جستجوی استاد راهنمای دانشگاهی

Anna Gautierمشاهده پروفایل

استادیار

• Multi-Agent Systems
• Robotics
• Artificial Intelligence
• +۳ مورد دیگر

Anna Gautier is an Assistant Professor in the Department of Computer Science at Chalmers University of Technology, affiliated with the Division of Data Science and AI. Previously, she was a Digital Futures Postdoctoral Fellow at KTH Royal Institute of Technology (2023–2025), focusing on mechanism design for multi-robot systems. Her research emphasizes planning under uncertainty, multi-agent systems, and human-robot interaction. She holds a PhD from the University of Oxford (2023), an MSc from the London School of Economics, and dual undergraduate degrees from Washington University in St. Louis. Education Background: PhD in Computer Science, University of Oxford (2023) MSc in Applied Mathematics, London School of Economics BA in Mathematics and BS in Computer Science, Washington University in St. Louis Research Interests: Dr. Gautier explores planning algorithms for multi-agent systems, particularly in uncertain environments. She designs mechanisms to coordinate robots and humans, leveraging game theory and formal methods. Her work addresses challenges like resource allocation, risk-aware decision-making, and trust in autonomous systems. Recent projects include contingency planning for autonomous vehicles and auction-based resource distribution. Professional Activities: She co-chairs the ECAI 2025 Demonstration Track and teaches the course Safe Robot Planning and Control at KTH. Her projects include collaborations with WASP-Nest (PerCorSo) and TECoSA on trustworthy autonomy. She actively publishes in top venues like AAMAS and AAAI. Labs and Teams: Affiliated with Chalmers' Data Science and AI division, she leads research in multi-agent systems and human-AI collaboration.

• Artificial Intelligence
• Machine Learning
• Game Theory
• +۷ مورد دیگر

Prof. Tansu Alpcan is a Professor and Reader in the Department of Electrical and Electronic Engineering at The University of Melbourne, Australia. He holds a PhD from the University of Illinois at Urbana-Champaign (UIUC) and has held academic positions at Technical University Berlin and Deutsche Telekom Laboratories. His research focuses on AI/ML applications in engineering, game theory, cybersecurity, Industry 4.0, quantum machine learning, smart grids, and communication networks. Education: PhD in Electrical and Computer Engineering (UIUC, 2006); MSc (UIUC, 2003); BEng (Bogazici University, 1999). Research interests include adversarial machine learning, cybersecurity games, quantum computing, and renewable energy systems. Authored over 200 papers and two books, including Network Security: A Decision and Game Theoretic Approach (Cambridge, 2011). Recipient of IEEE Senior Membership (2012) and multiple best paper awards. He leads the WILAB and has secured grants such as the ARC Training Centre in Optimisation Technologies. Current projects include quantum machine learning, adversarial reinforcement learning, and smart grid modeling. Supervised 17 PhD and 3 Master’s students.

Do Own (Donna) Kimمشاهده پروفایل

استادیار

• Digital Cultures
• Mediated Social Interactions
• Human-Machine Communication
• +۴ مورد دیگر

Do Own (Donna) Kim is an Assistant Professor at the University of Illinois Chicago's Department of Communication. Her research bridges technology studies, cultural studies, and computer-mediated communication, focusing on boundary-crossing practices in human-technology interactions. She examines questions of authenticity, hybrid spaces, and mediated identities through case studies like virtual influencers and K-pop digital cultures. Donna holds a Ph.D. in Communication from USC Annenberg (2022), with earlier degrees from Korea University (BA, 2015) and USC (MA, 2020). Her book project Virtually Real explores virtual influencers' cultural integration through cross-cultural fieldwork. She teaches courses on emerging technologies and communication theory, emphasizing qualitative research methods. Donna serves on Pop Junctions' editorial board and has written for platforms like In Media Res and Civic Imagination Project . Key awards include the KFAS Fellowship and a 2021 Browne Award for her chapter on Korean feminist activism. Her work appears in New Media & Society , International Journal of Communication , and CHI PLAY . Recent presentations include talks on AI ethics at UIC and virtual influencers at Curtin University's symposium.

Refik Soyerمشاهده پروفایل

استاد

• Bayesian Statistics
• Reliability Modeling
• Decision Analysis
• +۴ مورد دیگر

Refik Soyer is a Professor of Statistics at The George Washington University. His research focuses on Bayesian statistics, reliability modeling, decision analysis, and time series analysis. He has made significant contributions to the application of Bayesian methods in reliability engineering, queueing systems, and adversarial risk analysis. Education: D. Sc. in Statistics (1985), George Washington University His recent publications highlight advancements in Bayesian reliability analysis, adversarial decision frameworks, and computational methods for time series and queueing systems. Areas of emphasis include dynamic INAR processes, accelerated life testing, and software failure modeling. Soyer's work bridges theoretical statistics with practical applications in call centers, healthcare fraud detection, and risk management.

