Jiatao Gu is an Assistant Professor in the Department of Computer and Information Science (CIS) at the University of Pennsylvania, with a part-time role as Staff Research Scientist at Apple (MLR). He holds a Ph.D. in Electrical and Electronic Engineering from the University of Hong Kong (2018) and a B.Eng. in Electronic Engineering from Tsinghua University (2014). His research focuses on generative machine learning and AI agent interaction with the physical world, emphasizing multi-modal systems spanning language, images, videos, and 3D. Key themes include efficient modeling , flexible architecture design , and scalable decision-making frameworks . 2025: ICLR paper on DART framework 2024: TMLR work on GFlowNet alignment 2023: NeurIPS research on diffusion stability 2022: ACL papers on speech translation Recent publications explore diffusion models for text-to-image synthesis, 3D reconstruction, and efficient sampling techniques. His work addresses fundamental challenges in attention mechanisms, entropy collapse, and multi-stage distillation while advancing non-autoregressive translation and vision-language reasoning . Prospective students can apply through his recruitment process at UPenn. Prior affiliations include Meta AI (FAIR Labs) and academic collaborations with institutions like New York University's CILVR Lab.
Michael A Osborne is Professor of Machine Learning at the University of Oxford and leads the Bayesian Exploration Lab . He serves as Director of the EPSRC Centre for Doctoral Training in Autonomous Intelligent Machines and Systems and co-directs the Oxford Martin AI Governance Initiative. His research focuses on Bayesian optimization, Gaussian processes, and probabilistic numerics with applications in quantum devices, battery modeling, and AI governance. Key Positions: Professor of Machine Learning, University of Oxford Official Fellow, Exeter College Co-founder of Mind Foundry Lead Researcher, Oxford Martin Programme on Technology and Employment Research Themes: Probabilistic modeling for quantum systems Uncertainty quantification in energy storage AI safety and societal impact analysis Automated experimental design Quantum device calibration Probabilistic numerical methods Technical Contributions: Bridging reality gap in quantum devices Efficient Bayesian quadrature techniques Personalized neurostimulation algorithms Automated measurement protocols Quantum-classical hybrid ML
Lorraine (Xiang) Li is an Assistant Professor in the Department of Computer Science at the University of Pittsburgh’s School of Computing and Information (SCI). Her research focuses on the intersection of natural language processing, commonsense reasoning, knowledge representation, and machine learning, particularly in designing probabilistic models and evaluation methods for implicit commonsense knowledge in language. Li holds a PhD from the University of Massachusetts, Amherst, and previously worked as a young investigator with the Mosaic team at AI2. She has an M.S. in Computer Science from the University of Chicago, where she conducted research at TTIC. Her work emphasizes advancing AI’s ability to reason contextually and generate robust, human-like understanding through probabilistic frameworks. Key research themes include bias detection in reasoning models, iterative model editing, domain adaptation with LLMs, and evaluating commonsense through probabilistic measures. Her recent publications explore challenges like confirmation bias in chain-of-thought reasoning and geographical robustness in object recognition. Li actively contributes to the NLP community, serving on program committees for ACL, EMNLP, NAACL, and ARR. Though no formal awards are listed, her prolific publication record reflects her impact in AI research. She currently leads research in procedural knowledge models (e.g., Plasma) and long-tail knowledge generation, advancing foundational AI methodologies.
