جستجوی استاد راهنمای دانشگاهی

Julian McAuley is a Professor in the Department of Computer Science and Engineering at the University of California, San Diego's Jacobs School of Engineering. His research spans recommender systems, machine learning, natural language processing, music information retrieval, and multimodal learning. He maintains an active research group with numerous PhD students and postdocs working on cutting-edge AI problems. His research interests focus on developing advanced algorithms for personalized recommendation systems, with particular emphasis on sequential recommendation, multimodal learning, and integrating large language models with traditional recommendation approaches. His work bridges the gap between theoretical machine learning and practical applications across multiple domains including e-commerce, music, and healthcare. McAuley has published extensively in top-tier conferences including NeurIPS, ICML, KDD, SIGIR, and ACL, with his most recent work exploring the intersection of large language models and recommendation systems. His publications reveal a strong trend toward multimodal approaches that combine text, vision, and audio for more comprehensive understanding and recommendation. He has received significant research funding from major technology companies including Google, Amazon, Facebook, Adobe, and Samsung, as well as government agencies like the National Science Foundation and Department of Defense. His work has practical applications across multiple industries, with a focus on improving user experience through better personalization. McAuley advises numerous PhD students who have gone on to successful careers at leading technology companies and academic institutions. His former students include Wang-Cheng Kang and Jianmo Ni at Google DeepMind, Chris Donahue and Zachary Lipton as assistant professors at CMU, and Ruining He at Google Deepmind.

• Recommender Systems
• Machine Learning
• Natural Language Processing
• +۴ مورد دیگر

Luigi Acerbi is an Associate Professor in the Department of Computer Science at the University of Helsinki, where he leads the Machine and Human Intelligence research group. He is also an active member of the Finnish Center for Artificial Intelligence (FCAI) and ELLIS (European Laboratory for Learning and Intelligent Systems). His research focuses on probabilistic machine learning and computational neuroscience, particularly on developing efficient methods for statistical inference, Bayesian models of perception, and resource-constrained rationality. His work bridges machine learning and cognitive science, with applications in Bayesian optimization, simulation-based inference, and image completion. The recent publications highlight a strong trend toward unifying probabilistic conditioning across diverse tasks using transformer-based meta-learning frameworks like the Amortized Conditioning Engine (ACE). These works emphasize amortized inference, flexible latent variable modeling, and the integration of prior knowledge at runtime, enabling efficient and scalable Bayesian methods for complex problems. Scientific Affiliations: University of Helsinki, Department of Computer Science Finnish Center for Artificial Intelligence (FCAI) ELLIS (European Laboratory for Learning and Intelligent Systems) Education: PhD in Computational Neuroscience, Doctoral Training Centre, Edinburgh, UK Advisor: Sethu Vijayakumar and Daniel Wolpert Visiting work at Computational and Biological Learning Lab, Cambridge Postdoctoral Experience: Alex Pouget’s lab, University of Geneva, Switzerland Wei Ji Ma, New York University, USA Collaboration with the International Brain Laboratory Luigi Acerbi mentors PhD students including Daolang Huang and Nasrulloh Loka, and collaborates widely with researchers such as Samuel Kaski. He has contributed to open-source tools like PyVBMC and is involved in community initiatives such as the EurIPS conference. His work is supported by grants from the Research Council of Finland, Business Finland, and the UKRI Turing AI World-Leading Researcher Fellowship. He leads a research lab focused on amortized probabilistic inference, with ongoing projects including PriorGuide and Stacked VBMC, aiming to make Bayesian methods more practical and accessible for real-world scientific and engineering applications.

• Probabilistic Machine Learning
• Bayesian Models
• Statistical Inference
• +۱۲ مورد دیگر

Prof. Dr. Urs Fischbacher is a full-time Professor at the Department of Economics at the University of Konstanz, holding the Chair of Behavioral Economics. He is also affiliated with the Thurgau Institute of Economics in Switzerland. Research Focus: Behavioral Economics, Experimental Economics, Game Theory, Social Preferences, Public Goods, Trust and Cooperation, Decision-Making under Stress, and Neuroeconomics. Key Projects: Evaluation of educational interventions in Colombia, analysis of leadership selection in inequality perception, and studies on social pension targeting in Bangladesh. Academic Contributions: His recent articles focus on super-additive cooperation mechanisms, responsibility attribution in decision chains, stress-induced prosociality, and diversity policy impacts. Work spans interdisciplinary collaborations with psychologists and neuroscientists. Contact: Based at the University of Konstanz (Room F315) and Thurgau Institute of Economics. Office hours require appointment via email.

