Ruben Moreno Bote is a Serra Húnter Professor in the Department of Information and Communication Technologies at Universitat Pompeu Fabra. His research focuses on theoretical and computational neuroscience, with an emphasis on decision-making processes, neural coding, and computational models of cognitive dynamics. His work bridges disciplines such as neuroeconomics, systems neuroscience, and artificial intelligence, addressing topics like uncertainty management, value encoding, and neural network dynamics. Key research areas include: Computational principles of decision-making under uncertainty Neural mechanisms of economic choice and value representation Stochastic control in recurrent neural networks Cognitive dynamics of attention and confidence Integration of astrocytes in systems neuroscience His recent articles explore complex behaviors arising from intrinsic motivation, optimal sampling strategies in decision-making, and the role of neural oscillations in value-based decisions. The breadth of his work reflects a commitment to understanding how neural systems process information and guide adaptive behavior. Dr. Bote collaborates across disciplines, contributing to theoretical frameworks that connect neuronal activity with higher-order cognitive functions. His research has implications for understanding both healthy cognition and potential dysfunctions in neurological disorders.
Nir Lipovetzky is an Associate Professor in Artificial Intelligence at the School of Computing and Information Systems, University of Melbourne. He is affiliated with the Agent Lab and Digital Agriculture, Food and Wine Lab. His research focuses on AI planning, search, and applications in autonomous systems, particularly in agriculture. He holds a PhD from Universitat Pompeu Fabra (2012) and a BSc in Computer Science (2004). Education: PhD in Artificial Intelligence, Universitat Pompeu Fabra (2012) MEng in Artificial Intelligence, Universitat Pompeu Fabra (2007) BSc in Computer Science, Universitat Pompeu Fabra (2004) Graduate Certificate in University Teaching, University of Melbourne (2020) Research interests include AI planning, constraint programming, operations research, and intention recognition. He has developed novel width-based planning algorithms and contributed to planning competitions. Recent work includes adaptive goal recognition and reinforcement learning for decision-making under uncertainty. Publications span topics like count-based novelty exploration, process mining for goal recognition, and human-like goal inference models. Awards include the Best Student Paper Award at AAMAS 2024 and recognition in planning competitions. Advises 10+ PhD and Master’s students on topics like exploration methods, agricultural robotics, and multi-agent systems. Supervises Farm.bot, an open-source platform for AI-driven agriculture. Serves as Conference Chair for ICAPS 2025 and has held roles in AAAI, IJCAI, and ICAPS committees.
Dr. Ruth Horry is a Senior Lecturer in Psychology at Swansea University, part of the School of Psychology within the Faculty of Medicine, Health and Life Science. Her research focuses on cognitive psychology, particularly the intersection of psychology and law, including eyewitness identification, testimony, jury decision-making, and climate change education. She also advocates for open science practices in her work. Dr. Horry teaches statistics to undergraduates and investigative psychology to MSc students, while supervising research projects at both undergraduate and postgraduate levels. Research Interests Eyewitness identification and testimony, with a focus on improving reliability through innovative interview methods like the Self-Administered Interview. Jury decision-making dynamics, particularly regarding defendants with medical or neurological conditions. Climate change education strategies, including digital narratives and public engagement initiatives like the You and CO2 program. Meta-science practices, emphasizing reproducibility and open research methodologies. Recent Research Trends Dr. Horry’s recent work has emphasized climate education engagement, leveraging interactive digital tools to enhance youth understanding of environmental issues. Her contributions to legal psychology include refining eyewitness testimony protocols and evaluating lineup procedures to reduce errors. She has also pioneered collaborative research models involving undergraduate students in large-scale cognitive projects. Grants & Supervision Recipient of £77,884 from the Road Safety Trust (2017–2021) for optimizing witness evidence in traffic collisions. Supervision of PhD projects such as “Lineup Construction Methods” (ongoing) and “Perceptions of Defendants with Traumatic Brain Injury” (awarded 2023). Professional Contributions Dr. Horry contributes to the School of Psychology’s mission through teaching modules like Investigative Psychology and mentoring students in research design. She actively promotes interdisciplinary collaboration, bridging cognitive science, legal studies, and environmental education.
