جست‌وجوی استاد و پژوهشگر

Sara Ahmadianمشاهده پروفایل

پژوهشگر

• Optimization Algorithms
• Machine Learning
• Big Data Analysis
• +۵ مورد دیگر

Dr. Sara Ahmadian is a Researcher at the University of Waterloo's Department of Combinatorics and Optimization. She completed her Ph.D. in 2017 under the supervision of Prof. Chaitanya Swamy, earning the 2017 University of Waterloo Outstanding Achievement in Graduate Studies award. Her research focuses on designing efficient algorithms for optimization problems in machine learning and big data analysis, particularly in facility location and clustering. She has held visiting research positions at the University of Alberta, Hausdorff Research Institute for Mathematics, and École polytechnique fédérale de Lausanne. Education: Ph.D. in Combinatorics and Optimization, University of Waterloo (2017) Master's in Combinatorics and Optimization, University of Waterloo (2010) Bachelor's in Computer Engineering, Sharif University of Technology (2008) Research interests include approximation algorithms, online algorithms, and algorithmic game theory applied to clustering and facility location problems. Her work has led to advancements in k-means and k-median problems, with a notable improvement in the fundamental k-means algorithm. Scientific Awards: 2017 University of Waterloo Outstanding Achievement in Graduate Studies (Ph.D.) designation Advising and Grants: No specific advising or grant information is provided in the text. Labs/Teams: No specific lab or team affiliations mentioned.

• Machine Learning
• Game Theory
• Economics
• +۶ مورد دیگر

Vianney Perchet is an Associate Professor and Permanent Member at CREST, ENSAE Paris, specializing in the intersection of machine learning, game theory, and economics. His research spans theoretical foundations to practical applications in recommender systems and user behavior modeling, with a concurrent part-time role as principal researcher at Criteo AI Lab focusing on exploration efficiency. His research interests bridge mathematics, computer science, and economics, emphasizing reinforcement learning, social learning, online matching, bandit problems, and auction theory. Key contributions address optimal algorithm convergence rates, non-clairvoyant scheduling, fair resource allocation, and asynchronous multiplayer bandits, demonstrating strong interdisciplinary integration across theoretical and applied domains. Recent publications at NeurIPS, ICML, and AISTATS reveal a cohesive trend toward learning-augmented algorithms for scheduling and allocation problems, with growing emphasis on fairness constraints, communication limitations, and partial prediction scenarios. This work consistently connects theoretical guarantees with real-world economic and technical applications. No scientific awards were explicitly documented in the provided materials. Perchet advises 5 active PhD students on reinforcement learning, social learning, and online matching/bandits, while 4 former students have completed doctorates on bandits and auction theory. His industry collaboration with Criteo AI Lab provides practical validation for theoretical frameworks in recommender systems. As a core CREST research center member, he actively participates in academic initiatives including the Mediterranean Game Theory Symposium, Games and AI Summer School, and Learning in Games workshops, fostering cross-institutional collaboration in algorithmic economics.

• Transportation Modeling
• Applied Operations Research
• Sustainable Transportation
• +۳ مورد دیگر

Jee Eun (Jamie) Kang is an Associate Professor in the Department of Industrial and Systems Engineering at the University at Buffalo, located within the School of Engineering and Applied Sciences. Her research focuses on transportation modeling, applied operations research, and sustainable mobility systems. She has contributed extensively to studies on autonomous vehicles, emergency evacuation planning, and electric vehicle infrastructure. Her work bridges theoretical optimization frameworks with real-world applications, addressing challenges in multi-modal travel environments, disaster logistics, and shared mobility systems. Dr. Kang's research often involves data-driven methodologies, leveraging automated fare collection systems and smart card transactions to infer user behavior and optimize transportation networks. Her publications span topics such as strategic parking location models for autonomous vehicles, pricing mechanisms in bike-sharing systems, and scenario-based analyses for battery electric vehicle feasibility. She actively advises students on projects related to urban mobility, evacuation planning, and transportation sustainability. Scientific Awards: None explicitly mentioned in the provided texts. Dr. Kang's advising and collaborative work include projects on household activity patterns, hydrogen refueling station optimization, and evacuation warning systems. Her research has been applied to real-world scenarios like the Nepal earthquake relief efforts and California's hydrogen infrastructure planning.

