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.
Sean Welleck is an Assistant Professor at Carnegie Mellon University's School of Computer Science, Language Technologies Institute, leading the L3 Lab. His research focuses on bridging informal and formal reasoning with AI, spanning machine learning for mathematics and code, inference algorithms, and AI agents. PhD in Computer Science from New York University (advised by Kyunghyun Cho) Postdoctoral work at University of Washington (advised by Yejin Choi) His work explores AI-driven formal methods for mathematics and code generation, test-time compute scaling, and algorithms enabling AI improvement over time. Recent publications analyze reasoning evaluation, premise selection, and automated proof optimization in systems like Lean. Key article trends include neural theorem proving, code generation, and inference-time compute optimization. Awards: NVIDIA AI Labs Pioneering Research Awards (2017, 2018), NAACL 2025 Best Paper. Current advisees include PhD students Pranjal Aggarwal, Weihua Du (co-advised with Yiming Yang), Andre He (co-advised with Daniel Fried), and Seungone Kim (co-advised with Graham Neubig). He co-organizes workshops like Autoformalization for the Working Mathematician (ICERM 2025) and VerifAI: AI Verification in the Wild (ICLR 2025), and teaches Advanced NLP at CMU.
Helmholtz Association of German Research CentersGermany
Dr. Sabine Krabbe is a Group Leader at the German Center for Neurodegenerative Diseases (DZNE) in Bonn, Germany, where she leads research on neural circuit mechanisms underlying adaptive learning and state-dependent decision-making. Her work integrates neuroscience, molecular biology, and behavioral approaches to understand how internal states influence behavior and how these processes are disrupted in neurological disorders. Dr. Krabbe's research focuses on the interactions between midbrain circuits of the substantia nigra and ventral tegmental area with their output structures such as the striatum and amygdala. She investigates how these networks integrate internal states with environmental cues to produce appropriate behavioral responses. Her laboratory employs state-of-the-art techniques including deep-brain calcium imaging at single-cell resolution in mice, opto- and pharmacogenetic manipulations, anatomical tracings, and molecular approaches to characterize neural circuit elements in detail. Her recent publications reveal significant insights into amygdala interneuron plasticity during fear learning, brain-wide representational drift in memory consolidation, and the molecular mechanisms underlying Parkinson's disease progression. Her work demonstrates how activity patterns within specific neural circuits change in early stages of neurodegenerative diseases and how this dysfunction contributes to cognitive deficits and emotional disturbances. Dr. Krabbe is actively involved in the neuroscience community, organizing the BonnBrain Conference 2026 and sharing research through social media platforms. She has established herself as an emerging leader in the field of systems neuroscience with a particular focus on the neural basis of emotional states and decision-making processes.
Stefano Fusi is an Associate Professor of Neuroscience at Columbia University's Vagelos College of Physicians and Surgeons, with joint affiliations at the Mortimer B. Zuckerman Mind Brain Behavior Institute and Kavli Institute. His laboratory focuses on computational modeling of neural circuits and neuromorphic engineering. Education PhD in Physics, Hebrew University of Jerusalem (1999) BS in Physics, Sapienza University of Rome (1992) Research Focus Fusi investigates how biological complexity supports neural computation through three primary domains: theoretical analysis of neural circuit dynamics, representational geometry in learning systems, and hardware implementations of brain-inspired algorithms. His work bridges machine learning, neurophysiology, and theoretical physics, emphasizing high-dimensional representations and memory optimization. Recent publications demonstrate consistent focus on neural coding principles across hippocampus, prefrontal cortex, and sensory systems, with innovations in modeling working memory, stress responses, and cross-species computational paradigms. Collaborations & Labs Leads an interdisciplinary laboratory collaborating with Columbia experimental neuroscientists, MIT engineers, and Stanford computational researchers to validate theoretical models. Current projects include neuromorphic hardware development and neural decoding of emotional states.
