Find Academic Supervisors

• Neuroscience
• Neurophysiology
• Neuroplasticity
• +7 more

Paolo Belardinelli is an Associate Professor at the University of Trento’s Department of Cellular, Computational and Integrative Biology (CIBIO) and the Center for Mind/Brain Sciences (CIMeC). He holds a MS in Mathematical Physics from Sapienza University of Rome and a PhD in Neuroscience from the G. D’Annunzio University of Chieti. His academic career includes senior researcher roles at institutions like Aalto University (Finland), the MEG Center Tuebingen (Germany), and the University of Tuebingen. He has been an Assistant and Associate Professor at CIMeC since 2020. Belardinelli’s research focuses on understanding how brain states influence responses to stimuli and optimizing closed-loop neurostimulation. Key areas include EEG-TMS synchronization, brain connectivity, neuroplasticity, and clinical applications in stroke and psychiatric disorders. He has pioneered real-time EEG-triggered TMS methodologies to enhance rehabilitative outcomes. Education: MS in Mathematical Physics, Sapienza University of Rome PhD in Neuroscience, G. D’Annunzio University of Chieti His work combines EEG/MEG source localization with non-invasive stimulation techniques. He has authored/co-authored over 80 publications and serves on editorial boards for journals like Journal of Neuroscience Methods and Cognitive Processing . Notable grants include studies on Bayesian modeling for source reconstruction and functional connectivity mapping. Belardinelli advises PhD students and master’s theses, focusing on neurorehabilitation and neurophysiological mechanisms. His lab explores cutting-edge neurotechnology, including hyperbolic neural networks for aging trajectory modeling and reinforcement learning for closed-loop TMS optimization. Key achievements include defining EEG-TMS standards and demonstrating state-dependent stimulation efficacy.

Eric MedvetView profile

Associate Professor

• Evolutionary Computation
• Machine Learning
• Grammatical Evolution
• +3 more

Eric Medvet is an Associate Professor of Computer Engineering at the Department of Engineering and Architecture (DIA), University of Trieste, Italy. He leads the Evolutionary Robotics and Artificial Life Lab and co-leads the Machine Learning Lab. His research focuses on Evolutionary Computation, Machine Learning, and their applications to robotics, including Grammatical Evolution, Genetic Programming, and soft robotics. He received the Google Faculty Research Award 2019 for a project on modular soft robots and co-authored a best paper at EuroGP 2024. His recent work emphasizes interpretable control policies, quality-diversity optimization, and evolutionary learning of formal specifications. Medvet teaches advanced courses on machine learning, evolutionary robotics, and programming, consistently updating curricula to reflect cutting-edge research. His labs are hubs for interdisciplinary projects, blending theory with practical implementations in robotics and AI. Research Interests: Medvet’s research spans evolutionary algorithms applied to robotics, with a focus on modular systems. He explores how genetic programming and grammatical evolution can optimize robotic designs and controllers. His work on ‘totipotent neural controllers’ and ‘body-brain co-evolution’ exemplifies efforts to create adaptable, specialized robots. He also investigates formal methods (e.g., STL specifications) for system validation and interpretable AI models. Recent trends in his publications reflect a shift toward practical applications, such as wearable movement analysis and sim-to-real transfer in robotics. Awards: Google Faculty Research Award 2019, EuroGP 2024 Best Paper Award Labs: Evolutionary Robotics and Artificial Life Lab, Machine Learning Lab Teaching: Courses include Advanced Programming, Introduction to Machine Learning, and Evolutionary Robotics, with a focus on Java and practical software development. Medvet’s approach integrates theoretical rigor with hands-on experimentation, emphasizing open-source tools like JGEA and 2D-VSR-Sim. His work bridges computational intelligence and real-world robotics challenges, driving innovation in both fields.