Georgia Zellou is an Associate Professor in the Department of Linguistics at the University of California, Davis, where she co-directs the Phonetics Lab and conducts award-winning research at the intersection of phonetics, speech perception, and human-AI interaction. Her work investigates how phonetic detail is cognitively represented through variations in speech production, with significant contributions to understanding speech alignment with voice assistants, face-masked speech intelligibility, and cross-linguistic perception of synthetic voices. Her academic credentials include a Ph.D. in Linguistics from the University of Colorado at Boulder (2012), an M.A. in Linguistics from Stony Brook University (2007), and a B.A. in Linguistics & Anthropology from the University of Florida (2005, Cum Laude, Phi Beta Kappa). Ph.D., Linguistics, University of Colorado at Boulder (2012) M.A., Linguistics, Stony Brook University (2007) B.A., Linguistics & Anthropology, University of Florida (2005) Professor Zellou's research program centers on laboratory phonology approaches to real-world communication challenges, examining how acoustic-phonetic details influence speech perception across contexts. Her studies span speech alignment with voice-AI systems (e.g., Amazon Alexa), sociophonetic variation in bilingual speech, and the cognitive mechanisms underlying perceptual compensation for coarticulation. She employs experimental methods including eye-tracking, acoustic analysis, and perceptual testing to uncover how phonetic variation functions pragmatically in human communication and human-machine interaction. Analysis of her 15 most recent publications (2023-2025) reveals three dominant research trajectories: (1) human-AI voice interaction dynamics, including prosodic alignment and social evaluation of TTS voices; (2) intelligibility optimization in challenging contexts (face masks, clear speech for diverse listeners); and (3) cross-linguistic phonetic variation in vowelless words and consonant clusters. These works consistently bridge theoretical phonology with applied speech technology, demonstrating how fine-grained phonetic detail influences communication effectiveness in both human-human and human-machine contexts. Her scientific recognition includes: Fulbright Scholar (2022) for research in France Chancellor’s Award for Excellence in Undergraduate Mentoring (2019) Fellow of the Linguistic Society of America (2020) Amazon Faculty Research Award (2019) for Alexa-related speech studies Dean’s Fellow designation at UC Davis (2020-2023) Professor Zellou maintains an active mentoring practice recognized with the Chancellor’s Award, supervising undergraduate researchers in the Phonetics Lab while teaching core linguistics courses from introductory to advanced graduate levels. Her research program is supported by competitive grants including NSF funding, Amazon Research Awards, and UC Davis internal grants (Hellman Foundation, ISS Junior Faculty Grant), reflecting the translational value of her work for speech technology development. She has co-directed major initiatives including the 2019 LSA Linguistic Institute. The Phonetics Lab she co-leads serves as a hub for experimental phonetics research, focusing on speech production-perception relationships through projects investigating vocal accommodation to voice assistants, nasal coarticulation dynamics, and cross-linguistic prosody. Current collaborations with industry partners aim to implement human speech adaptation principles into voice assistant design to enhance naturalness and engagement.
Robert Dick is a Professor in the Department of Electrical Engineering and Computer Science at the University of Michigan, part of the College of Engineering. He previously held roles as Associate Professor at Northwestern University and Visiting Professor at Tsinghua University. He earned his Ph.D. from Princeton University and a Bachelor's degree from Clarkson University. His research focuses on Embedded Systems, Learning Dynamics, Efficient Machine Learning, Privacy, and Censorship Resistance. Key themes include defining problems with correct costs/constraints, broadening access to information technology, mitigating negative tech impacts, and solving inference problems with limited resources. He leads the Embedded Systems Graduate Program and is a member of the Michigan Integrated Circuits Laboratory (MICL). His work spans thermal management, energy-efficient computing, and low-power wireless networks. Notable contributions include innovations in embedded system design, machine vision frameworks, and sensor networks. Courses taught include EECS 507 (Embedded Systems Research), EECS 373 (Embedded System Design), and ENGR 100 (Autonomous Systems). His recent publications emphasize AI model analysis, energy-efficient networks, and environmental sensing. Projects include MemX (attention-aware wearable tech) and LoRa-based LPWAN protocols. Dick co-founded Stryd, a company commercializing embedded systems innovations.
