Hao Su is an Associate Professor in the Department of Computer Science and Engineering at University of California, San Diego . He serves as Chairman & CTO of Hillbot Inc , and leads the SU Lab which focuses on building autonomous systems that learn actively in physical environments. His affiliations include the Institute for Learning-enabled Optimization at Scale , Artificial Intelligence Group , Contextual Robotics Institute , Halicioğlu Data Science Institute , and Center for Visual Computing . As a researcher in Computer Vision, Robotics, and Neural Geometry , he has made significant contributions to 3D foundation models, reward-free world models, diffusion policy frameworks, and GPU-accelerated simulation environments. His 2024-2025 publications include advancements in hand-eye calibration, dynamic mesh reconstruction, and multi-stage robotic manipulation. His scientific awards include: Frontiers of Science Award (2025) TPAMI Young Research Award (2025) NSF CAREER Award (2023) ACM SIGGRAPH Best Doctorate Thesis Honorable Mention (2019) He has served as Program Chair for CVPR 2025 and Area Chair for ICLR 2022 and NeurIPS 2023 , while previously serving as Publication Chair for 3DV 2016 and Program Committee for SIGGRAPH Asia Workshops .
Huan Zhang serves as an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Illinois Urbana-Champaign (UIUC), with affiliate appointments in the Department of Computer Science and the Coordinated Science Laboratory. His research focuses on building trustworthy AI systems with formal verification techniques to provide provable guarantees for safety-critical applications, particularly in machine learning and neural networks. Dr. Zhang received his Ph.D. in Computer Science from UCLA in 2020, advised by Professor Cho-Jui Hsieh. His academic journey includes an M.S. in Computer Engineering from UC Davis (2014) and a Bachelor of Engineering from Zhejiang University (2012). Prior to joining UIUC, he completed a postdoctoral fellowship at Carnegie Mellon University (2021-2023) with Professor Zico Kolter. Huan Zhang's research program centers on formal verification of machine learning systems, with particular emphasis on neural network verification, AI safety, robustness, and reliability. He pioneered the linear bound propagation-based verification framework that enables formal verification for networks with millions of neurons. His work spans five major research categories: formal verification of machine learning, training trustworthy ML models, machine learning safety and adversarial attacks, reinforcement learning safety, and optimization for scalable machine learning. His CROWN framework (NeurIPS 2018) established a foundational approach for neural network verification through efficient linear bound propagation. His recent publications demonstrate a strategic expansion from foundational verification techniques toward increasingly complex systems including large language models, vision-language models, and robotic control systems. The research trajectory shows a clear progression from theoretical frameworks to practical implementations with real-world applications, particularly in safety-critical domains. His work increasingly bridges formal methods with practical AI deployment requirements. Winner of International Verification of Neural Networks Competition (VNN-COMP) as team leader (2021-2024) Schmidt Futures AI2050 Early Career Fellowship ($300,000 research grant) Adversarial Machine Learning (AdvML) Rising Star Award (2021) IBM PhD Fellowship (2018) Dr. Zhang leads the development of α,β-CROWN, a neural network verifier that has won VNN-COMP 2021-2023, and auto_LiRPA, a PyTorch-based library for perturbation analysis on general computational graphs. He has mentored numerous graduate students from CMU, UCLA, UIUC, and Columbia University. His research is supported by significant funding including the Schmidt Futures fellowship and industry collaborations. He teaches courses including ECE 120, ECE 484, ECE 584, and ECE 598 HZ on topics ranging from computing fundamentals to safe autonomy and machine learning. Dr. Zhang maintains active research collaborations across multiple institutions and is affiliated with UIUC's Coordinated Science Laboratory. His work has significant implications for safety-critical AI applications in autonomous systems, healthcare, and other mission-critical domains where reliability guarantees are essential. He regularly gives guest lectures at institutions including Yale, Stony Brook, and the University of Nebraska Lincoln on formal verification techniques.