Matilde BalbiView profile

Academic

• Neuroscience
• Stroke Recovery
• Neurovascular Biology
• +7 more

Dr. Matilde Balbi is a Senior Research Fellow at the Queensland Brain Institute, The University of Queensland. She holds a PhD in Neuroscience from Ludwig Maximilians University (Munich, Germany) and completed postdoctoral training at the University of British Columbia (Canada), supported by fellowships from the Leducq Foundation, CPSR, and MSFHR. Her research focuses on stroke recovery, neurovascular mechanisms, and neurodegenerative diseases, leveraging imaging techniques and brain stimulation in rodent models. Education: Bachelor and Master of Science in Medical Biotechnology (University of Naples, Italy) PhD in Neuroscience (Ludwig Maximilians University, Munich, Germany) Research Interests: Dr. Balbi's work centers on understanding cerebral blood flow dynamics in health and disease, particularly in stroke, traumatic brain injury, and subarachnoid hemorrhage. She investigates neuromodulation strategies (e.g., gamma oscillations, optogenetics) to enhance recovery. Her lab combines advanced imaging, electrophysiology, and closed-loop approaches to study neurovascular coupling and synaptic plasticity. Funding & Grants: Current: NHMRC IDEAS Grant (2023–2027) for cortical sensorimotor control in stroke recovery Past: Brain Foundation Grant, MNDRA Grant, Bionics Queensland Challenge, and U21 Health Sciences Fund Supervision: Available for PhD supervision. Current research topics include stress modulation in stroke recovery and AI-driven neuronal connectivity analysis. Completed projects include visual stimulation effects on neurovascular reactivity in stroke models. Labs & Teams: Leads her own laboratory at the Queensland Brain Institute, focusing on translational neuroscience and stroke recovery innovations.

Ken GoldbergView profile

Professor

• Artificial Intelligence
• Robotics
• Deep Reinforcement Learning
• +5 more

Ken Goldberg is a Professor of Industrial Engineering and Operations Research at UC Berkeley, with secondary appointments in EECS, Art Practice, and UCSF's Radiation Oncology. He joined UC Berkeley in 1995 and holds a PhD from Carnegie Mellon University (1990) and dual degrees from the University of Pennsylvania (1984). His research spans robotics, automation, and art, focusing on artificial intelligence, medical robotics, and human-robot interaction. He leads the AUTOLab and is a co-founder of the Berkeley Artificial Intelligence Research (BAIR) Lab. Goldberg's art installations, including collaborations with Sundance films, explore the intersection of technology and humanity. He has pioneered cloud robotics, surgical robotics, and collaborative filtering systems. Notable awards include the IEEE Fellow (2005), Joseph Engelberger Robotics Award (2000), and NSF Presidential Faculty Fellow (1995). His work integrates technical innovation with societal impact, emphasizing ethical AI and automation. Educations: PhD in Computer Science, Carnegie Mellon University (1990) BSEE in Electrical Engineering, University of Pennsylvania (1984) BSE in Economics, UPenn Wharton (1984) Research Interests: Goldberg's work bridges robotics, AI, and art. Key areas include cloud robotics (FogROS2), surgical robotics (Cal-MR), AI ethics, and interactive systems. His research in dexterous manipulation and grasp planning (Dex-Net) has advanced robotic capabilities in manufacturing and healthcare. Publications: Over 300 papers, including influential works on grasp planning, robotic learning, and collaborative filtering. Recent focus on AI in robotics, surgical assistance, and ethical automation. Awards: IEEE Fellow (2005) Joseph Engelberger Robotics Award (2000) NSF Presidential Faculty Fellow (1995) Advising and Grants: Supervised numerous students in robotics, automation, and AI. Active in funding initiatives like the National Robotics Initiative and NSF grants. Labs/Teams: Leads AUTOLab and co-founded the Berkeley Center for New Media (BCNM). Collaborates with industry partners like Ambi Robotics and Jacobi Robotics.