• Behavioral Economics
• Experimental Economics
• Game Theory
• +۶ مورد دیگر

Prof. Dr. Wolfgang Nejdl is a Professor at the Institute for Data Science within the Faculty of Electrical Engineering and Computer Science at Leibniz University Hannover. He serves as Executive Director of the L3S Research Centre and Leibniz Forschungszentrum Inclusive Citizenship. Web Science Information Retrieval Artificial Intelligence Deep Learning His recent research focuses on AI applications in medicine , multimodal data fusion , and ethical AI systems . Projects include CAIMed (AI in Causal Medicine) and DAISEC (AI & Cybersecurity). His publications span conferences like AAMAS, WWW, and SIGIR. Notable awards include membership in the National Academy of Science and Engineering (acatech) . Former students hold positions at institutions like Stanford, TU Dresden, and ETH Zürich. Current projects involve climate resilience AI , federated learning for healthcare , and quantum-inspired data science .

• Web Science
• Information Retrieval
• Artificial Intelligence
• +۴ مورد دیگر

Florian Brandl is an Argelander Professor (associate professor with tenure) at the University of Bonn, holding positions in both the Department of Economics and the Hausdorff Center for Mathematics. Previously, he was a postdoctoral research scholar at Princeton University and Stanford University. His academic career demonstrates a strong foundation in mathematical economics and game theory, with affiliations spanning multiple prestigious institutions. Brandl earned his Doctoral degree in Mathematics (summa cum laude) from the Technical University of Munich in 2018, following a Master's degree (2013) and Bachelor's degree (2011) from the same institution. His doctoral work focused on "Zero-Sum Games in Social Choice and Game Theory" under the supervision of Felix Brandt, establishing the foundation for his research trajectory. Prof. Brandl's research spans microeconomic theory with a focus on social choice theory, decision theory, and game theory. He is particularly interested in decision-making under uncertainty, connections between social choice and game theory, and dynamic processes converging to equilibrium. His work employs mathematical tools to analyze interactions of multiple entities in economic contexts, often incorporating algorithmic approaches and methods from theoretical computer science. He has made significant contributions to fair division, mechanism design, and probabilistic social choice. His publication record shows consistent contributions across multiple subfields, with recent work focusing on patience effects in fair division, social learning barriers, and axiomatic characterizations of equilibrium concepts. Brandl's research demonstrates strong interdisciplinary connections between economics, mathematics, and computer science, with publications in top journals across all three disciplines. Best Student Paper Award at WINE 2021 for "Funding Public Projects: A Case for the Nash Product Rule" Associate Editor for Theoretical Economics Co-organizer of the COMSOC Video Seminar Prof. Brandl actively contributes to academic service and community building. He co-organizes the COMSOC Video Seminar and will host a Trimester Program on "Advances in Mechanism Design" in Bonn in summer 2026. He serves on program committees for major conferences including COMSOC 2023 and EC 2023. His research has been supported through his position as a Bonn Junior Fellow at the Hausdorff Center for Mathematics since 2021. Based at the Institute for Microeconomics and affiliated with the Hausdorff Center for Mathematics, Brandl collaborates with a broad network of researchers across economics and computer science. His work often involves interdisciplinary collaboration, as evidenced by his numerous co-authored publications with researchers from various institutions worldwide. He maintains strong connections with the University of Oxford's Global Priorities Institute as a Research Affiliate.

• Microeconomics
• Social Choice Theory
• Decision Theory
• +۴ مورد دیگر

Dr. Kevin G. Jamieson is a faculty member at the University of Washington , School of Computer Science , with prior affiliations at the University of California, Berkeley (Department of Electrical Engineering and Computer Sciences) and the University of Wisconsin-Madison (Department of Electrical and Computer Engineering). His work spans machine learning, reinforcement learning, bandit algorithms, and robotics. Current university: University of Washington Academic rank: Professor His research focuses on: Bandit algorithms and sequential decision-making Optimization in non-stationary environments Reinforcement learning with real-world applications Multi-agent systems and game theory Efficient data selection for multimodal learning Human-in-the-loop AI systems Recent publications highlight his expertise in pure exploration strategies, robotic manipulation, and bridging simulation-to-reality gaps in RL. He has mentored numerous collaborators, though formal student advising details are not explicitly listed here. No scientific awards are mentioned in the provided data.