Shahin Kamaliمشاهده پروفایل

دانشیار

• Algorithms Design
• Algorithms Analysis
• Online Algorithms
• +۹ مورد دیگر

Shahin Kamali is an Associate Professor in the Department of Electrical Engineering & Computer Science at York University's Lassonde School of Engineering. Previously, he served as an Assistant Professor at York University (2022-2024) and the University of Manitoba (2017-2022), where he continues to hold an Adjunct Professor position. He completed his Ph.D. in Computer Science at the University of Waterloo in 2014 and was a postdoctoral fellow at MIT's CSAIL lab from 2015-2017. Dr. Kamali received his B.Sc. from the University of Tehran and his M.Sc. from Concordia University, both in Computer Science. He has earned teaching certifications including the Kaufman Teaching Certificate Program from MIT and the Certificate in University Teaching from the University of Waterloo. His research focuses on algorithms' design, analysis, and limitations, with particular emphasis on online problems such as bin packing, paging, list update, and k-Server. His work extends to big-data applications of algorithms in data compression, graph partitioning, resource allocation in the cloud, and algorithmic aspects of blockchain technology. His recent publications (2023-2025) demonstrate a strong focus on learning-augmented algorithms, online computation with predictions, and novel applications in blockchain and graph theory. His research has been supported by multiple NSERC grants since 2010, with recent projects investigating models, applications, and limitations of online algorithms. He has successfully supervised numerous graduate students and serves on various committees related to algorithms conferences and academic governance. University of Waterloo Doctoral Thesis Completion Award (2014) University of Waterloo Mathematics Graduate Experience Award (2008) Dr. Kamali teaches advanced courses in algorithms and data structures and is scheduled to teach Design and Analysis of Algorithms (EECS 3101) in Fall 2025 and Advanced Data Structures (EECS 4101/5101) in Winter 2026. He is actively involved in organizing major conferences, including CCCG and WADS 2025 at York University.

Albin Erlansonمشاهده پروفایل

مدرس

• Economic Theory
• Game Theory
• Mechanism Design
• +۳ مورد دیگر

Dr. Albin Erlanson is a Lecturer in the Department of Economics at the University of Essex since July 2019. His research focuses on economic theory, game theory, and mechanism design, with notable contributions to topics like resource allocation, epidemic modeling, and matching markets. He currently teaches Microeconomics courses and supervises PhD students Tejpal Singh Sandhu and Yun Xu. His work explores the intersection of economic systems and real-world policy challenges, such as optimizing personal assistance services and analyzing production-health trade-offs during epidemics. Dr. Erlanson’s research spans theoretical and applied economics, including studies on costly verification in collective decisions, time exchange systems, and multi-item auction mechanisms. His articles frequently address practical applications of economic theory to public policy and market design. He has secured significant funding, including a 2024 grant for exploring the role of interviews in labor market matching. Academic support hours are held Tuesdays 10-12 at 5B.325, Colchester Campus. Grants: 2024 British Academy grant for “Matching with Interviews: the importance of interviews in labor markets” Advising: Supervising two PhD candidates in Economics Contact: albin.erlanson@essex.ac.uk | +44 (0) 1206 873351

Steven O. Kimbroughمشاهده پروفایل

استاد

• Artificial Intelligence
• Computational Rationality
• Decision Support Systems
• +۶ مورد دیگر

Steven O. Kimbrough is a Professor of Operations, Information and Decisions at the Wharton School, University of Pennsylvania. His research spans artificial intelligence, computational rationality, and strategic optimization with applications to political science, economics, and service innovation. He teaches courses like Agents, Games, and Evolution and Thinking With Models , focusing on experimental approaches to bounded rationality and uncertainty in decision-making. Primary Email: kimbrough@wharton.upenn.edu Office: 3730 Walnut Street, 565 Jon M. Huntsman Hall, Philadelphia, PA 19104 His research interests include: Artificial intelligence and metaheuristics for constrained optimization Evolutionary computation in electoral redistricting Agent-based modeling of market dynamics Logic modeling for normative reasoning Text mining applications in event analysis Publications demonstrate expertise in computational economics, political modeling, and service analytics. Recent work focuses on: Empirical validation of electoral compactness Strategic learning in oligopolies Multi-objective matching algorithms Feasible-infeasible solution spaces Service network optimization Teaching emphasizes: Game-theoretic approaches to strategic behavior Modeling life-cycle for energy sustainability Computational experiments in social science

Hedyeh Beyhaghiمشاهده پروفایل

استادیار

• Algorithmic Game Theory
• Mechanism Design
• Machine Learning Theory
• +۳ مورد دیگر

