Lee Miller is a Professor of Physiology, Physical Medicine & Rehabilitation, and Biomedical Engineering at the University of Chicago. His research focuses on understanding how the brain encodes movement commands through neural signals, with applications in developing brain-machine interfaces (BMIs) to restore motor function in paralyzed patients. His work integrates neuroscience, engineering, and computational methods to study neural networks in motor systems. Key research areas include decoding cortical signals to predict muscle activity, developing closed-loop BMIs, and investigating functional connectivity in neural circuits. Miller collaborates extensively with the Biomedical Engineering Department and the Interdepartmental Neuroscience Program (NUIN). His lab combines experimental approaches (e.g., chronic neural recordings) with computational tools to study neural dynamics and develop therapeutic technologies. Recent work emphasizes restoring hand function via cortically controlled functional electrical stimulation (FES), translating neural signals into muscle activation. His publications span neural decoding algorithms, sensory feedback systems, and the neurobiology of motor control. Miller’s contributions bridge fundamental neuroscience and clinical neuroengineering, with potential impacts on spinal cord injury rehabilitation and prosthetic control systems.
Kevin F. Kelly is an Associate Professor in the Department of Electrical and Computer Engineering at Rice University. He was formerly the Chair of the Applied Physics Program and is affiliated with the Smalley-Curl Institute. Additionally, he has been a member of the Penn State Center for Nanoscale Science and the Mid-Infrared Technologies for Health and the Environment (MIRTHE) Center at Princeton University. Dr. Kelly co-founded Inview Technology Corporation as its Chief Scientist, focusing on commercializing compressive imaging technologies. He has also consulted for the Baker Institute for Public Policy regarding photovoltaics and taught courses in anthropology and history at Rice. Dr. Kelly holds a B.S. in Engineering Physics from the Colorado School of Mines (1993), followed by an M.S. (1996) and Ph.D. (1999) in Applied Physics from Rice University. His postdoctoral work included fellowships at the Institute for Materials Research in Sendai, Japan, and the Chemistry Department at Penn State University. His research interests span Optics and Photonics, Imaging and Spectroscopy at the nanoscale, and the role of mathematics in image acquisition. He develops Scanning Probe Microscopy techniques and studies Electronic Materials such as graphene and topological insulators. A major focus is Compressive Hyperspectral Imaging systems, including single-pixel camera innovations and advanced microscopy methods. He also pioneers molecular machines like the Nanocar and investigates charge transport in polymer photovoltaics. Over recent years, his work emphasizes interdisciplinary applications, such as integrating compressive sensing with neural networks for machine vision and exploring technological disaster analysis through history courses. His contributions have been recognized with awards like the IEEE Fellow (2022) and Technology Review’s Top 10 Emerging Technologies (2007). In addition to academic roles, Dr. Kelly has co-founded Inview Technology and contributed to grants and collaborations through his involvement in the MIRTHE Center and other institutes. While no formal advisees are listed, his teaching includes courses on nanotechnology since 2009 and he actively engages in policy consultations for photovoltaic commercialization. His lab at Rice and collaborations with the Smalley-Curl Institute drive advancements in nanotechnology and imaging, with a particular emphasis on practical applications of compressive sensing and molecular-scale devices.
Peter Desain is a Professor and Principal Investigator at the Donders Institute for Brain, Cognition and Behaviour, Radboud University. His work focuses on developing advanced brain-computer interfaces (BCI) leveraging evoked potentials, particularly through code-modulated visual and auditory stimuli. He pioneers methods like noise-tagging and Bayesian dynamic stopping to enhance BCI efficiency and accessibility. His research spans neurotechnology, electrophysiological modeling, and clinical applications such as objective EEG audiometry and ALS communication aids. Recent studies emphasize gaze-independent systems, semantic decoding, and minimizing BCI calibration requirements. Key contributions include optimizing c-VEP code-books, real-time fMRI neurofeedback for memory contexts, and literature reviews on BCI design trends. Experimental pilot studies explore auditory attention and high-frequency SSVEP dynamics. No scientific awards are explicitly mentioned. His work integrates multidisciplinary approaches, bridging neuroscience, machine learning, and engineering to advance human-computer interaction and clinical tools.