Preeya KhannaView profile

Assistant Professor

• Sensorimotor learning and control
• Neural engineering
• Brain-machine interfaces
• +5 more

Preeya Khanna is an Assistant Professor in the Department of Bioengineering within the College of Engineering at the University of California, Berkeley, with affiliations at the Helen Wills Neuroscience Institute. Her research integrates sensorimotor neuroscience, computational modeling, and neurotechnology development to restore movement in damaged sensorimotor systems. Her educational background includes: PhD in Bioengineering from UC Berkeley (2017) BSE in Bioengineering & Mathematics from the University of Pennsylvania (2012) Dr. Khanna's research focuses on uncovering neural principles of dexterous movement control through studies of sensorimotor learning, brain-machine interfaces, and rehabilitation engineering. She develops neurophysiologically-grounded therapies including neurofeedback and neuromodulation approaches for stroke rehabilitation, bridging computational neuroscience with clinical translation. Her recent publications (2022-2025) reveal a strong trajectory in decoding motor cortex dynamics, particularly beta oscillations and ensemble reactivations, for therapeutic applications. Key themes include closed-loop BMI systems for severe stroke patients, low-frequency stimulation protocols, and kinematic-based impairment assessment, demonstrating convergence of computational modeling, neural engineering, and clinical rehabilitation. Her scientific recognition includes: Sloan Research Fellow (2024) Google Faculty Research Award (2024) NIH Director's New Innovator Award (2024) As Principal Investigator of the Sensorimotor Neural Engineering Lab, Dr. Khanna leads NIH and Google-funded research on neurotechnology for motor rehabilitation. Her lab actively recruits researchers to develop next-generation brain-machine interface therapies targeting sensorimotor restoration. The Sensorimotor Neural Engineering Lab investigates how sensory and motor brain signals coordinate dexterous behaviors, leveraging these principles to design neurotechnology for damaged sensorimotor networks. Current work focuses on closed-loop neurostimulation systems and real-time neural decoding for stroke rehabilitation.

Madineh Sedigh-SarvestaniView profile

Assistant Professor

• Visual Neuroscience
• Sensory Processing
• Embodied Cognition
• +5 more

Madineh Sedigh-Sarvestani is an Assistant Professor in the Department of Neurobiology & Behavior at Cornell University and a Howard Hughes Medical Institute Freeman Hrabowski Scholar. Her research investigates how the brain's sensory systems integrate with bodily movements to shape visual perception. BA in Engineering from Harvey Mudd College PhD in Engineering Science & Mechanics from Penn State University Her lab employs in vivo two-photon imaging , electrophysiology , and machine learning to study visuomotor circuits in tree shrews and rats , focusing on embodied cognition principles. Recent publications highlight her work on visual field mapping, thalamocortical dynamics, and cross-species comparisons. Scientific contributions include: Defining sensorimotor signatures in visual circuits Advancing closed-loop experimental paradigms Organizing Tree Shrew Discovery meetings to promote species diversity in neuroscience The lab offers mentorship emphasizing teamwork, scientific rigor, and public communication of research, with support for conference participation and career development.

Zakaria DjebbaraView profile

Associate Professor

• Neuroarchitecture
• Cognitive Neuroscience
• Virtual Reality
• +3 more

Zakaria Djebbara is an Associate Professor at Aalborg University's Department of Architecture, Design and Media Technology, part of The Technical Faculty of IT and Design. His research explores how built environments influence human cognitive processes, focusing on neural synchronization patterns and the interplay between architectural features and attention/working memory. He employs Virtual Reality (VR) combined with mobile EEG and body tracking to study these phenomena. Key Projects: "Neuronal and Urban rhythms effect on memory" (2024-2027, PhD supervision) "Architecture & Transitions: an enactive and electrophysiological approach" (2017-2020) Research Themes: Neuroarchitecture and cognitive neuroscience Embodied cognition and sensorimotor interactions Mobile brain/body imaging (MoBI) His work bridges architecture, neuroscience, and technology, examining how spatial rhythms and visual patterns shape neural activity. Recent studies include EEG investigations in historical cities like Ghardaïa, Algeria, and VR experiments analyzing navigation strategies. Awards: 2021 Spar Nord Fondens Forskningspris 2020 Brain Products MoBI Award Key Contributions: Developed the BeMoBIL pipeline for multimodal neuro-architectural data analysis Advocated for biophilic design and adaptive facades to enhance well-being Co-organized the Designing Atmospheres symposium (2023)

