Nadia Figueroa is the Shalini and Rajeev Misra Presidential Assistant Professor in the Mechanical Engineering and Applied Mechanics (MEAM) Department at the University of Pennsylvania. She holds secondary appointments in Computer and Information Science (CIS) and Electrical and Systems Engineering (ESE), and is a core faculty member at the GRASP Lab. Prior to Penn, she was a Postdoctoral Associate at MIT’s CSAIL and earned her PhD in Robotics from EPFL under Prof. Aude Billard. Her research focuses on physical and perceptual adaptive intelligence for robots, enabling fluid collaboration with humans in dynamic environments. Key applications include robot learning from demonstration , human-robot co-manipulation , safe navigation in human-centric spaces , and rehabilitation robotics . Her work integrates machine learning control theory artificial intelligence biomechanics psychology with guarantees of stability, safety, and robustness . Recent publications highlight advancements in reactive collision avoidance dynamical system learning intent estimation EEG-driven assistive control origami-based reconfigurable robots across platforms like autonomous vehicles and humanoid robots. She has authored a 2022 textbook on dynamical systems for robot control and received the Presidential Assistant Professorship at Penn.
Lily L. Tsai is the Ford Professor of Political Science at the Massachusetts Institute of Technology (MIT) and founder/director of the MIT Governance Lab (MIT GOV/LAB). She previously chaired the MIT Faculty and focuses her research on governance, accountability, and political participation in developing contexts, particularly Asia and Africa. Her research explores how decentralization, democratic reforms, informal institutions, and economic development influence public goods provision. She also investigates the dual impacts of social capital and civil society on political outcomes, and analyzes political behavior in authoritarian and transitional systems through surveys, interviews, and field experiments. Recent publications span topics including collective governance, vaccine compliance in Uganda, anticorruption strategies in China, and the role of informal institutions in rural China. Tsai’s work appears in journals like American Political Science Review and World Development . Dogan Award for best book in comparative research (2007-08) She leads interdisciplinary projects such as the Compass Initiative (SHASS Education Innovation Fund) and The Science of Respect with MIT’s Brain and Cognitive Sciences department. MIT GOV/LAB, which she founded in 2014, emphasizes real-time empirical collaboration between researchers and practitioners to address governance challenges.
Mark Steedman is a Professor in the School of Informatics at the University of Edinburgh, where he conducts research in Artificial Intelligence, Computational Cognitive and Social Science, and Natural Language and Speech Processing. He is affiliated with the Institute for Language, Cognition and Computation (ILCC), the Centre for Speech Technology Research (CSTR), and the Human Communications Research Center (HCRC). He also holds an adjunct professorship in Computer and Information Science at the University of Pennsylvania. His research focuses on Combinatory Categorial Grammar (CCG) , computational linguistics , prosody and intonation , temporal semantics , gesture in communication , and computational music analysis . He has authored foundational books including Surface Structure and Interpretation , The Syntactic Process , and Taking Scope . The recent publications reflect a strong trend toward integrating formal grammatical frameworks like CCG with modern neural and distributional models, particularly in semantic parsing, entailment reasoning, and cognitive modeling. His work bridges symbolic and statistical approaches in NLP, often focusing on robust, wide-coverage parsing and semantic interpretation. Best Paper Award at AACL/IJCNLP 2023 for 'Smoothing Entailment Graphs with Language Models' Best Paper Award at ACL 2023 for 'Extrinsic Evaluation of Machine Translation Metrics' Influential Paper Award 2017 from IFAAMAS for 'Animated Conversation' Mark Steedman has supervised numerous PhD students and collaborated widely across institutions. He leads research in formal grammar applications to cognitive modeling, dialogue, and multimodal communication. His lab contributes to CCG software and semantic parsing tools, and he continues to be actively involved in advancing the integration of symbolic and neural AI.
