Brian Scholl is a Professor of Psychology at Yale University, specializing in visual cognition, perception, and cognitive science. His research explores how visual perception interacts with higher-level cognition, including object persistence, event perception, causality, and intuitive physics. He leads the Yale Perception & Cognition Lab, focusing on dynamic events, attention, and computational models. He earned his Ph.D. from Rutgers University in 1999. His work spans experimental psychology, neuroscience, and computational modeling, with a focus on phenomena like inattentional blindness, scaffolded attention, and event segmentation. He has authored numerous papers and presented at international conferences, contributing to debates on perception vs. cognition interactions. Key research areas include the perception of dynamic event types, the role of intuitive physics in visual processing, and the influence of event boundaries on memory and decision-making. His lab employs computer-based experiments and collaborations with computational models to study perception across species and clinical populations.
Claus Lamm is a Full Professor of Biological Psychology at the University of Vienna , where he leads the Social, Cognitive and Affective Neuroscience Unit (SCAN-Unit) . He serves as Vice Dean for Research and Advancement of Early Career Researchers at the Faculty of Psychology and holds affiliations with the Vienna Cognitive Science Hub , Environment & Climate Change Hub , and Austrian Academy of Sciences . His academic career spans international collaborations and formative research experience abroad. Scientific Focus: Lamm investigates the neural underpinnings of empathy and prosocial behavior , employing multi-modal approaches combining neuroimaging, psychopharmacology, and psychoneuroendocrinology . His work extends to comparative studies with ravens and dogs, and explores environmental social neuroscience through climate change decision-making research. Recent publications show trends in cross-cultural psychology , machine learning applications , and neurobiological pathways related to social behavior. Awards & Grants: Recipient of the APS Mentor Award for his support of early career researchers. Funded by European Research Council , Austrian Science Fund , Vienna Science and Technology Fund , and intramural grants exceeding €10 million. Key projects include "Unravelling the opioid system in empathy" and "Comparative dog-human fMRI" . Media Engagement: A prominent public science communicator, Lamm has appeared in Nature , Science Magazine , and Austrian media outlets like Ö1 Mittagsjournal and ORF2 , discussing topics from pandemic psychology to social media effects . He maintains active outreach through Science TV and educational programs .
Anocha Aribarg is a Professor of Marketing and Area Chair of Marketing at the Ross School of Business, University of Michigan, with additional faculty affiliation at the Center for Southeast Asian Studies (CSEAS). Her interdisciplinary research bridges psychological theory, consumer behavior, and advanced statistical modeling to address complex marketing challenges. Education: PhD in Marketing, University of Wisconsin, 2004 MBA, University of Wisconsin-Milwaukee, 1998 BS in Statistics, Chulalongkorn University, Thailand, 1994 Research Focus: Prof. Aribarg investigates cognitive processes in consumer decision-making, specializing in individual/joint choice dynamics, product search behaviors, and responses to marketing stimuli. Her methodology integrates Bayesian econometrics with physiological measures (eye tracking, skin conductance) and multi-method experimental designs to uncover hidden decision mechanisms. Publication Trends: Her 14 recent publications (2009-2024) reveal consistent innovation in choice modeling, with increasing emphasis on attention dynamics (2020), moral psychology in aesthetics (2022), and high-frequency service data integration (2023). Work appears predominantly in Marketing Science Journal of Marketing Research, and Psychological Science, demonstrating methodological rigor across consumer behavior, advertising, and service contexts. Academic Service: She serves as Associate Editor for Marketing Science, Journal of Marketing, and Journal of Marketing Research, while contributing to the Journal of Retailing editorial board. Teaching: Prof. Aribarg delivers graduate-level courses in Marketing Research and Analytics and Predictive Analytics at Ross, emphasizing data-driven decision frameworks.
Stephen Cushman is the Robert C. Taylor Professor at the University of Virginia, specializing in American literature and poetry. His interdisciplinary work bridges Civil War studies, 19th-century poetry, and environmental literature. He earned his Ph.D. (1982), M.Phil. (1981), and M.A. (1980) from Yale University, and a B.A. from Cornell University (1978). His research explores how literary forms shape historical understanding, particularly through works like *The Generals' Civil War* (2021) and *Belligerent Muse* (2014). He has edited major volumes such as *The Princeton Handbook of Poetic Terms* (2016) and *Civil War Writing* (2019). His poetry collections include *Keep the Feast* (2022) and *Hothead* (2018), blending personal reflection with historical inquiry. Cushman has received prestigious awards including the Cavaliers' Distinguished Teaching Professorship (2014–2016) and the All-University Teaching Award (1992). His writing often intersects with Virginia’s literary landscape, as seen in *After Great Pain, Dickinson in Charlottesville* (2018). He actively engages with environmental themes in works like *Pope Francis, Laudato Si’, and U.S. Environmentalism* (2017). His articles critically analyze figures like Lincoln, Sherman, and Thoreau, while exploring modernist engagements with biblical texts and Civil War memory. He serves as a leading voice in American literary history, emphasizing the interplay between form, content, and cultural context.
