Professor Thomas Lukasiewicz is a Full Professor and Head of the Artificial Intelligence Techniques research group at the Faculty of Informatics, Vienna University of Technology (TU Wien). His research focuses on enabling machines to mimic human-like intelligence through techniques spanning deep learning, symbolic reasoning, and predictive coding. Key areas include explainable AI, hybrid neurosymbolic systems, and applications in healthcare and law. He teaches courses such as Deep Learning for Natural Language Processing, Scientific Research and Writing, and multiple seminars in artificial intelligence and knowledge representation. His research projects include Explainable AI in Healthcare (2023–2027) and foundational work on predictive coding networks. His publications (15+ recent articles) address medical image segmentation, neurosymbolic frameworks, and language model evaluation in mathematics. Notable work includes neurosymbolic hybrid models (CCN⁺), reinforcement learning for medical report generation, and theoretical foundations of predictive coding networks.
Leslie Valiant is the T. Jefferson Coolidge Professor of Computer Science and Applied Mathematics in Harvard University's School of Engineering and Applied Sciences, where he has held a faculty position since 1982. A foundational figure in theoretical computer science, his work bridges artificial and natural computational phenomena across multiple disciplines. His academic background includes education at: King's College, Cambridge Imperial College, London Ph.D. in Computer Science from Warwick University (1974) Valiant's research spans computational complexity , machine learning theory , parallel systems , and computational neuroscience . He pioneered the PAC (Probably Approximately Correct) learning framework that established computational learning theory as a rigorous field. His holographic algorithms work revealed deep connections between computational complexity and statistical physics, while his neuroidal model and evolvability theory provide computational explanations for cognitive processes and biological evolution. Current investigations focus on cortical computation primitives and knowledge infusion architectures. His publication trends show increasing integration of neuroscience with computational theory since 2010, with dominant themes in holographic computation (2006-2018), cortical modeling (2012-2018), and evolvability (2009-2017). The work consistently applies computational complexity analysis to biological and cognitive systems. Major recognitions include: Nevanlinna Prize (1986) for mathematical aspects of computer science Knuth Award (1997) for foundational algorithms contributions EATCS Award (2008) for theoretical computer science impact Turing Award (2010) for computational learning theory and complexity Fellowship in the Royal Society and National Academy of Sciences Valiant's research has been supported by NSF and international grants enabling cross-disciplinary work in computational neuroscience and evolutionary algorithms. While specific advisees aren't documented in source materials, his theoretical frameworks have shaped generations of researchers in machine learning and complexity theory. His current research group explores neuroidal architectures for cognitive computation, investigating how cortical circuits achieve robust information processing through in-circuit testing methodologies. Ongoing projects aim to identify fundamental computational primitives in neural systems and develop biologically inspired AI frameworks.
Professor Michael Milford is a robotics and computer vision expert at Queensland University of Technology (QUT), serving as Joint Director of QUT's Centre for Robotics. His research bridges robotics, neuroscience, and computer vision, focusing on biologically inspired navigation systems for autonomous vehicles and drones. He has pioneered projects like SeqSLAM and RatSLAM, emphasizing the synergy between biological intelligence and robotic systems. Research Interests: Milford explores navigation algorithms inspired by animal behavior, energy-efficient neural networks, and autonomous systems. His work aims to create robots capable of operating in dynamic environments through interdisciplinary collaboration with institutions like MIT, Harvard, and NASA's Jet Propulsion Laboratory. Awards & Recognition: Awarded the 2019 Batterham Medal and a $2.7M Australian Laureate Fellowship for his project on GPS-independent positioning systems. His work has produced highly cited papers in robotics and computer vision, including breakthroughs in visual SLAM and place recognition under varying conditions. Teaching & Collaboration: Milford mentors students in robotics and AI, launching initiatives like the STEM Storybook to inspire youth. He collaborates globally, emphasizing cross-disciplinary research to bridge gaps between fundamental and applied sciences. Labs & Projects: Leads QUT's Centre for Robotics, advancing technologies for autonomous systems. His projects explore neuromorphic computing, SLAM systems, and bio-inspired navigation, with applications in defense, transport, and environmental monitoring.