Pengtao Xie is an Associate Professor (with tenure as of June 2025) in the Department of Electrical and Computer Engineering at the University of California San Diego. He also serves as Associate Adjunct Professor in the Division of Biomedical Informatics, Department of Medicine, and holds affiliate appointments with the Halıcıoğlu Data Science Institute, School of Biological Sciences, Shu Chien-Gene Lay Department of Bioengineering, Skaggs School of Pharmacy and Pharmaceutical Sciences, and multiple research institutes including the AI Group, Center for Machine-Intelligence, Computing and Security, Institute of Engineering in Medicine, and Institute for Genomic Medicine. Education: PhD in Machine Learning, School of Computer Science, Carnegie Mellon University Research Interests: His research focuses on machine learning inspired by human learning skills, such as self-explanation, small-group learning, and learning by teaching. He applies these techniques to large language models, foundation models, healthcare, and biomedicine. His work spans generative AI, medical imaging, protein modeling, and drug discovery. Recent Research Trends: His 2024–2025 publications emphasize generative AI for ultra-low-data medical image segmentation, multimodal large language models for biomedical applications, protein function prediction, and novel training strategies like task-adaptive pretraining and bi-level optimization for model adaptation. Scientific Awards: NIH MIRA Award (2025) NSF CAREER Award (2024) Best Graduate Teacher Award – UCSD ECE (2023) ICLR Notable-Top-5% Paper (2023) Global Top-100 Chinese Young Scholars in AI (2022) UCSD Faculty Career Development Award (2022) Tencent Faculty Award (2021) Outstanding Reviewer – ICLR (2021) AMIA Doctoral Dissertation Award Finalist (2020) Amazon AWS Research Award (2020) Tencent AI-Lab Faculty Award (2020) Innovator Award – Pittsburgh Business Times (2018) Siebel Scholarship (2014) Advising and Grants: He currently advises PhD students, postdocs, and master’s students. He has received major grants including the NIH MIRA and NSF CAREER awards, and actively mentors Schmidt AI in Science postdocs and graduate students. Teaching and Labs: He teaches ECE285 Deep Generative Models and ECE175B Probabilistic Reasoning and Graphical Models . His lab focuses on foundational and translational AI research with applications in biomedicine and healthcare.
Danqi Chen is an Associate Professor in the Department of Computer Science at Princeton University's School of Engineering and Applied Science. Their research focuses on advancing large language models (LLMs), with emphasis on model alignment, safety, and long-context reasoning capabilities. Key research areas: LLMs, AI safety, retrieval systems, and model optimization Recent work explores theorem proving, context encoding, and ethical content generation Their 2025 publications highlight innovations in formal verification scaffolding, attention mechanism efficiency, and copyright-aware generation. 2024 studies investigate continual memorization, rule-based chatbot representations, and scientific literature retrieval benchmarks. Current projects demonstrate commitment to improving model robustness, interpretability, and security compliance in multimodal systems.
Michael J. Frank is the Edgar L. Marston Professor of Psychology and Professor of Brain Science at Brown University's School of Cognitive, Linguistic, and Psychological Sciences. He holds academic affiliations with the Carney Institute for Brain Science and specializes in cognitive neuroscience, computational neuroscience, and decision-making processes. Frank earned his Ph.D. in Neuroscience & Psychology from the University of Colorado at Boulder in 2004, and joined Brown University in 2011 after serving as a Professor at the University of Arizona. His research integrates computational modeling and experimental methods to explore neural mechanisms underlying reinforcement learning, decision-making, and cognitive control, with a focus on prefrontal cortex-basal ganglia interactions and dopamine modulation. Frank's honors include the Troland Research Award (2021), Kavli Fellowship (2016), and the Cognitive Neuroscience Society Young Investigator Award (2011). He is an editor for eLife, Behavioral Neuroscience, and the Journal of Neuroscience. His lab, based at http://ski.clps.brown.edu, investigates topics such as neural circuit models of cognitive control, neuropsychological testing, and translational applications of computational models in psychiatry. Frank's research emphasizes interdisciplinary approaches, combining behavioral experiments, neuroimaging (fMRI, EEG), and pharmacological studies to dissect brain-behavior relationships. Key findings include insights into dopamine's role in motivation, decision-making deficits in schizophrenia, and computational phenotyping of mental disorders. His work bridges basic science and clinical applications, aiming to inform therapeutic strategies for neurological and psychiatric conditions.
