Find Academic Supervisors

• Machine Learning
• Reinforcement Learning
• Optimization
• +4 more

Kishan Panaganti Badrinath is a Postdoctoral Scholar Research Associate in the Computing and Mathematical Sciences department at the California Institute of Technology (Caltech), affiliated with the Division of Engineering and Applied Science. Hosted by Prof. Adam Wierman and Prof. Eric Mazumdar, his work focuses on theoretical foundations of reinforcement learning algorithms and bridging simulation-to-real-world performance gaps. Education: PhD in Electronics and Communication Engineering & Mathematical Optimization (2023) from Texas A&M University Masters in Communication and Networks (2018) from Indian Institute of Science (IISc) Bachelors in Electronics and Communication Engineering (2017) from PES University Research Interests: Kishan’s work spans reinforcement learning theory , addressing robustness in multi-agent systems , imitation learning , and human feedback integration . His expertise includes high-dimensional probability , optimization , and stochastic theory , with applications to autonomous solutions and environmental uncertainties . Recent Articles (2025): His recent publications focus on robust sequential decision-making , including KL-regularization privacy , multi-agent tractability via behavioral economics , and distributionally robust LLM alignment . These contributions reflect his emphasis on theoretical rigor and practical adaptability. Scientific Awards: PIMCO Postdoctoral Fellow in Data Science ML and Systems Rising Stars 2025 awardee (selected from 150+ applicants) Current Status: Kishan is actively seeking full-time faculty or core industrial research positions starting in 2025, with recent speaking engagements at IIT Bombay, Microsoft Research, and the Theory CS Winter School at IISc.

Jan FransooView profile

Professor

• Supply Chain Management
• Operations Management
• Retail Operations
• +6 more

Jan Fransoo is a Full Professor of Operations and Logistics Management at Tilburg University's School of Economics and Management. He leads research in supply chain management, focusing on nanostores, retail operations in emerging markets, and urban logistics. His work bridges academic rigor with industry collaboration, involving companies like ABInbev and intergovernmental agencies like the World Bank. Education: PhD in Production Control and Demand Management, Eindhoven University of Technology (1989–1993) Master's in Industrial Engineering and Management Science, Eindhoven University of Technology (1984–1989) Research Interests: Professor Fransoo's research emphasizes fragmented retail networks in developing economies, particularly nanostores. His studies explore inventory models, intermodal transportation, and the integration of human-AI systems in supply chain decisions. He is also known for his work during the pandemic on ad hoc supply chains for PPE distribution. Notable Contributions: Authored Reaching 50 Million Nanostores , a seminal work on retail distribution in emerging megacities. Developed frameworks for supply chain agility under time pressure, highlighted in Journal of Operations Management . Awards: Salzberg Medallion (2022) M&SOM Meritorious Service Award (2016) Advising & Grants: Supervised over 30 PhD students across institutions like TU Eindhoven and Kuehne Logistics University. Active in grants focusing on urban logistics, nanostore digitization, and supply chain resilience. Labs/Teams: Leads research teams at Tilburg, MIT's CTL, and TU Eindhoven, collaborating with industry partners on projects like the Dutch vaccination supply chain during the pandemic.

Vitali GretschkoView profile

Professor

• Market Design
• Environmental Economics
• Game Theory
• +3 more

Prof. Vitali Gretschko is a Professor of Sustainable Market Design at the University of Münster's Department of Economics (School of Business and Economics). He leads the Chair of Sustainable Market Design and focuses on research areas such as auction design, procurement strategies, environmental economics, and game theory. His work bridges theoretical economics with practical market mechanisms, addressing topics like resource allocation, strategic behavior in auctions, and policy design for sustainable markets. Prof. Gretschko teaches advanced courses in environmental economics, energy economics, and game theory at both undergraduate and doctoral levels. His research team includes associates like Dr. Daniil Larionov and student assistants such as Noah Ryll. He collaborates with institutions like the ZEW Mannheim on policy recommendations for market design in sectors like telecommunications and energy. Key research themes include optimizing procurement mechanisms to prevent discrimination, analyzing sequential procurement under incomplete contracts, and designing social norms-based market entry rules for externalities. His work has been published in journals such as Theoretical Economics , Games and Economic Behavior , and Management Science .