Mahzarin R. Banaji is the Richard Clarke Cabot Professor of Social Ethics at Harvard University and a Harvard College Professor. She is affiliated with the Department of Psychology and is a key figure in the Mind, Brain, and Behavior (MBB) Interfaculty Initiative. Her research is centered at the intersection of social cognition, implicit bias, and ethical behavior. Institution: Harvard University School: Harvard College Department: Psychology Email: banaji@fas.harvard.edu Dr. Banaji earned her Ph.D. from Ohio State University and has been a leading scholar in the study of unconscious bias. Her work explores how implicit attitudes shape perception, judgment, and behavior outside conscious awareness. She co-developed the Implicit Association Test (IAT) , a groundbreaking tool for measuring unconscious biases related to race, gender, age, and other social categories. Her research spans social cognition, prejudice, stereotyping, moral psychology, and the neuroscience of social behavior . More recently, she has extended her work into the domain of artificial intelligence, investigating how human-like biases emerge in large language models. The 15 most recent publications reflect a strong trend toward computational social science , combining psychological theory with natural language processing and AI. Her team analyzes bias in digital corpora, studies the transmission of stereotypes in AI systems, and develops tools to measure intersectional and implicit attitudes at scale. These works bridge psychology, ethics, and technology, highlighting the societal implications of implicit cognition. Among her notable scientific honors are: Fellow of the American Academy of Arts and Sciences William James Fellow Guggenheim Fellowship Kurt Lewin Award (SPSSI) Harvard College Professorship Dr. Banaji has advised numerous graduate students, including Tessa Charlesworth and Kerry Morehouse, many of whom are now active researchers in social and cognitive psychology. She has secured major grants through the Mind, Brain, and Behavior Initiative and has led interdisciplinary teams exploring bias in education, law, and technology. She is also the co-creator of OutsmartingHumanMinds.org , a public education platform on implicit bias. Her lab serves as a hub for collaborative research on implicit social cognition, bringing together psychologists, neuroscientists, and computer scientists to understand and mitigate unconscious bias in human and artificial systems.
Jesse Cougle is a Professor of Psychology at Florida State University, focusing on anxiety disorders , obsessive-compulsive and related disorders (OCRD), and problematic anger . He leads the Cougle Lab, which develops computerized treatments and investigates distress intolerance , courage , and transdiagnostic factors . Education: University of Texas at Austin (2008), Oxford University (postgraduate) Research: Cognitive-behavioral mechanisms in anxiety disorders, technology-based interventions for social anxiety and BDD, and biological processes in problematic anger Students: Mentoring Tapan Patel, James Zech, Victoria Swaine, and others His work spans interpretation bias modification , safety behavior reduction , and 'not just right' experiences in OCRD. Recent publications emphasize digital interventions for mental health and distress tolerance as a key transdiagnostic factor. Scientific Awards: President’s New Researcher Award (ABCT) Graduate Faculty Mentor Award (FSU) He serves as Editor-in-Chief of the Journal of Obsessive-Compulsive and Related Disorders and holds editorial roles in major journals. The Cougle Lab explores anxiety , perfectionism , and appearance-related psychopathology through multimodal research .
Dr. Kathryn Lester is an Associate Professor in Developmental Psychology at the University of Sussex's School of Psychology. She leads internationally recognized research on childhood anxiety, focusing on intergenerational transmission, cognitive biases, and school mental health interventions. Her work includes developing evidence-based programs for emotionally-based school avoidance and evaluating whole-school approaches to mental health. She holds leadership roles in Sussex’s senior management team, including Subject Group Lead for Developmental Psychology and Deputy Director for Postgraduate Research. She co-leads the Sussex Foundation Partnership Trust School Mental Health Research Team Clinic and has secured funding from the National Institute for Health Research, ESRC, and The National Lottery Community Fund. Her academic journey includes a D.Phil. in Psychiatry from the University of Oxford (2008) and postdoctoral research at the University of Sussex and King’s College London. Key research interests include anxiety prevention, school-based interventions, and the impact of parenting behaviors on child mental health. She has collaborated with organizations like the Sussex Wildlife Trust and provided consultancy for educational content development, such as children’s book series on emotions and ITV’s ‘Planet Child’ series. Her research spans mixed-methods approaches, including participatory methods with children and caregivers. Notable projects include NIHR-funded studies on digital mental health toolkits and Kavli Trust-funded interventions to reduce anxiety transmission from parents to children. She actively engages in knowledge exchange and mentoring early-career researchers.