Jason LiView profile

Assistant Professor

• Theoretical Computer Science
• Algorithms Design
• Computational Complexity
• +3 more

Jason Li is an Assistant Professor in the Computer Science Department at Carnegie Mellon University. He specializes in robotics and artificial intelligence, focusing on theoretical foundations of algorithms and complexity. His research emphasizes robotic manipulation, simulation frameworks, and perception systems for real-world applications. He teaches advanced courses including 15-750 (Algorithms), 15-754 (Advanced Algorithms), 15-451 (Algorithm Design and Analysis), 15-651 (Algorithms in the Real World), and 15-850 (Special Topics in Robotics) , reflecting his expertise in algorithmic theory and practical implementation. Key research contributions include frameworks like ORBIT for surgical robotics and DexWild for dexterous human-robot interaction. His work bridges simulation and real-world deployment, with a focus on synthetic data (Synthetica) and physically consistent perception models. He currently advises PhD students Henry Fleischmann and George Li. His teaching spans both undergraduate and graduate levels, emphasizing hands-on projects and theoretical rigor.

Eugenia ChiappeView profile

Researcher

• Neuroscience
• Sensorimotor Integration
• Neural Circuits
• +3 more

Eugenia Chiappe is a Principal Investigator at the Champalimaud Center for the Unknown , leading the Chiappe Lab . Her research focuses on the neural circuits involved in self-movement estimation and sensorimotor integration in Drosophila melanogaster , combining behavioral paradigms , neural activity recording , and genetic tools to uncover mechanistic explanations for brain function in adaptive behaviors. Research highlights : Investigation of HS and VS cells in flies as self-motion estimators . Development of head-fixed walking paradigms for reversible neural perturbation. Identification of nonvisual signals modulating visual responses during locomotion. Exploration of neural dynamics encoding combined visual and motor inputs. Scientific contributions : Marie Curie Career Integration Grant PCIG13-GA-2013-618854 Bial Foundation grant 191/12 Collaborative projects on state-dependent sensory processing (Journal of Neuroscience, 2017) Advising and Collaborations : Mentored Daniel Tendero (External PhD Student) and Miguel Paço (INDP PhD Student). Collaborations with teams at Yale University , MIT , and University College London .

Siddhartha SikdarView profile

Professor

• Biomechanics
• Assistive Technology
• Biomedical Imaging
• +9 more

Siddhartha Sikdar is a Professor in the Department of Bioengineering at George Mason University and Director of the Center for Adaptive Systems of Brain-Body Interactions (CASBBI). His work focuses on developing wearable ultrasound imaging systems for prosthetic control and studying brain-body interactions in chronic pain, stroke, and spinal cord injury. PhD, Electrical Engineering, University of Washington (2005) MS, Electrical Engineering, University of Washington BS, Electrical Engineering, Indian Institute of Technology, Kanpur Research interests span biomechanics , neuroprosthetics , and rehabilitation engineering , with a focus on wearable ultrasound sensors and hybrid imaging techniques . His publications highlight trends in prosthetic control systems, muscle fatigue monitoring, and biomedical imaging innovations. Key scientific awards include: NSF CAREER Award Volgenau School of Engineering Rising Star Award Mason’s Emerging Researcher/Scholar/Creator Award Active grants from the National Science Foundation , National Institutes of Health , Department of Defense , and Veterans Health Administration support his research on intuitive prosthetic control and brain-body interactions. His lab (CASBBI) integrates engineering and neuroscience to address public health challenges.