Heng Huang is the Brendan Iribe Endowed Professor in the Department of Computer Science and the Department of Electrical and Computer Engineering at the University of Maryland, College Park . He earned his Ph.D. in Computer Science from Dartmouth College and holds prior degrees from Shanghai Jiao Tong University. His research focuses on advancing the foundations and applications of artificial intelligence, particularly in machine learning, data mining, natural language processing, computer vision, and biomedical informatics . His work integrates large-scale optimization, fairness, and robustness in deep learning systems. Heng Huang’s recent publications demonstrate a strong trend in large language models, federated learning, model watermarking, continual learning, and medical image analysis . His work appears consistently in top venues like NeurIPS, ICML, CVPR, ICLR, and MICCAI, reflecting a broad impact across theoretical and applied AI. He actively mentors students and postdocs, seeking highly motivated researchers in machine learning and related domains. His work has significant implications for healthcare, privacy, and trustworthy AI.
Tanzeem Choudhury is a Professor at the Jacobs Technion-Cornell Institute at Cornell Tech, specializing in Information Sciences. She co-founded HealthRhythms Inc. and directs the People-Aware Computing lab, focusing on machine learning for human activity and social network analysis. Her work pioneered the 'Reality Mining' field, leveraging mobile sensors to model social interactions. Choudhury holds a Ph.D. from MIT's Media Lab and B.S. in Electrical Engineering from the University of Rochester. Her research spans wearable tech, mental health interventions, and ubiquitous computing. Notable projects include affective touch wearables for anxiety reduction, stress detection systems (e.g., StressSense), and the BeWell app for well-being monitoring. Choudhury has been recognized as an ACM Fellow and SIGCHI Academy member, with impactful contributions to healthcare technology and sensor-based behavioral analysis. Current lab initiatives include developing closed-loop interventions for mental health, equity-driven sensing systems, and exploring AI integration in psychotherapy. She advises PhD students on topics like digital biomarkers, burnout detection in healthcare workers, and smartphone-based mental health assessment. Education: Ph.D. in Media Arts & Sciences (MIT), M.S. (MIT Media Lab), B.S. in Electrical Engineering (University of Rochester) Lab Team: Includes PhD candidates (Dan Adler, Yiran Zhao) and researchers focusing on health AI, wearable devices, and clinical applications. Major Awards: ACM Fellowship (2020), SIGCHI Academy (2018), Best Paper Awards at Ubicomp and MobileHCI conferences. Key Projects: OptoBeat (skin-tone calibrated blood oxygen sensor), EmotionCheck (anxiety intervention), and Affective Touch devices.
Ole Winther is Professor in High dimensional biological data analysis/Machine learning at the Department of Biology, University of Copenhagen and Professor in Data science and complexity at DTU Compute, Technical University of Denmark. He serves as CRO and co-founder of raffle.ai, CTO and co-founder of FindZebra, Head of ELLIS Unit Copenhagen, and co-PI of the Machine Learning for Life Science Center. His research spans Bioinformatics , Machine Learning , and AI for Science , focusing on applying deep learning to biological sequence analysis, latent variable models, and medical NLP. Winther's work develops predictive and generative models for bioinformatics, with significant contributions to protein localization tools (SignalP, DeepLoc, DeepTMHMM), single-cell genomics, and novel deep learning architectures like variational autoencoders and diffusion models. Analysis of Winther's recent publications (2023-2025) reveals a strong trend toward integrating protein language models with traditional bioinformatics approaches and applying diffusion models to scientific problems. His work bridges theoretical machine learning advancements with practical applications in biology and medicine, particularly in protein sequence analysis, medical search engines, and scientific simulation acceleration. Winther currently supervises a diverse research group including Panagiotis Antoniadis, Rachael M. DeVries, Jun Wang, Beatrix M. G. Nielsen, Felix G. Teufel, Irene R. Rodriguez, Anders Christensen, and Christopher Heje Grønbech. His former students have established successful careers at institutions including Google, Apple, and various startups, with notable alumni like Casper Sønderby (Google Brain) and Søren Sønderby (Apple). He leads significant research initiatives including the ELLIS Unit Copenhagen and the Machine Learning for Life Science Center, while maintaining active industry partnerships through his co-founded companies raffle.ai (enterprise search using NLP) and FindZebra (search engine for rare diseases). His teaching includes Deep Learning courses at both DTU (02456) and University of Copenhagen (NDAK24002U).
