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Adriana RicklinView profile

Researcher

• Data Science
• Reinforcement Learning
• Machine Learning
• +3 more

Adriana Ricklin is a Research Associate at Lucerne University of Applied Sciences and Arts, affiliated with the Lucerne School of Business and the Institute of Communication and Marketing. Concurrently, she is a PhD candidate at Eindhoven University of Technology's Department of Mathematics and Computer Science, researching sequential decision-making methods for e-commerce optimization under Professor Maurits Kaptein. Education: PhD candidate at Eindhoven University of Technology (2024-present) MSc Applied Information & Data Science, Hochschule Luzern (2019-2022) BSc Business Administration, Hochschule Luzern (2015-2018) Research Focus: Her expertise spans reinforcement learning, predictive analytics, and machine learning applications in sustainability-focused e-commerce. Key projects include Algorithmic Nudging for Sustainability, Sharing Potential Accelerator, and real-time sentiment analysis for brand reputation. Publication Trends: Recent works demonstrate consistent focus on machine learning adaptations for consumer behavior prediction and sharing economy optimization, utilizing advanced methods like LSTMs and graph neural networks across e-commerce and peer-to-peer platforms. Professional Engagements: Contributes to multiple HSLU research initiatives including Sustainable Consumer Advisor and AI-assisted talent acquisition projects, collaborating with teams at the Teaching & Research Institute for Data Science.

Fateme JamshidiView profile

Researcher

• Causal Inference
• Bayesian Networks
• Multi-Armed Bandits
• +3 more

Fateme Jamshidi is a doctoral researcher at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the College of Management of Technology and the Institute for Management, Technology and Economics. Her research focuses on: Causal inference and discovery Bayesian network modeling Multi-armed bandit algorithms Context-specific independence analysis Hidden confounding in causal networks Nonparametric statistical testing Recent work trends demonstrate expertise in causal modeling under uncertainty, with applications to machine learning and decision systems. Her publications explore graph-dependent regret bounds, causal imitability, and structural side information integration.

• Mechanical Engineering
• Process Engineering
• Transport Systems
• +3 more

Adrian Müller is a Lecturer at the Department of Mechanical and Process Engineering within the ZHAW School of Management & Law at Zurich University of Applied Sciences (ZHAW). His contact details include the email mueladri@ethz.ch and a phone number. He is based in Winterthur, Switzerland. His research interests focus on Mechanical Engineering , Process Engineering , and Transport Systems , with recent work exploring regret minimization , constrained Markov decision processes , and optimization algorithms within artificial intelligence and operations research . His recent publications highlight advancements in regret minimization and constrained MDPs, emphasizing decision-making algorithms and their applications in machine learning and operations research. Adrian Müller has not been listed with any scientific awards in the provided information. No information is available regarding his advising of students or grants received. Details about his involvement in specific labs or research teams are not provided in the available data.

• Behavioral Finance
• Corporate Governance
• Sustainable Investing
• +4 more

Kelly Shue is a Professor of Finance at Yale School of Management, with additional affiliations at the National Bureau of Economic Research (NBER) and the European Corporate Governance Institute (ECGI). Her research bridges behavioral finance, corporate governance, and labor economics, with a particular focus on decision-making patterns in financial markets and organizations. Professor Shue's research interests span multiple domains of finance and economics. She investigates how behavioral biases affect financial decision-making, as evidenced by her work on the gambler's fallacy among professionals and contrast effects in financial markets. Her research on sustainable investing examines the impact elasticity of ESG considerations, while her gender economics work explores the gender gap in promotions and housing returns. She also studies executive compensation structures, corporate governance mechanisms, and the role of networks in firm policies. Her publication record shows a consistent focus on behavioral aspects of finance, with recent work increasingly incorporating AI applications and examining gender disparities across multiple economic domains. Shue frequently collaborates with leading scholars in finance and economics, producing work that appears in top journals including the Quarterly Journal of Economics and Journal of Finance. Professor Shue has advised numerous PhD students and supervised research across behavioral finance, corporate finance, and labor economics domains. Her work has received substantial attention in academic circles, as evidenced by her strong citation record and download statistics on platforms like SSRN.

