guarantees His work emphasizes developing algorithms with strong theoretical guarantees in both computational and statistical complexity while maintaining practical implementability. Dr. Neu's research program seeks to bridge the gap between theoretical foundations and practical applications of machine learning systems, particularly in sequential decision-making contexts where performance guarantees are crucial. Analysis of Dr. Neu's recent publications reveals a strong focus on advancing the theoretical understanding of reinforcement learning, bandit algorithms, and generalization in machine learning. His work often employs techniques from convex analysis, information theory, and regret analysis to derive novel theoretical results with practical implications. A notable trend in his recent work is the exploration of offline reinforcement learning, contextual bandits, and the development of tighter generalization bounds using innovative analytical approaches that connect online learning with PAC-style guarantees. Dr. Neu has received numerous prestigious awards and recognitions for his research contributions: ERC Starting Grant (2020) Bosch AI Young Researcher Award (2020) Google Faculty Research Award (2019) "la Caixa" Junior Leader fellowship (2018) ELLIS Scholar appointment (2021) Outstanding Reviewer Award from ICML'16 Blue Ribbon for Outstanding Reviewing from IJCAI'16 Dr. Neu actively mentors several PhD students and postdoctoral researchers, fostering the next generation of theoretical machine learning researchers. His ERC Starting Grant provides substantial funding for his research program, enabling him to pursue ambitious theoretical investigations while training early-career researchers. He has successfully placed alumni in prestigious positions including assistant professorships at TU Delft and Politecnico di Milano. Dr. Neu's research group operates within the vibrant AI and Machine Learning research community at Universitat Pompeu Fabra, collaborating closely with other researchers in the Department of Information and Communication Technologies and maintaining strong ties with the Statistics group at the Department of Economics and Business. The group participates actively in the international machine learning theory community, regularly contributing to top conferences such as COLT, NeurIPS, ICML, and ALT, where Dr. Neu has served in leadership roles including Program Chair for COLT'23.

Davud Rostam-Afsharمشاهده پروفایل

استاد

• Accounting
• Taxation
• Macroeconomics
• +۷ مورد دیگر

Professor Davud Rostam-Afshar is a Professor (W2) at the University of Mannheim Business School, where he serves as academic director of the German Business Panel. Previously, he held positions at the University of Hohenheim, Free University of Berlin, and University of Potsdam, with research stays at UC Berkeley and Harvard University. He also serves as a consultant to the European Commission and the OECD. Education: PhD (summa cum laude) in Economics, Free University of Berlin (2010-2015), Supervisors: Viktor Steiner (FU Berlin), Richard Blundell (UCL) Diploma (M.Sc. equivalent) in Economics, Free University of Berlin (2005-2010) Rostam-Afshar's research spans several interconnected fields with a focus on empirical analysis. His work in accounting and taxation examines corporate decision-making, regulatory effects, and transparency issues. In public finance, he investigates optimal taxation, tax incidence, and the relationship between taxation and economic behavior. His labor economics research explores occupational regulation, migration effects, and precautionary behavior. Methodologically, he specializes in econometrics, survey design, and experimental approaches, often combining theoretical modeling with empirical validation. His recent publications reveal a strong focus on business decision-making during economic disruptions (pandemics, wars), with particular attention to how firms respond to regulatory changes, taxation policies, and external shocks. The German Business Panel serves as a key data source for much of this research, providing insights into managerial perceptions and expectations that complement traditional accounting data. Scientific Awards: Principal Investigator, German Research Foundation (2023) University of Turin Fellowship (2020) Excellence in Reviewing Award, Labor Economics (2020) Best Teaching Award, University of Hohenheim (2019-2020 and 2019) Ph.D. Fellowship, German National Academic Foundation (2011-2013) Scholarship, German National Academic Foundation (2009-2010) Rostam-Afshar has advised numerous master's and PhD students, including Laura Arnemann (now at OECD) and Lisa Feil (now at SAP). His grant portfolio includes significant funding from the German Research Foundation and collaborations with major institutions including the European Commission. He has developed specialized Stata modules for econometric analysis, including BBANDITS for multi-armed bandit experiments. As academic director of the German Business Panel, Rostam-Afshar leads a major research initiative that collects semi-annual survey data from managers responsible for accounting and tax matters in German firms. This panel provides unique insights into firm-level decision-making processes that complement traditional financial reporting data.