Amilcare Porporato is the Thomas J. Wu '94 Professor of Civil and Environmental Engineering at Princeton University, with joint appointment at High Meadows Environmental Institute. His research integrates hydrology, ecology, and thermodynamics to study soil-water interactions and sustainable resource management. Education includes: Ph.D. Polytechnic of Milan (1996) M.S. Civil Engineering, Polytechnic of Milan (1992) Research explores nonlinear hydrologic systems, soil moisture dynamics, ecohydrology, and environmental complexity using theoretical and experimental approaches. His work advances understanding of water-biota interactions across scales from pore-level processes to landscape evolution. Honors and Awards: Hydrologic Sciences Award, AGU Borland Lecturer American Geophysical Union Fellow Inaugural Landolt Chair, EPFL Teaches courses on Environmental Thermodynamics (ENV 385/CEE 385) and Ecohydrology (CEE 587/ENV 587).
Ali Vakilian is a Research Assistant Professor at the Toyota Technological Institute at Chicago (TTIC), with a strong academic background in theoretical computer science and algorithms. He will join the Department of Computer Science at Virginia Tech as an Assistant Professor in Fall 2025. His research bridges algorithmic theory and machine learning, focusing on scalable, fair, and efficient algorithms for massive data. Education: Ph.D. in EECS, Massachusetts Institute of Technology (MIT), advisors: Erik Demaine and Piotr Indyk M.S. in Computer Science, University of Illinois at Urbana-Champaign (UIUC), advisor: Chandra Chekuri B.S. in Computer Engineering, Sharif University of Technology Research Interests: Ali Vakilian's work centers on the algorithmic foundations of machine learning and data science. He develops streaming, sketching, and sublinear-time algorithms for massive datasets, and pioneers learning-augmented algorithms that use machine learning predictions to improve performance while maintaining worst-case guarantees. His research in trustworthy ML includes algorithmic fairness, fair clustering, and learning with strategic agents. He also contributes to combinatorial optimization and approximation algorithms for network design, set cover, and low-rank approximation. His recent publications (2023–2025) show a consistent focus on fair clustering (individual and group fairness), streaming graph algorithms , learning-augmented methods , and frequency estimation . These works appear in top venues such as NeurIPS, ICML, SODA, and ICALP, often with recognitions like oral or spotlight presentations. Scientific Awards: Outstanding Student Paper Highlight Award, AISTATS 2024 Notable-top-25% paper, ICLR 2023 Oral presentation, AISTATS 2024 Spotlight presentation, NeurIPS 2023 Advising and Grants: Ali Vakilian mentors several students and interns, including summer interns at TTIC and Fatima Fellows. His research is supported by the National Science Foundation (TRIPODS program), as noted in the press coverage of his work on LearnedSketch. He actively contributes to the academic community through advising, organizing workshops (e.g., Algorithms with Predictions, Learning-Augmented Algorithms), and serving on program committees (e.g., NeurIPS, ICML, AISTATS). Labs and Teams: He is affiliated with the theory and algorithms group at TTIC and collaborates with researchers at MIT, UIUC, and other institutions. His work on learning-augmented algorithms has led to influential workshops and collaborations with leading figures such as Piotr Indyk and Erik Demaine.