Subrata Kundu is a Professor of Statistics and Director of the Data Science Program at George Washington University (GWU), affiliated with the Columbian College of Arts and Sciences. He has held academic positions at GWU since 1997, progressing from Assistant Professor (1997–2001, 2001–2008) to Associate Professor (2009–2022), and became a full Professor in 2022. Prior to GWU, he taught at American University (1999–2001) and the University of California, Santa Barbara (1994–1997). Education: Ph.D. in Statistics from the University of Illinois at Urbana-Champaign (1994), advised by Adam T. Martinsek; M.Stat. and B.Stat. (Hons.) from the Indian Statistical Institute (1989 and 1987). His research focuses on advanced statistical methodologies, including Joint Modeling, Sequential Analysis, Density Estimation, Software Reliability, Hypothesis Testing, and Nonparametric Statistics . These areas emphasize rigorous mathematical foundations and practical applications in data-driven decision-making. No specific awards, grants, or lab affiliations are detailed in the provided text.
Yiangos Papanastasiou is an Associate Professor of Management – Operations Management at the Jesse H. Jones Graduate School of Business, Rice University. He previously held tenured positions at UC Berkeley's Haas School of Business. His research focuses on operations management, pricing strategies, and business analytics, particularly in online platforms and social learning dynamics. Education: BEng in Engineering for Life Sciences (University of Cambridge), MSc in Computer & Information Engineering (University of Cambridge), PhD in Management Science & Operations (London Business School). Research explores optimal information provision in platforms, social learning mechanisms, and misinformation economics. Recent work addresses challenges like review blackmail in two-sided platforms and fake news detection. Teaches MBA courses on business analytics and data analysis.
Jason Ford is a Professor of Electrical Engineering at Queensland University of Technology (QUT), leading research in trustworthy autonomous systems and decision-making under uncertainty. He holds positions in QUT's Centre for Robotics and Centre for Data Science. Ford earned his PhD from the Australian National University (ANU) and has over 20 years of experience in aerospace, energy, and defense sectors. His research focuses on model-based filtering, estimation, and decision systems for dynamic environments, with applications in aerial autonomy, infrastructure inspection, and low-signal-to-noise detection. Education: B.Sc. and B.E. (1995), PhD (1998), all from ANU. Professional Experience: Research Scientist at DSTG (1998-2004), Research Fellow at UNSW (2004-2005), QUT faculty since 2005, promoted to Professor in 2019. Research Interests: Robust autonomous systems, vision-based sense-and-avoid, model-driven decision systems, and resilient robotic technologies. His work on ROAMES asset management systems has saved Queensland $40M/year and won international awards. Awards: 2019 Academic of the Year (Australian Defence Industry), multiple best paper awards, and industry recognition for collision avoidance and UAV technologies. Grants: Over $10M in competitive research funds since 2009. Supervision: 8 completed HDR students since 2008, teaching control systems and autonomous systems to 1000+ undergraduates. Labs/Teams: Program Lead for Decision and Control in QUT's Robotics Centre, collaborator with industry partners like Insitu Pacific and Caterpillar.
Vicenç Gómez is an Associate Professor in the Department of Engineering at Universitat Pompeu Fabra (UPF), where he leads research in artificial intelligence and machine learning. He serves as Coordinator of the Erasmus Mundus Joint Master in Artificial Intelligence and the MSc program in Intelligent and Interactive Systems, and teaches in the BSc in Mathematical Engineering in Data Science. His research interests include machine learning, approximate inference, optimal control, and complex networks , with applications in social networks, robotics, brain-computer interfaces, and urban systems. He applies advanced AI techniques to model human behavior, network dynamics, and decision-making processes. The recent publications highlight a strong focus on graph-based learning, reinforcement learning, social network analysis, and health informatics . His work integrates theoretical advances in probabilistic modeling with real-world applications in digital platforms, environmental monitoring, and mental health. There is a consistent theme of modeling complex systems through structured AI and interpretable models. Scientific Awards and Recognition: Coordinator of the prestigious Erasmus Mundus Joint Master in Artificial Intelligence Local Chair of UAI 2024, a top-tier conference in AI Program Committee member for ICAPS 2024 Organizer of the EMAI Summer School in collaboration with UCL Advising and Grants: Vicenç Gómez actively supervises PhD and Master's students, including Nur Alvarez-Gonzalez, Roger Garriga, Emily Theophilou, and Sergio Calo. His advising spans topics in emotion detection, mental health modeling, air quality prediction, and representation learning. He has been involved in organizing major academic events and leading international educational programs, indicating significant leadership and collaborative grant activity. Labs and Research Groups: He is a key member of the Artificial Intelligence and Machine Learning group at UPF’s Department of Engineering, based at the Roc Boronat building in Barcelona. His team engages in interdisciplinary research combining AI theory with applications in social, health, and urban domains.