Ken Paller is Professor of Psychology and Director of the Cognitive Neuroscience Program at Northwestern University, where he holds the James Padilla Chair in Arts & Sciences. He serves as Associate Director of the NIH/NINDS-funded Training Program in the Neuroscience of Human Cognition, dedicated to training future cognitive neuroscientists. Dr. Paller's research focuses on the intricate relationships between brain activity and conscious experience, particularly investigating how memories are formed, stored, and re-experienced. His laboratory has pioneered methods to strategically influence the mind during sleep to improve memory, creativity, and psychological well-being. His work spans multiple domains including memory consolidation during sleep, targeted memory reactivation, lucid dreaming research, and the neural correlates of conscious experience. Notably, he collaborates with Tibetan Monastic Scholars in research on sleep and dreaming, bridging scientific and contemplative traditions. Analysis of his recent publications reveals a strong focus on sleep-dependent memory processes, with particular emphasis on targeted memory reactivation techniques, dream manipulation, and memory modification during sleep. His research demonstrates how sleep can be leveraged to enhance cognitive functions and potentially treat conditions like nightmares in narcolepsy. Dr. Paller received the prestigious Director's Pioneer Award from NIH in 2024 and holds the James Padilla Chair in Arts & Sciences at Northwestern University. His work has garnered significant media attention, featured in outlets including BBC World Service, The World Science Festival, Discover Magazine, NY Times, LA Times, The Economist, NPR Science Friday, and CBC Radio. As Director of the Cognitive Neuroscience Program and Associate Director of the NIH-funded Training Program, Dr. Paller plays a significant role in mentoring the next generation of cognitive neuroscientists. His Cognitive Neuroscience Laboratory (CNL) conducts cutting-edge research at the intersection of sleep science, memory research, and consciousness studies, with implications for both basic science and clinical applications.
Adam Fox is the Sarah Johnson '82 Professor in the Sciences within the Psychology Department at St. Lawrence University. His research focuses on the experimental analysis of human and non-human behavior, particularly in the context of choice, time, and learning. He investigates variables controlling behavior using quantitative methods and laboratory models, including studies on ADHD and ASD in rats, as well as applied animal behavior with domesticated animals like horses. His work bridges basic and translational research, emphasizing principles of behavior analysis. Education : PhD in Psychology (Behavior Analysis specialization), West Virginia University MA, BS in Psychology, Western Michigan University Research Interests : Fox explores timing mechanisms and decision-making processes in behavior analysis. His lab uses rat models to study neurodevelopmental disorders (e.g., ADHD, ASD) and employs translational approaches to connect findings to clinical and applied settings. He also applies behavior analytic principles to improve human-animal relationships, particularly in equine behavior modification. Recent work includes investigating precrastination, the impact of environment on impulsive choice, and the role of genetics in behavioral timing. Recent Work Trends : Publications emphasize translational research in neurodevelopmental disorders, cross-species temporal judgment studies, and behavioral interventions. His 2024 work on high-fat diets and impulsive choice highlights interdisciplinary approaches, while 2025 studies on FMR1 knockout rats explore genetic influences on timing. Grants & Advising : Advises graduate and undergraduate students (e.g., Bibiana Thieret, William DeCoteau) on experimental and applied projects. Active in collaborative research with institutions like the Behavior Analyst Certification Board. Labs & Teams : Research conducted in St. Lawrence’s Psychology Department labs, focusing on behavioral analysis, animal models, and applied behavior studies. Collaborates with interdisciplinary teams on translational and animal welfare projects.