Joachim Kruegerمشاهده پروفایل

استاد

• Social Psychology
• Cognitive Psychology
• Game Theory
• +۵ مورد دیگر

Joachim Krueger is a Professor of Cognitive and Psychological Sciences at Brown University. He holds editorial roles at the Personality and Social Psychology Review and the American Journal of Psychology . His research focuses on social judgment, decision-making, and the intersection of cognitive psychology with behavioral economics and organizational behavior. Key themes include self-perception, intergroup relations, free will belief paradoxes, and the volunteer’s dilemma. Dr. Krueger earned his Ph.D. from the University of Oregon (1988) and completed postdoctoral research at the Max Planck Institute in Berlin before joining Brown in 1991. He teaches courses on psychology in business, happiness philosophy, and behavioral decision sciences. His work critiques positivist methodologies while exploring creativity, leadership, and the replication crisis in psychological research. He is a Fellow of the Association for Psychological Science, Society for Experimental Social Psychology, and Eastern Psychological Association. His writings include books on social judgment, rationality, and happiness, alongside a Psychology Today blog. Krueger collaborates across disciplines, emphasizing pragmatic approaches to understanding human social behavior.

Ryan Murrayمشاهده پروفایل

استادیار

• Calculus of variations
• Partial Differential Equations
• Machine Learning
• +۵ مورد دیگر

Ryan Murray is an Assistant Professor in the Department of Mathematics at North Carolina State University (NC State). His research focuses on developing mathematical tools to address problems in applied analysis, including calculus of variations, partial differential equations (PDEs), and their applications to machine learning, fluid dynamics, and control theory. He holds a PhD in Mathematics from Carnegie Mellon University (2016). His expertise spans regularization methods for machine learning, singular perturbations in materials science, algorithms for distributed optimization, and singularity formation in fluid dynamics. His work is supported by the National Science Foundation (NSF) and the Simons Foundation. He actively collaborates with researchers in data science, PDE analysis, and optimization. Key research areas include adversarial training in classification, geometric data analysis via statistical depths, and the analysis of vortex sheet singularities. His teaching experience includes courses on partial differential equations, optimal control theory, and linear control systems. Ryan has published extensively in journals such as SIAM Journal on Mathematics of Data Science , Archive for Rational Mechanics and Analysis , and Journal of Machine Learning Research . His articles explore topics ranging from graph-based learning to fluid dynamics instabilities.

• Number Theory
• Algebraic Geometry
• Modular Forms
• +۵ مورد دیگر

Dr. Helena Verrill is an Assistant Professor at the Department of Mathematics, University of Warwick, focusing on teaching and outreach. Her research interests span Number Theory, Algebraic Geometry, Combinatorial Game Theory, and the Mathematics of Origami, with applications in generative art. She actively mentors students through third-year essays, research projects, and outreach initiatives like the Royal Institute Masterclasses. Her research explores modular forms, computational algebra, and arithmetic geometry, with notable contributions to ℓ-adic representations and noncongruence cuspforms. She bridges pure mathematics with artistic expression through origami tessellations and fractal patterns. Notable works include studies on Fano plane automorphisms and Harter-Heighway dragon curves. Her publications span journals like the *Proceedings of the American Mathematical Society* and *Journal of Number Theory*, alongside creative outputs in *Generative Art* and *Bridges Conferences*. She maintains a personal homepage with resources for mathematical art and education.

Lea Hellmuellerمشاهده پروفایل

دانشیار

• Global Journalism
• Journalistic Role Performance
• Media and Terrorism
• +۷ مورد دیگر