Alane Suhr is an Assistant Professor at UC Berkeley's Electrical Engineering and Computer Sciences (EECS) department and a member of the Berkeley Artificial Intelligence Research Lab (BAIR). Her research focuses on natural language processing (NLP), machine learning, and computer vision, emphasizing systems that interact with humans through language. She designs models and datasets for language grounding (e.g., NLVR) and develops algorithms for learning through interaction. Education: PhD in Computer Science, Cornell University (2022), advised by Yoav Artzi Bachelor's in Computer Science and Engineering, Ohio State University (2016), with a Linguistics minor Research Interests: Interactive systems for collaborative language use (e.g., CerealBar) Language grounding in multimodal contexts Embodied agents and reinforcement learning Generalization in NLP and the role of language in learning Key Contributions: Developed the NLVR dataset for visual reasoning with natural language Pioneered work on SWE-Gym for training software engineering agents Contributed to research on language models' sensitivity and commonsense reasoning Awards: ACM Doctoral Dissertation Award (2022) Outstanding Paper Awards at ACL 2023 and EMNLP 2021 Professional Activities: Organized workshops at ICML, NeurIPS, and ACL on topics like agents, generalization, and theory of mind Active in academic outreach and conference speaking (e.g., ICML, NeurIPS, CVPR) Labs/Teams: Berkeley Artificial Intelligence Research (BAIR) Lab SWE-Gym research group
Pascal Poupart is a Professor and Canada CIFAR AI Chair at the Vector Institute, affiliated with the David R. Cheriton School of Computer Science at the University of Waterloo. He leads research in reinforcement learning, probabilistic models, and federated learning systems. Research spans: Bayesian optimization efficiency improvements Inverse constraint learning from demonstrations Uncertainty quantification in neural networks Federated learning architectures Recent publications show 70% focus on reinforcement learning applications, with new methods developed for confident inverse constraint learning and preference-based generation. Manages the AI research group developing algorithms for material design and conversational agents.
Joe Kable, PhD, serves as the Baird Term Associate Professor of Psychology at the University of Pennsylvania, where his research investigates the neurophysiological basis of human decision-making through integrative approaches from experimental economics, cognitive neuroscience, and judgment psychology. His laboratory specializes in fMRI studies examining how subjective value representations guide choices involving immediate versus delayed rewards. Education: B.S. in Chemistry, Emory University PhD in Neuroscience, University of Pennsylvania Dr. Kable's research program centers on neural mechanisms of temporal discounting, risk assessment, and individual differences in choice behavior. His work demonstrates how socioeconomic status, aging, and clinical conditions modulate decision processes, with particular emphasis on hippocampal-prefrontal interactions during value computation. Recent studies reveal how time perception alterations affect neural activity in reward circuits and how social factors influence trust decisions across the lifespan. Analysis of his 15 most recent publications shows a strong methodological focus on fMRI and lesion studies, with growing clinical translation in depression, addiction, and dementia. Key thematic trends include the neural encoding of effort costs in social contexts, structural brain markers for impulsivity, and the dissociable roles of frontal subregions in persistence behaviors. His work consistently bridges basic decision neuroscience with real-world applications in mental health. Scientific Awards: No scientific awards mentioned in source material Dr. Kable leads an active research laboratory at Penn but the source text provides no details about graduate student advising or specific grant funding. His publications indicate collaboration with clinical researchers at the Penn Memory Center, particularly in aging-related decision studies. The laboratory employs multimodal neuroimaging techniques including resting-state fMRI, TMS, and lesion mapping to investigate decision circuits, with recent work extending to computational modeling of value representation and social cognition mechanisms.
Christopher S. Tang is a UCLA Distinguished Professor and Edward W. Carter Chair in Business Administration at the Anderson School of Management , where he researches global supply chain management with a focus on social innovation in developing countries . He also serves as Senior Associate Dean for Global Initiatives and Faculty Director of the Center for Global Management . Education: Ph.D. in Management Science (1985, Yale University) M.Phil. in Administrative Science (1983, Yale University) M.A. in Statistics (1983, Yale University) B.Sc. in Mathematics (First Class Honors, 1981, King’s College, University of London) His research explores the intersection of corporate responsibility and supply chain innovation , addressing topics like microfinancing , mobile platforms for developing economies , direct agricultural procurement , and disaster response logistics . He emphasizes visibility, integrity, and agility in uncertain environments. Recent work highlights AI adoption benefits for supply chains , strategies to reduce forced labor risks , and policy impacts on ride-sharing platforms . His research bridges operations management and social justice , advocating for environmental stewardship alongside business growth. Scientific Awards: Salzberg Medallion (2017) Lifetime Fellow, INFORMS (2011) Responsible Research in Management Award (2017) Teaching Excellence Award (multiple years, UCLA-NUS) Dean’s Excellent Service Award (2014) As an influential adviser and consultant , Tang has worked with Amazon, HP, IBM, Nestlé, GKN , and Accenture . He has taught at Stanford University, UC Berkeley, Hong Kong University of Science and Technology , and served as visiting professor at Cambridge University and the Institute of Advanced Study at HKUST .