• Artificial Intelligence
• Machine Learning
• Speech Emotion Recognition
• +7 more

Rajib Rana is a Professor of Computer Science at the University of Southern Queensland, affiliated with the School of Mathematics, Physics and Computing. He holds a PhD from the University of New South Wales (UNSW), completed in 2011. His research focuses on AI-driven solutions for mental health, speech emotion recognition, and healthcare informatics, with a strong emphasis on machine learning, deep learning, and domain adaptation techniques. His work spans clinical applications, wearable sensor systems, and mobile health technologies. Dr. Rana has supervised numerous doctoral candidates, including studies on AI tools for youth mental health interventions, adversarial attack robustness in speech emotion systems, and data analytics for school mental health monitoring. His research outputs include over 50 peer-reviewed articles, with notable contributions in IEEE Transactions on Affective Computing, Computers in Biology and Medicine, and other leading journals. His technical expertise includes federated learning, compressive sensing-based encryption for IoT, and context-aware affect sensing via smartphones. He collaborates widely with clinical partners and industry, addressing challenges like mental health triage prioritization and ICU admission prediction during pandemics. His work bridges computational methods with real-world healthcare and educational applications.

Nicholas FurlView profile

Senior Lecturer

• Social Perception
• Decision Making
• Face Perception
• +3 more

Nicholas Furl is a Senior Lecturer in Psychology at Royal Holloway University of London (RHUL), UK. His research focuses on social perception, decision-making mechanisms, and face perception, employing neuroimaging techniques. He holds a PhD in Cognition and Neuroscience from UT Dallas and completed postdoctoral training at University College London (UCL) and the NIH. Prior to RHUL, he was affiliated with the MRC Cognition and Brain Sciences Unit in Cambridge. Education: B.S. in Psychology (University of Illinois, Urbana-Champaign), Ph.D. in Cognition and Neuroscience (UT Dallas). His work spans 32 peer-reviewed publications, including contributions to journals like Cognitive, Affective & Behavioral Neuroscience and Trends in Cognitive Sciences . He leads three funded research projects on decision-making mechanisms, facial expression encoding, and neural representation of faces, supported by the Bial Foundation and ESRC. Research highlights include studies on foraging strategies, delusional belief resistance, and the role of caricatured facial movements in emotion perception. His lab (FurlLab) collaborates internationally and maintains an active Twitter presence (@FurlLab). No specific awards are listed, though his projects indicate sustained research impact.

Albert N. ShiryaevView profile

Professor

• Probability theory
• Mathematical statistics
• Stochastic processes
• +3 more

Albert N. Shiryaev is a distinguished Professor at Moscow State University's Faculty of Mechanics and Mathematics, where he has served as Head of the Probability Theory Department since 1996. He has been a Professor at the university since 1970 and a member of the Steklov Mathematical Institute of the Russian Academy of Sciences since 1957. His research spans Probability theory , Mathematical statistics , Stochastic processes , and Financial mathematics , with particular expertise in sequential analysis and optimal stopping rules. His theoretical work has significantly influenced both pure and applied mathematics, especially in financial modeling where his monographs have become standard references. Shiryaev's publication record includes over 170 scientific papers and numerous influential monographs such as Statistics of Random Processes (with Liptser), Limit Theorems for Stochastic Processes (with Jacod), and Essentials of Stochastic Finance . His research demonstrates consistent focus on foundational probability theory while expanding into financial applications in later decades. Markov Prize of Academy of Sciences of the USSR (1974) Honorary Fellow of the Royal Statistical Society (1985) Member of the Academia Europea (1990) Kolmogorov Prize of the Russian Academy of Sciences (1994) Humboldt Research Award (1996) Doctor Rerum Naturalium Honoris Causa of Albert Ludwigs University (2000) Professor Honoris Causa of Amsterdam University (2002) Distinguished Professor of Moscow State University (2003) Shiryaev has advised 52 PhD dissertations across multiple countries and held significant academic leadership positions. He served as President of the Bernoulli Society (1989-1991), President of the Actuarial Society of Russia (1994-1998), and President of the Bachelier Finance Society (1998-1999). He currently serves as Deputy Head of the journal Theory of Probability and its Applications and Co-editor of Finance and Stochastics , while also sitting on multiple editorial boards including Markov Processes and Related Fields and Quantitative Finance . His research group at the Steklov Mathematical Institute and Moscow State University has maintained continuous activity in probability theory research for decades, contributing to both theoretical advances and practical applications in finance and statistics.