Lea Hellmueller is Associate Dean of Research in the School of Communication & Creativity and Reader (Associate Professor) in the Journalism Department at City, University of London. She is also an Affiliated Researcher with the Violence & Society Centre. Previously, she served as Assistant Professor at the University of Houston and Texas Tech University, and was a visiting professor at the University of Zurich and visiting fellow at the Reuters Institute for the Study of Journalism, University of Oxford. Dr. Hellmueller holds a Ph.D. in Economics and Social Sciences from the University of Fribourg, Switzerland, with dissertation co-supervision from the University of Missouri-Columbia. She completed postdoctoral training at the University of Texas at Austin. Her research focuses on the role of media and journalism in a globalized world, particularly in areas such as global risk journalism, transnational reporting, journalistic role performance, media and social justice, and violence & media. Fluent in multiple languages, she brings a comparative and international perspective to her scholarship. Her recent publications explore transnational journalism, digital media discourse, journalistic cultures, credibility, and terrorism coverage. The articles reflect a strong trend toward computational and comparative analysis of global media practices, emphasizing power, identity, and the transformation of journalistic norms across borders. Faculty Research Award, AEJMC (2019) Honorary Mention, ICA Journalism Studies Division (2018) Outstanding Research Award, University of Houston (2018) First Place Teaching Contest, AEJMC (2016) Top Faculty Paper, AEJMC (2016) Top Paper Award, ICA (2016) Dr. Hellmueller has secured multiple competitive grants, including from the Swiss National Science Foundation (SNF) and Fulbright. She has supervised PhD students, currently mentoring Namrata Acharya. She serves on editorial boards of Journalism Practice and International Communication Gazette , and holds leadership roles in AEJMC and ICA. She is also the North America Regional Lead for the Global Risk Journalism Hub. She is active in collaborative research networks and has contributed to major reports, including with the Reuters Institute. Her work bridges academic research, policy, and global media practices.

Tianyu Guanمشاهده پروفایل

استادیار

• Sports Analytics
• Machine Learning
• Functional Data Analysis
• +۶ مورد دیگر

Tianyu Guan is an Assistant Professor in the Department of Mathematics and Statistics at York University, Faculty of Science. He previously served as an Assistant Professor at Brock University and joined York University in 2024. He holds a PhD in Statistics from Simon Fraser University (2020), an MSc in Actuarial Science from the same institution (2014), and a BSc in Statistics from Jilin University (2011). PhD in Statistics, Simon Fraser University, 2020 MSc in Actuarial Science, Simon Fraser University, 2014 BSc in Statistics, Jilin University, 2011 His research centers on sports analytics, functional data analysis, and nonparametric statistics, with strong applications in machine learning and data science. He applies statistical methodologies to understand sports performance, player behavior, and game dynamics. His work also extends to theoretical developments in sparse modeling and functional regression. The recent publications highlight a clear trend toward integrating advanced statistical techniques with real-world sports and entertainment data. His work combines functional data analysis, machine learning, and probabilistic modeling to extract insights from complex longitudinal and high-dimensional datasets. Topics span soccer, rugby, football, and movie reviews, demonstrating interdisciplinary reach. While no formal scientific awards are listed in the provided text, his publications in high-impact journals such as Annals of Applied Statistics and Statistics and Computing reflect strong academic recognition. Tianyu Guan actively advises multiple graduate students at both MSc and PhD levels, primarily at Brock and Simon Fraser Universities. His teaching portfolio includes advanced courses in nonparametric statistics, sampling theory, and experimental design at the undergraduate and graduate levels. He has not received external grant information in the provided text, but his research output suggests active engagement in funded or independent research projects. He leads methodological and applied research in sports analytics, often co-supervising students with colleagues across institutions. His lab or research group appears focused on developing and applying statistical tools for performance analysis and decision-making in sports, supported by computational implementations such as the R package ngr .

Jalal Etesamiمشاهده پروفایل

استادیار

• Machine Learning
• Statistical Decision Making
• Causal Inference
• +۴ مورد دیگر

Prof. Jalal Etesami is an Assistant Professor in the Department of Computer Science at Technical University of Munich (TUM), leading the Decision Sciences & Systems group. He holds a Ph.D. in Industrial and Systems Engineering from the University of Illinois at Urbana-Champaign and was a Postdoctoral Fellow at EPFL in Switzerland. His research focuses on machine learning, causal inference, multi-agent systems, and game theory, with applications to systemic risk modeling and market design. He teaches advanced courses such as Causal Inference in Time Series , Algorithmic Game Theory , and Optimization, Learning, and Market Design . Notable contributions include work on causal structure learning, stochastic optimization, and non-Gaussian causal models. Recent research explores causal effect identification under confounding, neural networks for market analysis, and optimal experiment design. Prof. Etesami’s work appears in top venues like NeurIPS, AAAI, and IEEE journals. He actively contributes to the academic community, organizing seminars and workshops on topics ranging from causal reasoning to computational social choice.