Harris H. Wang is an Associate Professor in the Department of Systems Biology and Department of Pathology and Cell Biology at Columbia University's Vagelos College of Physicians and Surgeons, where he also serves as Interim Chair of Systems Biology. He is affiliated with the Center for Computational Biology and Bioinformatics (C2B2) and the Integrated Program in Cellular, Molecular and Biomedical Studies (CMBS). B.S., Physics and Mathematics, MIT Ph.D., Biophysics, Harvard University Dr. Wang's research lies at the intersection of systems and synthetic biology, focusing on developing foundational technologies for genome engineering, microbiome manipulation, and synthetic genomics. His lab pioneers methods such as MAGE, MAGIC, CAST, and CAMII to enable high-throughput genetic manipulation, in situ microbiome engineering, and AI-driven microbial culturomics. Key research themes include understanding microbial community dynamics, engineering cellular memory systems, designing biocontained genetic circuits, and applying synthetic biology to human health challenges in personalized medicine and infectious disease. His recent publications reveal a strong trend in spatial and functional metagenomics, CRISPR-based microbiome editing, and synthetic biology tools for data storage and genetic stability. The articles span high-impact journals like Nature , Science , and Nature Biotechnology , reflecting his leadership in developing scalable, programmable biological systems. Scientific Awards: NIH Director’s Early Independence Award Forbes 30 Under 30 in Science Sloan Research Fellowship NSF CAREER Award ONR Young Investigator Award Burroughs Wellcome Fund PATH Award Schaefer Scholar Blavatnik National Award Vilcek Prize PECASE Dr. Wang has advised numerous PhD and postdoctoral researchers, many of whom have gone on to independent scientific careers. His lab is supported by major grants from NIH, NSF, DARPA, DOE, and foundations including the Bill & Melinda Gates Foundation and CZ Biohub NY. He is actively involved in educational initiatives, including organizing Columbia’s iGEM team and the Cold Spring Harbor Laboratory Synthetic Biology course. The Wang Lab is based at the Columbia University Irving Medical Center and is part of national consortia such as the Engineering Biology Research Consortium (EBRC) and the Genome Project-Write (GP-Write) initiative. The lab develops and applies cutting-edge technologies in automation, machine learning, and synthetic biology to engineer microbiomes for applications in medicine, global health, and climate change.
Bo Dai is an Assistant Professor at the School of Computational Science and Engineering, Georgia Institute of Technology, and a Staff Research Scientist at Google DeepMind. His research focuses on Agent AI, Generative Models, and Representation Learning, aiming to create decision-making agents through world modeling. He holds a Ph.D. from Georgia Tech (2013–2018) and previously worked at Google Brain. Dai has authored numerous influential papers in top conferences like NeurIPS, ICML, and ICLR, and received the AISTATS Best Paper Award (2016). Education: Ph.D., School of Computational Science and Engineering, Georgia Tech (2013–2018) Research Interests: Reinforcement Learning and Representation Learning for decision-making agents Generative Models and their integration with Agent AI Provable and scalable algorithms for real-world applications His work bridges theory and practice, emphasizing spectral representations and provable guarantees in complex systems. Key Article Trends: His recent work emphasizes scalable spectral methods for multi-agent systems, diffusion policies, and representation-based techniques in reinforcement learning. He also explores LLM alignment and sim-to-real transfer learning. Awards: AISTATS Best Paper Award (2016) NeurIPS Workshop Best Paper (2017) Ross Fellowship (2011–2012) Advising & Grants: Supervises 6 current Ph.D. and M.S. students. Active in organizing workshops on reinforcement learning and serves as an Area Chair for top conferences. Labs & Software: Leads development of Representation-based Reinforcement Learning and Repr-Control toolboxes for nonlinear control and stochastic systems.