John P. O'Doherty serves as the Fletcher Jones Professor of Decision Neuroscience within Caltech's Division of Humanities and Social Sciences, holding continuous faculty appointments since 2004 (Assistant Professor 2004-07, Associate Professor 2007-09, Professor 2009-present, Fletcher Jones Professor 2021-present). He previously directed the Caltech Brain Imaging Center (2013-17) and maintains affiliations with the T&C Chen Center for Social and Decision Neuroscience. His educational background includes a B.A. from University of Dublin, Trinity College (1996) and D.Phil. from University of Oxford (2000). His research focuses on computational and neural mechanisms of reward-based learning and decision-making , employing fMRI, intracranial recordings, and mathematical modeling to investigate how the brain solves complex decision problems through evolutionarily conserved algorithms. Key areas include Reinforcement learning systems (model-based/model-free arbitration) Observational and social learning mechanisms Neural representation of value, risk, and uncertainty Computational phenotyping of mental disorders Temporal dynamics of goal persistence Analysis of his 2023-2025 publications reveals dominant trends in computational psychiatry (problem gambling, autism traits), hierarchical decision-making, and neuroeconomic modeling of social behavior. His work consistently integrates cross-species computational frameworks with human neuroimaging to identify transdiagnostic mechanisms. While specific awards beyond his endowed professorship aren't detailed, his leadership as Brain Imaging Center Director and prolific high-impact publications demonstrate significant recognition. Current advising includes graduate researcher Sneha Aenugu on goal-persistence projects, with administrative support from Mary A. Martin (mmartin@caltech.edu). His active research program continues to pioneer computational approaches to understanding decision pathologies.
Eero Hyvönen is a Professor of Computer Science at Aalto University and Director of the Helsinki Centre for Digital Humanities (HELDIG) at the University of Helsinki. He leads the Semantic Computing Research Group (SeCo), specializing in Semantic Web technologies and applications. Hyvönen holds an Adjunct Professor title at the University of Helsinki's Department of Computer Science. His work focuses on developing national-level semantic web infrastructure, with applications in digital humanities, cultural heritage, and AI. He has published over 500 research articles and books, receiving numerous awards including the Decoration of Knight, First Class, of the Order of the White Rose of Finland (2018). Hyvönen chairs editorial boards of journals like Semantic Web and serves on committees for major conferences. His research spans semantic web technologies, linked open data, and AI applications in cultural heritage domains. Key projects include Sampo portals (e.g., LetterSampo, OperaSampo) for cultural data analysis, ParliamentSampo for parliamentary data, and FindSampo for archaeological finds. His work emphasizes interdisciplinary collaboration, integrating law, history, and computing through initiatives like FIN-CLARIAH. Hyvönen advises doctoral and master's students, notably Annastiina Ahola and Jouni Tuominen, who have won awards under his supervision. He directs research infrastructures, chairs international boards, and actively contributes to open data policies and standards development.
Naresh R. Shanbhag is the Jack Kilby Professor in the Department of Electrical and Computer Engineering and the Coordinated Science Laboratory at the University of Illinois at Urbana-Champaign. He serves as Director of the Systems on Nanoscale Information fabriCs (SONIC) Center and held the D.J. Gandhi Distinguished Visiting Professorship at IIT Mumbai from 2015-2020. Previously, he was a visiting faculty member at National Taiwan University (2007) and Stanford University (2014). Dr. Shanbhag received his doctorate from the University of Minnesota (1993) in Electrical Engineering. From 1993 to 1995, he worked at AT&T Bell Laboratories as the lead chip architect for AT&T's 51.84 Mb/s transceiver chips over twisted-pair wiring for Asynchronous Transfer Mode (ATM)-LAN and very high-speed digital subscriber line (VDSL) chip-sets. His research focuses on the design of energy-efficient machine learning, communications, and signal processing systems on resource-constrained embedded platforms. He explores fundamental trade-offs between energy efficiency, latency and accuracy of decision-making systems implemented in nanoscale technologies, with applications to computer vision, biomedicine, automatic target recognition, and imaging. His work spans four primary focus areas: Resource-efficient Machine Learning for the Edge, In-memory Computing (IMC), Energy-efficient High Data Rate Communications, and Shannon-inspired Statistical Error Compensation (SEC). Analysis of his recent publications reveals a strong emphasis on in-memory computing architectures (SRAM, MRAM, RRAM) for machine learning acceleration. His work consistently addresses energy-accuracy trade-offs, with increasing attention to security aspects of hardware implementations and applications to MIMO signal processing and edge AI systems. His research demonstrates a progression from theoretical foundations to practical silicon implementations. 2024 Semiconductor Research Corporation Innovation Award 2018 Semiconductor Industry Association/Semiconductor Research Corporation University Researcher Award 2018 IEEE International Symposium on Circuits and Systems Best Paper Award 2006 IEEE Fellow 1996 National Science Foundation CAREER Award Professor Shanbhag has mentored over 50 graduate students who now work at leading technology companies including Qualcomm, Amazon, Nvidia, Intel, and Apple. His research has been generously supported by the National Science Foundation, DARPA, AFRL, Semiconductor Research Corporation, Texas Instruments, Sandia National Laboratories, and industry partners including IBM, GlobalFoundries, and Intel Corporation. He led the Alternative Computational Models research theme (2006-2012) and was the founding Director of the SONIC Center (2013-2017), a 5-year multi-university center funded by DARPA and SRC. Currently, he leads research themes in the SRC and DARPA funded JUMP 2.0 Program's Center for Co-Design of Cognitive Systems and the Center for Ubiquitous Connectivity, and in the NSF IUCRC Center for Advanced Semiconductor Chips with Accelerated Performance (ASAP). As Director of the Systems on Nanoscale Information fabriCs (SONIC) Center, Professor Shanbhag leads a multidisciplinary team exploring novel computing paradigms for the nanoscale era. His group has benchmarked an extensive collection of in-memory computing and digital accelerator IC designs, maintaining a publicly available IMC benchmarking repository of metrics extracted from published IC prototypes. His research philosophy integrates concepts from information theory, statistical signal processing, detection and estimation, VLSI architectures, and digital and analog integrated circuits to develop energy-efficient systems from algorithms to silicon implementations.