Zhu-Tian Chenمشاهده پروفایل

استادیار

• Data Visualization
• Human-Computer Interaction
• Augmented Reality
• +۷ مورد دیگر

Zhu-Tian Chen is an Assistant Professor in the Department of Computer Science and Engineering at the University of Minnesota, Twin Cities, where he leads research in data visualization, human-computer interaction, and augmented reality. Prior to this, he held postdoctoral positions at Harvard University and UC San Diego, working with leading researchers in visual computing and interactive design. Ph.D. in Computer Science, Hong Kong University of Science and Technology B.Eng. in Software Engineering, South China University of Technology His research focuses on augmenting human intelligence through hybrid human-AI systems, particularly in everyday and outdoor environments. He specializes in designing intelligent AR interfaces, embedded visualizations, and language-oriented interactions for applications in sports analytics, education, and data analysis. His work integrates human-centered design with applied machine learning to create intuitive and effective visualization tools. The recent trend in his publications shows a strong emphasis on intelligent AR systems for dynamic scenes, LLM-based code generation interfaces, and real-time augmentation of sports videos using natural language and gaze-based interactions. His work frequently appears in top-tier venues such as IEEE VIS, ACM CHI, and UIST. Best Paper Award, ACM CHI'23 Best Short Paper Honorable Mention, EuroVis'23 Best Paper Honorable Mention, IEEE VIS'22 (twice) Certificate of Distinction and Excellence in Teaching, Harvard University Hong Kong Ph.D. Fellowship Dr. Chen actively mentors undergraduate, master’s, and PhD students, as well as visiting scholars and interns, and is building a new research lab focused on visualization for intelligent AR systems. He has served on program committees for major conferences including ACM CHI, IEEE VIS, and EuroVis, and has been invited to speak at institutions such as Apple, JP Morgan, and multiple universities worldwide. He also contributes to the academic community through grant reviewing for NSF and the Department of Energy. He leads research projects in intelligent AR systems for sports, language-oriented interactions with LLMs, and immersive data visualization, often in collaboration with institutions like Harvard, UC San Diego, and HKUST. His lab welcomes students and collaborators interested in visualization, HCI, and applied AI.

• Federated Learning
• Optimization for Machine Learning
• Privacy-Preserving Machine Learning
• +۱۲ مورد دیگر

Sai Praneeth Karimireddy is an Assistant Professor in the Thomas Lord Department of Computer Science at the University of Southern California (USC), with a courtesy appointment in the Ming Hsieh Department of Electrical and Computer Engineering. He previously held an SNSF postdoctoral fellowship at UC Berkeley under Michael I. Jordan and earned his PhD at EPFL advised by Martin Jaggi. He co-leads the Federated Learning and Data Quality working group at MONAI (NVIDIA) and collaborates with researchers at Apple Research. His research lies at the intersection of optimization, machine learning, statistics, and economics, with a strong focus on federated learning, privacy-preserving machine learning, data valuation, and AI for healthcare. He investigates how data quality, privacy, and incentives shape collaborative ML systems, especially in high-stakes domains like medicine. His work has been deployed at companies such as Meta, Google, OpenAI, and Owkin. His recent publications span top-tier venues including NeurIPS, ICML, ICLR, and JMLR, with influential contributions such as the SCAFFOLD algorithm for federated learning. His research shows a consistent trend toward building robust, private, and incentive-compatible collaborative learning systems, with increasing emphasis on real-world deployment in healthcare and decentralized data markets. 2023 SNSF Mobility Fellowship 2022 Patrick Denantes Memorial Prize for best thesis in computer science 2022 EPFL thesis distinction (top 8%) 2021 Chorafas Foundation Prize for exceptional applied research Capitol One Fellow (2025) He is actively mentoring PhD students and leads a research group focused on foundational and applied challenges in federated and privacy-preserving ML. He teaches graduate courses at USC, including CSCI 599 on Optimization for Machine Learning and CSCI 699 on Privacy-Preserving Machine Learning. He serves as an area chair for ICLR 2025 and co-organizes major workshops on incentives in data sharing and federated learning. His lab collaborates with institutions like NVIDIA, Apple, and Argonne National Laboratory, and he is building a research program centered on sustainable, equitable, and trustworthy AI ecosystems.