Dr James Herbert-Read is an Associate Professor and Whitten Lecturer in Marine Biology at the Department of Zoology, University of Cambridge. He serves as Deputy Head of Department (Postgraduate Education) and leads the Marine Behavioural Ecology Group. His research focuses on understanding how animals, particularly marine organisms, collect and process information from their environments to make behavioral decisions, with emphasis on social interactions, adaptation mechanisms, and ecological constraints. His group employs theoretical frameworks, controlled experiments, and quantitative field studies to investigate behavioral diversity in marine species. Key themes include collective behavior, predator-prey dynamics, camouflage strategies, and the impacts of environmental stressors on animal decision-making. Recent publications highlight work on lionfish vocalization mechanisms, cuttlefish camouflage, citizen science applications in marine research, and behavioral responses to visual and acoustic noise. Scientific awards and affiliations include: Whitten Lecturer in Marine Biology Associate Professor, University of Cambridge He has supervised research projects on topics such as: Social attraction in invasive fish species Evolution of coordinated movement Neurophysiological basis for leadership in shoals Maternal effects on offspring exploration
Professor Sangbae Kim is the Jerry McAfee (1940) Professor in Engineering at the Massachusetts Institute of Technology (MIT), School of Engineering, Department of Mechanical Engineering. His research focuses on bio-inspired robotics, extracting principles from animal biomechanics to develop high-performance robotic systems. Education: B.S. from Yonsei University (2001), M.S. (2004) and Ph.D. (2008) from Stanford University. Research Interests: Bio-inspired Robotics, Robotic Actuators, Locomotion Dynamics, Composite Sensor Fabrication, and Minimally Invasive Surgical Robotics. His notable achievements include the MIT Cheetah robot capable of 13mph outdoor running and autonomous obstacle jumping, and Stickybot, a climbing robot featured in TIME's Best Inventions (2006). Recent publications emphasize soft robotics, energy-efficient legged locomotion, and bio-inspired actuator design. Kim has received prestigious awards including the NSF CAREER Award (2014), DARPA Young Faculty Award (2013), and Ruth and Joel Spira Award for Distinguished Teaching (2015). Scientific Awards: NSF CAREER (2014), DARPA YFA (2013), TIME Best Invention (2006), multiple best paper awards. Professional Service: Associate Editor roles, NSF review panels, and leadership in IEEE and ASME organizations.
Zeynep Temel is an Assistant Professor at Carnegie Mellon University's College of Engineering, jointly appointed in the Biomedical Engineering and Robotics Institute. She leads the Zoom Lab, focusing on bio-inspired compliant mechanisms for robotic systems. Current research emphasizes adaptable robots for complex environments through mechanical intelligence and embedded control . Key application areas include surgical robotics , search-and-rescue , and micromanipulation . Her work spans bio-inspired design, compliant robotics, and human-centered applications. Recent publications highlight advancements in: Swarm robotics for collaborative exploration Soft actuators using bioplastics and gelatin Dexterous manipulation via delta robot frameworks The Zoom Lab trains students in robotic fabrication and biological modeling, with members transitioning to roles in academia and industry.
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.
Dr. Tan Viet Tuyen Nguyen is a New Frontiers Fellow (Lecturer) in AI at the University of Southampton, specializing in Human-Centered Artificial Intelligence and Social Human-Robot Interaction. His research focuses on multimodal learning for robots to adapt their behavior to human social needs, with applications in healthcare, education, and service environments. Prior to this role, he was a Research Associate at King’s College London and a Research Assistant on the EU-funded CARESSES project, developing culturally-aware assistive robots for elderly support. Education: PhD in Information Science (Robotics) from Japan Advanced Institute of Science and Technology. He has organized conferences such as the IEEE RO-MAN 2022 special session on nonverbal communication and served as a reviewer for top-tier robotics and AI conferences. Research Interests include: Human-Robot Collaboration, Multimodal Perception, Generative AI for Social Interaction, and Context-Aware Robot Behavior Generation. His work has been recognized with awards including the Best Paper Award at ROMAN 2022 and the Prospective Research Award at ICServ 2023. Teaching Responsibilities include courses on Biologically Inspired Robotics, High-Level Programming, and MSc/Undergraduate project supervision. He currently oversees two PhD students and collaborates on projects like 'Exploring the impact of AI-driven writing of engagement in climate change' and 'Bridging Generations and Cultures through Generative AI.' Labs/Teams: Member of the Agents, Interaction and Complexity Centre and the Centre for Robotics Research at Southampton.