Wendell Gilland is an Associate Professor of Operations and Associate Dean overseeing the Evening Executive MBA, Weekend Executive MBA, and Charlotte Executive MBA Programs at the University of North Carolina's Kenan-Flagler Business School. He holds a PhD and MBA from Stanford University and an AB from Harvard University. His research focuses on optimizing operations through multi-channel strategies, supply chain resilience, and healthcare operations. Key areas include inventory prepositioning for humanitarian logistics, cancer screening efficiency, and hospital staffing optimization. Gilland combines academic rigor with industry experience from firms like Monitor Company and CSC Index, advising Fortune 500 clients on strategic planning and operational improvements. His work spans diverse industries including healthcare, retail, and disaster relief, emphasizing practical solutions for complex operational challenges. With over 25 publications, his recent research highlights include UNICEF supply chain strategies (2021), healthcare access optimization (2015-2019), and supply risk mitigation frameworks (2010-2011). Gilland is recognized as an award-winning teacher in operations management, specializing in supply-chain management and decision-making models. His consulting projects span global corporations like Apple, Intel, and Sony, bridging academic theory with real-world business challenges.
Jan Stuckatz is an Assistant Professor of Business and Government at the Department of International Economics, Government and Business at Copenhagen Business School (CBS). He is also a Marie Skłodowska-Curie Fellow and an Affiliate Fellow at the Stigler Center, University of Chicago, Booth School of Business. His research focuses on political economy, particularly money in politics, corporate political strategies, and the influence of workplace dynamics on political behavior. He holds a Ph.D. in Political Science from the London School of Economics (2019). Stuckatz specializes in quantitative methods, including computational social science, causal inference, machine learning, and natural language processing. He co-leads the DeNazDB project, digitizing denazification questionnaires from post-WWII Germany to study motivations for Nazi membership and its economic benefits. His work has been supported by grants from DFG, ANR, and institutions like IAST and Stanford. He teaches Research Design and Quantitative Methods at CBS and advises students on political economy topics. Upcoming roles include a visiting fellowship at Princeton University's School of Public and International Affairs (2024–2025). Grants & Awards: Marie Skłodowska-Curie Fellowship, DFG/ANR funding. Labs/Teams: Co-Principal Investigator of DeNazDB, part of historical political economy research networks.
Ziping Xu is an Assistant Professor at the University of North Carolina at Chapel Hill's School of Data Science and Society. His primary research focuses on statistical machine learning, reinforcement learning (RL), and their applications in digital interventions, particularly in mobile health. He joined UNC in July 2025 after a postdoc at Harvard University with Susan Murphy and a PhD from the University of Michigan under Ambuj Tewari. His educational background includes a B.S. in data science from Peking University, advised by Song Xi Chen. Xu is actively involved in designing RL components for real mobile health clinical trials, such as ADAPTS-HCT targeting medication adherence in adolescents and young adults undergoing bone marrow transplants. Ziping’s academic journey includes: Bachelor of Science in Data Science, Peking University (2018), advised by Prof. Song Xi Chen. PhD in Statistics, University of Michigan (advisor: Prof. Ambuj Tewari). Postdoctoral Fellow in Statistics at Harvard University (2023-2025, advised by Prof. Susan Murphy). His research explores data-driven decision-making approaches, particularly reinforcement learning (RL) and its applications in digital interventions. Key areas include: Sample-efficient algorithms leveraging structural information for mobile health. Statistical inference for adaptively collected data in clinical trials. Transfer learning methodologies addressing domain shifts in healthcare data. Designing RL-based solutions for real-world mobile health trials, such as ADAPTS-HCT. Ziping Xu advises students in his research areas but no specific advisees are listed. Details on grants are not provided in the text. He leads the RL algorithm design for the ADAPTS-HCT clinical trial, collaborating with healthcare teams to enhance medication adherence through digital interventions.