• Machine Learning
• Reinforcement Learning
• Bandit Algorithms
• +۵ مورد دیگر

Mahsa Ghasemi is an Assistant Professor in the Elmore Family School of Electrical and Computer Engineering at Purdue University, leading the AKADEMI Group. Her research focuses on theoretical advancements in trustworthy sequential decision-making for autonomous systems, emphasizing human-aware collaboration and adaptation to dynamic environments. She is affiliated with the Institute for Control, Optimization and Networks (ICON). Education: PhD in Electrical and Computer Engineering from The University of Texas at Austin (2021), MSE in Mechanical Engineering (2017), and BSc in Mechanical Engineering from Sharif University of Technology (2014). Research Interests: Reinforcement learning, control theory, active perception, multi-agent systems, robotics, and online learning. Applications span disaster response, healthcare, and autonomous systems design. Key methodological directions include compositional learning, human-AI collaboration, and adaptive decision-making under uncertainty. Teaching: Courses include Reinforcement Learning Theory (ECE 59500), Introduction to Reinforcement Learning (ECE 49595), and Python for Data Science (ECE 20875). Awards: Finalist for Student Best Paper Award at the 2018 American Control Conference (ACC). Students: Current advisees include Maheed H. Ahmed, Jayanth Bhargav, and Somtochukwu Oguchienti. Past members include Lai Wei and Juan Sebastian Mateo Ruiz Bulla. Service: Editorial roles at ICRA, ICCPS, and IFAC workshops. Reviewer for top conferences (NeurIPS, ICML) and journals (Automatica, IEEE TAC). Labs/Teams: Leads the AKADEMI Group, focusing on algorithmic and theoretical research in autonomous decision-making systems.

• Reinforcement Learning
• Control Theory
• Active Perception
• +۳ مورد دیگر

Zhen Liu is an Assistant Professor at the School of Data Science, CUHK-Shenzhen. His research focuses on generative models, 3D representations, and the synergy of spatial and semantic understanding in AI systems. With a PhD from Mila and Université de Montréal, he develops foundational methods for physics simulation, 3D assembly, and semantic reasoning in neural networks. His work bridges machine learning with applications in computer vision and graphics, emphasizing: Generative architectures for 3D content creation Diffusion model alignment techniques Efficient parameter finetuning strategies Dr. Liu mentors students in AI research and contributes to advancing 3D generative modeling paradigms.

• Generative Models
• 3D Representation Learning
• Computer Vision
• +۲ مورد دیگر

Amelie Schiprowski serves as an Assistant Professor of Economics at the University of Bonn and is a faculty member of ECONtribute, the Cluster of Excellence "Markets & Public Policy". She concurrently holds a Research Fellow position at the Max Planck Institute for Research on Collective Goods and maintains affiliations with CEPR, IZA, and CESifo, demonstrating deep integration within international economic research networks. Her research centers on Labor Economics and Public Economics, with rigorous empirical investigations into unemployment insurance systems, job search behavior, and caseworker-citizen interactions. She employs high-frequency survey data combined with administrative records to isolate causal mechanisms, recently expanding into gender differences in committee deliberation and bias mitigation in interviewing processes through experimental approaches. Analysis of her 2019-2025 publications reveals a cohesive focus on micro-level labor market dynamics, particularly how policy design and frontline implementation shape individual behavior. A dominant trend examines the nuanced effects of caseworker interventions on search effort and employment outcomes, while newer work explores gender disparities in decision-making and the efficacy of bias-awareness training. Her scientific recognition includes: Joachim Herz Award in Economics (2020) Dr. Schiprowski maintains extensive collaborative networks across institutions as evidenced by co-authored publications with researchers from multiple German universities. Her affiliations with major research centers indicate substantial ongoing grant support, though specific funding details are not disclosed in available materials. She actively contributes to policy-relevant economics through publications in premier journals and media coverage in outlets like Frankfurter Allgemeine Zeitung.