Rodrigo Ventura is an Associate Professor at the Department of Electrical and Computer Engineering, Instituto Superior Técnico (IST), University of Lisbon. He is also a senior researcher at the Institute for Systems and Robotics (ISR-Lisbon), leading the Space and Aeronautics thematic line. His research focuses on the intersection of Robotics and Artificial Intelligence, emphasizing human-robot interaction, space robotics, and cognitive architectures. He coordinates the Minor in Space Sciences and Technologies at IST and the MBE on Space Systems for Tecnico+. As Adjoint Faculty at the International Space University (ISU), he contributes to global academic initiatives. His work includes experiments on the International Space Station and participation in analog space missions. Research interests span biologically inspired systems, machine learning, and teleoperation interfaces. Recent publications address reinforcement learning for UAVs, microgravity experiments, and pseudo-haptic feedback for robotic control. He teaches subjects like Artificial Intelligence and Decision Systems, Satellite Engineering, and Autonomous Systems.
Hyosang Lee is an Assistant Professor in the Robotics Section of the Mechanical Engineering Department at Eindhoven University of Technology (TU/e). He holds a PhD from KAIST and has held research positions at the Max Planck Institute and University of Stuttgart. His work focuses on tactile sensing technologies, including artificial skin development, soft robotics, and integration of sensory systems with AI. Bachelor's: Mechanical Engineering, Korea University Master's: Robotics and Mechanical Engineering (double major) PhD: Mechanical Engineering, KAIST (2017) Research interests span tactile sensor design, electrical impedance tomography (EIT), and human-robot interaction. His group emphasizes creating scalable, flexible tactile systems for robots. Recent work includes air pressure sensing for force estimation and biomimetic skin materials. Publications highlight innovations in multi-directional force sensing, soft component technologies, and haptic interfaces for autism therapy. He teaches 'Dynamics and Control of Robotic Systems' and serves on the editorial board of npj Robotics . No formal student advisees are listed, though his lab, the Tactile Sensing and Robotic Skin Group , likely involves graduate researchers. His research contributes to UN Sustainable Development Goals related to health and technology.
Sergii Strelchuk is an Associate Professor of Computer Science at the University of Oxford, specializing in quantum computing and its applications. His research sits at the intersection of quantum information theory, computer science, and bioinformatics, with a focus on developing quantum algorithms for practical problems in genomics and beyond. Professor Strelchuk's primary research interests include quantum algorithms and their applications (particularly in bioinformatics), classical simulation methods for quantum computation, quantum complexity theory, and quantum learning theory. His work bridges theoretical quantum computing with practical applications, especially in the emerging field of quantum genomics and pangenomics, with significant implications for understanding human and pathogen genomes. His recent publications demonstrate a strong focus on applying quantum computing techniques to genomic data analysis, developing efficient fermion-qubit mappings for quantum simulation, and exploring fundamental aspects of quantum complexity theory. His research shows a clear trajectory toward making quantum computing practically applicable to biological data analysis and advancing our theoretical understanding of quantum computational models. Among his notable scientific achievements are: Royal Society University Research Fellow Leverhulme Early Career Fellow John and Delia Agar Research Fellow Professor Strelchuk leads several significant research projects including the Wellcome Leap "Human and Pathogen Quantum Pangenomics" project (2023-2026), which recently entered Phase 3 in April 2025, the EPSRC "Structure and symmetry in quantum verification" grant (2023-2025), and the "Quantum Algorithms for Quantum Field Theory" project (2022-2025). His research has attracted substantial funding for quantum computing applications in genomics. His work has received significant attention in both academic and popular science media, including coverage in Quanta Magazine and collaborations with institutions like the Sanger Institute to tackle complex genomic challenges using quantum computing approaches, with recent publicity about his leadership in the final phase of the Wellcome Leap-funded quantum pangenomics project.