Daniel HuberView profile

Professor

• Neuroscience
• Neurophysiology
• Optical Imaging
• +2 more

Prof. Daniel Huber is a leading neuroscientist at the University of Geneva , focusing on understanding sensorimotor integration and cortical dynamics. His work bridges motor cortex research , optical imaging , and computational modeling to decode how neural circuits govern voluntary movements and tactile perception. Research spans rodent models and primate evolution through projects like the eLemur 3D brain atlas Key methodologies: in vivo two-photon microscopy , optogenetic manipulation , and biomechanical simulations Lab members include graduate students and collaborators studying Pacinian corpuscle physiology and forelimb motor control Recent publications reveal groundbreaking insights on lamellar Schwann cells' role in vibration perception , tonotopic mapping in the brainstem , and cross-species neural circuit comparisons . His team's 2025 Science Advances paper demonstrates Schwann cell contributions to mechanosensitivity using optogenetic inactivation. Current projects focus on neuronal network dynamics in decision-making and 3D anatomical frameworks for comparative studies. The lab's Swiss National Science Foundation grants and European Research Council funding enable these interdisciplinary approaches.

• Neuroscience
• Machine Learning
• Control Theory
• +4 more

Dr. LIU Quanying is an Associate Professor in the Department of Biomedical Engineering at the Southern University of Science and Technology (SUSTech), where she has been a faculty member since September 2019. She serves as the Principal Investigator of the Neural Computing and Control Laboratory (NCC lab) and is a doctoral supervisor. Prior to joining SUSTech, she earned her PhD in Biomedical Engineering from ETH Zurich and conducted postdoctoral research at Caltech. Education: PhD in Biomedical Engineering, ETH Zurich (2013-2017) Master in Computer Science, Lanzhou University (2010-2013) Bachelor in Electrical Engineering, Lanzhou University (2006-2010) Research Interests: Dr. Liu’s research integrates neuroscience, machine learning, and control theory. Her work focuses on multi-modal neural signal processing (EEG, sEEG, fMRI, DTI), explainable AI for neuroscience, and optimization techniques for neuromodulation (tES, TMS). She has developed high-density EEG source localization algorithms and data-driven brain network modeling frameworks, aiming to enhance precision in neural stimulation and control. Scientific Awards: The New Brain 30 (2023) AAIC Travel Award (2019) Estes Stars Award (2018) 深圳市孔雀人才计划C类 Laboratory and Team: As the PI of the NCC lab, Dr. Liu leads a team focused on machine learning algorithms, neurocomputational modeling, and neurofeedback control. The lab actively recruits graduate students, postdocs, and visiting researchers, emphasizing interdisciplinary collaboration in neuroscience and AI.

Shlomi HaarView profile

Senior Lecturer

• Neurosciences
• Cognitive Sciences
• Psychology
• +5 more

Dr. Shlomi Haar is a Senior Lecturer in Cognitive Neuroscience at the University of Surrey and an Honorary Senior Lecturer at the Department of Brain Sciences, Imperial College London. He also serves as the Movement Data and Living Lab Lead at the UK Dementia Research Institute Care Research and Technology Centre since 2023. Education: BSc, Biomedical Engineering (Ben-Gurion University, 2007-2011) MSc, Biomedical Engineering (Ben-Gurion University, 2010-2012) PhD, Brain and Cognitive Sciences (Ben-Gurion University, 2013-2017) Dr. Haar investigates neurobehavioural mechanisms of human movement in health and disease, with a focus on Parkinson's disease (PD) and Deep Brain Stimulation (DBS). His interdisciplinary research bridges engineering, neuroscience, and data science to develop Real-World Motor Neuroscience approaches through: Novel sensor technology Adaptive AI models Ecologically valid motor learning paradigms Digital biomarkers for neurodegeneration Closed-loop therapeutic systems Explainable neural network architectures His recent 2023-2025 publications demonstrate trends in: Applying embodied VR for motor rehabilitation Quantifying cerebellar role in PD Developing AI-driven biomarkers from EEG/fMRI Validating markerless motion capture for clinical use Understanding motor variability in PD and DBS Improving clinical outcome measures for long-term trials Scientific awards include: Royal Society – Kohn International Fellowship (2017-2020) Edmond and Lily Safra Research Fellowship (2020-present) Dr. Haar leads the Haar Lab at Surrey, focusing on: Developing digital biomarkers for motor conditions Integrating robotics and VR in motor rehabilitation Creating adaptive closed-loop therapies for PD Quantifying individual differences in motor learning Translating lab findings to real-world applications Collaborations with Milbotix Ltd and SERG Technologies Ltd

