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Katerina Fragkiadaki is the JPMorgan Chase Associate Professor of Computer Science in the Machine Learning Department at Carnegie Mellon University. She works at the intersection of Artificial Intelligence, Computer Vision, Machine Learning, Language Understanding, and Robotics. PhD from GRASP Lab, University of Pennsylvania Postdoctoral researcher at UC Berkeley (with Jitendra Malik) and Google Research Recipient of NSF CAREER, DARPA Young Investigator, Amazon, Google, Sony, UPMC, and AFOSR awards Organizer of CoRL 2023 Workshop on Generalist Robots ICLR 2024 Program Chair, multiple area chair roles Her research group focuses on developing machines that autonomously improve world models through human-environment interactions, with specific emphasis on: Representation learning and video understanding 2D/3D unified vision-language models Generative simulation and reinforcement learning Real2Sim/Sim2Real robot learning Continual learning and spatial common sense 3D scene reconstruction and dynamics Recent publications highlight advancements in: 3D mesh generation with compositional transformers Unified 2D/3D perception frameworks Physics-aware generative models Diffusion-based robotic manipulation policies Embodied agents with memory prompting Awards include: 2024: DARPA Young Investigator Award 2023: Amazon Faculty Award 2022: Sony Faculty Research Award 2021: UPMC Faculty Research Award 2020: NSF CAREER Award 2019: Google Faculty Award Key collaborations span institutions including UC Berkeley, Google Research, Stanford, MIT, and University of Tsukuba. Her work bridges theoretical innovation with practical applications in: Autonomous robot manipulation 4D world modeling Language-grounded perception Visual dynamics prediction Embodied program synthesis Physics-based simulation engines

• Artificial Intelligence
• Computer Vision
• Machine Learning
• +8 more

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 .

• Microelectromechanical Systems (MEMS)
• Advanced Manufacturing
• Nanomanufacturing
• +6 more

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.

• Human-AI Interaction
• Human-Robot Interaction
• AI Safety and Alignment
• +5 more

Byron Yu is a Professor in Electrical & Computer Engineering and Biomedical Engineering at Carnegie Mellon University, with affiliations to the Neuroscience Institute and Robotics Institute. He is a core faculty member of the Center for the Neural Basis of Cognition. Research focuses on computational neuroscience , neural dynamics , and brain-machine interfaces . Key contributions include dimensionality reduction techniques and neural population activity analysis. Recent publications explore topics such as neural dynamics during motor imagery, BCI optimization, and attentional processing. His work has appeared in Nature Neuroscience , Neuron , and eLife , often as cover articles. Awardees include the Gerard G. Elia Career Development Professorship and AIMBE Fellowship . His lab has mentored numerous PhD and postdoctoral researchers, many now in academic and industry leadership roles.

• Computational Neuroscience
• Neural Dynamics
• Motor Control
• +7 more

Steve Chase is a Professor at Carnegie Mellon University , affiliated with the Biomedical Engineering , Electrical and Computer Engineering , Neuroscience Institute , and Robotics Institute departments. His research spans Computational Neuroscience , Neural Engineering , and Systems Neuroscience , with a focus on neural circuits, motor control, and brain-computer interfaces (BCI). Research Areas: Sensation & Perception, Methods Development, Diseases & Disorders, Physiological & Anatomical Methods. Lab Highlights: Development of the RotaWheel, memory trace studies in the motor cortex, and investigations into BCI stabilization and learning dynamics. Scientific Contributions: His lab has published extensively in journals like Neuron , Nature Computational Science , eLife , and PNAS , with notable works on neural activity patterns, dimensionality reduction in calcium imaging, and sensory constraints on motor cortex modulation. Students and postdocs in his lab have received awards, including the CNBC best paper award.

• Computational Neuroscience
• Neural Engineering and Technology
• Systems Neuroscience
• +4 more