Daniel E. Koditschek is the Alfred Fitler Moore Professor in the Department of Computer and Information Science at the University of Pennsylvania’s School of Engineering and Applied Science. He also holds primary appointments in the Department of Electrical and Systems Engineering and a research affiliation with the Department of Mechanical Engineering and Applied Mechanics. He is a leading figure in the GRASP Lab, where he leads the Kod*lab, a specialized group focused on physical interaction and locomotion in autonomous robots. His research lies at the intersection of dynamical systems theory and robotics, emphasizing legged locomotion, hybrid control systems, and bio-inspired design. Koditschek's work integrates formal mathematical modeling with empirical testing of physical robots that run, jump, climb, and manipulate objects. He actively explores how biological insights into animal mobility can inform robotic autonomy and control. His group maintains strong collaborations with biologists and emphasizes embodied intelligence in machine behavior. The recent publications reflect a strong trend in applying theoretical control frameworks—such as hybrid dynamical systems, averaging methods, and navigation functions—to practical robotic challenges in unstructured environments. Topics include terrain adaptation, energy-efficient locomotion, reactive planning, and affordance-based interaction. There is a clear focus on bridging abstract mathematical models with real-world robotic performance, particularly in legged and mobile manipulation systems. IEEE RAS Pioneer Award Heilmeier Research Award AFOSR MURI Award (2010) Daniel Koditschek has advised numerous PhD students and postdoctoral researchers, many of whom now hold faculty positions or leadership roles in robotics companies like Ghost Robotics and Boston Dynamics. His research is supported by major grants from the NSF and AFOSR, including the MURI award and REU/RET programs that engage K-12 and undergraduate educators. He has also been involved in international outreach, including activities at the Penn Wharton China Center. Koditschek leads the Kod*lab within the GRASP Lab’s PERCH facility, which houses advanced legged robots such as the Ghost Minitaur, XRHhex, Inu, Delta Hopper, and Jerboa platforms. The lab emphasizes experimental validation of control theories using custom hardware and real-world terrain challenges.
Marcelo Mattar is an Assistant Professor of Psychology and Neural Science at New York University, leading the Mattar Lab. His research focuses on the neural computations underlying memory, decision-making, and reinforcement learning. He holds a Ph.D. in Psychology from the University of Pennsylvania and has held academic positions at NYU, UC San Diego, and postdoctoral roles at Princeton University and the University of Cambridge. His work bridges computational neuroscience and artificial intelligence, aiming to model how the brain uses internal models for planning and decision-making. Education: Ph.D. in Psychology (Computational and Cognitive Neuroscience), University of Pennsylvania, 2016 M.A. in Statistics, University of Pennsylvania, 2016 B.A. in Electronics Engineering, Instituto Tecnologico de Aeronautica, Brazil, 2010 Research Interests: The lab develops mathematical models of learning and decision-making, leveraging reinforcement learning, Bayesian statistics, and neural networks. Experiments involve human behavioral studies and neuroimaging, with collaborations in animal electrophysiology and computational psychiatry. Key Contributions: His work explores how episodic memory and hippocampal replay support flexible decision-making. Recent studies highlight parallels between human cognition and AI systems, such as language models' metacognitive abilities and brain-inspired algorithms. Awards: Newton International Fellowship, Royal Society (2018–2019) Lab Team: The lab includes postdocs, PhD students, and undergraduates from diverse fields like cognitive science, neuroscience, and computer science. Current members are listed on the lab's website. Lab Location: Meyer Hall, 6 Washington Place, New York, NY 10003.