Chaowei Xiao is an Assistant Professor at the University of Wisconsin-Madison (starting 2023), affiliated with the School of Computer, Data & Information Sciences. His research focuses on securing AI systems, particularly exploring robustness in trustworthy machine learning, autonomous systems, and large language models (LLMs). He holds a Ph.D. from the University of Michigan, Ann Arbor, and a B.S. from Tsinghua University. Before joining UW-Madison, he worked as a research scientist at NVIDIA (2020–2022) and at Arizona State University (2022–2023). His work bridges model and system perspectives to ensure practical robustness and provable guarantees in AI applications like autonomous driving, healthcare, and IoT. Key research areas include adversarial robustness, AI security, and ethical AI. Recent contributions include frameworks for detecting LLM hallucinations, mitigating jailbreak attacks, and securing multi-modal systems. His work on diffusion models for adversarial purification and physical-world attacks on autonomous driving systems has been widely recognized. Notable awards include the 2024 USENIX Security Distinguished Paper Award, ACM Gordon Bell Finalist (2024), and Schmidt Sciences AI2050 Fellowship. He has advised students like Xiaogeng Liu (NVIDIA Fellow) and secured grants from Amazon, Apple, and UW-Madison. His lab actively publishes at top venues like NeurIPS, ICML, and CVPR.
Chuang Gan is an Assistant Professor at the University of Massachusetts Amherst, affiliated with the College of Information and Computer Sciences and the Department of Computer Science. His work focuses on advancing artificial intelligence, robotics, computer vision, and embodied agents through interdisciplinary research combining neural networks, physical simulations, and multimodal learning. Research interests include generative models, reinforcement learning, vision-language integration, and scalable autonomous systems. He explores topics like world modeling for robots, adaptive policy learning, and physics-driven AI. His projects often involve creating systems that learn from visual, auditory, and tactile inputs to perform complex tasks such as object manipulation, navigation, and decision-making in dynamic environments. Recent research trends emphasize embodied AI systems capable of long-horizon planning, compositional reasoning, and efficient learning from limited data. His work bridges theory and practice, with applications in robotics, simulation platforms, and multimodal generation. Key contributions include frameworks for 3D scene understanding, adaptive world models, and novel training paradigms for large language models. His research has been applied to robotics platforms like RoboDreamer and UBSoft, focusing on unbounded soft environments. Collaborations involve designing benchmarks for physical scene understanding (e.g., Physion++), and creating tools like DiffTactile for tactile simulation. His work often integrates principles from differential geometry, PDE dynamics, and game theory. Chuang Gan’s research group develops open-source tools and benchmarks, such as the SoftZoo robot co-design platform and the SOK-Bench situated reasoning benchmark. His team emphasizes scalable alignment methods beyond human supervision and explores ethical AI through principles like symmetry-enhanced training.
Ralf Haefner is an Assistant Professor in the Departments of Brain & Cognitive Sciences and Physics & Astronomy at the University of Rochester, holding this joint appointment since 2014. His interdisciplinary research bridges neuroscience and physics to investigate computational principles of perception and decision-making. Education and professional background: PhD, Oxford University, 1999 Visiting Research Fellow, Department of Neurobiology, Harvard Medical School Swartz Fellow, Sloan-Swartz Center for Theoretical Neurobiology, Brandeis University Haefner's research program centers on computational neuroscience , with primary focus on how the brain forms perceptual beliefs and uses them for decisions through Bayesian modeling . He employs machine learning tools to construct mathematical models explaining neural responses and behavior, particularly in the visual domain. His work addresses neural representation of uncertainty, causal inference mechanisms, and probabilistic computation in cortical circuits. Analysis of recent publications (2023-2025) reveals three dominant trends: (1) causal inference frameworks applied to motion perception and segmentation, (2) Bayesian modeling of perceptual biases and confidence computations, and (3) integration of generative and discriminative neural computations. His work extends beyond traditional neuroscience into scientific methodology through 'Generative Adversarial Collaborations' for improving research discourse. Honors and Awards: Swartz Fellowship, Sloan-Swartz Center for Theoretical Neurobiology NSF CAREER Award (2022) for 'Approximate inference at the intersection of neuroscience and machine learning' Haefner secured significant research funding through his NSF CAREER award, which supports foundational work on probabilistic inference at the neuroscience-ML interface. While specific students aren't listed, his active publication record and lab infrastructure suggest ongoing mentorship of graduate students and postdocs. His research has clinical relevance as shown by studies on perceptual abnormalities in autism spectrum disorder, indicating translational potential for understanding neurological conditions.