John R Anderson is the Richard King Mellon University Professor of Psychology and Computer Science at Carnegie Mellon University (CMU), affiliated with the Department of Psychology within the Dietrich College of Humanities and Social Sciences. His research focuses on understanding higher-level cognition, particularly mathematical problem-solving, through the development of the ACT-R cognitive architecture—a computational framework simulating human cognitive processes. This architecture integrates behavioral, neural, and educational data to model learning and decision-making. Anderson’s work bridges cognitive science, neuroscience, and educational technology. He investigates how brain imaging (e.g., fMRI, EEG) can reveal the temporal dynamics of cognitive processes and improve instructional methods. His research emphasizes analyzing brain activity time courses to uncover underlying mechanisms of problem-solving and skill acquisition. Key Research Themes: Cognitive architectures, neural correlates of learning, computational models of memory, and intelligent tutoring systems. Notable Contributions: Development of the ACT-R architecture, integration of neuroimaging with cognitive modeling, and studies on skill transfer and learning strategies. Anderson’s publications include seminal books like Cognitive Psychology and Its Implications and How Can the Human Mind Occur in the Physical Universe? His work has advanced understanding of associative memory, strategic decision-making, and the application of cognitive models in educational technology. His lab, the ACT-R Research Group, collaborates across disciplines to model complex cognitive tasks and their neural foundations. Current projects analyze real-time brain activity to refine educational interventions and improve human-machine interaction.

• Cognitive Neuroscience
• Cognitive Science
• Computational Psychology
• +3 more

Tom Mitchell is the Fredkin Professor of AI and Learning and Director of the Center for Automated Learning and Discovery (CALD) at Carnegie Mellon University's School of Computer Science. His research focuses on machine learning, computational neuroscience, and their applications in neuroimaging and natural language processing. He is renowned for pioneering work in developing algorithms to decode brain activity and for contributions to foundational machine learning theory, including co-training and explanation-based learning. Mitchell authored the seminal textbook *Machine Learning* (McGraw Hill, 1997) and has led projects like Never-Ending Learning (NELL), an AI system that autonomously learns from web content. His work bridges computer science and cognitive science, exploring how machines can learn from data and human interaction. Notable research interests include brain-computer interfaces, automated knowledge extraction, and ethical AI. Mitchell's publications span influential journals like *Science* and *Nature*, and he has been recognized for advancing interdisciplinary research in AI and neuroscience. He has advised numerous students and contributed to initiatives like the AAAI Presidential Address on AI and brain sciences. Mitchell's current projects include studying the neural basis of language and developing AI tools for education and healthcare.

• Machine Learning
• Artificial Intelligence
• Neuroimaging
• +4 more

Xu Zhang is an Assistant Professor in Electrical and Computer Engineering at Carnegie Mellon University's College of Engineering. He directs the Zhang Lab, focusing on atomically precise 2D materials (e.g., graphene, tellurium) for nanoelectronic/photonic devices in computing, sensing, energy, and healthcare. His research bridges metamaterials, neuromorphic systems, and scalable nanofabrication. Zhang holds a PhD from MIT and BS from USTC, with honors including MIT Technology Review's Innovators Under 35 (2022), NSF CAREER Award (2023), and multiple MIT fellowships. His group's recent work demonstrates programmable mid-infrared metasurfaces, high-mobility tellurium photodiodes, and kirigami-actuated optical systems for biomedical imaging and AR/VR. Advisees include Kevin St. Luce, Yibai Zhong, and Tianyi Huang. Zhang has secured research funding from NSF and industry partners.

• Nanoelectronics
• Nanophotonics
• 2D Materials
• +3 more

Jonathan Tsay is an Assistant Professor in the Department of Psychology at Carnegie Mellon University, affiliated with the Dietrich College of Humanities and Social Sciences. His research focuses on understanding human motor learning through computational modeling, neuropsychology, and psychophysics, with applications to clinical rehabilitation and brain-computer interfaces. Education: B.A. in Mathematics from Northwestern University; D.P.T. from Northwestern University's Feinberg School of Medicine; Ph.D. in Psychology from UC Berkeley. Research Interests: Investigating how humans master complex movements through cognitive and neural mechanisms. Key areas include sensorimotor adaptation, implicit learning processes, and the interplay between perception and action. His work integrates experimental methods with computational models to explore motor control in health and disease. Labs/Teams: Leads the Physical Intelligence Lab (Pi-Lab), studying movement diversity and optimization through interdisciplinary approaches. The lab emphasizes translational research to improve clinical interventions and human performance technologies.