Swiss Federal Institute of Technology in LausanneSwitzerland
Yuval Noah Harari is a historian, philosopher, and lecturer at the Department of History at the Hebrew University of Jerusalem. Born in Haifa, Israel in 1976, he received his PhD from the University of Oxford in 2002. He is best known as the author of the internationally acclaimed books Sapiens: A Brief History of Humankind (2014), Homo Deus: A Brief History of Tomorrow (2016), 21 Lessons for the 21st Century (2018), Nexus: A Brief History of Information Networks from the Stone Age to Artificial Intelligence , and the Unstoppable Us children's series. Harari's research focuses on macro-historical questions including the relationship between history and biology, the essential difference between Homo sapiens and other animals, justice in history, historical directionality, human happiness throughout history, and the ethical questions raised by science and technology in the 21st century. His work bridges history, biology, philosophy, and economics, taking both macro and micro perspectives to understand not only what happened and why, but also how it felt for individuals. His books have sold over 45 million copies in 65 languages, with Sapiens alone selling 25 million copies since its publication. He is considered one of the world's most influential public intellectuals today. Harari has given keynote speeches at major international forums including the World Economic Forum in Davos and has presented as a digital avatar in a TED talk. Sapiens spent 96 consecutive weeks in the top 3 of the Sunday Times bestseller list Co-founded Sapienship, an international social impact company focused on education and storytelling, with his husband Itzik Yahav Regularly speaks at major international events on topics of technology, history, and the future of humanity Harari's recent work focuses on the challenges of the information age, particularly the crisis of truth and trust in an era of disinformation and artificial intelligence. He explores how humans can navigate the complex ethical questions raised by emerging technologies while maintaining democratic values and human dignity.
Yang Liu is an Assistant Professor in the Department of Electrical and Computer Engineering at the Baskin School of Engineering, University of California, Santa Cruz. Previously, they were affiliated with Harvard University and earned their PhD in 2015 from the Department of EECS at the University of Michigan, Ann Arbor. Their research lies at the intersection of machine learning, fairness, and trustworthy AI, with a strong focus on large language models, federated learning, and causal reasoning. Their research interests include: Machine Learning and Fairness Federated and Privacy-Preserving Learning Large Language Model Safety and Unlearning Causal Inference and Counterfactual Reasoning Anomaly Detection and Robust Forecasting Human-AI Interaction and Ethical AI Recent publications (2024–2025) demonstrate a strong trend in developing methods for machine unlearning, fairness in LLMs, and robustness under label noise and distribution shifts. Their work frequently appears in top-tier venues such as NeurIPS, ICLR, ICML, AAAI, and KDD, often in collaboration with researchers like Zhaowei Zhu, Mingyan Liu, Jiaheng Wei, and Kun Zhang. Themes include algorithmic fairness, model accountability, and human-aligned AI systems. Scientific contributions include: Frameworks for LLM unlearning and model editing Methods for fair classification and recourse Robust time series forecasting under anomalies Test-time adaptation in multimodal models Causal approaches to debiasing and policy learning While no formal advising list is provided, the depth and volume of collaborative work suggest active mentorship of graduate students and postdocs. Their research program is highly active, with numerous ongoing projects in trustworthy and socially responsible AI.
Anne-Marie Oswald is an Associate Professor in the Department of Neurobiology within the Biological Sciences Division at the University of Chicago. Her research profile indicates active engagement in neuroscience research with a particular focus on cortical circuits, neural coding, and sensory processing systems. She maintains an active research program with publications spanning from 2011 to the present. Dr. Oswald's research interests span multiple areas of neuroscience, with particular emphasis on cortical circuit function, neural coding mechanisms, and sensory processing. Her work investigates how inhibitory interneurons shape cortical dynamics, how neural assemblies form during learning, and how sensory information is processed across different brain regions. Notably, she has also contributed to discussions on diversity in science through her publication "Curating more diverse scientific conferences" in Nature Reviews Neuroscience (2020). Her research employs a combination of electrophysiological, computational, and behavioral approaches to understand neural circuit function. Analysis of her publication record reveals a strong focus on cortical circuit mechanisms, particularly in the olfactory system. Her work demonstrates expertise in understanding how different interneuron subtypes (particularly parvalbumin and somatostatin-positive cells) regulate cortical dynamics, assembly formation, and sensory processing. Over time, her research has evolved from examining basic circuit mechanisms to investigating how these circuits support complex cognitive functions like odor discrimination and associative learning. The consistent presence of computational and systems neuroscience approaches throughout her publication history indicates a rigorous quantitative approach to understanding neural function. Dr. Oswald appears to be actively mentoring students and postdoctoral researchers, as evidenced by her consistent publication record with multiple collaborators. While specific grant information isn't provided in the available data, her sustained publication output suggests successful funding of her research program. Her work bridges cellular and systems neuroscience, contributing to our understanding of how microcircuit properties shape sensory processing and behavior.