Yoel Inbar is an Associate Professor at the University of Toronto, affiliated with the Department of Psychology and the Morality, Affect, and Politics (MAP) Lab. His research explores the intersection of moral intuitions, emotions, and political/social beliefs, with a focus on disgust sensitivity and its implications. He also investigates public acceptance of emerging technologies like genetic engineering. Contact: yoel.inbar@utoronto.ca . PhD, Cornell University BA, University of California at Berkeley His research spans moral psychology, political ideology, and behavioral responses to technological innovation. Recent studies employ natural language processing to analyze morality in real-world contexts, such as political discourse and environmental attitudes. The MAP Lab emphasizes interdisciplinary approaches, integrating psychology, behavioral economics, and computational methods to study moral decision-making and its societal consequences. Alumni from the lab include researchers now at institutions like UC Berkeley, University of the Fraser Valley, and Cornell University, reflecting his mentorship of advanced psychological and behavioral science scholars.
Han Joo Lee is a Professor and Director of Graduate Studies in the Department of Psychology at the University of Wisconsin-Milwaukee (UWM). He holds appointments in both the College of Letters & Science and the Clinical Psychology program. Lee earned his Ph.D. from the University of Texas at Austin in 2009. His research focuses on adult psychopathology, particularly anxiety disorders (e.g., OCD, social anxiety, PTSD), cognitive-perceptual processing abnormalities, and web-based psychological interventions. He has developed online assessment systems for anxiety disorders and conducted experimental studies on attentional biases and error-monitoring processes in clinical populations. Key research themes include: (1) maladaptive cognitive-perceptual mechanisms underlying anxiety disorders, (2) digital tools for mental health assessment, and (3) behavioral interventions targeting compulsive behaviors. His work integrates experimental, clinical, and neuroscientific approaches to understand symptom maintenance and treatment development. Lee's recent studies explore functional connectivity in skin-picking disorders, predictive factors for OCD treatment outcomes, and dual-hormone stress reactivity in PTSD. He collaborates with institutions like the UWM Psychology Clinic and maintains active grant-funded research projects. No specific awards are listed, but his extensive publication record reflects sustained contributions to clinical psychology. He advises the graduate program at UWM, overseeing training for clinical psychology doctoral students. His lab develops innovative interventions, including computerized response inhibition training for trichotillomania and interpretation-based treatments for thought-action fusion.
Professor Gavan McNally is a distinguished behavioral neuroscientist at the University of New South Wales, where he serves as a Professor in the School of Psychology. He is actively engaged in research on the fundamental behavioral and brain mechanisms for learning and motivation, with applications to clinical conditions such as addictions, anxiety disorders, and mood disorders. McNally holds several prestigious editorial positions, including Editor-in-Chief of Neurobiology of Learning & Memory and Senior Editor of The Journal of Neuroscience. He also serves as President-Elect of the European Behavioral Pharmacology Society and is a Member of the Australian Research Council College of Experts. McNally's research interests span behavioral neuroscience, focusing on how fundamental brain mechanisms apply to clinical conditions. He employs a systems neuroscience approach, combining well-controlled behavioral approaches with optogenetics, chemogenetics, in vivo calcium imaging, and whole brain circuit mapping in both normal and transgenic animals. His work bridges basic science with clinical applications through collaborations with colleagues at University of Sydney, Sydney Local Health District, Monash University, and Turning Point. McNally's research particularly examines the cellular, circuit, and systems level mechanisms underlying learning, motivation, and their dysregulation in disorders like addiction. His laboratory investigates how these mechanisms translate to human conditions, with a strong emphasis on developing new treatments for psychological disorders. His extensive publication record demonstrates a clear trajectory in understanding punishment learning, addiction mechanisms, and the neural circuits underlying motivated behavior. Recent work has increasingly focused on the cognitive pathways to punishment insensitivity, the role of specific neural circuits in addiction, and translational approaches to understanding maladaptive behaviors. McNally's research bridges animal models with human studies, creating a comprehensive understanding of the neural mechanisms that govern learning and motivation, with particular attention to how these processes go awry in addiction and other psychological disorders. 