Hedyeh Beyhaghi is an Assistant Professor in the Department of Computer Science at the University of Massachusetts Amherst , affiliated with the Manning College of Information and Computer Sciences. She holds a PhD in Computer Science from Cornell University and completed postdoctoral research at the Toyota Technological Institute at Chicago , Northwestern University , and Carnegie Mellon University . Research Interests : Her work focuses on algorithmic game theory , mechanism design , machine learning theory , and algorithms under uncertainty . She investigates strategic agent behavior, fairness in algorithmic systems, revenue maximization in auctions, and optimization under stochastic constraints. Recent Publications address topics like the Strategic Perceptron , Pandora’s Box Problem , and Fair Incentive Design , reflecting trends in strategic learning , multi-agent optimization , and fairness-aware algorithms . These studies often intersect economics , machine learning , and theoretical computer science . Teaching : She teaches COMPSCI 611 - Advanced Algorithms , covering randomized algorithms, approximation techniques, and computational complexity. Weekly quizzes and biweekly assignments emphasize collaboration policies and academic integrity in algorithm design. PhD Advisee : Amirmahdi Mirfakhar. No scientific awards are currently documented.

Sheldon Howard Jacobsonمشاهده پروفایل

استاد

• Operations Research
• Aviation Security
• Optimization
• +۳ مورد دیگر

Sheldon Howard Jacobson is a Founder Professor at the Siebel School of Computing and Data Science, University of Illinois at Urbana-Champaign. He holds cross-appointments in Electrical and Computer Engineering, Industrial and Enterprise Systems Engineering, Biomedical and Translational Sciences, Mathematics, and Statistics. His research focuses on operations research, optimization, and security systems, with notable contributions to aviation security, pediatric vaccines, and homeland security. Jacobson has been recognized with prestigious awards including the AAAS Fellowship (2019), George E. Kimball Medal (2020), and Guggenheim Fellowship (2003). His work bridges theoretical and applied domains, addressing real-world challenges in public health, policy, and technology. He has contributed to media discussions on topics like pandemic impacts and coronavirus safety measures. In research, Jacobson emphasizes interdisciplinary approaches, combining mathematical modeling with policy analysis. His recent work includes optimizing political redistricting and analyzing viral transmission risks in aviation. Collaborations span multiple disciplines, reflecting his role as a bridge between academia and practical problem-solving.

Suchi Rajendranمشاهده پروفایل

استادیار

• Prescriptive Analytics
• Predictive Analytics
• Applied Operations Research
• +۱۰ مورد دیگر

Suchi Rajendran is an Assistant Professor with a joint appointment in the Department of Industrial and Systems Engineering and the Department of Marketing at the University of Missouri, Trulaske College of Business. She is also the Director of Undergraduate Studies in ISE and actively leads research in prescriptive analytics, operations research, and data-driven decision-making across healthcare, transportation, and business systems. Education: PhD in Industrial Engineering, Pennsylvania State University Her research focuses on applying advanced analytics to solve complex real-world problems. Key areas include optimizing healthcare delivery systems, supply chain and logistics (notably blood supply chains and port operations), marketing data analytics, and emerging transportation systems such as air taxis and electric vehicle infrastructure. She leverages predictive and prescriptive artificial intelligence to forecast demand and recommend optimal operational strategies. Her recent work, highlighted in university news up to 2025, demonstrates a strong trend toward interdisciplinary, AI-powered solutions in logistics, sustainability, and public service. She frequently collaborates with industry partners like Case New Holland and Schneider Electric, and has secured funding from agencies such as the Alaska Department of Transportation and the National Science Foundation. Scientific Awards and Recognitions: Richard Wallace Faculty Incentive Grant Bob Bloss Faculty Enhancement Grant Winemiller Excellence Award in Data Analytics NSF CHOT Scholar (Penn State) Service Enterprise Engineering Fellow DAAD-WISE Fellowship (Germany) Lean Six Sigma Black Belt Dr. Rajendran is actively involved in mentoring students, having advised honors-winning graduate and undergraduate researchers. She leads an NSF-funded Research Experiences for Undergraduates (REU) program that brings 10 students annually to Mizzou to work on AI-enabled operations engineering. Her grants support hands-on, interdisciplinary research that prepares the next generation of engineers and data scientists. She is affiliated with cutting-edge research initiatives involving simulation modeling, digital communication platforms, and AI integration in industrial systems. Her lab and team focus on developing practical, scalable solutions for logistics, healthcare, and sustainable transportation, often through collaborative, real-world projects with public and private stakeholders.