Dr. Daniel Berio is a researcher at Goldsmiths, University of London, specializing in computational models for human-like movement in digital art and robotics. His work bridges computer graphics, cognitive psychology, and robotic manipulation, focusing on stylized stroke generation, graffiti analysis, and kinematic modeling. He collaborates with Frederic Fol Leymarie and Rejean Plamondon, utilizing the Sigma Lognormal model to simulate human handwriting dynamics. Education : Doctoral thesis on AutoGraff (2021), exploring computational understanding of graffiti and calligraphy. Research Themes : Human-like motion in digital art, kinematic reconstruction from static traces, robotic graffiti generation, and perceptual fluency in aesthetic evaluation. Publications : 15+ works since 2015, spanning ACM Transactions on Graphics, British Journal of Psychology, and conferences like MOCO and IROS. Applications : Font stylization tools, synthetic graffiti generation, compliant robot control, and semantic typography systems.
Alexander Sumich is Professor of Mental Health and Biopsychology at Nottingham Trent University , School of Social Sciences. He co-directs the Centre for Public and Psychosocial Health and co-leads the Affect, Personality and Embodied Brain (APE) Research Group . He also serves as Adjunct Professor in Psychology at Auckland University of Technology, New Zealand , and holds editorial and leadership roles in organizations like the British Society for the Psychology of Individual Differences (Treasurer) and Personality and Individual Differences (Associate Editor). Education: MA in Psychology (First Class Honors), University of Auckland PhD in Psychology as Applied to Medicine, Institute of Psychiatry, London His research spans neuroimaging and electrophysiological methods to investigate the neurobiology of affect-driven behaviors (depression, aggression, hallucinations) and the immune system's role in mental health . He employs virtual reality exposure therapy (VRET) , spiking neural networks , and computational models to enhance mental health interventions. Key areas include gaming disorder , mindfulness effects , and gut-brain axis interactions . Notable research grants include: BIAL Foundation (2023-2024): €58,765 for trauma-paranormal-mental health links MBIE-Catalyst Programme (2020-2024): NZ$2,134,847 for computational neurogenetics UKRI KTP (2021-2024): £204,000 for therapeutic digital displays His external roles include: Treasurer, British Society for the Psychology of Individual Differences Associate Editor, Personality and Individual Differences Co-host, APE2020 Conference
Jian Shi is a Professor in both the Department of Materials Science and Engineering and the Department of Physics, Applied Physics, and Astronomy at Rensselaer Polytechnic Institute (RPI). He also holds a Simons Foundation Pivot Fellowship and has been a Visiting Scholar at the Pritzker School of Molecular Engineering at the University of Chicago. Ph.D. in Materials Science, University of Wisconsin-Madison (2012) Postdoc in Applied Physics, Harvard University (2014) His research focuses on understanding and engineering the optical, electronic, and spintronic properties of novel materials, particularly van der Waals solids, polar/ferroelectric crystals, chiral systems, and materials with tunable Berry parameters. His group develops experimental approaches for energy-efficient quantum and spintronic devices, utilizing strain engineering, symmetry manipulation, and heterostructure design. Recent publications highlight advancements in halide perovskite engineering, strain-induced topological phases, and quantum device applications. Key trends include spin-orbit coupling, ferroelectricity, and 2D materials for computing and energy conversion. Simons Foundation Pivot Fellowship (2023) IEEE Ferroelectrics Young Investigator Award (2023) School of Engineering Outstanding Research Team Award (2024) Early Career Editor roles at Journal of Applied Physics (2020–present) His group has advised numerous Ph.D. students and postdocs now placed at institutions like Applied Materials, Apple, and Micron Technology. Funding sources include NSF, AFOSR, ARO, and IBM. Key lab equipment includes customized ALD, PLD, and CVD systems, cryogenic transport and optical stages, high-pressure reactors, and advanced spectroscopy tools. Collaborative research spans quantum computing, neuromorphic devices, and energy materials.
