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Kevin Jamieson is an Associate Professor at the Paul G. Allen School of Computer Science & Engineering and an Adjunct Professor in the Department of Statistics at the University of Washington . His academic journey includes a B.S. (2009) , M.S. (2010) , and Ph.D. (2015) in electrical engineering from the University of Washington, Columbia University, and University of Wisconsin–Madison respectively. He completed a postdoc at UC Berkeley's AMP Lab before joining UW in 2017. Ph.D., Electrical Engineering, University of Wisconsin–Madison (2015) M.S., Electrical Engineering, Columbia University (2010) B.S., Electrical Engineering, University of Washington (2009) Jamieson's research lies at the intersection of interactive machine learning , active learning , and sequential decision making . His work focuses on: Adaptive sampling strategies in multi-armed bandits and reinforcement learning (RL) Developing instance-dependent optimal algorithms that adapt to problem difficulty Applications in robotics , human perception studies , and hyperparameter optimization Representation learning for large models and experimental design frameworks His 15 most recent publications (2025-2022) demonstrate expertise in bandit theory , contextual RL , and game-theoretic learning . Notable trends include sample-efficient optimization , adaptive A/B testing , and sim-to-real transfer in robotics. Jamieson has received: NSF CAREER award for foundational contributions Amazon Faculty Research award for innovation in learning systems He actively recruits graduate students and postdocs , emphasizing collaboration in areas like: Multi-agent RL and strategic actor learning Empirical process suprema and adaptive sampling theory Applications in robotics , large language model finetuning , and biomedical data analysis Jamieson leads the Washington AI Lab (WAIL) and develops open-source learning systems like the NEXT framework for real-world adaptive data collection. He serves as co-PI for the Institute for the Foundations of Data Science (IFDS) and co-organizes the Distinguished Seminar in Optimization & Data .

• Machine Learning
• Active Learning
• Multi-Armed Bandits
• +4 more

Prof. Hedan Bai is an Assistant Professor at the Department of Materials, ETH Zürich, specializing in robotics materials, bio-inspired systems, and soft robotics. Their research focuses on developing advanced materials for sensing, energy-efficient systems, and biomedical applications. Notable projects include the SmartSuit architecture for space exploration and self-healing optical sensors for soft robots. Research interests span bioelectronics, stretchable sensors, haptic interfaces, and biomimetic materials. Bai's work integrates material science with robotics to create adaptive, sustainable, and intelligent systems. Key areas include wireless implants for neuromodulation, environmental-responsive textiles, and energy-harvesting devices. Publications highlight innovations in optical waveguides, self-healing materials, and wearable technologies. Their interdisciplinary approach bridges robotics, biomedical engineering, and aerospace applications. No formal awards are listed, but contributions to next-gen materials for robotics are prominent. Advising and grants are not detailed in the text, but Bai's lab focuses on projects like SmartSuit for extravehicular activities and synthetic afferent neural networks. Collaborations likely involve aerospace and biomedical sectors.

• Robotics Materials
• Bioelectronics
• Sensors Technology
• +3 more

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.

• Ubiquitous Computing
• Mobile Sensing Systems
• Wearable Technology
• +4 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

Silvestro Micera is a Full Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) and holds the Bertarelli Foundation Chair in Translational Neuroengineering. He directs the Translational Neural Engineering Laboratory and teaches courses including Neural signals and signal processing and Translational neuroengineering . His research bridges neural interfaces, robotics, and neuroprosthetics to restore motor functions in spinal cord injuries, stroke, and amputations. Micera's research integrates implantable neural interfaces, robotic rehabilitation, and hybrid neuro-prosthetic systems. Key focus areas include: Robotic neurorehabilitation for mobility restoration Neural control mechanisms in movement CNS/PNS neural interface development Bioelectronic modulation for sensory feedback His recent publications emphasize machine learning-driven motor recovery prediction, closed-loop sensory feedback systems, and minimally invasive neuroprosthetics. Trends include AI-optimized stimulation protocols, multimodal data fusion for rehabilitation, and clinical translation of neural bypass technologies. Awards: IEEE EMBS Early Career Achievement Award (2009) IEEE EMBS Technical Achievement Award (2021) Micera leads EU-funded projects such as TIME, CLONS, and NeuWalk, focusing on neural prostheses. He advises 8 current and 18 former PhD students in neuroengineering. His lab collaborates with MIT, Harvard, and industry partners (e.g., Plexon) to advance translational neurotechnologies.