Gary Fedder is the Howard M. Wilkoff Professor of Electrical and Computer Engineering at Carnegie Mellon University (CMU), with courtesy appointments in Biomedical Engineering, Mechanical Engineering, and Robotics. He serves as Faculty Director of the Manufacturing Futures Institute (MFI) and previously held roles such as Vice Provost for Research and Interim CEO of the Advanced Robotics for Manufacturing (ARM) Institute. Fedder’s research focuses on MEMS, advanced manufacturing, and implantable microsystems. He earned his B.S., M.S., and Ph.D. in EECS from MIT and UC Berkeley, respectively. Education: Ph.D., Electrical Engineering and Computer Science, UC Berkeley (1994) M.S., Electrical Engineering and Computer Science, MIT (1984) B.S., Electrical Engineering and Computer Science, MIT (1982) Research Interests: Microelectromechanical systems (MEMS), digital twins, aerosol jet printing, stretchable electronics, and manufacturing innovation. His work integrates MEMS with CMOS processes, emphasizing low-cost, high-performance systems. Key Contributions: Co-founded the ARM Institute; developed MEMS-based sensors and actuators; pioneered methods for manufacturing innovation through projects like America Makes. His research spans over 300 publications and 21 patents. Awards: IEEE Fellow (2007), Ross Tucker Award (1993), NSF CAREER Award (1996), and leadership roles in Manufacturing USA initiatives. Leadership & Outreach: Directed the Institute for Complex Engineered Systems and led national initiatives to advance U.S. manufacturing competitiveness. Active in editorial roles for journals like IoP Journal of Micromechanics .
Anca Dragan is an Associate Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley, where she runs the InterACT Lab focused on algorithms for human-AI and human-robot interaction. Currently on leave from Berkeley, she leads AI Safety and Alignment at Google DeepMind, overseeing safety for Gemini models and preparing for future advancements. Dragan has been a co-PI of the Center for Human-Compatible AI and served on the steering committee for the Berkeley AI Research (BAIR) Lab. B.Sc. in Computer Science from Jacobs University Bremen, Germany Ph.D. from Carnegie Mellon University Dr. Dragan's research focuses on enabling AI agents to work effectively with, around, and in support of people. Her work bridges robotics, machine learning, and game theory to create systems that better understand human preferences and coordinate with users. Key areas include AI alignment (ensuring AI does what people actually want), learning reward functions from diverse human feedback forms, and developing algorithms for human-AI collaboration across domains like autonomous vehicles, brain-machine interfaces, and recommender systems. Her research emphasizes maintaining uncertainty about human preferences and accounting for the plurality of human values. Dr. Dragan's recent publications reveal a strong focus on addressing fundamental challenges in AI safety and alignment. Her work spans theoretical foundations of reward learning, practical implementations for human-AI coordination, and critical examinations of limitations in current approaches. There's a clear trajectory toward more robust, safe, and value-aligned AI systems that can handle complex human preferences while avoiding both present-day harms and potential catastrophic risks. IEEE RAS Early Academic Career Award in Robotics and Automation (2021) McEntyre Award for Excellence in Teaching (2020) PECASE (Presidential Early Career Award for Science and Engineering) (2019) Sloan Fellowship (2018) NSF CAREER Award (2017) Okawa Foundation Award (2017) MIT Tech Review 35 Innovators Under 35 (2017) Multiple best paper awards at top robotics and AI conferences Dr. Dragan has mentored numerous successful students who have gone on to faculty positions at MIT, Stanford, CMU, and Princeton, as well as industry roles at DeepMind, Waymo, and Meta. Her advising philosophy emphasizes both technical rigor and consideration of broader societal impacts. She has secured significant research funding including NSF CAREER, ONR Young Investigator, and Okawa Foundation awards, supporting work on human-AI interaction and alignment. Dragan has also consulted for Waymo for six years, helping develop roadmaps for deploying increasingly learning-based safety-critical systems. Dr. Dragan leads the InterACT Lab at UC Berkeley, which has produced influential work on Cooperative Inverse Reinforcement Learning, Inverse Reward Design, and other foundational concepts in human-AI interaction. The lab's research has significantly shaped the field of AI alignment, with applications spanning autonomous vehicles that coordinate with human drivers, brain-machine interfaces that adapt to user needs, and language models that better understand human preferences. Current work focuses on scaling safety approaches as AI capabilities advance, ensuring alignment keeps pace with technological progress.