Fan Yao is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Central Florida's College of Engineering and Computer Science. She received her Ph.D. in Computer Engineering from The George Washington University in 2018 and currently leads the Computer Architecture and Systems Research (CASR) lab. Her research focuses on the intersection of computer architecture, security, and machine learning, with particular emphasis on hardware-based security vulnerabilities and defenses. Dr. Yao's research interests span computer architecture, hardware and system security, AI security, energy-efficient computing, and cloud computing. Her work addresses critical security challenges in modern computing systems, particularly focusing on microarchitecture attacks, hardware-based model tampering in deep learning systems, and information leakage threats in emerging non-volatile memory systems. She has developed innovative defense mechanisms against cache timing channels, branch predictor vulnerabilities, and GPU-based side channels. Her recent publications demonstrate a strong focus on AI security (particularly Deep Neural Network vulnerabilities), hardware security (including cache and branch predictor attacks), and secure memory architectures. The research shows an evolution from traditional computer architecture topics toward the security implications of AI hardware and emerging memory technologies, with increasing emphasis on practical attacks and defenses in real-world systems. NSF GW I-Corps Site Grant Award, 2018 Best Dissertation Award, GWU, 2018 The Norris & Betty Hekimian Engineering Endowment Fellowship, GWU, 2017 Top Picks in Hardware and Embedded Security, 2019 NSF CAREER project award, 2024 Dr. Yao currently leads multiple NSF-funded research projects including 'Understanding and Taming Deterministic Model Bit Flip Attacks in Deep Neural Networks' (NSF SaTC, 2020-2023), 'Towards Secure-By-Design Integration of Emerging Non-Volatile Memory in Future System' (NSF CNS, 2020-2023), and 'Architecting Secure-by-Design Memristor-Based Memories' (NSF CNS, 2019-2022). She has successfully mentored numerous PhD students, many of whom appear as first authors on top-tier conference publications, demonstrating her commitment to graduate education and research mentorship. As the leader of the CASR lab, Dr. Yao oversees a vibrant research group focused on building secure-by-design, efficient, and advanced future systems through novel techniques spanning hardware, computer architecture, and systems. The lab actively publishes at top computer architecture and security conferences including ISCA, MICRO, HPCA, IEEE S&P, and USENIX Security, with multiple papers accepted to these venues annually. The group has developed several influential tools and frameworks for security analysis, including proof-of-concept code for BranchSpec exploits that has been widely cited in the hardware security community.
Raviraj Nataraj is an Associate Professor in the Department of Biomedical Engineering at Stevens Institute of Technology, leading the Movement Control Rehabilitation (MOCORE) Laboratory. He holds a PhD from Case Western Reserve University (2010) and an MS from Stanford University (2003). His research focuses on integrating control systems with assistive technologies to enhance motor function recovery for individuals with neurotrauma, including spinal cord injury, stroke, and amputation. Key areas include wearable sensors, virtual reality environments, and neuroprosthetics. Education: PhD in Biomedical Engineering, Case Western Reserve University (2010) MS in Mechanical Engineering, Stanford University (2003) Research interests emphasize sensory feedback mechanisms, cognitive factors in motor rehabilitation, and the development of personalized computerized interfaces. His lab creates instrumented wearables and VR tools to improve functional movement. Recent work includes optimizing feedback modalities for upper-limb rehabilitation and studying neural responses to altered visual feedback. Notable awards include the NSF CAREER Award (2023) and Stevens’ Harvey Davis Distinguished Teaching Award. He has secured grants from NSF, Department of Veterans Affairs, and New Jersey Health Foundation for projects like ‘Personalizing sensory-driven interfaces for motor rehabilitation’. Professional roles include NIH Review Panelist and Associate Editor for Frontiers of Medical Engineering. He chairs Stevens’ Institutional Review Board and oversees BME faculty searches. The MOCORE Lab (www.mocorelab.com) collaborates on clinical solutions like the ‘Cognition Glove’ and muscle-training braces.