• Neural Engineering
• Neuroprosthetics
• Robotics
• +3 more

Michel M. Maharbiz is a Professor in the Department of Electrical Engineering and Computer Science at the University of California, Berkeley. He leads research on miniaturized bioelectronic interfaces, including neural dust implants and cyborg insects. He holds affiliations with the Berkeley Sensor & Actuator Center (BSAC), Center for Neural Engineering & Prostheses (CNEP), and SWARM Lab. His education includes a Ph.D. in EECS from UC Berkeley (2003) and a B.S. in EE from Cornell University (1997). Maharbiz's research integrates MEMS, ultrasonic systems, and synthetic biology to develop wireless neural interfaces, implantable sensors, and biohybrid devices. Key focus areas are neural dust technology for peripheral nerve recording, magnetoelastic strain sensors for medical applications, and electrochemical biosensing using bacterial flagellar motors. His publications emphasize neural interfaces, ultrasonic implants, and biomedical monitoring. Recent articles explore ultrasonic power delivery (2025), radiation detectors for oncology (2025), and fracture-healing smart plates (2019). Trends include miniaturization of wireless implants, closed-loop therapeutic systems, and novel biomaterials. Scientific Awards: McKnight Technological Innovations in Neuroscience Award (2017) Chan-Zuckerberg Biohub Investigator (2017) NSF CAREER Award (2009) MIT TR10 Top Emerging Technology (2009) Bakar Fellows Spark Award (2012) He directs the Maharbiz Lab, advancing neural dust and bioelectronic interfaces. Projects include impedance-based fracture monitoring, carbon fiber neural arrays, and hernia repair sensors. Funding includes NSF and industry partnerships for implantable device development.

• Neural Interfaces
• Micro/Nano Electro Mechanical Systems (MEMS)
• Biosystems & Computational Biology
• +3 more

Nima Mesgarani is an Associate Professor of Electrical Engineering at Columbia Engineering, Columbia University, affiliated with the Sense, Collect and Move Data Committee. His research bridges engineering and neuroscience through reverse-engineering neural signal processing mechanisms, leading to advancements in brain-machine interfaces, neural prosthetics, and speech processing algorithms. He received his PhD in Electrical Engineering from the University of Maryland and completed postdoctoral training at Johns Hopkins University's Center for Language and Speech Processing and UC San Francisco's Neurosurgery Department. Research Focus Professor Mesgarani's lab integrates computational neuroscience and engineering to study acoustic signal processing. Key areas include: Neural decoding of speech and auditory attention in multi-talker environments Development of brain-controlled hearing technologies Novel speech separation and synthesis algorithms inspired by cortical processing Cross-modal learning between auditory and visual systems Applications of large language models in neural signal interpretation Publication Trends Analysis of his 15 most recent articles (2025) reveals dominant themes: neural decoding techniques using intracranial EEG, brain-inspired speech separation models (e.g., Mamba architectures), applications of large language models in auditory neuroscience, cross-modal distillation methods, and clinical translation of audio processing algorithms. A strong emphasis emerges on real-time brain-computer interfaces and noise-robust speech processing. Laboratory and Collaborations Mesgarani directs an interdisciplinary lab developing neurotechnology for hearing restoration. His team collaborates with neurosurgery departments and speech processing centers, focusing on translating theoretical models into clinical brain-machine interfaces. The lab's work has yielded patents for brain-informed speech separation systems and attention-decoding frameworks.

• Acoustic Signal Processing
• Computational Neuroscience
• Brain-Machine Interface
• +3 more