Syed Hani Hassan Abidi is an Associate Professor at the Department of Biomedical Sciences , School of Medicine , Nazarbayev University , Kazakhstan. His research integrates virology , immunology , viral oncology , and bioinformatics , with a focus on HIV molecular epidemiology , viral evolution , and drug resistance . He has led international projects across Pakistan, Kenya, Afghanistan, and Kazakhstan, and is recognized for innovative teaching and MOOC development. Education: PhD in Virology and Immunology Research Interests: His laboratory employs bioinformatics (machine learning, AI), genomics , and proteomics to study HIV phylodynamics , viral co-infections , and oncogenic viruses like EBV in prostate cancer. He also explores microbiome-immunity interactions and designs antiviral drugs/vaccines . Recent Research Trends: His 2025 publications emphasize COVID-19 immunopathology , HIV/syphilis epidemiology in Pakistan , and particle physics contributions via ATLAS , showcasing interdisciplinary impact. Awards & Recognition: Outstanding Teachers Award (2019, Aga Khan University) Fellowship of Higher Education (UK, 2022) Teaching & Grants: He pioneered Pakistan’s first MOOC on Computer-Based Drug Discovery (2014) and received a 2022 SoTL grant for MOOC-based molecular biology education. His teaching integrates animations , films , and flipped classrooms . Collaborations & Labs: Leads projects on HIV drug resistance , HCV genomics in Kazakhstan , and AI-driven dementia diagnostics (Kazakh Brain Atlas). His lab collaborates with global institutions to advance viral disease surveillance and therapeutic innovation .
Joseph Kable is the Jean-Marie Kneeley President's Distinguished Professor of Psychology at the University of Pennsylvania's Department of Psychology within the School of Arts and Sciences. His research integrates experimental economics, cognitive neuroscience, and personality psychology to investigate the neural and psychological mechanisms underlying decision-making. He explores how subjective value is represented in the brain, deviations from rational choice theory, and individual differences in decision processes. His lab employs fMRI and interdisciplinary methods to study topics like risk tolerance, impulsivity, and neural plasticity. Education: BS in Chemistry from Emory University (undergraduate), PhD in Neuroscience from the University of Pennsylvania (doctoral). Research focuses on behavioral and cognitive neuroscience mechanisms of choice, including studies on temporal predictions, value signals, and brain structure-function relationships. His recent work examines how neural markers correlate with decision-making variability across individuals. He currently advises graduate students in Psychology and has post-doctoral researchers in his lab. Associated with MindCORE, Penn's hub for integrative mind research. Lab activities include undergraduate research programs and active studies exploring decision neuroscience and neuroeconomics.
Jiaxuan You is an Assistant Professor at the Siebel School of Computing and Data Science, University of Illinois at Urbana-Champaign, leading the U Lab focused on achieving Artificial General Intelligence (AGI) in digital environments. His research spans graph neural networks (GNNs), relational data, foundation models, and machine learning systems. PhD and MS in Computer Science from Stanford University (2021) Developed GraphGym and PyTorch Geometric (PyG) for graph learning Core member at Kumo AI (2021-2023) His research explores: Graph-enhanced LLMs: Integrating relational structures into foundation models AGI Development: Self-optimizing AI agents and tool utilization ML Systems: Scalable architectures and redundancy-free computation Interdisciplinary Applications: Financial networks, crop yield prediction, and metro systems Recent publications focus on temporal reasoning, multi-agent dynamics, and hybrid architectures for LLMs. He actively develops open-source tools like DBGYM and GraphRouter. Scientific recognition includes: JPMC PhD Fellowship Baidu Scholarship Best Student Paper at AAAI 2017 World Bank Big Data Innovation Challenge winner He mentors PhD and intern students, emphasizing machine learning systems expertise. His lab collaborates on AGI workshops (e.g., ICLR 2024) and industry projects.