Mohammad Mohammadi Amiri serves as an Assistant Professor in the Department of Computer Science at Rensselaer Polytechnic Institute (RPI), appointed in Fall 2023. His research focuses on advancing artificial intelligence through strategic data utilization, with emphasis on large language models, data valuation, federated learning, and deep learning. Previously, he held postdoctoral appointments at Princeton University and MIT Media Lab, building on his strong educational foundation from Imperial College London, University of Tehran, and Iran University of Science and Technology. Education: Ph.D. in Electrical and Electronic Engineering, Imperial College London (2019) - Best Ph.D. Thesis Award recipient M.Sc. in Electrical and Computer Engineering, University of Tehran (2014) - Ranked 1st among all M.Sc. students B.Sc. in Electrical Engineering, Iran University of Science and Technology (2011) - Ranked 1st among all B.Sc. students Dr. Amiri's research centers on optimizing artificial intelligence systems through innovative data strategies. His work addresses critical challenges in large language models including efficiency, memory usage, alignment, and reasoning capabilities. In data valuation, he develops principled methods to quantify data worth for fair trading platforms. His federated learning research tackles privacy concerns, heterogeneous data distribution, and communication overhead in decentralized environments. The deep learning component explores theoretical foundations to improve model interpretability and robustness. Analysis of his recent publications reveals a strong focus on making AI systems more efficient and accessible, with particular emphasis on large language model optimization, federated learning advancements, and data valuation frameworks. His work bridges theoretical foundations with practical applications in wireless communications and distributed computing environments. Scientific Awards: IEEE Communications Society Young Author Best Paper Award (2022) Best PhD Thesis Award from IEEE Information Theory Chapter of UK and Ireland (2019) Eryl Cadwallader Davies Prize for Outstanding PhD Thesis (2019) EEE Departmental Scholarship at Imperial College London (2015-2019) Ranked 1st among M.Sc. students at University of Tehran (2014) Ranked 1st among B.Sc. students at Iran University of Science and Technology (2011) Dr. Amiri actively mentors graduate students, currently supervising five Ph.D. candidates and one M.Sc. student working on efficient LLM fine-tuning, inference, and storage. His research has attracted significant attention, evidenced by numerous keynote invitations at prestigious institutions including Bell Labs, MIT, King's College London, and various IEEE conferences. He serves on program committees for major conferences including IEEE Globecom and ICC, demonstrating his growing influence in the academic community. His research group operates at the intersection of machine learning and wireless communications, developing innovative solutions for resource-constrained environments while addressing fundamental theoretical challenges in AI systems. Current projects focus on making advanced AI more scalable and accessible through efficiency improvements in model training and inference.
Pengtao Xie is an Associate Professor (tenured) in the Department of Electrical and Computer Engineering at UC San Diego, with cross-appointments in the Division of Biomedical Informatics and affiliations across multiple schools and institutes including the Halıcıoğlu Data Science Institute, School of Biological Sciences, and Skaggs School of Pharmacy. His research bridges human-inspired machine learning and healthcare applications. Education: PhD in Machine Learning, Carnegie Mellon University (2018) MS from Tsinghua University BS from Sichuan University Research Interests: His work focuses on machine learning inspired by human learning strategies , including learning by testing, interleaving, self-explanation, and teaching. These techniques are applied to large language models , foundation models , healthcare , and biomedicine . Recent efforts emphasize generative AI for medical image segmentation and protein function prediction. Scientific Awards: NIH MIRA Award (2025) NSF Career Award (2024) Best Graduate Teacher Award, ECE UCSD (2023) ICLR Notable-Top-5% Paper (2023) Global Top-100 Chinese Young Scholars in AI (2022) Tencent Faculty Award (2021) AMIA Doctoral Dissertation Award Finalist (2020) Siebel Scholarship (2014) Teaching & Mentorship: He has developed and taught courses such as Deep Generative Models , Probabilistic Graphical Models , and Linear Algebra and Applications . He actively mentors PhD, master's, and undergraduate students, with alumni now at CMU, Stanford, Mila, and industry roles. Labs & Teams: He leads a research group within the Center for Machine-Intelligence, Computing and Security and collaborates with the Institute for Genomic Medicine and Institute of Engineering in Medicine at UC San Diego.