Dr. Birgit Frauscher is the Lincoln Financial Group Distinguished Professor in Neurobiology at Duke University School of Medicine, where she serves as Professor of Neurology and holds a secondary appointment in the Department of Biomedical Engineering at the Duke Pratt School of Engineering. She is currently the Director of the Duke Comprehensive Epilepsy Center and leads the Analytical Neurophysiology (ANPHY) Lab. Her clinical and research work focuses on epilepsy and sleep medicine, utilizing both invasive and non-invasive electrical recordings to study brain activity in humans. Dr. Frauscher completed her medical training, neurology residency, and subspecialty training in electroencephalography, epilepsy, and sleep medicine at the Medical University of Innsbruck in Austria. After completing her clinical training in 2008, she earned her habilitation degree in 2011. She further specialized in intracranial EEG and signal analysis during a visiting professorship at the Montreal Neurological Institute and Hospital, McGill University (2013-2015), where she later served as an Attending Epileptologist and Group Leader of Epilepsy. Her research interests focus on developing novel seizure-independent EEG markers for the epileptogenic zone, investigating sleep-epilepsy interactions, and using intracranial EEG to study brain physiology during wakefulness and sleep. Her work aims to improve epilepsy diagnosis, prognosis, and treatment outcomes by better localizing the epileptic focus. Dr. Frauscher's recent publications demonstrate her continued leadership in epilepsy research, with over 170 peer-reviewed papers and an H-index of 62. Dr. Frauscher has received several prestigious awards including the Clinician-Scientist awards of the FRSQ (2018-2023), the Michael Prize of the International League against Epilepsy (2019), and the Ernst Niedermeyer Prize from the Austrian Epilepsy Society (2015). Her scholarly work has significantly advanced clinical knowledge in epilepsy and sleep medicine, establishing her as a leading figure in the field. As Director of the Duke Comprehensive Epilepsy Center and head of the ANPHY lab, Dr. Frauscher oversees a research program dedicated to advancing neuroscience through innovative approaches to studying brain activity. Her lab employs quantifiable tools to investigate neurophysiological and pathological processes related to epilepsy and sleep, with the ultimate goal of improving patient outcomes through better understanding of brain function.

• Epilepsy
• Sleep Medicine
• Electroencephalography
• +4 more

Noorbakhsh Amiri Golilarz is an Assistant Professor in the Department of Computer Science at The University of Alabama, College of Engineering. He has established himself as a prominent researcher in artificial intelligence, particularly in computer vision, deep learning, and image processing. His educational background includes: Postdoctoral Research Fellow, Computer Science, Boston College (2023) Ph.D., Electrical and Computer Engineering, Southern Illinois University Carbondale (2023) D. Eng., Computer Science and Technology, University of Electronic Science and Technology of China (2021) M.S., Electrical and Electronic Engineering, Eastern Mediterranean University (2017) B.S., Electrical Engineering, University of Guilan (2012) Dr. Golilarz's research spans multiple domains of artificial intelligence with a particular focus on computer vision, deep learning, and image processing applications. His work addresses challenges in medical imaging, satellite imagery, and cognitive neuroscience. He has made significant contributions to image denoising techniques, control chart pattern recognition, and AI applications in healthcare. His recent work has expanded into generative AI, large language models, and secure machine learning operations. His publication portfolio demonstrates consistent productivity with over 2500 citations and an h-index of 25. His most impactful work includes applications of blockchain and federated learning for COVID-19 detection, optimized support vector machines for medical diagnosis, and innovative image denoising techniques using metaheuristic optimization algorithms. Among his professional achievements: Co-founded AI Letters journal in 2024, serving as Associate Editor-in-Chief Served as Lead Guest Editor and Topic Editor for several SCI-indexed journals Held the role of Conference Program Chair Dr. Golilarz has supervised numerous graduate students and research projects, with his work spanning theoretical advancements in AI algorithms to practical applications in healthcare, energy systems, and cybersecurity. His research group has established collaborations with institutions including Boston College and Mississippi State University.

• Artificial Intelligence
• Computer Vision
• Deep Learning
• +13 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

Huining Li is an Assistant Professor in the Department of Computer Science at North Carolina State University . Her research focuses on Internet of Things (IoT) , cybersecurity , and mobile computing , with a specialized emphasis on mobile health (mHealth) technologies. Education: Ph.D. in Computer Science and Engineering from University at Buffalo (2024). Her work addresses privacy-preserving sensing mechanisms , biomarker measurement , and fairness in dynamic mobile environments , developing systems for chronic wound care, Parkinson’s disease management, and mental health therapy. Recent publications highlight innovations in mmWave biometrics , machine learning for health diagnostics , and non-contact monitoring . Scientific Awards: Best Paper Awards (SenSys 2019, BodyNet 2021, ICHI 2022) Best Paper Candidate (SenSys 2022) Harold O. Wolf Achievement Award (2024) EECS Rising Star (2023) NIH mHealth Training Institute Scholar (2025) She teaches courses in Mobile Health Systems and Applications and Computer Networks , and actively serves on NSF panels , TPC committees (ACM MobiSys, IEEE-EMBS BSN), and as Associate Editor for journals like Elsevier Smart Health.