Haimonti DuttaView profile

Associate Professor

• Machine Learning
• Distributed Inference
• Distributed Optimization
• +5 more

Haimonti Dutta is an Associate Professor in the Department of Management Science and Systems at the University at Buffalo School of Management. Her research focuses on machine learning, distributed inference, optimization, and large-scale computing with applications in healthcare informatics, electrical grid analysis, and cultural heritage technology. She holds a PhD and has contributed to projects involving clinical data analysis (e.g., predicting hospital stays using EHRs), fraud detection in financial reporting, and developing recommendation systems for folk art. Her work integrates distributed algorithms and graph neural networks to solve real-world problems in edge computing and healthcare. Her research outputs span topics like consensus-based machine learning, data cleaning during pandemics, and optimizing neural network training through creative architectures. Despite no listed awards, her extensive publications reflect impactful contributions to interdisciplinary computational methods.

Elea McDonnell FeitView profile

Associate Professor

• Marketing Analytics
• Advertising Incrementality
• Bayesian Hierarchical Models
• +12 more

Elea McDonnell Feit is Associate Professor of Marketing and Associate Dean for Research at Drexel University’s LeBow College of Business. She bridges academic research and industry practice, with prior roles at Amazon Ads, General Motors R&D, The Modellers, and Wharton Customer Analytics. Her work focuses on data-driven marketing, experimentation, and Bayesian modeling. Research Interests: Elea's research centers on improving marketing decisions through rigorous analytics. She specializes in advertising incrementality, causal inference, Bayesian hierarchical models, missing data, data fusion, and conjoint analysis. Her methodological expertise enables robust measurement of advertising effectiveness and consumer choice. She develops accessible tools and frameworks for practitioners through open-source workshops and books. Publication Trends: Her recent publications (2008–2025) reflect a sustained focus on marketing science, particularly experimentation (A/B testing, test-and-roll), advertising measurement (attribution, multi-channel effectiveness), and advanced statistical modeling (Bayesian, hierarchical, data fusion). She frequently publishes in top journals such as Marketing Science , Journal of Marketing Research , and Management Science . Scientific Awards: Editorial Review Board Service Award (Marketing Science, 2022) MSI Scholar (Marketing Science Institute, 2022) Top Analytics Educator Award (Digital Analytics Association, 2021) Allen Rothwarf Award for Teaching Excellence (Drexel, 2018) Data Science Research Award (Adobe, $25,000, 2017) Best Software Demo Award (AMA ART Forum, 2017) Excellence in Research Award (LeBow, 2016) Junior Teaching Award (LeBow, 2016) 4 under 40 Award (American Marketing Association, 2013) Advising and Grants: While formal student advisees are not listed, her leadership in Wharton Customer Analytics and academic collaborations indicate mentorship roles. She has secured significant research support, including a $25,000 Data Science Research Award from Adobe. Her editorial roles in top journals and industry partnerships reflect her influence and funding potential. Labs and Teams: Elea leads initiatives in marketing analytics education and open-source tools. Her GitHub presence and development of workshops (e.g., on Stan, R, Python) suggest active engagement with data science communities. She contributes to teams advancing Bayesian modeling and experimentation in marketing through editorial and collaborative research roles.