Dr. Mohammad Reza Abidian is an Associate Professor in the Department of Biomedical Engineering at the University of Houston, part of the Cullen College of Engineering. His research focuses on integrating organic bioelectronics with neural tissue to develop neuroelectronic devices for treating neurological disorders. Key areas include materials science (design of biocompatible devices), electronics (neural interface technologies), neuroscience (in vivo testing), and biomedical translation (clinical applications). Education: PhD in Biomedical Engineering from University of Michigan, Ann Arbor; Master’s and Bachelor’s in Biomedical and Mechanical Engineering from Amirkabir University of Technology, Tehran. His work emphasizes multidisciplinary innovation, such as 3D-printed organic semiconductor devices, conductive polymer nanotubes for drug delivery, and femtosecond laser fabrication. He leads the Abidian Lab, pioneering bioelectronic medicine and neural prosthetics. Awards: Single-PI NIH R01 grant (5-year award). His research bridges fundamental science with clinical applications, addressing challenges in neural regeneration, tumor-targeted therapy, and biosensor development. Grants and collaborations support his mission to advance biomedical technologies.
Dr. Michael P. Barry is the Associate Director for Translational Research and a Senior Research Fellow at the Pritzker Institute of Biomedical Science and Engineering, Illinois Institute of Technology. His work focuses on neuroprosthetic design, artificial vision systems, and low-vision rehabilitation. He earned a PhD in Biomedical Engineering from Johns Hopkins University (2018) and a B.A./M.S. in Neuroscience from the same institution (2010). His research includes pioneering contributions to the Argus II retinal prosthesis and the Intracortical Visual Prosthesis (ICVP), emphasizing psychophysical evaluations and device optimization. Dr. Barry has published over 40 peer-reviewed articles and holds a patent for spatial fitting by percept location tracking (2018). He received the Envision-Atwell Award for Low Vision Research in 2017. Key projects include managing the RES-MATCH program for IIT undergraduates and advancing thermal imaging and distance-filtering systems to enhance prosthetic vision. His work integrates neurophysiological studies, clinical trials, and software development to improve visual perception for blind individuals. Collaborations span academic institutions and industry partners like Second Sight Medical Products. Professional memberships include the Association for Research in Vision and Ophthalmology (2010–2020, 2023–2024) and the Society for Neuroscience (2019, 2023). Current research emphasizes optimizing ICVP performance through EEG recordings and electrode stability analysis, while exploring applications in mobility assistance and environmental interaction.
Cristian Ciobanu serves as Professor in the Department of Mechanical Engineering at Colorado School of Mines, where he has maintained continuous faculty appointment since 2004. His academic journey includes postdoctoral research at Brown University prior to joining Mines, with progressive promotions from Assistant to Associate to full Professor by 2014. Educational background: PhD in Physics, The Ohio State University (2001) MS in Physics, The Ohio State University (1998) BS in Physics, University of Bucharest (1995) His research program integrates computational and experimental approaches to address fundamental challenges in nanoscale surface physics and two-dimensional materials . Specialized expertise includes evolutionary algorithms for atomic structure optimization, development of materials for renewable energy applications, and investigation of self-organized nanostructures on crystal surfaces. Current work emphasizes machine learning applications in high-entropy alloy design and piezoelectric property engineering of layered systems. Publication trends reveal sustained focus on transition metal dichalcogenides, computational materials discovery, and piezoelectric response enhancement through alloying. Recent work increasingly incorporates machine learning for materials design while maintaining strong experimental validation through advanced microscopy and spectroscopy techniques. Key recognitions include: NSF Career Award (2009-2014) Research Excellence Award at Colorado School of Mines (2013) Fellow of the Institute of Physics (elected 2014) Ohio State Presidential Fellowship (2000-2001) Research funding has been secured through competitive mechanisms including the NSF Career Award, supporting his authorship of over 60 technical publications and a coauthored book on atomic structure determination. He actively advises graduate students in computational materials science and nanotechnology research within the Mechanical Engineering department. His scholarly activities are complemented by professional memberships in the Materials Research Society, American Physical Society, and American Vacuum Society. While specific laboratory facilities aren't detailed in source materials, his publication record indicates capabilities in computational modeling, scanning probe microscopy, and thin film characterization relevant to nanoscale materials research.