• Labor Economics
• Public Economics
• Behavioral Economics
• +۳ مورد دیگر

Claire Vernade is a Group Leader at the University of Tübingen within the Cluster of Excellence Machine Learning for Science. She holds an Emmy Noether award (2022) and an ERC Starting Grant (2024) for her projects FoLiReL and ConSequentIAL , focusing on theoretical reinforcement learning and non-stationary environments. Education: PhD from Telecom ParisTech (2017) Post-doctoral researcher at University of Magdeburg (2018) Her research bridges sequential decision making, bandit problems, and reinforcement learning theory. Recent work explores lifelong learning, distributional RL, and game-theoretic approaches to PCA, emphasizing mathematical rigor and algorithmic innovation. Recent publications highlight trends in non-stationary RL , continual learning , and bandit algorithms with complex feedback structures. Key subfields include meta-learning, adaptive control, and theoretical guarantees in dynamic programming. Scientific Awards: Emmy Noether Award (2022) ERC Starting Grant (2024) Outstanding Paper Award & Oral Presentation, ICLR (2021) She mentors PhD and master's students in theoretical machine learning, with current advisees including Nicolas Nguyen, Onno Eberhard, and Ziyad Sheebaelhamd. Her lab actively recruits candidates in bandit algorithms and RL theory through the IMPRS-IS and ELLIS doctoral programs. Claire co-leads diversity initiatives like Tübingen Women in Machine Learning and Women in Learning Theory, advocating for inclusivity in AI research. She has organized workshops at ICML and EWRL, and contributed to union activism in the tech industry.

• Reinforcement Learning
• Bandit Algorithms
• Learning Theory
• +۴ مورد دیگر

Benny Moldovanu is Professor of Economics at the University of Bonn, where he holds the Chair of Microeconomics. He is a principal investigator in the Collaborative Research Center TR 224 (CRC TR 224) and a member of the Cluster of Excellence ECONtribute: Markets & Public Policy, as well as the Hausdorff Center for Mathematics and the Bonn Graduate School of Economics (BGSE). Education: Detailed curriculum vitae available at CV link . Research Interests: His research focuses on auctions and mechanism design , microeconomic theory , information design , voting theory , and dynamic allocation problems . He has made significant contributions to understanding strategic behavior in auctions, optimal voting rules, and the design of mechanisms under incomplete information. Recent Publications Trend: Recent work (2020–2025) explores entropy-regularized optimal transport in information design, optimal security design for risk-averse investors, order independence in sequential voting, and insurance design under adverse selection. These studies combine sophisticated theoretical tools with applications in finance, political economy, and market design. Scientific Awards and Honors: Winner of the first AEA Best Paper Award for the American Economic Journal: Microeconomics (2009) for “Dynamic Revenue Maximization with Heterogeneous Objects: A Mechanism Design Approach”. Advising and Doctoral Training: Professor Moldovanu has supervised a large cohort of doctoral students and postdocs, many of whom now hold faculty positions worldwide (e.g., Philipp Strack—Clark Medal recipient, Alex Gershkov, Andreas Kleiner, Xianwen Shi, Thomas Kittsteiner). He teaches Microeconomics II (summer 2025) and offers thesis supervision to BGSE students. Labs and Research Groups: He leads the Microeconomics Research Group at Bonn and coordinates research activities in the CRC TR 224 project B01, focusing on market design and information economics.

• Auctions and Mechanism Design
• Microeconomic Theory
• Information Design
• +۳ مورد دیگر

Claire Vernade is a Group Leader at the University of Tübingen in the Cluster of Excellence Machine Learning for Science. She leads an active research group focused on theoretical aspects of sequential decision making, with particular expertise in bandit problems and reinforcement learning theory. Her work bridges theoretical foundations with practical applications in scientific discovery. Her research interests span sequential decision making, bandit problems, theoretical Reinforcement Learning, Learning Theory, and principled learning algorithms. She has made significant contributions to understanding non-stationary environments, lifelong learning frameworks, and the theoretical foundations of bandit algorithms. Her work on "Eigengame: PCA as a Nash Equilibrium" received an Outstanding Paper Award at ICLR 2021. Dr. Vernade has been awarded prestigious grants including an Emmy Noether award (2022) for her FoLiReL project and an ERC Starting Grant (2024) for her ConSequentIAL project. Her current ERC project explores the role of Reinforcement Learning in developing Continual Learning agents, with applications to scientific domains like drug discovery and micro-chemistry. Emmy Noether award under the AI Initiative call (2022) ERC Starting Grant (2024) Outstanding Paper Award at ICLR 2021 She currently supervises three PhD students and actively recruits postdocs and PhD candidates through the IMPRS-IS and ELLIS doctoral programs. Her group collaborates extensively with the broader machine learning community, organizing workshops like FoRLaC at ICML 2024 and serving as co-chairs for tutorials at major conferences. Dr. Vernade is also deeply committed to diversity and inclusion in machine learning, co-leading initiatives like Women in Learning Theory and Tübingen Women in Machine Learning.