Dr. Sarah Schwöbel is a PostDoc researcher at the Chair of Neuroimaging within the Faculty of Psychology at Technische Universität Dresden since 2020. She holds a Dr. rer. nat. (PhD) in Psychology from TU Dresden (2020), an MSc and BSc in Physics from Ludwig-Maximilians-Universität München (2013-2020). Her research focuses on computational modeling of decision-making processes, integrating Bayesian approaches to study habitual vs. goal-directed behavior, and the neural mechanisms underlying predictive processing. She has contributed to studies on active inference frameworks, contextual behavioral control, and sequential decision-making tasks. Her academic journey includes roles as a software developer at Engineering Bureau Cichon, teaching assistant in physics and computer science, and internships in child psychiatry. Her work bridges psychology, neuroscience, and computational methods, with a particular emphasis on applying mathematical models to understand cognitive processes. Notable publications include studies on rational trade-offs in cognitive control, computational models of narcissism etiology, and Bayesian interpretations of behavioral balancing. She collaborates across disciplines, contributing to the ReCoDe addiction research consortium and exploring predictive minds in neurobiological contexts.
Isaac Davis is a Lecturer at Yale University with affiliations across multiple disciplines including Anthropology, Child Study Center, Cognitive Science, Computer Science, Haskins Laboratories, Linguistics, Music, Philosophy, Psychology, Law, Management, and Medicine. His research focuses on cognitive processes, social cognition, and computational modeling of human knowledge and decision-making. Key interests include social biases, epistemic reasoning, causal inference in neuroscience, and the dynamics of collective behavior. His work bridges cognitive science with social psychology, exploring how people infer others' knowledge and intentions through minimal cues. Recent studies examine information cascades, prosocial behavior, and the cognitive frameworks underlying transformative experiences. He also contributes to debates on bounded rationality and uncertainty representation in decision-making systems. Notable contributions include analyzing the US repo market's financial mechanisms (2012-2015) and foundational work in nonstandard analysis (2009). His interdisciplinary approach integrates experimental psychology, computational modeling, and philosophical inquiry to address core questions about human cognition and social interaction. Labs/Teams: Active collaborations with Haskins Laboratories and cross-disciplinary teams at Yale's Cognitive Science and Philosophy departments. Current projects explore the neural correlates of social perception and the computational underpinnings of collective knowledge systems.
Sanjiban Choudhury is an Assistant Professor at Cornell University's Ann S. Bowers College of Computing and Information Science and a Machine Learning Researcher at Aurora. He leads the PoRTaL group, focusing on interactive AI agents that self-align through few-shot human interactions. His research emphasizes reinforcement learning (RLHF), imitation learning (IRL), and foundation models for robotics, planning, and code generation. Key achievements include receiving the 2025 ONR Young Investigator Award for multistep robot task learning, the OpenAI Superalignment Award (2024), and a Google Research Award for LLM-based planning. His group develops modular robotics foundation models (MOSAIC), earning best paper awards at ICRA 2024 workshops. Research projects aim to bridge AI language models with robotic execution, enabling robots to interpret manuals/videos and perform complex tasks like engine repairs in hazardous environments. Lab members include doctoral students Gonzalo Gonzalez, Yuki Wang, Kushal Kedia, and master’s student Prithwish Dan. Ongoing work focuses on task super-alignment, human-robot transfer learning, and open-source training models for the robotics community. Current funding supports developing robots capable of fluid, multi-step tasks through integrated AI systems.