Paul R. SchraterView profile

Associate Professor

• Machine Learning
• Probabilistic Inference
• Perception
• +5 more

Paul R. Schrater is an Associate Professor with a joint faculty appointment in the Departments of Psychology and Computer Science at the University of Minnesota. His research bridges computational approaches with cognitive and neural sciences, focusing on understanding human perception, decision-making, and motor control through probabilistic and machine learning frameworks. Dr. Schrater's research interests span across several interconnected domains in cognitive science and artificial intelligence. His work primarily investigates how humans use probabilistic methods to navigate perception and motor control challenges. He approaches these questions through a structured interdisciplinary lens, placing computational theory at the forefront to drive empirical work. His research aims to develop precise, testable theories of brain computations that can be validated through behavioral measures and brain imaging techniques. His publication record reveals consistent work in human perception, decision theory, and motor control, with a strong emphasis on Bayesian modeling approaches. Over the past decade, his research has evolved to incorporate increasingly sophisticated probabilistic models to explain phenomena ranging from perceptual bistability to optimal grasping strategies under uncertainty. His work demonstrates a clear trajectory toward integrating multiple sensory modalities and cognitive processes within unified computational frameworks. Dr. Schrater directs the Computational Perception and Action Laboratory, located in Elliott Hall at the University of Minnesota. The lab is equipped with a Visuo-Haptic workbench for testing visuo-motor behavior with virtual objects, and an adjustable reach laboratory for studying reaching behavior with actual objects. His teaching includes graduate courses such as "Mathematical Models of Human Behavior" (PSY 5018H) and "Artificial Intelligence II" (CSCI 5512), reflecting his interdisciplinary approach that bridges psychology and computer science.

Patrick GanzerView profile

Assistant Professor

• Neurotechnology
• Closed-Loop Neuromodulation
• Machine Learning
• +5 more

Patrick Ganzer serves as an Assistant Professor in the Department of Biomedical Engineering within the College of Engineering at the University of Miami. His research focuses on developing closed-loop neuromodulation therapies, with particular emphasis on integrating machine learning approaches to decode biosignals and promote neuroplasticity for neurological recovery. Ganzer's research interests span multiple interconnected domains including closed-loop neuromodulation systems, machine learning applications for physiological signal processing, and neuroplasticity mechanisms following neurological injury. His laboratory investigates how reactive nerve stimulation can be precisely timed to disease events to optimize therapeutic outcomes, with applications in spinal cord injury, stroke recovery, and cardiovascular dysfunction. His recent publications reveal a strong focus on using artificial intelligence to detect and reverse physiological abnormalities, particularly in cardiovascular and neurological contexts. The research demonstrates how machine learning can decode sympathetic responses, optimize vagus nerve stimulation timing, and predict clinical outcomes in conditions like atrial fibrillation. Krishna Kumar New Investigator Award (2022) Ganzer leads the Ganzer-Kanumuri Neurotechnology Lab, mentoring multiple PhD students, postdoctoral fellows, and undergraduate researchers. His research is supported by significant NIH funding (R01 NS131493) and Florida Department of Health grants (COPBC), indicating strong institutional confidence in his research direction. Current projects explore how chronic stress alters supraspinal blood pressure control, the relationship between cardiovascular remodeling and deficits after spinal cord injury, and state-dependent vagus nerve stimulation approaches. The Ganzer-Kanumuri Neurotechnology Lab operates as a collaborative environment with Dr. Vivek V. Kanumuri as Co-Principal Investigator, focusing on closed-loop neuromodulation therapies that integrate disease event detection with precisely timed nerve stimulation to promote functional recovery.