• Sequential Decision Making
• Bandit Problems
• Theoretical Reinforcement Learning
• +۶ مورد دیگر

Professor Dietrich Darr is a faculty member at Hochschule Weihenstephan-Triesdorf (HSWT), specifically within the Department of Agriculture, Food, and Nutrition. His academic journey includes previous professorship at Hochschule Rhein-Waal (2012-2024) and visiting positions at Universidade de Brasília (2023-2024), Vietnam National University of Forestry (2020), and ICRAF Central Asia office (2018). Dr. Darr's research focuses on sustainable and resilient farming and food systems with particular expertise in agribusiness, corporate management, and sustainable resource utilization. His work prominently features baobab tree applications in African and European food industries, agroforestry systems, non-timber forest products, and sustainable land management. His international research projects span Sub-Saharan Africa, Central Asia, and Southeast Asia. His recent publications reveal strong trends in sustainable value chain development, climate-smart agriculture, and innovative approaches to rural livelihood improvement. The articles demonstrate consistent focus on practical applications of agroforestry systems, analysis of non-timber forest products commercialization, and sustainable land management solutions for smallholder farmers in developing regions. Dr. Darr leads numerous research projects including SUFACHAIN (2022-2025) on sustainable agroforestry value chains in Central Asia, CONFARMED (2024) on edible insects for circular food economy in Uganda, and previously led BAOQUALITY (2019-2022) on baobab quality improvement for food security in Africa. His projects are primarily funded by German institutions including BMBF, BMEL, and DFG. As an active member of professional organizations including ATSAF, EFARD, IUFRO, DeFAF, and EURAF, Dr. Darr contributes to international agricultural research communities with focus on tropical and subtropical agriculture, agroforestry, and forest research.

• Sustainable Agriculture
• Agroforestry Systems
• Non-Timber Forest Products
• +۶ مورد دیگر

Elena Katok is the Ashbel Smith Professor of Operations Management at the University of Texas at Dallas, where she serves in the Naveen Jindal School of Management. She is also Co-Director of the Laboratory for Behavioral Operations and Economics (LBOE) and has been a Visiting Scholar at the University of Cologne, Germany (2007-2018). Previously, she held faculty positions at Penn State University, Harvard University, and Colorado School of Mines. Dr. Katok earned her Ph.D. in 1996 and MBA in 1992 from Penn State University, and her B.S. in Business Administration with emphasis on Finance and Economics from the University of California, Berkeley in 1987. Dr. Katok is a pioneer in Behavioral Operations Management (BOM), a field that studies how human behavior factors into managerial decisions. Her research focuses on market design, strategic procurement, auctions, and supply chain management. She has made significant contributions to understanding how behavioral factors influence supply chain coordination, contract design, and procurement auctions. Dr. Katok helped establish the Behavioral Operations Management section of INFORMS and organizes the annual Behavioral Research in Operations Management conference. Her recent publications (2018-2022) show a continued focus on trust mechanisms in procurement, contract design under behavioral considerations, auction theory, and supply chain coordination. Her work often combines theoretical modeling with laboratory experiments, demonstrating how behavioral insights can improve operational decision-making. She has particularly examined fairness considerations in supply chains, trust dynamics in procurement relationships, and the impact of behavioral factors on auction outcomes. Franz Edelman Award for achievement in the practice of OR&MS (2000) As Co-Director of the Laboratory for Behavioral Operations and Economics, Dr. Katok leads research on how human behavior affects operational decisions. She has developed numerous classroom simulations to teach supply chain management concepts and has written several case studies used in business education. Her work bridges academic research and practical application in operations management.

• Behavioral Operations Management
• Market Design
• Auctions
• +۳ مورد دیگر

Prof. Michael N. Smolka is a Professor in the Department of Psychiatry and Psychotherapy, focusing on neuro-cognitive mechanisms underlying addictive behaviors. His research employs longitudinal approaches to study addiction development, maintenance, and recovery, integrating computational modeling of behavior with functional MRI to map brain systems. Key areas include executive functions, decision-making, learning, and motivation. He contributes to research consortia such as SFB 940 and TRR 265. Research interests emphasize brain-behavior interactions in mental health, with studies on genetic risk factors, environmental influences, and neuroimaging correlates of psychiatric disorders. His work bridges clinical psychiatry with cutting-edge computational methods, aiming to develop diagnostic tools and personalized interventions. Recent studies explore machine learning applications for predicting substance use disorders and eating disorders, alongside investigations into developmental trajectories of brain morphology and cognitive control mechanisms. Publications highlight interdisciplinary approaches, linking genetic, environmental, and neurobiological factors to addictive behaviors and mental health outcomes. His contributions to frameworks like brain-derived nosology and diathesis-stress models reflect his commitment to advancing translational research in psychiatry.

• Addictive Behavior
• Neuroscience
• Psychiatry
• +۷ مورد دیگر