• Dialogical Approaches to Mental Health
• Community Mental Health
• Co-Production Research
• +۸ مورد دیگر

Dr. Anna Sidis is a Senior Lecturer at the University of Wollongong's School of Psychology within the Faculty of the Arts, Social Sciences and Humanities. With over 20 years of clinical experience as a Clinical Psychologist specializing in youth mental health, she holds dual appointments as both Senior Lecturer and Deputy Director of Postgraduate Psychology Programs (since January 2024). She serves as Chair of the Social Sciences Human Research Ethics Committee, demonstrating significant institutional leadership. Her educational background includes a Doctorate of Clinical Psychology (2001-2004) and Bachelor of Arts (Hons) from the University of Sydney. Her teaching portfolio spans undergraduate and postgraduate programs covering Family Therapy, Narrative Therapy, Psychosis assessment, Suicide Prevention, Grief counseling, and Critical Psychology. Dr. Sidis' research focuses on dialogical and community approaches to mental health care, with particular expertise in co-production methodologies, qualitative research, and critical psychology. Her work addresses critical areas including youth mental health, early psychosis intervention, suicide prevention, bereavement support (especially cancer-related grief), and service user involvement in care design. Analysis of her 22 publications reveals consistent emphasis on qualitative methodologies, therapist-client interaction dynamics, and innovative service delivery models like Open Dialogue. Her funded research includes projects examining barriers to mental health access for youth with co-morbid substance use, dialogical therapy implementation ('Always opening and never closing' grant), and community-based suicide prevention initiatives. She actively supervises five higher degree research students working on topics ranging from virtual reality exposure therapy to sports-based suicide prevention. Barriers to access and engagement of young people with co-morbid substance use (2022-2024) 'Always opening and never closing' dialogical therapy study (2022) Innovation and Evaluation Grant for youth mental health access (2022) Dr. Sidis maintains active clinical research networks through her role as Deputy Director of Postgraduate Programs and Ethics Committee leadership, while her 15 most recent publications demonstrate growing international impact with citations across multiple platforms including Wikipedia references and social media engagement.

Roie Levinمشاهده پروفایل

استادیار

• Approximation Algorithms
• Online Algorithms
• Submodular Functions
• +۵ مورد دیگر

Roie Levin is an Assistant Professor at Rutgers University's Department of Computer Science. He received his PhD in Algorithms, Combinatorics and Optimization from Carnegie Mellon University in 2022, advised by Anupam Gupta. Prior to that, he worked at the Allen Institute for Artificial Intelligence (2015-2017) and earned dual BSc degrees in Computer Science/Applied Mathematics and Mathematics from Brown University (2015). Before joining Rutgers, he was a Fulbright Postdoctoral Fellow at Tel Aviv University under Niv Buchbinder. Current Role: Assistant Professor in Computer Science Academic Training: PhD (2022) CMU, BSc (2015) Brown University Postdoctoral: Fulbright Fellow at Tel Aviv University Levin's research focuses on approximation algorithms for uncertain environments (online/dynamic/streaming models) and submodular function optimization. His work spans theoretical foundations and practical implementations across distributed systems, geometric constraints, and reinforcement learning paradigms. Teaching includes graduate and undergraduate algorithms courses (CS 344, CS 513) with emphasis on problem-solving techniques, computational complexity, and modern algorithmic trends. His publications showcase expertise in online algorithms, submodular optimization, and approximation theory with applications in clustering, caching, and machine learning. The 2025 articles demonstrate continued exploration of online consistency and contention resolution, while 2023-2024 works focus on submodular optimization under uncertainty and dynamic environments. Earlier publications (2015-2017) cover semantic parsing, geometric approximation, and planar graph optimization. Fulbright Postdoctoral Fellow Levin's research connects theoretical guarantees with practical implementations, bridging classical algorithm design with modern machine learning applications. His recent work explores primal-dual methods in online settings and robust subspace approximation techniques for streaming data environments.