Neil D. B. Bruce is an Associate Professor in the School of Computer Science at the University of Guelph, Canada. His research focuses on computer vision, deep learning, and computational neuroscience, with a strong emphasis on visual saliency, neural networks, and semantic segmentation. He holds a BSc in Computer Science & Pure Math from the University of Guelph, an MASc in Systems Design Engineering from the University of Waterloo, and a PhD in Computer Science from York University. Prior to Guelph, he held academic positions at Ryerson University and the University of Manitoba. Dr. Bruce leads the Vision Lab, exploring topics like attention mechanisms, image processing, and AI-driven solutions for visual computing challenges. His work bridges theoretical models with practical applications, including real-world gaze behavior analysis and exposure blending techniques. Key contributions include saliency prediction frameworks (e.g., AIM model) and semantic segmentation networks (EML-Net, Iterative Gating Networks). His research also intersects with interdisciplinary fields such as neuroscience and healthcare informatics, as seen in recent studies on avian influenza outbreak detection using social media data. Teaching highlights include courses in neural networks, data science, and machine learning. He actively supervises graduate and undergraduate research projects, emphasizing computational methods and AI innovation.
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.
Yujia Zhang is a Tenure Track Assistant Professor at the School of Engineering , École Polytechnique Fédérale de Lausanne (EPFL), leading the Laboratory for Bio-Iontronics (BION) since January 2025. His work focuses on developing iontronic biointerfaces and hybrid intelligent systems for biomedical applications. Academic Affiliations: EPFL School of Engineering, STI-SMT SMT-ENS PhD program committee Research Themes: Droplet-based iontronics, synthetic tissues, advanced manufacturing Research Trends from his publications emphasize microscale droplet iontronics , soft energy systems , and biohybrid interfaces , with applications in neurostimulation , tumor modeling , and biomedical devices . Scientific Awards : 2023: Early-career Research Scientist Representative, UK Parliamentary & Scientific Committee 2022: Excellent Doctoral Dissertation, Chinese Academy of Sciences 2021: Outstanding Doctoral Thesis, Chinese Institute of Electronics 2020: Special Prize for President Scholarship, Chinese Academy of Sciences Academic Contributions include mentoring PhD students and teaching microfabrication technologies. His lab develops 3D-printed synthetic tissues and droplet networks for interactive biological communication.
Dr Christopher Spicer is a Lecturer in Chemistry at the Department of Chemistry, University of York. His research focuses on developing synthetic biomolecules and materials to modulate biological systems, with particular emphasis on tissue regeneration and biomaterial design. The Spicer Lab employs interdisciplinary approaches combining chemical biology, materials chemistry, and bioengineering to create platforms that mimic natural regenerative processes. Key research areas include bioconjugation chemistry, dynamic material systems, and the design of 3D scaffolds for controlled cellular behavior. The lab's work bridges chemistry, biology, and materials science to address challenges in regenerative medicine. Current PhD opportunities are available in these fields. Dr Spicer's research emphasizes modular biomaterial platforms, enzymatically triggered delivery systems, and the development of novel bioactive materials. His work is supported by cutting-edge techniques such as solid-phase peptide synthesis, metadynamics simulations, and advanced microscopy. Contact: chris.spicer@york.ac.uk / Spicer Lab Website