Virginia Polytechnic Institute and State UniversityUnited States
Brenda M. Davy is a Professor in the Department of Human Nutrition, Foods and Exercise at Virginia Tech. Her work focuses on obesity prevention, dietary intake assessment, and the role of beverages in health. She holds a PhD in Human Nutrition from Colorado State University (2001), an MS in Exercise Physiology (Virginia Tech, 1992), and a BS in Human Nutrition (Virginia Tech, 1989). Her research investigates diet, physical activity, and beverage consumption's impact on cardiovascular health, type 2 diabetes risk, and cognitive function. Notable projects include studies on ultra-processed foods' effects and hydration's role in metabolism. Dr. Davy has received prestigious awards, including Fellowships from The Obesity Society (2013) and the American College of Sports Medicine (2007), and the Excellence in Outcomes Research Award (2019). Her work bridges clinical practice and population health, with a focus on translating research into actionable public health strategies. Education: PhD, Human Nutrition (Colorado State University 2001) Experience: Over 30 years in academia and clinical nutrition roles, including leadership in clinical trials and research coordination. Key Research: Behavioral interventions, dietary biomarkers, and longitudinal studies on food intake. Her recent studies emphasize ultra-processed foods' metabolic consequences and hydration's cognitive benefits in aging populations. Collaborative projects include validating dietary assessment tools and exploring exercise timing's effects on cardiometabolic outcomes.
Robert D. Zettle is a Professor of Psychology in the Clinical-Community Psychology Program at Fairmount College of Liberal Arts and Sciences, Wichita State University. He directs the Contextual Behavioral Science Lab and teaches Abnormal Psychology, Cognitive Behavioral Therapy, and Ethical and Professional Issues in Clinical Psychology. Dr. Zettle's educational background includes: Ph.D. in Psychology from University of North Carolina-Greensboro (1984) M.A. in Psychology from Bucknell University (1976) B.A. in Psychology from Wilkes College (1974) His research centers on Contextual Behavioral Science , examining how languaging and verbal behavior contribute to human suffering. Key areas include: Developing measures of psychological flexibility and self-as-context Experimental manipulation of processes in anxiety and mood disorders Comparing traditional CBT with Acceptance and Commitment Therapy (ACT) for depression Investigating mechanisms like defusion and acceptance in ACT Current projects focus on dissociative symptom induction, mood-enhancing versus value-congruent behavioral activation, and client treatment preferences in spider fear interventions. Dr. Zettle's recent publications (2019-2025) reveal a strong emphasis on self-as-context measurement , cross-cultural adaptations of ACT tools, and depression relapse prevention . His work increasingly integrates attention bias modification with ACT and explores client preference dynamics, while expanding applications to university counseling centers and international contexts. He mentors graduate students including Nakisha Carrasquillo, Angela Cathey, Suzanne Gird, Angie Hardage-Bundy, Sarah Staats, and Jeff Swails. His research is supported by ongoing projects in the Contextual Behavioral Science Lab investigating core ACT processes and clinical applications. The Contextual Behavioral Science Lab, directed by Dr. Zettle, maintains a collaborative team environment focused on both experimental psychopathology and clinical treatment development. Current work spans measurement validation, interoceptive exposure techniques, and comparative treatment studies for depression and anxiety disorders.