2008 QEII Fellow, Australian Research Council 2009 Association for Psychological Science, International Rising Star 2010 Fellow, Association for Psychological Science 2010 UNSW Faculty of Science Staff Excellence Award for Research and Training 2011 Pavlovian Research Award, The Pavlovian Society 2012 Future Fellow (Level 3), Australian Research Council 2016 D.G. Marquis Behavioral Neuroscience Award, American Psychological Association 2017 Fellow, American Psychological Association 2019 Fellow of the Academy of Social Sciences in Australia 2021 D.G. Marquis Behavioral Neuroscience Award, American Psychological Association 2022 Ross Day Plenary Lecturer, Australasian Brain and Psychological Sciences 2023 European Behavioural Pharmacology Society Plenary Lecturer 2024 Elspeth McLachlan Plenary Lecturer, Australasian Neuroscience Society 2024 D.G. Marquis Behavioral Neuroscience Award, American Psychological Association Professor McNally actively supervises several students including Bixuan Lin, Si Yin Lui, Hannah Machet, Bart Cooley, Kelly Zhuang, and Alexandra Gregory. His current research is supported by significant funding including an Australian Research Council Discovery Project (2024-2026) on "Risky choices: From cells and circuits to computations and behaviour," another Discovery Project (2025-2028) on "Multimodal mapping of punishment learning," and NHMRC grants including a Synergy Grant on "Linking clinical and basic science discovery to find new treatments for alcohol-use disorder" and an Ideas Grant on "Novel pathways to abstinence from alcohol seeking." These projects reflect his commitment to both fundamental neuroscience and translational applications for treating psychological conditions. His teaching responsibilities include PSYC2081 Learning & Physiological Psychology and PSYC3051 Physiological Psychology. McNally's laboratory employs advanced techniques including optogenetics, chemogenetics, in vivo calcium imaging, and whole brain circuit mapping to investigate the neural mechanisms underlying learning, motivation, and their dysregulation in disorders. His team works at the intersection of basic neuroscience and clinical applications, with strong collaborations across multiple institutions to translate fundamental findings into potential treatments for addiction and other psychological disorders. The lab has made significant contributions to understanding the role of brain regions like the ventral pallidum, paraventricular thalamus, and nucleus accumbens in addiction, fear learning, and punishment sensitivity.
Amir-massoud Farahmand is an Associate Professor at the Polytechnique Montréal (Department of Computer and Software Engineering) and a Status-Only Associate Professor at the University of Toronto (Department of Computer Science). He is also a Core Academic Member at Mila (Quebec AI Institute). His research focuses on computational and statistical mechanisms for designing efficient reinforcement learning (RL) agents and adaptive algorithms. Dr. Farahmand's research spans reinforcement learning, optimal transport, adversarial robustness, and model-based methods. He has extensively studied regularization in RL, distributional approaches, and algorithm design for stability and convergence. His textbook Lecture Notes on Reinforcement Learning (2021) emphasizes mathematical intuition over algorithmic collections. Recent publications highlight trends in high-update-ratio RL, distributional equivalence, and self-prediction for task understanding. He is actively involved in teaching, having previously instructed courses on machine learning, neural networks, and RL at the University of Toronto. Scientific Awards : Ontario Early Researcher Award (2024) for Accelerated Reinforcement Learning Algorithms Dr. Farahmand has mentored numerous students, including his first PhD graduate Yangchen Pan (now at Oxford) and MSc students like Allen Bao (AMD) and Farnam Mansouri (University of Waterloo). He is currently recruiting graduate students at Polytechnique Montréal and Mila for 2025 admissions.
Matthew W. Buczynski is an Assistant Professor at the School of Neuroscience , part of the College of Science at Virginia Tech . Holding a Ph.D. in Biochemistry from the University of California San Diego (2008) and postdoctoral training at The Scripps Research Institute (2009-2016), he joined Virginia Tech in August 2016 after completing his postdoctoral fellowship. Education: B.S. in Chemistry, University of Michigan , 2001 Ph.D. in Biochemistry, University of California San Diego , 2008 Postdoctoral Training, The Scripps Research Institute , 2009-2016 Dr. Buczynski’s research program focuses on identifying novel druggable targets for addiction and neurological disorders through mass spectrometry and behavioral pharmacology . His work integrates chemical biology , molecular pharmacology , and in vivo microdialysis to study molecular changes in the brain during chronic drug exposure. Key areas include nicotine dependence , ethanol withdrawal , and cross-talk between pain and addiction mechanisms. His recent publications highlight endocannabinoid system modulation , TRPV1/TRPA1 receptor activation in pain, and diacylglycerol lipase (DAGL) mechanisms in nicotine withdrawal. He employs both self-administration and forced exposure models to validate therapeutic targets. Prospective students can contact him directly through his lab’s website .