• Econometrics
• Statistical Theory
• High-Dimensional Testing
• +4 more

Univ.Prof. David Preinerstorfer is a Professor at the Department of Statistics and Mathematics at WU Vienna University of Economics and Business. His research focuses on econometric theory, statistical methodology, and high-dimensional testing. He has published extensively in top journals such as Econometrica , Journal of Econometrics , and Annals of Statistics . Key research areas include heteroskedasticity and autocorrelation robust testing, high-dimensional statistical inference, bootstrap methods, and optimal policy learning. His work addresses challenges in econometric testing under complex data conditions and develops methods to enhance statistical power and validity. Recent publications analyze topics like modern Gauss-Markov theorems, superconsistency in high dimensions, and treatment allocation with distributional targets. Preinerstorfer collaborates frequently with researchers such as A. B. Kock and B. M. Pötscher on projects addressing robust testing procedures and statistical methodology advancements.

Sverre Ole GrepperudView profile

Professor

• Health Economics
• Health Policy
• Regulatory Economics
• +3 more

Sverre Ole Grepperud is a Professor at the Department of Health Management and Health Economics, University of Oslo. His research focuses on health economics, regulatory frameworks, and healthcare policy, with emphasis on topics like accreditation systems, voluntary health insurance, and provider incentives. He has contributed extensively to journals such as Health Economics Review , BMJ , and European Journal of Political Economy . Key research areas include analyzing healthcare financing mechanisms, evaluating policy impacts on healthcare quality, and understanding strategic behavior in regulated markets. His work integrates microeconomic theory with empirical applications, addressing issues such as medical error reporting, pharmaceutical pricing negotiations, and catastrophic health expenditure in low-income regions like rural Tanzania. Grepperud collaborates with institutions like NORCHER (Norwegian Centre for Health Services Research) and has authored over 70 peer-reviewed articles. His recent publications examine accreditation effects in healthcare markets, incentive design for performance-based pay, and the role of informal penalties in medical error prevention.

• Artificial Intelligence
• Machine Learning
• Virtual Environments
• +7 more

Dr. Thomas Hartley is a Senior Lecturer in Computer Science at the University of Wolverhampton, affiliated with the Faculty of Science and Engineering and the School of Mathematics and Computer Science. His work bridges artificial intelligence, virtual environments, and game technology, with a strong emphasis on adaptive systems and educational applications. Research Interests: Thomas specializes in artificial intelligence for virtual environments, with core interests in in-game learning, machine learning, adaptive navigation, and non-entertainment uses of game technology. His research explores how AI agents can learn and adapt in real time within interactive systems, particularly games and simulations. His recent publications reflect a consistent focus on adaptive AI in games, including pathfinding, tactic selection, and agent decision-making. Themes across these works include real-time adaptation, use of Markov Decision Processes, and emotional modeling for intelligent agents in training simulations. Professional Engagement: He is actively involved in Knowledge Transfer Partnerships (KTPs), applying research to real-world problems. He serves on the International Programme Committee for the CGAMES Conference and is a reviewer for the International Journal on Intelligent Games and Simulation (IJIGS) . Advising and Grants: While specific students are not listed, his engagement in KTPs and research supervision indicates active mentoring. His work has been supported through academic and industry collaborations, particularly in applied game technology projects. Labs and Teams: His research is conducted within the School of Mathematics and Computer Science, likely in collaboration with AI and game development research groups focused on intelligent agents and interactive systems.

Xiao ShouView profile

Assistant Professor

• Machine Learning
• Deep Learning
• Data Science
• +6 more

Dr. Xiao Shou is an Assistant Professor in the Department of Computer Science at Baylor University, within the College of Arts and Sciences. He holds a PhD in Applied Mathematics and an MS in Computer Science, both from Rensselaer Polytechnic Institute (RPI), where he also served as a visiting researcher prior to joining Baylor. His academic journey includes an MS from Ohio State University and a BA from Wittenberg University. His research centers on machine learning for sequential data , with a focus on temporal point processes and time series. He develops deep learning models that incorporate causality, uncertainty, and attention mechanisms to better understand complex event sequences. His work bridges theoretical advances with real-world applications in data-intensive domains. The selected publications highlight a strong trend in modeling event sequences using advanced neural architectures. Key themes include causal learning, probabilistic attention, and multi-label prediction in streaming environments. These works have been published in top-tier conferences such as NeurIPS, ICML, and AAAI, reflecting high impact and technical rigor. Scientific Awards: 2023 IBM Research Accomplishment Award 2021 ACM BCB Best Student Paper Award Dr. Shou has served as a Program Committee (PC) member for premier conferences including NeurIPS, ICML, ICLR, AISTATS, and AMIA, indicating active engagement in the research community. While specific grant details are not mentioned, his collaborative work with IBM suggests industry-academic partnerships. He is likely involved in mentoring students, though no advisees are currently listed. His research group likely focuses on developing next-generation machine learning models for dynamic systems, with potential applications in healthcare, finance, and network analysis. Future work may expand into interpretable AI, real-time decision systems, and causal discovery in high-dimensional event streams.