• Cognitive Neuroscience
• Computational Modeling
• Neuropsychology
• +3 more

Helen N. Schwerdt is an Assistant Professor in the Department of Bioengineering at the University of Pittsburgh, affiliated with the Swanson School of Engineering and the Center for the Neural Basis of Cognition (CNBC). Her research focuses on developing implantable tools for probing electrical and chemical neural activity, with a particular emphasis on long-term monitoring in both rodents and primates. Key areas include dopamine signaling in Parkinson’s disease, neural interface design, and chronic neural recording systems. Her lab’s work spans interdisciplinary fields such as bioengineering, materials science, and neuroscience. Notable contributions include scalable carbon fiber electrode arrays for neurochemical monitoring and wireless passive neurorecording systems. Schwerdt’s team collaborates extensively, addressing challenges in neural prosthetics and translational neuroscience. Recent research highlights include the 2023 publication on rodent neurochemical arrays and 2020 studies on dopamine and motor control in primates. The lab actively recruits students and postdoctoral researchers across disciplines, emphasizing training in microfabrication, electrophysiology, and computational neuroscience.

• Neuroscience
• Bioengineering
• Materials Science
• +6 more

Valerie Ventura is a Professor in the Department of Statistics and Data Science at Carnegie Mellon University's Dietrich College of Humanities and Social Sciences. She serves as Co-Director of the Ph.D. Program and maintains dual affiliations as affiliated faculty in the Machine Learning Department and the Center for the Neural Basis of Cognition (CNBC). As a graduate advisor for the Program in Neural Computation (PNC), she bridges statistical methodology with neuroscience applications. Her research focuses on computational neuroscience, particularly time series forecasting, networks, algorithms, and nonparametric methods. Key research areas include computational neuroscience, biostatistics & epidemiology, graphical models & networks, and DELPHI lab projects. Her work integrates advanced statistical techniques to solve complex problems in neural data analysis, motor control, and neural decoding. Analysis of her publication record reveals consistent contributions to neural decoding methodologies, spike train analysis, and cortical covariance modeling. Her recent work emphasizes practical applications in brain-machine interfaces and neural prosthetics, with a strong focus on improving decoding accuracy through novel statistical approaches to spike sorting and waveform analysis. The integration of machine learning with traditional statistical methods characterizes her interdisciplinary approach. Scientific Awards: 2006 Canadian Journal of Statistics Best Paper Award As an educator, Ventura serves as Co-Director of the Ph.D. Program in Statistics and Data Science while advising students through the CNBC's Program in Neural Computation. Her collaborations span multiple departments and research centers, particularly through the DELPHI Lab Group which focuses on computational neuroscience applications. Current research directions suggest continued innovation in neural decoding algorithms and applications to motor control systems.

• Computational Neuroscience
• Time Series Forecasting
• Graphical Models
• +6 more

Matthew A. Smith is a Professor in the Department of Biomedical Engineering and the Carnegie Mellon Neuroscience Institute, where he serves as Co-Director of the Center for the Neural Basis of Cognition. His research bridges computational and experimental neuroscience to understand visual perception, cognition, and motor control. Dr. Smith's research focuses on neural engineering, visual perception, cognition, eye movements, and neural circuits . His laboratory investigates how groups of neurons interact to construct visual perception and translate it into cognitive processes and motor outputs. The lab employs a multi-scale approach combining single-neuron electrophysiology with global signals like EEG and near-infrared imaging, examining both normal and disease states of the brain. Analysis of his recent publications reveals strong trends in brain stimulation optimization (e.g., MiSO/OMiSO frameworks), neural population dynamics during cognitive tasks, visual cortex plasticity , and non-invasive neurotechnology development. His work spans fundamental neuroscience questions about working memory and perception while developing practical tools for brain monitoring and intervention. NIH K99/R00 Pathway to Independence Award Research to Prevent Blindness Career Development Award Dr. Smith has secured substantial funding from NIH, NSF, Research to Prevent Blindness, Schaffer Foundation for Glaucoma Research, and Hillman Foundation to support his research program. His laboratory actively develops novel methodologies for neural recording and stimulation while investigating fundamental mechanisms of visual processing and cognition. The lab maintains strong collaborative ties within Carnegie Mellon's neuroscience ecosystem through the Center for the Neural Basis of Cognition.