• Mobile Computing
• Internet of Things
• Cybersecurity
• +5 more

Prof. Dr. Oliver Müller is a Professor at the Department of Philosophy, University of Freiburg, and serves as Vice Dean of the Faculty of Philosophy. His academic journey includes a doctorate from Humboldt University Berlin (2005) and a habilitation in Philosophy (2012). Müller leads research on Philosophical Anthropology, Philosophy of Technology, and Ethics, with a focus on human-machine interactions and Anthropocene ethics. He supervises doctoral and postdoctoral researchers across projects like livMatS and BrainLinks-BrainTools. Research interests span neuroethics, transhumanism, and socio-technological systems, emphasizing ethical frameworks for emerging technologies. Publications explore AI ethics, drone warfare, and phenomenological embodiment, reflecting interdisciplinary engagement with neuroscience and environmental philosophy. Honors include the MTZ Bioethics Award and Heisenberg Fellowship. Müller directs participatory discourse initiatives bridging art and science, fostering public engagement with technological ethics. His work advances critical dialogues on human enhancement and ecological responsibility in the digital age.

• Philosophical Anthropology
• Philosophy of Technology
• Ethics
• +3 more

Jason Ritt is an Associate Professor of Brain Science (Research) and Scientific Director of Quantitative Neuroscience at the Robert J. and Nancy D. Carney Institute for Brain Science, Brown University. He holds affiliations with the Data Science Institute and collaborates across disciplines on quantitative research methods. Education : B.S., M.A., and Ph.D. in Neuroscience from Boston University (1997–2003). Research : Focuses on neural processing during active sensing and neuroengineering for neurostimulation. Combines electrophysiology, optogenetics, and theoretical approaches in rodent models. Develops closed-loop systems for studying sensory neural prosthetics and brain-machine interfaces. Key areas include synaptic diversity, neurocontrol algorithms, and sensory restoration. Teaching : Instructs NEUR 2100 NeuroPracticum, integrating hands-on neuroscience research training.

• Computational Neuroscience
• Data Science
• Neuroengineering
• +3 more

John A. Rogers is the Louis Simpson and Kimberly Querrey Professor at Northwestern University, holding joint appointments in Materials Science and Engineering, Biomedical Engineering, Mechanical Engineering, Chemistry, and Neurological Surgery. He directs the Querrey-Simpson Institute for Bioelectronics. His work bridges soft materials science, bio-integrated electronics, and nanotechnology, with a focus on wearable medical devices, bioresorbable systems, and neural interfaces. Educations: B.A. & B.S. from University of Texas Austin (1989), S.M. from MIT (1992), Ph.D. in Physical Chemistry from MIT (1995). Prior roles include Director of Bell Labs' Condensed Matter Physics Department and faculty positions at University of Illinois at Urbana-Champaign. Research emphasizes soft materials for bio-inspired electronics, including flexible sensors, microfluidic platforms, and bioresorbable implants. His lab has pioneered epidermal electronics, injectable optoelectronics, and neural interfacing systems. Over 1000 peer-reviewed publications and 100+ patents highlight his contributions to nanotechnology and biomedical engineering. Awards include the Benjamin Franklin Medal (2019), MRS Medal (2018), and MacArthur Fellowship (2009). He is a member of the National Academies of Sciences, Engineering, and Medicine. Current projects span bioelectronic medicines, wearable health monitors, and advanced medical imaging tools.

• Bioengineering
• Materials Science
• Biomedical Engineering
• +8 more

Daniel Wolpert is a Professor of Neuroscience at Columbia University , where he is also Vice-Chair of the Department of Neuroscience and a key member of the Zuckerman Mind Brain and Behavior Institute . Additionally, he holds a part-time position as Director of Research at the Department of Engineering, University of Cambridge, and is a Fellow of the Royal Society and the Academy of Medical Sciences . Education: Medical Doctor (1989), D.Phil. in Physiology from the University of Oxford (1992) Previous Positions: Lecturer at Sobell Department of Motor Neuroscience (Institute of Neurology), Professor of Engineering at University of Cambridge (2005–2018) Wolpert is a world leader in sensorimotor control , combining computational neuroscience , Bayesian inference , and robotic/virtual reality technologies to reverse-engineer how the brain generates movements. His work emphasizes the brain's role in reducing sensorimotor uncertainty through predictive modeling and has implications for understanding disorders like autism and Parkinson’s disease. His awards include: Royal Society Ferrier Medal (2020) Minerva Foundation Golden Brain Award (2010) Royal Society Francis Crick Prize Lecture (2005) Daniel Wolpert actively contributes to public science communication, including a 2011 TED Talk on the computational role of the brain in movement, and leads the Wolpert Lab at Columbia, which investigates the neural basis of decision-making , motor learning , and reinforcement learning in both healthy and clinical populations.

• Neuroscience
• Computational Neuroscience
• Sensorimotor Control
• +4 more