Steve Chase is a Professor at Carnegie Mellon University , affiliated with the Biomedical Engineering , Electrical and Computer Engineering , Neuroscience Institute , and Robotics Institute departments. His research spans Computational Neuroscience , Neural Engineering , and Systems Neuroscience , with a focus on neural circuits, motor control, and brain-computer interfaces (BCI). Research Areas: Sensation & Perception, Methods Development, Diseases & Disorders, Physiological & Anatomical Methods. Lab Highlights: Development of the RotaWheel, memory trace studies in the motor cortex, and investigations into BCI stabilization and learning dynamics. Scientific Contributions: His lab has published extensively in journals like Neuron , Nature Computational Science , eLife , and PNAS , with notable works on neural activity patterns, dimensionality reduction in calcium imaging, and sensory constraints on motor cortex modulation. Students and postdocs in his lab have received awards, including the CNBC best paper award.
John D. Murray is the Gregg L. Engles Associate Professor of Psychological and Brain Sciences at Dartmouth College and an Adjunct Associate Professor of Psychiatry at Yale School of Medicine. He holds a PhD in Physics from Yale University (2013) and a BS in Physics and Mathematics from Yale (2006). His research focuses on computational neuroscience and computational psychiatry, with secondary appointments in Physics and Neuroscience at Yale until 2023. His work integrates computational modeling, neuroimaging, and systems neuroscience to study decision-making processes, cortical organization, and psychiatric disorders. Collaborators include prominent researchers like Dr. John Krystal and Dr. Anticevic. Research interests include hierarchical brain organization, neuroimaging analysis techniques, and pharmacological effects on neural circuits. His lab (Murray Lab) develops computational tools like PsychRNN for cognitive task modeling. Notable contributions include linking transcriptomic data to neuroimaging patterns and modeling LSD’s effects on brain topography. He has been featured in YaleNews and Nature Communications for innovations in mapping mental illness variability and neural circuit dynamics. Grants and collaborations span translational neuroscience, addiction, and PTSD research through partnerships with Yale’s Center for Biomedical Data Science and VA National Center for PTSD. His interdisciplinary approach bridges physics, computer science, and clinical psychiatry to advance understanding of brain function and dysfunction.
Professor Beatrice Heuser is a distinguished academic in the Department of Politics within the School of Social & Political Sciences at the University of Glasgow. Her career spans multiple prestigious institutions across Europe, including King's College London, University of Reading, and visiting professorships at Sciences Po Paris, Bundeswehr University Munich, and MGIMO. She maintains significant affiliations with major strategic research institutes including Chatham House, RUSI, IFRI, and IRSEM. Education: BA from Bedford College London, MA from LSE, DPhil from Oxford (St Antony's/St John's), Habilitation from University of Marburg Professional Experience: Former Director of Studies at German Bundeswehr's military history research office, NATO Headquarters consultant, and extensive teaching across European institutions Her research centers on strategic studies with particular focus on the cultural dimensions of warfare, nuclear strategy, and the historical evolution of strategic thought. Key research areas include strategic culture, military exercises as political instruments, laws of war development, and European defense policy. Her work bridges historical analysis with contemporary security challenges, examining how cultural narratives shape strategic decision-making from antiquity to the nuclear age. Professor Heuser's recent publications demonstrate consistent scholarly output across strategic theory, military history, and contemporary security issues. Her work shows particular emphasis on the conceptual foundations of strategic culture, historical analysis of warfare evolution, and practical applications to modern security challenges including nuclear deterrence and asymmetric warfare. The interdisciplinary nature of her research connects political theory, historical analysis, and security studies. She actively supervises graduate students in strategic theory, nuclear strategy, insurgency studies, and defense policy-making within NATO/EU contexts. Her teaching portfolio includes War and International Relations, Defense Policy, and Strategic Theory courses.