• Computational Neuroscience
• Systems Neuroscience
• Neural Engineering
• +7 more

Krishna V. Shenoy is the Hong Seh and Vivian W. M. Lim Professor of Engineering at Stanford University, an Investigator at the Howard Hughes Medical Institute (HHMI), and Director of the Neural Prosthetic Systems Laboratory (NPSL). He also co-directs the Neural Prosthetics Translational Laboratory (NPTL) and serves on scientific advisory boards for institutions like the University of Washington’s Center for Sensorimotor Neural Engineering and companies such as CTRL-Labs Inc., MIND-X Inc., and Heal Inc. Research Interests Shenoy’s work bridges neuroscience and neuroengineering. His neuroscience research explores the neural basis of movement preparation and generation using electrophysiological, behavioral, computational, and theoretical approaches. His neuroengineering focus centers on developing high-performance neural prosthetic systems (brain-machine interfaces) to restore function for individuals with paralysis. Scientific Awards Burroughs Wellcome Fund Career Award in the Biomedical Sciences McKnight Technological Innovations in Neurosciences Award NIH Director’s Pioneer Award 2010 Stanford Postdoc Mentoring Award Alfred P. Sloan Fellowship AIMBE Fellow The Andrew Carnegie Prize in Mind and Brain Sciences (2018) Labs & Collaborations Shenoy leads the Neural Prosthetic Systems Laboratory (NPSL) and co-directs the Neural Prosthetics Translational Laboratory (NPTL), emphasizing translational research to bridge basic science and clinical applications.

• Neuroscience
• Neuroengineering
• Neural Prosthetics
• +5 more

Douglas Weber is a Professor in the Department of Mechanical Engineering at Carnegie Mellon University (CMU), where he leads research at the intersection of neuroscience and engineering. He is a founding member of DARPA’s Biological Technologies Office and has led major neurotechnology programs, including HAPTIX, ElectRx, and TNT. His research is centered at the NeuroMechatronics Lab, with collaborations at the University of Pittsburgh and industry partners like Meta. He is actively engaged in developing neuroprosthetics, spinal cord stimulation, and wearable devices for sensory and motor restoration. Ph.D., Bioengineering, Arizona State University (2001) MS, Bioengineering, Arizona State University (2000) BS, Biomedical Engineering, Milwaukee School of Engineering (1994) Post-doctoral training, Centre for Neuroscience, University of Alberta Douglas Weber's research focuses on understanding sensory feedback in motor control, neuroplasticity, and the development of neurotechnologies for individuals with stroke, spinal cord injury, or limb loss. His work spans neural engineering , biomechanics , neuromodulation , and bioelectronic medicine . He aims to translate academic research into real-world applications, such as brain-computer interfaces and sensory-restoring prosthetics. His lab explores high-density electromyography (HD-sEMG), spinal cord stimulation, and closed-loop systems to enhance human-machine interaction. The recent articles reflect a strong emphasis on spinal cord and peripheral nerve stimulation , brain-computer interfaces , and sensory feedback restoration . Many studies investigate neuromodulation for motor recovery , endovascular BCIs , and non-invasive stimulation techniques . There is a recurring theme of clinical translation , with multiple early feasibility trials and human studies. The work integrates electrophysiology , signal processing , and rehabilitation engineering to improve outcomes for patients with neurological impairments. Douglas Weber has mentored over 100 undergraduate, graduate, and medical students, as well as several postdoctoral fellows. He holds eight issued U.S. patents and has secured significant research funding, including awards from ARPA-H and collaborations with the Pitt CTSI. His work has led to practical technologies aimed at improving independence for individuals with paralysis or limb loss. He is actively involved in several research teams and labs, most notably the NeuroMechatronics Lab at CMU. His team collaborates with institutions like the University of Pittsburgh and industry partners such as Meta to develop wearable interfaces and implantable devices. Projects include wristband-based sEMG control systems, spinal cord stimulation for stroke recovery, and bioelectronic implants for chronic disease management.

• Neural Engineering
• Neuroprosthetics
• Sensory Feedback
• +12 more

Shihong Huang is a Teaching Professor in the Information Systems Program at Carnegie Mellon University's Dietrich College of Humanities and Social Sciences. Her research focuses on human-system co-adaptation, integrating software engineering with human-computer interaction to optimize symbiotic collaboration between humans and autonomous systems. Education: Ph.D. and M.Sc. in Computer Science from University of California, Riverside Her work explores how mutual adaptation between humans and systems—using tools like EEG monitoring—can enhance decision-making in AI-driven environments. Current research includes brain-computer interaction (BCI), health informatics, and clinical applications of computer systems. She received FAU's Award for Excellence and Innovation in Undergraduate Teaching twice (2008, 2015) and the Engineer’s Council Merit Award in 2022. Her teaching interests include web application development, software engineering, and information visualization.

• Software Engineering
• Human-Computer Interaction
• Brain-Computer Interfaces
• +3 more