Raul Vicente Zafra is a Professor of Data Science at the University of Tartu, Faculty of Science and Technology, Institute of Computer Science, where he has been working since 2013. His research spans computational neuroscience, artificial intelligence, and data science, with a particular focus on bridging biological and artificial models of intelligence. Education: PhD in Physics (2001-2006), University of the Balearic Islands BSc in Physics (1997-2001) Professor Zafra's research interests center on computational neuroscience and artificial intelligence, with specific expertise in brain-computer interfaces, reinforcement learning, neural modeling, and explainable AI. His work bridges the gap between biological and artificial intelligence systems, exploring how neural principles can inform machine learning algorithms and vice versa. He has made significant contributions to understanding neural coherence, time interval learning in neural systems, and the application of information theory to brain-computer interfaces. His research often involves interdisciplinary collaboration between computer science, neuroscience, and medicine. Analysis of Zafra's recent publications reveals a strong focus on the intersection of artificial intelligence and neuroscience. His work spans explainable AI methods, brain-computer interfaces, reinforcement learning models that mimic cognitive processes, and neurophysiological studies of brain activity. A notable trend is his exploration of how biological principles of neural computation can inform and improve artificial intelligence systems, particularly in areas like time-based learning, consciousness modeling, and neural coherence. Scientific Awards: 2012: Attendee at the 62nd Lindau Nobel Laureate Meeting 2007: Quantum Electronics and Optics Division Prize of the European Physical Society for the best PhD Thesis in Applied Optics in Europe 2006: PhD Extraordinary Award of the Physics Department of the University of the Balearic Islands 2001: Physics Degree Extraordinary Award (First Class Honors, best GPA) 1997: Bronze Medal in the "8th Spanish Physics Olympiad" Professor Zafra has been principal investigator on numerous significant research projects including the Estonian Centre of Excellence in Artificial Intelligence, Cardiovascular Stress Impacts On Neuronal Function, and Bridging biological and artificial models of vision. His grant portfolio demonstrates strong funding support from the Estonian Research Council, European Commission, and other major funding bodies. He has supervised multiple PhD students and mentored early-career researchers in computational neuroscience and AI. His laboratory work focuses on developing computational models of neural systems and applying these insights to artificial intelligence. Current research directions include explainable AI methods, brain-computer interfaces, modeling of consciousness and cognitive processes, and the application of AI to healthcare challenges.
Erniel Bayhon Barrios is a Professor at the Malaysia School of Business, Monash University. Formerly a professor at the University of the Philippines Diliman and a visiting scholar at Karlstad University (Sweden) and the Asian Development Bank Institute (Japan). He holds a PhD in Statistics (1990) from the University of the Philippines Diliman. His research focuses on computational statistics, nonparametric methods, data science, and computational econometrics, with applications in spatiotemporal modeling, time series, and financial markets. He has contributed to high-dimensional data analysis, volatility modeling, and robust statistical techniques. Key projects include the National Mental Health Survey and Well-Being (2019–2021) and building a data-driven organization for the Habib Group (2025–2026). He is an elected member of the International Statistical Institute (2012), associate editor of Communications in Statistical Applications and Methods , and served on the board of the International Association of Statistical Computing (2022–2025). He advises PhD students on topics like stochastic frontier models, data assignment in big data, and high-frequency time series analysis. His work aligns with UN Sustainable Development Goals related to education and economic growth.