Andrés CristiView profile

Assistant Professor

• Algorithmic Game Theory
• Mechanism Design
• Sequential Decision-Making
• +7 more

Andrés Cristi is a Tenure Track Assistant Professor at EPFL's College of Management of Technology and heads the Chair of Game Theory and Operations (GO). He is affiliated with the CDM (College of Management of Technology) and its subunits MTEI (Management and Technology Education Initiative) and GO (Game Theory & Operations). Current Position: Tenure Track Assistant Professor, EPFL Previous Roles: Postdoc at Center for Mathematical Modeling (CMM), Universidad de Chile; Research Member at Simons-Laufer Mathematical Sciences Institute Education: PhD in Engineering Systems (2023), Universidad de Chile MS in Operations Management, Universidad de Chile Mathematical Engineer, Universidad de Chile His research focuses on the intersection of Algorithmic Game Theory , Mechanism Design , and Sequential Decision-Making , studying how optimization interacts with strategic agent incentives in dynamic allocation problems. He employs data-driven approaches to analyze platforms like routing apps and online marketplaces. Recent work trends include Prophet Inequalities , Combinatorial Auctions , Online Resource Allocation , and Fairness in Algorithmic Systems , with applications to real-time decision-making and bias reduction. Scientific Awards: Meta Research PhD Fellowship (2021) EURO Excellence in Practice Award Finalist (2019) IFORS Prize for OR in Development Runner-up (2020) He advises PhD student Zhang Jiechen and has taught courses on Algorithmic Game Theory and Applied Probability & Stochastic Processes .

Marcel BinzView profile

Researcher

• Machine Learning
• Cognitive Science
• Artificial Intelligence
• +7 more

Dr. Marcel Binz is a research scientist and deputy head at the Institute for Human-Centered AI at Helmholtz Munich. His work bridges machine learning and cognitive science to develop foundation models of human cognition, aiming to unify theories of human behavior through computational modeling.

• Computational Game Theory
• Multi-Agent Systems
• Artificial Intelligence
• +7 more

Jiarui Gan is a Lecturer in the Department of Computer Science at the University of Oxford, where they conduct research at the intersection of computational game theory, multi-agent systems, and artificial intelligence. Their work focuses on understanding and shaping interactions among intelligent agents in complex real-world scenarios, with applications spanning transportation systems, digital platforms, and societal ecosystems. Dr. Gan's academic journey includes: PhD in Computer Science from the University of Oxford, supervised by Edith Elkind and Michael Wooldridge Postdoctoral research at the Max Planck Institute for Software Systems (MPI-SWS) with Rupak Majumdar Dr. Gan's research program centers on computational approaches to game theory and multi-agent systems. They investigate how to design incentive mechanisms that effectively coordinate autonomous agents toward organizational objectives, while addressing critical issues of fairness, security, and sustainability. Their work spans several interconnected themes: Principal-agency problems and dynamic mechanism design Stackelberg games and robust equilibrium concepts Fair resource allocation and envy-freeness in multi-agent settings Bayesian persuasion and information design Applications to security, transportation, and societal challenges Analysis of Dr. Gan's publication record reveals a consistent pattern of bridging theoretical insights with practical applications. Their work demonstrates sophisticated mathematical modeling combined with algorithmic innovations, resulting in computationally tractable solutions for complex multi-agent problems. The research shows particular strength in developing frameworks that unify previously disparate problem domains, such as their generalized principal-agency model that encompasses contract design, information design, and Bayesian Stackelberg games. Dr. Gan is actively involved in the academic community, mentoring students and collaborating with researchers across institutions. They are currently seeking motivated PhD students interested in computational game theory and multi-agent systems, with opportunities to work on both theoretical foundations and practical applications that address societal challenges.

• Machine Learning Theory
• Game Theory
• Information Theory
• +5 more

Prof. Wouter M. Koolen serves as Professor of Mathematical Machine Learning in the Statistics group at the University of Twente and as Senior Researcher in the Machine Learning group at Centrum Wiskunde & Informatica (CWI). He maintains active research affiliations with INRIA-CWI associate teams 6PAC (with Inria Lille) and 4TUNE (with Inria Paris and Grenoble), and holds the distinction of ELLIS Scholar. Dr. Koolen earned both his MSc and PhD cum laude from the Institute of Logic, Language and Computation at the University of Amsterdam, completing his doctoral work titled 'Combining Strategies Efficiently: High-quality Decisions from Conflicting Advice' in January 2011. His academic journey includes being designated a Master of Logic. Prof. Koolen's research spans theoretical machine learning with deep connections to game theory, information theory, statistics, and optimization. His current work focuses on pure exploration in multi-armed bandit models, game tree search algorithms, and provably accelerated learning methods in statistical and individual-sequence settings, which he characterizes as 'learning faster from easy data.' His theoretical contributions consistently demonstrate practical relevance in sequential decision making and statistical inference. Analysis of his recent publications reveals three dominant research threads: martingale-based methods for anytime-valid statistical inference using e-values, adaptive optimization algorithms with provable guarantees, and theoretical foundations of multi-armed bandit problems. His work increasingly bridges theoretical computer science with modern statistical methodology, particularly in sequential analysis and adaptive experimentation. His notable achievements include: NWO VENI grant for innovative research QUT Vice-Chancellor's postdoctoral research fellowship Designation as ELLIS Scholar recognizing European research excellence cum laude distinctions for both master's and doctoral degrees Prof. Koolen actively mentors the next generation of researchers, having supervised multiple PhD students to completion including Hongwei Wen, Clément Lezane, and Tyron Lardy with defenses scheduled for 2025. His research program is supported by competitive grants focusing on theoretical machine learning and statistical methodology. He maintains an active presence in the international research community through conference presentations, workshop organization, and collaborations across European institutions. Within the Machine Learning group at CWI and Statistics group at the University of Twente, Prof. Koolen contributes to a dynamic research environment focused on theoretical foundations with practical applications. His work often intersects with colleagues investigating sequential decision processes, game-theoretic approaches to learning, and robust statistical inference methods.