Karthik Menon serves as an Assistant Professor with a joint appointment in the Woodruff School at Georgia Institute of Technology and the Coulter Department of Biomedical Engineering. His research integrates fluid mechanics, computational modeling, and data-driven methodologies to address critical challenges in healthcare, renewable energy, and bio-inspired engineering systems. His academic credentials include: Ph.D. in Mechanical Engineering, Johns Hopkins University (2021) M.S. in Mechanical Engineering, Johns Hopkins University (2019) B.E. in Mechanical Engineering, Birla Institute of Technology and Science, Pilani, India (2015) Menon's research program centers on three interconnected domains: cardiovascular flows for personalized treatment of heart disease, fluid-structure interactions in biological systems like heart valves and bio-mimetic robots, and vortex-dominated flows for renewable energy applications. His approach combines high-fidelity computational modeling with machine learning to uncover fundamental physics and develop clinical solutions, such as cardiovascular digital twins for non-invasive risk assessment. Current projects focus on patient-specific hemodynamics using CT imaging and uncertainty quantification to improve surgical planning. Analysis of his 15 most recent publications (2023-2025) reveals a dominant focus on advancing multi-fidelity computational frameworks for cardiovascular applications. Key trends include Bayesian uncertainty quantification, zero-dimensional solver development, and integration of clinical imaging data to create predictive digital twins. His work bridges fluid dynamics with clinical cardiology, targeting improved outcomes in coronary artery disease and Kawasaki-related complications through physics-informed machine learning. Menon's scholarly contributions have been recognized through competitive awards: WCCM-PANACM 2024 Travel Award, U.S. Association for Computational Mechanics (2024) Future Faculty Symposium Travel Award, Society of Engineering Science Conference (2023) Mark O. Robbins Prize in High-performance Computing, Johns Hopkins University (2021) Corrsin-Kovasznay Outstanding Paper Award, Johns Hopkins University (2020) Prosperetti Travel Award, Johns Hopkins University (2017) Mechanical Engineering Departmental Fellowship, Johns Hopkins University (2016) As principal investigator of the ComBiNE Fluid Dynamics Lab, Menon mentors graduate students in developing computational tools for fluid-structure interaction problems. His collaborative projects with cardiologists at Stanford and Emory hospitals translate engineering principles into clinical applications for cardiovascular disease management. Current grant activities focus on NSF and NIH-funded initiatives for uncertainty-aware cardiovascular modeling and bio-inspired flow energy harvesting. The ComBiNE Fluid Dynamics Lab operates as an interdisciplinary hub where engineers, clinicians, and data scientists collaborate on fluid mechanics challenges. Current lab initiatives include developing real-time hemodynamic simulators for surgical planning, creating reduced-order models for cardiac device optimization, and investigating vortex dynamics in fish schooling for underwater vehicle design. The lab maintains strong partnerships with Children's Healthcare of Atlanta and the Parker H. Petit Institute for Bioengineering and Bioscience.
Lara A. Estroff is a Full Professor and the current Chair of the Department of Materials Science and Engineering at Cornell University's College of Engineering. She has been a faculty member since 2005 and served as Director of Graduate Studies from 2015 to 2019. Her academic leadership and research excellence position her at the forefront of bio-inspired materials and biomineralization research. Her educational background includes a B.A. in Chemistry from Swarthmore College (1997) and a Ph.D. in Chemistry from Yale University (2003), followed by an NIH-funded postdoctoral fellowship at Harvard University in the lab of Prof. George M. Whitesides. Dr. Estroff's research centers on the fundamental mechanisms of crystal growth, biomineralization, and pathological mineralization. She investigates how organisms control mineral formation and applies these principles to engineer synthetic materials with complex structures and functionalities. Her work spans biomaterials, tissue engineering, and energy materials—particularly hybrid organic-inorganic perovskites for photovoltaics. She employs advanced characterization techniques and has pioneered in situ methods to monitor crystallization dynamics. Her recent publications reveal a strong trend toward interdisciplinary research, integrating materials science with cancer biology, immunology, and machine learning. The articles emphasize bio-inspired synthesis, mineral-tissue interactions, and the development of functional crystalline materials for medical and energy applications. Faculty Early CAREER Award, National Science Foundation (2009) Fiona Ip Li '78 and Donald Li '75 Excellence in Teaching Award, Cornell College of Engineering (2007) Marilyn Emmons Williams Award, Cornell Undergraduate Research Board (2009) Keynote Speaker, Gordon Research Seminar on Biomineralization (2012) Lawrence Berkeley National Lab Affiliate (2013) Dr. Estroff leads a major DOE-funded project titled “Formulation Engineering of Energy Materials via Multiscale Learning Spirals,” a $3 million, three-year initiative using machine learning to optimize perovskite synthesis for solar cells. She has advised numerous graduate students and postdoctoral researchers, and her lab is known for fostering collaborative, cross-disciplinary research. She has also contributed to educational initiatives at Cornell, particularly in undergraduate research and materials education. Her research group operates at the intersection of chemistry, engineering, and biology, focusing on high-resolution characterization of biominerals, in situ crystal growth studies, and the design of in vitro models for cell-mineral interactions. The lab actively collaborates with institutions including Lawrence Livermore National Laboratory, National Renewable Energy Laboratory, and Johns Hopkins University.