Professor Muhammad A S Abdel Haleem, OBE, is the King Fahd Professor of Islamic Studies at SOAS University of London, within the School of Languages, Cultures and Linguistics. He holds a BA from Cairo University and a PhD from the University of Cambridge (Cantab), with additional qualifications including FCIL (London). His research focuses on Quranic language, style, and translations, alongside Islamic family law and Arab society. Key interests include Qur’anic exegesis, comparative religious studies, and the intersection of scripture with contemporary issues like disability ethics and interfaith relations. Professor Haleem has supervised numerous PhD candidates exploring topics such as divine states in Sufism, gender relations in the Quran, and charitable work in multicultural societies. He has authored over 60 works, including translations of the Quran and influential studies on Quranic structure and theology. Awards: Order of the British Empire (OBE) Publications: Over 60 books and articles, including Understanding the Qur'an: Themes and Style and translations of the Quran. His work bridges academic theology with practical ethics, emphasizing contextual interpretation and intercultural dialogue. Current research continues to explore Quranic coherence, legal hermeneutics, and the Quran’s role in modern society.
Sherry Tongshuang Wu is an Assistant Professor at Carnegie Mellon University's School of Computer Science, with primary appointments in the Human-Computer Interaction Institute (HCII) and secondary affiliation with the Language Technology Institute (LTI) . Trained at the University of Washington under Jeffrey Heer and Dan Weld, she bridges HCI and NLP to study human interactions with AI systems across diverse user groups. Her educational background includes a Ph.D. (2016-22) and M.S. (2016-18) in Computer Science and Engineering from the University of Washington, and a B.Eng. (2012-16) from Hong Kong University of Science and Technology. Industry experience includes research internships at Google Brain, Microsoft Research, and Apple. Wu's research focuses on three interconnected pillars: Real-world AI Evaluation (developing frameworks like SPHERE for systematic assessment), Task-specific AI Test & Distill (optimizing general-purpose models for specific use cases), and Human-AI Task Delegation (designing optimal collaboration between humans and AI). Her work emphasizes practical deployment, user-specific net gains, and error recovery mechanisms. Analysis of her 15 most recent publications reveals a strong trend toward evaluation frameworks (35%), human-AI collaboration systems (30%), and specialized model distillation (25%), with growing emphasis on educational applications (10%). Key methodological themes include checklist-based evaluation, perspective-aware retrieval, and structural analysis of AI outputs. Google Academic Research Award (2024) Amazon Research Awards (2024) AIED 2024 Best Paper Award ACL 2020 Best Paper Award Rising Stars in EECS Workshop (2020) Wu actively mentors 19 students across PhD, Master's, and undergraduate levels, with notable projects including synthetic data generation, LLM literacy tools, and retrieval system optimization. She leads significant grant-funded work through Amazon Research Awards and Google Academic Research Awards, focusing on deployable model generation and human-AI collaboration frameworks. Her lab develops practical tools like Promp2Model and SPHERE that bridge theoretical research with industry applications.
Yiming Yang is a Professor at the Language Technologies Institute and Machine Learning Department within the School of Computer Science at Carnegie Mellon University , where he has held faculty positions since 2003. His research spans foundational and applied aspects of machine learning , artificial intelligence , and scientific computing . Professor, Carnegie Mellon University (2003–Present) Associate Professor, Carnegie Mellon University (1996–2003) Yang's research focuses on LLM-based problem-solving agents , combinatorial optimization , and scalable oversight frameworks . His work explores diffusion models, Langevin dynamics, and Fourier neural operators for NP-hard problems, while advancing reinforcement learning techniques for self-play supervision and principle-driven fine-tuning of large language models. Recent publications highlight his contributions to code synthesis , PDE solving , and multi-agent reinforcement learning . Key methodologies include demonstration-guided control, retrieval-augmented reasoning, and test-time scaling laws. His team has developed frameworks like FEEDER for efficient in-context learning and μTransfer-FNO for zero-shot hyperparameter transfer in PDE solvers. Notable scientific achievements include: Best Student Paper Runner Up (2013) Best Theoretical Paper Award (1994) Best Theoretical Paper Award (1993) Yang has mentored over 20 PhD students and postdocs, including Shengyu Feng , Zhiqing Sun , and Aman Madaan , across domains like graph learning , extreme multi-label classification , and language model alignment .