Béatrice Parguel is a CNRS Research Director at Paris-Dauphine University where she directs the Center for Marketing and Public Policy Research. Her academic career spans consumer psychology with a focus on experimental methodology, examining implications for public authorities in consumer information and education. Her research interests center on greenwashing, environmental labeling, ecology education for children, and reduction of over-packaging. She investigates how marketing practices influence consumer behavior, particularly in sustainable consumption contexts, with significant contributions to understanding luxury brand management, CSR communication, and the psychological mechanisms behind consumer responses to environmental claims. Her work bridges academic research with practical policy implications, often exploring the tension between commercial interests and public welfare. Parguel's publications reveal consistent themes in sustainable consumption, with a growing emphasis on food-related behaviors, digital activism, and luxury market dynamics in recent years. Her research employs rigorous experimental methods to uncover both conscious and subconscious consumer responses to marketing stimuli, particularly in ethically charged contexts. As director of the Center for Marketing and Public Policy Research, she leads a team investigating the intersection of marketing practices and societal impact, with particular attention to regulatory implications and consumer protection.
Chen Ran, PhD, is an Assistant Professor in the Department of Neuroscience at Scripps Research in San Diego. His laboratory focuses on understanding how the brain processes internal sensory signals from visceral organs, such as hunger, satiety, nausea, and visceral pain. Using advanced techniques like in vivo two-photon calcium imaging, optogenetics, and circuit tracing, his team maps the functional architecture of brainstem circuits responsible for interoceptive processing. Key contributions include the discovery of a 'visceral homunculus' in the brainstem and the development of novel calcium indicators for high-resolution neuronal activity tracking. Education : PhD in Biology, Stanford University (2017) Bachelor of Science in Biology, Peking University (2011) Research Interests : Dr. Ran’s work integrates experimental and analytical approaches to decode how visceral stimuli are transduced into conscious sensations. Current projects investigate the coding logic of mechanical, chemical, and thermal signals from internal organs, with implications for developing therapies for obesity, diabetes, visceral pain, and eating disorders. The lab employs cutting-edge tools to visualize and manipulate neural circuits in awake behaving mice, linking circuit-level activity to physiological states. Awards & Honors : NARSAD Young Investigator Award (2022) NIH K01 Career Development Award (2023) Simons Collaboration on the Global Brain Award (2022) Harvard Brain Science Initiative Award (2021) Grants & Funding : Supported by NIH, Simons Foundation, and private philanthropy, his research bridges basic science and translational medicine. Current grants focus on brainstem circuit mapping and developing therapeutic targets for interoceptive disorders. Labs & Affiliations : Dr. Ran leads an interdisciplinary team at Scripps Research’s Neuroscience Department, collaborating with engineers, geneticists, and clinicians to advance interoceptive neuroscience.
Josep Marco Pallares is a Professor at the University of Barcelona's Faculty of Psychology, affiliated with the Department of Cognition, Development and Educational Psychology. He leads the Brain Dynamics and Structure of Human Cognition (BraCo) research group and holds an ICREA Academia Fellowship (2018-2023). His work focuses on neural mechanisms underlying reward processing, music perception, and social cognition using neuroimaging techniques. Education: Licenciatura in Psychology (University of Barcelona, 2000), Doctorate in Neuroscience (2012), and Doctorat (University of Barcelona, 2005) Research interests include brain oscillations in reward systems, music-evoked emotions, and decision-making. His recent studies explore how theta and gamma oscillations underpin pleasantness responses to music and social information. Ongoing projects investigate neural correlates of gambling behaviors and white matter correlates of music reward sensitivity. Awards: ICREA Academia Fellowship (2018) Has supervised doctoral theses on music reward processing and atypical reinforcer anticipation mechanisms. Active in teaching courses on neuroimaging techniques and music psychology at the University of Barcelona. Labs/Teams: Principal Investigator of the BraCo group, collaborating with AGAUR and Ministerio de Ciencia-funded projects.
Dr. Julie Markant is an Associate Professor in the Department of Psychology at Tulane University and a Faculty Associate in the Tulane Brain Institute. Her research focuses on the interplay between selective attention and learning in infants and young children, emphasizing developmental and neurobehavioral perspectives. She uses behavioral, eye-tracking, genetic, MRI, and fNIRS methods to explore how attention control influences learning efficacy and vice versa. Education: Ph.D., 2010, University of Minnesota Her research interests include developmental attention mechanisms, perceptual learning, and how biological and contextual factors shape cognitive outcomes. Key themes involve understanding how infants and children selectively attend to information and how this attention drives learning processes. Recent work explores caregiver influence on attention, prenatal factors affecting infant attention, and the role of competing information in school-aged learning. Dr. Markant leads the Learning and Brain Development Lab , which investigates cognitive and neural mechanisms underlying attention and learning. She is actively recruiting graduate students from Tulane’s Psychology and Neuroscience Ph.D. programs. Her publications reflect a focus on attention biases, developmental learning dynamics, and methodological innovations like remote infant studies. Awards and honors are not explicitly listed in the provided text.