Amitis Shidani is a DPhil in Statistics student at the Department of Statistics, University of Oxford, supervised by Prof. Arnaud Doucet and Prof. George Deligiannidis. She is broadly interested in Applied Statistics and Machine Learning, with a focus on Sequential Decision-Making, Bandit Learning, and the integration of optimization, robust statistics, fairness, and causal inference for real-world applications. Education: Bachelor’s in Electrical Engineering and Computer Science (double major) from Sharif University of Technology, where she worked on Causal Inference in Computational Genomics with Babak Khalaj. Professional Background: Lead data scientist at CafeBazaar (Iranian Android app-store with 40M+ users) for two years prior to Oxford. Her research interests span Sequential Decision-Making, Game Theory, Optimization, Robust Statistics, Conformal Prediction, and Causal Inference. She is affiliated with the Computational Statistics and Machine Learning and Statistical Theory and Methodology research groups. Recent publications highlight her work in diverse areas, including martingale concentration inequalities, AI deployment in healthcare, transformer architectures, and theoretical machine learning. Her office is located at G.01, and she uses she/her pronouns.
Hamsa Sridhar Bastani is an Associate Professor of Operations, Information, and Decisions (OID) as well as Statistics and Data Science at the Wharton School of the University of Pennsylvania, where she co-directs the Wharton Healthcare Analytics Lab. Her academic journey includes graduating summa cum laude from Harvard in 2012 with an A.M. in physics and an A.B. in physics and mathematics, completing her PhD in Stanford's Electrical Engineering department under Mohsen Bayati, and spending a year as a Herman Goldstine postdoctoral fellow at IBM Research. Dr. Bastani's research focuses on developing novel machine learning algorithms for learning and optimization, including methods for sequential decision-making (bandits, reinforcement learning, active learning), learning from auxiliary data sources (transfer learning, meta-learning, surrogates), and designing effective human-AI interfaces (interpretability, fairness). She is passionate about applying machine learning and AI to tackle high-impact societal problems across domains like healthcare, public policy, and education. Her recent work explores how AI systems affect and augment human behavior, with the goal of designing AI tools that help humans thrive. She has collaborated with national governments to deploy algorithms at country scale for improving public health outcomes, including working with the Government of Greece to nearly double the efficacy of their national border COVID-19 screening via reinforcement learning, and with the Government of Sierra Leone to improve patient access to essential medicines by nearly 20% via decision-aware learning. She also co-led the first large field study deploying generative AI tutors in high school math classes, demonstrating critical risks for human overreliance and deskilling. Dr. Bastani's publications reveal trends in applying advanced machine learning techniques to real-world problems, particularly in healthcare and social impact domains. Her work often combines theoretical rigor with practical implementation through randomized controlled trials and field evidence. Recent publications show increasing focus on the human-AI interface, especially examining risks of generative AI in educational contexts and developing frameworks for responsible human-AI collaboration. Her research has been published in leading outlets including Nature, Management Science, Operations Research, and PNAS. Scientific Awards Wagner Prize for Excellence in Operations Research Practice (2021) INFORMS Pierskalla Award for Best Paper in Healthcare (2021, 2019, 2016) Public Sector in OR Best Paper Award (2024) INFORMS Data Mining Best Paper Award (2022) Wharton Teaching Excellence Awards (2021, 2020, 2019) George Nicholson Best Student Paper Competition (2016) MSOM Best Student Paper Competition (2016, 2020) National Science Foundation Fellow (2012-2017) As an advisor, Dr. Bastani has mentored numerous PhD students who have gone on to prestigious positions including Assistant Professors at Cornell Johnson, ASU Carey, and UC Berkeley Haas, as well as Director of Responsible AI at PwC. She has secured significant research funding through collaborations with government entities and has served as an Associate Editor for Operations Research, M&SOM, and OR Letters. Her work has been supported by partnerships with national governments and organizations like the Penn Center for Health Incentives and Behavioral Economics. She primarily teaches OIDD 321: Introduction to Management Science, for which she received multiple Wharton Teaching Excellence Awards. Dr. Bastani co-directs the Wharton Healthcare Analytics Lab, which focuses on applying data science and machine learning to healthcare challenges. She also serves on the Steering Committee for the Penn Center for Health Incentives and Behavioral Economics and on the statistics advisory committee for the AHA Food is Medicine Initiative. Outside academia, she serves on the Workday AI Advisory Board, connecting her research with industry applications.