Ilai Bistritzمشاهده پروفایل

استادیار

• Game Theory
• Distributed Control
• Multiagent Learning
• +۱۰ مورد دیگر

Dr. Ilai Bistritz is an Assistant Professor at the School of Industrial and Intelligent Systems Engineering and School of Electrical and Computer Engineering , Tel Aviv University , with a PhD in Electrical Engineering (2023) from Stanford University under Nicholas Bambos . His research bridges Game Theory , Distributed Control , and Multiagent Learning , focusing on decentralized decision-making in networked systems like autonomous vehicles , smart grids , and epidemic modeling . His work addresses challenges in Distributed Optimization where agents operate with limited communication and feedback, such as in multiplayer bandits and delayed adversarial environments . He has developed algorithms for max-min fairness , non-myopic informational cascades , and delay-robust regret minimization , achieving theoretical breakthroughs in Networked Artificial Intelligence . Scientific awards include the Best Student Paper Award at IEEE WCNC 2018 and Best Student Paper Finalist at WODES 2020 . His research emphasizes privacy-preserving protocols, scalable architectures, and applications in health monitoring , energy systems , and wireless networks .

Bruno Gaujalمشاهده پروفایل

استاد پژوهشی

• Performance Evaluation
• Optimization
• Discrete Event Dynamic Systems
• +۵ مورد دیگر

Bruno Gaujal is a Research Professor at Inria Grenoble-Rhône-Alpes, affiliated with Université Grenoble Alpes. He obtained his PhD from the University of Nice in 1994 under François Baccelli's supervision and has held positions at AT&T Bell Labs, INRIA, and École Normale Supérieure de Lyon. He previously led the MESCAL (now POLARIS) research group focused on large-scale computing until 2015. His research interests center on performance evaluation, optimization, and control of discrete event dynamic systems with stochastic inputs. Specific areas include: Markov Chains and Markov Decision Processes Reinforcement Learning and stochastic optimization Queueing theory and scheduling algorithms Energy-efficient computing in distributed systems Game-theoretic approaches in network optimization Gaujal's recent publications show strong emphasis on reinforcement learning applications in queueing networks, energy optimization for real-time systems, and scalable algorithms for Markov Decision Processes. His work bridges theoretical frameworks like Whittle indices with practical implementations in cloud computing and distributed systems. He has supervised numerous PhD students including Nicolas Gast (now Inria researcher), Anne Bouillard (Huawei researcher), and Emmanuel Hyon (Paris Nanterre professor). Current students include Hélène Arvis and Romain Cravic. Gaujal co-founded RTaW, a startup specializing in real-time network design tools. At Inria, he leads research in the POLARIS group, focusing on optimization methods for large-scale distributed computing infrastructures. His work involves collaborations with 85+ co-authors across institutions globally.

Panayotis Mertikopoulosمشاهده پروفایل

استادیار

• Game Theory
• Machine Learning
• Optimization
• +۴ مورد دیگر

Dr. Panayotis Mertikopoulos is a CNRS researcher (chargé de recherche) at the Laboratoire d'Informatique de Grenoble, part of Université Grenoble Alpes. He is affiliated with the Inria/LIG joint team POLARIS and has held visiting positions at UC Berkeley, EPFL, LUISS University of Rome, and NKUA. His academic journey includes completing his PhD at the University of Athens in 2010 on "Stochastic perturbations in game theory and applications to networks" and his Habilitation à Diriger des Recherches (HDR) in 2019 on "Online optimization and learning in games: Theory and Applications". Dr. Mertikopoulos' research spans several interconnected fields at the intersection of mathematics, computer science, and economics. His primary research interests include: Game theory and its applications to network design and resource allocation Online learning algorithms and their convergence properties Optimization methods for non-convex and stochastic problems Applications to machine learning, signal processing, and wireless networks Quantum game theory and quantum computing applications His extensive publication record shows a clear evolution from foundational work in game dynamics and learning theory toward increasingly sophisticated applications in machine learning and network optimization. Recent work demonstrates growing interest in quantum game theory, non-convex optimization, and the mathematical foundations of deep learning. His research consistently bridges theoretical insights with practical applications, particularly in communication networks and distributed systems. Among his notable achievements is receiving the INFORMS best paper award in the network analytics section in 2022 for his work on "Robust power management via learning and game design". His publications have appeared in top venues including NeurIPS, ICML, COLT, IEEE Transactions, and leading economics and operations research journals. Dr. Mertikopoulos has supervised numerous PhD students and postdoctoral researchers, though specific names are not listed in the available information. He has secured research funding for projects at the intersection of game theory, optimization, and machine learning, with applications to network design and resource allocation. His collaborative work spans multiple institutions across Europe and North America. As a member of the POLARIS research team at Inria/LIG, he contributes to a vibrant research environment focused on parallel and distributed systems. His work often intersects with colleagues researching optimization algorithms, machine learning theory, and network science, creating opportunities for cross-disciplinary collaboration on complex computational problems.