Alberto MarchesiView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Algorithmic Game Theory
• +3 more

Alberto Marchesi is an Assistant Professor at the Department of Electronics, Information and Bioengineering (DEIB) at Politecnico di Milano, where he earned his Ph.D. in Computer Science under the supervision of Prof. Nicola Gatti. His research bridges Artificial Intelligence and economics, focusing on algorithmic game theory , online learning , and computational complexity . Education: Ph.D. in Computer Science, Politecnico di Milano His work develops no-regret learning dynamics and strategic reasoning algorithms for game-theoretic scenarios. Key publication venues include ICML , NeurIPS , EC , and Journal of the ACM , with recent articles analyzing constrained MDPs , Bayesian persuasion , and contract design in multi-agent systems. Scientific Recognition: PRIN 2022 grant for research in online learning and computational game theory NeurIPS 2020 Best Paper Award (top 3/9476 submissions) NeurIPS 2020 Spotlight presentation (top 2.96%) Currently recruiting a postdoctoral research fellow to advance projects on steering collective behavior of no-regret learners in computational game theory, with collaboration opportunities at Bocconi University.

• Product Management
• Supply Chain Management
• Artificial Intelligence
• +3 more

Amin Kaboli is a Lecturer at the Swiss Federal Institute of Technology in Lausanne (EPFL), holding positions in the School of Mechanical and Process Engineering and the School of Computer and Communication Sciences . As Co-Director of the AI Product Management program , he specializes in teaching students to apply AI/ML principles to real-world product development. His academic roles include designing curricula that blend technical engineering with business leadership, such as courses on Sustainable Products and Production Management. He earned a Ph.D. in Manufacturing Systems & Robotics from EPFL and holds a graduate degree in Industrial Engineering. His leadership training at IMD Business School complements his engineering background, enabling him to advise Fortune 500 corporations like Philip Morris International, Nespresso, and Rolex, as well as Swiss startups. His professional experience includes roles as an executive and COO of a startup, alongside work as an executive coach (PCC-certified). Kaboli’s research interests span AI-driven innovation , supply chain optimization , and sustainability . He explores solutions for energy management in smart grids, waste reduction in construction, and fault detection in industrial machinery. His work integrates fuzzy logic, multi-criteria decision-making (MCDM), and hybrid machine learning frameworks to address complex challenges. He teaches courses such as AI Product Management (focus on practical AI/ML applications), Sustainable Products and Supply Chains (sustainability principles and logistics optimization), Production Management (cost-effective manufacturing strategies), and Continuous Improvement of Manufacturing Systems (process optimization and team leadership). No scientific awards or grants are explicitly listed. His contributions include developing frameworks for eco-city solutions and waste management strategies. Kaboli’s office is located at ME A2 408 , EPFL’s Lausanne campus, where he also engages in administrative and educational leadership roles.

• Machine Learning
• Biomedical Informatics
• Medical Genomics
• +3 more

Prof. Gunnar Rätsch is a Full Professor in the Department of Computer Science at ETH Zürich and Deputy Head of the Institute for Machine Learning. His research focuses on developing machine learning methods for biomedical applications, including genomics, medical imaging, and clinical decision support systems. He specializes in integrating multi-omics data, spatial transcriptomics, and time-series analysis to address challenges in precision medicine and critical care. His work emphasizes ethical AI frameworks, algorithmic fairness, and robust clinical prediction models. Key research areas include: Deep learning for medical imaging and histopathology Single-cell analysis and tumor profiling Reinforcement learning for treatment optimization in ICUs Multimodal data integration for clinical applications Recent work highlights advancements in: Standardizing single-cell cytometry readouts for clinical use Developing foundation models for critical care time-series analysis Creating interpretable survival models for ICU patients His contributions span foundational machine learning theory and applied healthcare technologies, with a focus on translational research to improve clinical outcomes. Current projects include the Tumor Profiler Study for multi-omic tumor analysis and ethical frameworks for clinical AI systems.