Todd H. Hall is a Professor of International Relations at the University of Oxford's Department of Politics and International Relations (DPIR) and a Tutorial Fellow in Politics at St Anne's College. He directs the University of Oxford China Centre and holds affiliations with the International Relations Network and the Centre for International Studies (CIS). His research focuses on international relations theory, emotional dimensions of diplomacy, and Chinese foreign policy, with particular emphasis on Sino-Japanese relations and East Asian security dynamics. Education: PhD in Political Science from the University of Chicago (2008). Postdoctoral fellowships at Princeton and Harvard Universities, and visiting scholar appointments at Free University of Berlin, Tsinghua University, and University of Tokyo. Research interests include the role of emotions in international politics, China's foreign policy, and the historical parallels between early 20th-century European tensions and contemporary East Asian geopolitics. His award-winning book *Emotional Diplomacy* (2015) explores how states strategically deploy emotion in international relations. Recent publications analyze Huawei's 5G policy dilemmas, China's Belt and Road Initiative, and dispute escalation mechanisms. He advises on East Asian security, including repeated tensions in the South China Sea and Sino-Japanese territorial disputes. Awards include the ISSA 2016 Diplomatic Studies Book Award. Active in policy commentary, his op-eds address Taiwan Strait tensions, China's diplomatic rhetoric, and historical memory conflicts. Labs/Teams: Leads the Oxford China Centre's research initiatives on modern Chinese diplomacy and emotional geopolitics. Collaborates with institutions like the National University of Singapore and Missouri S&T on East Asian security studies.
Prof. Karen Alim is a Professor of Biological Physics and Morphogenesis at the Department of Physics, Technische Universität München (TUM), affiliated with the TUM School of Natural Sciences. She holds a PhD from the Ludwig-Maximilians-Universität München (2010) and conducted postdoctoral research at Harvard University (2010–2015) before leading a Max Planck Research Group in Göttingen. Her research focuses on non-neuronal information processing in living systems, particularly using Physarum polycephalum to study physical principles of network adaptation, fluid dynamics, and morphogenesis. Education: PhD in Physics, Ludwig-Maximilians-Universität München (2010) Studies at Universität Karlsruhe, LMU München, and University of Manchester Research Interests: Prof. Alim explores how biological systems process information without neurons, emphasizing adaptive flow networks, mechanical signaling in plants, and collective behavior in active matter. Her work combines theoretical modeling with experimental systems like slime molds and plant tissues. Awards: ERC Starting Grant (2020) Elisabeth-Schiemann-Kolleg Fellowship (2013–2018) DAAD Stipendium (2011–2014) John Birks Award (2004) Advising & Grants: While specific grant details beyond the ERC award are not listed, her research has been supported by major funding bodies. No student advisees are explicitly listed in the provided materials. Labs/Teams: Leads the Biological Physics and Morphogenesis group at TUM, focusing on interdisciplinary studies of living systems' physical principles.
Laurence Hunt is an Associate Professor of Experimental Psychology at the University of Oxford and a Tutorial Fellow in Psychology at St John's College. He leads the Laboratory of Decision Dynamics, focusing on neural mechanisms underlying decision-making. His work integrates mathematical models with electrophysiological techniques in humans and animal models to study behavioral and neural data. Key roles include the Wellcome/Royal Society Sir Henry Dale Fellowship and prior postdoctoral positions at UCL and Oxford's Department of Psychiatry. Education: D.Phil in Neuroscience from the University of Oxford (2007-2011), pre-clinical Medicine at Cambridge University. Research emphasizes cognitive computational neuroscience, particularly how neural systems process value, uncertainty, and learning. Collaborators include notable figures like Matthew Rushworth and Christopher Summerfield. Scientific Awards: Wellcome/Royal Society Sir Henry Dale Fellow Advising & Grants: His research is supported by fellowships, though specific grants or advisees are not detailed. The Hunt Lab actively explores topics such as reward processing, decision dynamics, and neural geometry using advanced analytical methods. Labs/Teams: Director of the Laboratory of Decision Dynamics, part of the Department of Experimental Psychology. Engaged in interdisciplinary collaborations across Oxford and international institutions.