• Multi-Criteria Decision Making
• Operations Research
• Optimization Algorithms
• +5 more

Grzegorz Filcek is a Researcher at the Department of Computer Science and Systems Engineering , Faculty of Information and Communication Technology , Wrocław University of Science and Technology, Poland. His work focuses on multi-criteria optimization , evolutionary algorithms , and scheduling with applications in transportation systems , logistics , and industrial automation . Email: grzegorz.filcek@pwr.edu.pl Office: Building C-3, Room 14, Janiszewskiego 11/17, 50-372 Wrocław His research spans multi-criteria decision-making , location-scheduling integration , and transportation network optimization . Recent work includes frameworks for Pareto optimal solutions in MIP and rescheduling trains under track closures . He also explores evolutionary computation for freight parking planning and carpooling systems . Key trends in his publications include multi-criteria optimization (2019-2024), evolutionary algorithms (2020-2021), and transportation/logistics applications (2016-2023). His work often combines mathematical programming with real-world problem-solving in supply chains , railways , and emergency power systems .

• Decision Neuroscience
• Computational Modeling
• Social Contexts Research
• +4 more

Christoph W Korn is a researcher at the University of Hamburg focusing on human decision-making and learning in complex multimodal and social contexts. His work integrates computational modeling , fMRI analysis , and pupillometry to study neural mechanisms underlying decision processes. His research highlights include Investigating hippocampus and medial prefrontal cortex roles in threat minimization Exploring language proficiency's impact on future optimism Developing psychophysiological models for fear learning Examining how font readability affects framing effects He serves as a Review Editor for Cognitive Neuroscience at Frontiers in Human Neuroscience, contributing to editorial roles in computational and cognitive neuroscience domains.

• Machine Learning
• Reinforcement Learning
• Bandit Problems
• +7 more

Claire Vernade is a Group Leader at the University of Tübingen within the Cluster of Excellence Machine Learning for Science. Her research focuses on sequential decision making and theoretical reinforcement learning (RL), particularly in non-stationary environments, bandit problems, and principled learning algorithms. She has received prestigious awards including the Emmy Noether Award (2022) and an ERC Starting Grant (2024) for her projects FoLiReL and ConSequentIAL , respectively. Claire has previously worked as a Research Scientist at DeepMind (London) and as a part-time Applied Scientist at Amazon (Berlin). Education : PhD in Machine Learning from Telecom ParisTech (2017), under Prof. Olivier Cappé. Research Interests include: Sequential Decision Making Bandit Problems (Combinatorial, Delayed Feedback, Sparse Actions) Reinforcement Learning Theory Meta-Learning and Lifelong Learning Optimization Algorithms Game-Theoretic Approaches to PCA Publications highlight trends in non-stationary environments, contextual bandits, and theoretical foundations of RL and bandit algorithms. Her work spans applications in scientific discovery, statistical testing, and optimization. Scientific Awards : Emmy Noether Award (2022) ERC Starting Grant (2024) Outstanding Paper Award at ICLR 2021 Advising and Grants : Claire leads a research group with ongoing PhD and postdoc opportunities through IMPRS-IS and ELLIS doctoral programs. Her projects receive funding from the European Research Council and DFG, with focus on continual learning and adaptive AI systems. Labs/Teams : She coordinates the Tübingen Women in Machine Learning (TWiML) initiative and co-leads the Women in Learning Theory (WiML-T) website. Her group emphasizes diversity, inclusivity, and collaborative research in theoretical machine learning.