Aaron Bernsteinمشاهده پروفایل

دانشیار

• Graph Algorithms
• Dynamic Algorithms
• Sublinear Algorithms
• +۲ مورد دیگر

Aaron Bernstein is Charles S. Baylis Associate Professor in Computer Science at NYU Tandon School of Engineering. His research advances theoretical foundations of graph algorithms, with breakthroughs in dynamic algorithms, shortest path problems, and distributed computing. Research contributions: Pioneering work on near-linear time algorithms for shortest paths with negative weights Fundamental advances in dynamic graph algorithms for connectivity and matching Innovative frameworks for distributed load balancing and network optimization Breakthroughs in maximum flow and matching problems Publications demonstrate consistent theoretical innovation with numerous best paper awards at top theory conferences. Recent work resolves long-standing open problems in graph algorithms and establishes new algorithmic paradigms. Current research group includes PhD students working on graph algorithms and distributed computing. Recipient of prestigious awards including Presburger Award and Sloan Research Fellowship. Research funded by NSF CAREER grant and Google Research Scholar award.

Arash Asadpourمشاهده پروفایل

دانشیار

• Data Science/Analytics
• Operations Management
• Operations Research
• +۷ مورد دیگر

Arash Asadpour is an Associate Professor at the Narendra Paul Loomba Department of Management, Zicklin School of Business, Baruch College, City University of New York (CUNY). His research focuses on operations research, optimization, and marketplace dynamics, particularly in gig economy platforms and online retailing. PhD in Management Science and Engineering, Stanford University 2010 B.Sc. in Computer Engineering, Sharif University of Technology, Tehran, Iran 2005 His work spans data science , dynamic pricing , stochastic optimization , and matching markets , with applications to ridesharing, online advertising, and resource allocation. Recent publications emphasize platform economics, algorithmic game theory, and submodular function optimization. Arash has received prestigious awards including the Best Paper Award at the ACM-SIAM Symposium on Discrete Algorithms (SODA) in 2010, top honors in Iran’s national graduate entrance exam (2004), and silver medals in national informatics olympiads (1999-2000).

• Optimization under Uncertainty
• Robust Optimization
• Combinatorial Optimization
• +۷ مورد دیگر

Christina Büsing is a Professor in Combinatorial Optimization at RWTH Aachen University. She leads the Teaching and Research Group on Combinatorial Optimization and is a principal investigator in the UnRAVeL Graduate College. Her research focuses on optimization under uncertainty , robust optimization , and combinatorial optimization , with applications to healthcare , energy systems , and transportation logistics . Alumni of TU Berlin, WWU Münster, and Universidad Complutense de Madrid Junior Professor for Robust Planning in Medical Care at RWTH Aachen (2016-2021) Her methodological expertise spans exact algorithms , heuristics , and complexity theory , with recent publications addressing network flows, facility location, and healthcare scheduling. She has received awards for teaching excellence and gender equality advocacy. 2022 Brigitte Gilles Award for women in science 2019 FAMOS Award for family-friendly leadership 2018 Best Teaching Award at RWTH Aachen Her work combines theoretical rigor with practical applications in urban railway traffic management , solar power systems , and agent-based pandemic response modeling .

Irène Marcoviciمشاهده پروفایل

استاد

• Probability Theory
• Dynamical Systems
• Cellular Automata
• +۴ مورد دیگر

Irène Marcovici is a Professor in the Department of Mathematics at the University of Rouen Normandy, affiliated with the Raphaël Salem Mathematics Laboratory (LMRS). She leads the Probability and Dynamical Systems team within LMRS and participates in the ALEA and SDA2 working groups of the GDR Informatique Fondamentale et ses Mathématiques. PhD in Mathematics (2013, University of Paris Diderot) Habilitation à Diriger des Recherches (2021, University of Lorraine) Her research focuses on probability theory , dynamical systems , and cellular automata , with significant contributions to percolation theory, self-organization phenomena, and combinatorics on words. She investigates how randomness influences complex systems, particularly through probabilistic cellular automata and their ergodic properties. Analysis of her recent publications (2021-2025) reveals a strong emphasis on percolation dynamics (e.g., corner percolation with directional bias), self-stabilization mechanisms in cellular systems, and combinatorial structures like Kolakoski sequences. Her work bridges theoretical computer science and pure mathematics, often employing stochastic methods to solve problems in symbolic dynamics and discrete geometry. Supervision & Collaborations: Currently supervising Maxence Poutrel (with Jérôme Casse) Previously supervised Pierrick Siest (2021-2024), Jocelyn Begeot (now Associate Professor), and Pierre-Adrien Tahay (now PRAG at Telecom Nancy) Regular collaborations with Régine Marchand, Nazim Fatès, and Pascal Moyal Laboratory Context: As leader of the Probability and Dynamical Systems team at LMRS, she contributes to France's national research infrastructure in fundamental mathematics, with connections to CNRS and international working groups focused on automata theory and discrete probability.