Tianyi Linمشاهده پروفایل

استادیار

• Optimization
• Game Theory
• Machine Learning
• +۳ مورد دیگر

Tianyi Lin serves as an Assistant Professor in the Department of Industrial Engineering and Operations Research (IEOR) at Columbia Engineering, Columbia University, a position he assumed in 2024. He holds dual affiliations as a verified Data Science Institute (DSI) Member and an Affiliated Member of both the Financial and Business Analytics Center and the Foundations of Data Science Center. His academic credentials include: Ph.D. in Electrical Engineering and Computer Science, UC Berkeley Postdoctoral Researcher, Laboratory for Information & Decision Systems (LIDS), MIT (2023-2024) M.S. in Operations Research, UC Berkeley M.S. in Pure Mathematics and Statistics, University of Cambridge B.S. in Mathematics, Nanjing University Dr. Lin's research spans optimization theory , game-theoretic models , and machine learning algorithms , with emphasis on nonconvex minimax problems , variational inequalities , and data science applications . His work bridges theoretical guarantees with practical implementations in high-dimensional settings, particularly focusing on convergence properties and computational efficiency in complex systems. Analysis of his 15 most recent publications (2022-2025) reveals dominant themes in high-order optimization methods , no-regret learning in games , and optimal transport algorithms . His contributions demonstrate consistent innovation in developing doubly optimal algorithms for monotone games, spectral regularization techniques for policy optimization, and structure-driven approaches for nonconvex problems, reflecting strong interdisciplinary connections between operations research, computer science, and applied mathematics. No scientific awards or honors were documented in the provided source material. Information regarding student advising and research grants remains unspecified in the current documentation, though his center affiliations suggest active participation in collaborative research initiatives. Dr. Lin maintains significant interdisciplinary engagement through his affiliations with Columbia's Data Science Institute and specialized research centers, positioning his work at the intersection of theoretical optimization and real-world data science applications.

• Operations Research
• Management Science
• Dynamic Pricing
• +۴ مورد دیگر

Arash Asadpour Rahimabadi is an Associate Professor at the N. P. Loomba Department of Management within the Zicklin School of Business at Baruch College, CUNY . His research bridges Operations Research and Management Science , focusing on algorithmic design, dynamic pricing, and optimization in gig economy platforms. Education: Ph.D. in Operations Research, Stanford University (2010) BSc in Computer Engineering, Sharif University of Technology (2004) His research interests include stochastic optimization , submodular maximization , marketplace stability , and fair allocation . Recent work explores dynamic pricing in extreme value regimes , shared ride sustainability , and regulation of gig economy platforms . His scientific contributions span algorithmic game theory, combinatorial optimization, and resource allocation, with key publications in Management Science and Operations Research . Current projects analyze escrow payment mechanisms , shared mobility efficiency , and hotel reservation systems . Scientific Awards Best Paper Award, ACM-SIAM Symposium on Discrete Algorithms (SODA), 2010 1st Rank in Iran’s National Graduate Entrance Exam in Computer Engineering, 2004 Silver Medals in Iranian National Olympiads in Informatics, 1999–2000 He serves on graduate and PhD committees at CUNY and has reviewed for journals including Management Science and Operations Research . His teaching includes courses like Decision Models and Analytics and Advanced Discrete Optimization .

Martín Hötzel Escardóمشاهده پروفایل

استاد

• Homotopy Type Theory
• Univalent Mathematics
• Martin-Löf Type Theory
• +۱۲ مورد دیگر

Martín Hötzel Escardó is a Professor of Theoretical Computer Science in the School of Computer Science at the University of Birmingham, UK. He has been a faculty member since 2000, following previous academic positions at Imperial College London, the University of Edinburgh, and the University of St Andrews. His research bridges theoretical computer science and pure mathematics, with a strong emphasis on foundational aspects of computation. His educational background includes a BSc and MSc from Universidade Federal do Rio Grande Sul (Brazil) and a PhD from Imperial College London (1997) under Michael B. Smyth. Escardó's research interests center on topology in computation , constructive mathematics , dependent and univalent type theory (including Homotopy Type Theory and Cubical Type Theory), domain theory , locale theory , and exact real-number computation . His work explores deep connections between logic, topology, and programming, often using functional languages like Haskell and Agda to formalize and experiment with theoretical ideas. He is particularly known for his discoveries on exhaustively searchable infinite sets and the topological nature of computability. The trend in his recent publications reflects a sustained focus on univalent foundations, constructive domain and order theory, game semantics with dependent types, and the logical structure of type universes. His work consistently advances the formalization and understanding of higher-type computation and constructive mathematics within modern type theories. He has no listed scientific awards in the provided text, but his influence is evident through his extensive publication record and software developments like TypeTopology. Escardó has advised several students and collaborators, though specific names are not listed. He has been involved in significant research projects, particularly in the formalization of mathematics in type theory and the semantics of programming languages. His work often involves developing Agda libraries to formalize new mathematical results constructively. He leads and contributes to a vibrant research group in theoretical computer science at Birmingham, with a focus on logic, semantics, and type theory. His public research blog, lecture notes, and open-source Agda code (e.g., TypeTopology, HoTT-UF-in-Agda) serve as important resources for the community.