Find Academic Supervisors

• Brain-Computer Interfaces
• Neuroscience
• Human-Computer Interaction
• +3 more

Matt Russell is a PhD candidate in Computer Science at Tufts University, focusing on Brain-Computer Interfaces (BCI) within the Human-Computer Interaction Lab. His research emphasizes measuring mental workload via fNIRS and EEG, with applications in LLM-based interfaces and BCI design. He has taught Data Structures (C++) twice as a professor and served as a teaching assistant for multiple computer science courses. His work bridges neuroscience and engineering to enhance adaptive interface technologies. Education: PhD Candidate in Computer Science, Tufts University Research Interests: Russell’s multidisciplinary research explores implicit BCI design, mental workload analysis, and neuroergonomics. Key areas include fNIRS/EEG-based state classification, LLM interface integration, and real-time BCI systems for memory enhancement. His studies often involve human subject trials to evaluate cognitive and physiological responses. Publications: His articles span 2011 to 2025, focusing on neuroimaging techniques (fNIRS/EEG), BCI innovation, and HCI applications. Recent work explores AI collaboration impacts and low-cost EEG systems for cognitive task decoding. Teaching & Advising: Russell has instructed Data Structures (C++) and supported courses in graphics, cybersecurity, and concurrency. He actively contributes to pedagogical efforts in computer science education. Labs & Projects: His research is conducted in the Human-Computer Interaction Lab at Tufts, with open-source projects hosted on GitHub.

Emma ColamarinoView profile

Researcher

• Electroencephalographic (EEG) Signal Processing
• Electromyographic (EMG) Signal Processing
• Brain-Computer Interfaces (BCI)
• +4 more

Emma Colamarino is a Researcher at the Department of Computer, Control and Management Engineering "Antonio Ruberti" of Sapienza University of Rome. She holds an M.Sc. in Biomedical Engineering (2014, cum laude) and a Ph.D. in Bioengineering (2019). Since 2015, she has been a research collaborator at the Neuroelectrical Imaging and Brain-Computer Interfaces Lab of IRCCS Fondazione Santa Lucia in Rome and served as a Visiting Ph.D. student at Imperial College London (2018). From 2019 to March 2023, she was a Post-Doctoral Fellow at Sapienza University. Her research focuses on Advanced electroencephalographic (EEG) and electromyographic (EMG) signal processing Brain-Computer Interface (BCI) protocols for cerebral function recovery Machine learning in neurorehabilitation Hybrid BCIs integrating cortico-muscular networks Recent publications address stroke rehabilitation, BCI design, spectral graph theory, and EMG-EEG integration. Her work spans biomedical data analysis, neuroengineering, and rehabilitation technology validation. Scientific awards include multiple grants from Sapienza University and the Italian Ministry of Health, a Student Award at the 7th International BCI Meeting (2018), and recognition as a Subject Expert (2019). She has supervised/co-supervised 18 MD theses across Biomedical, Management, and Robotics Engineering disciplines.

Surjo R. SoekadarView profile

Professor

• Clinical Neurotechnology
• Brain-Computer Interfaces (BCI)
• Neuromodulation
• +8 more

Surjo R. Soekadar is the Einstein Professor of Clinical Neurotechnology at Charité – University Medicine Berlin. He leads the Clinical Neurotechnology Laboratory , which focuses on developing noninvasive neurotechnologies for treating neurological and psychiatric disorders through closed-loop brain stimulation and advanced brain-machine interfaces (BCI/BMI). His work integrates real-time EEG/MEG monitoring with electromagnetic stimulation to modulate pathological brain oscillations and enhance neuroplasticity in conditions like stroke, spinal cord injury, and psychiatric disorders. Education : Studied medicine in Mainz, Heidelberg, and Baltimore Clinical Training : Residency in Psychiatry and Psychotherapy at University of Tübingen Academic Journey : 2008-2011 Research Fellow at NINDS (USA); 2017 Venia Legendi at University of Tübingen; 2018 First Professor of Clinical Neurotechnology in Germany His research interests span: • Closed-loop neurostimulation combining real-time brain state monitoring with targeted intervention • Next-generation BCI using optically pumped magnetometers (OPM) for mobile MEG recordings • Neurorehabilitation through exoskeleton control and sensory feedback • Neurophysiological modeling of entropy measures and phase flows Recent publications highlight: • Adaptive deep brain stimulation protocols • Real-time phase-sensitive tACS applications • OPM-based BCI innovations • Stroke recovery mechanisms through corticospinal tract analysis Scientific recognition includes: International BCI Research Award BIOMAG Award NARSAD Young Investigator Award Funded by the European Research Council (ERC) , his lab trains doctoral students like David Haslacher (EEG/MEG integration), Khaled Nasr (multicoil TMS optimization), and Annalisa Colucci (entropy-driven BCI development). The team also explores quantum AI applications in clinical decision-making and bidirectional BCI systems using OPM and tES.

Alan RubelView profile

Professor

• Information Ethics
• Privacy and Security
• Surveillance
• +4 more

Alan Rubel is a Professor and Director of the Information School at the University of Wisconsin-Madison. He also serves as a member of the Department of Medical History & Bioethics and an affiliate of the UW Law School. His research focuses on information ethics, policy, and law, particularly addressing privacy, surveillance, algorithmic decision-making, and bioethics. Rubel holds a PhD in Philosophy (2006) and a JD (2003), both from the University of Wisconsin-Madison. He has held academic positions since 2010, including visiting researcher roles at the 4TU Centre for Ethics and Technology in the Netherlands. His work bridges philosophy, law, and technology, emphasizing ethical challenges in automated systems and data-driven societies. Education: PhD in Philosophy (2006), JD (2003), both from UW-Madison. Professional roles include former Director of the Center for Law, Society & Justice, and prior fellowships at Johns Hopkins and Georgetown Universities. Rubel’s interdisciplinary approach addresses contemporary issues like learning analytics, neurotechnology ethics, and digital privacy. His research highlights privacy in higher education, ethical implications of AI, and bioethical dilemmas in emerging technologies. Notable contributions include the book Algorithms and Autonomy: The Ethics of Automated Decision Systems (2021) and influential articles on algorithmic transparency, surveillance, and information fiduciaries.

Colin ConradView profile

Associate Professor

• Artificial Intelligence
• Cognitive Neuroscience
• Educational Technology
• +3 more

Colin Conrad is an Associate Professor of Digital Innovation at Dalhousie University’s Faculty of Management. He also serves as Co-Director of the College of Digital Transformation and Principal of the Cognition and Organizations Research Group. His research focuses on interdisciplinary projects spanning information systems, computer science, and cognitive neuroscience, with particular emphasis on human factors in educational technology and artificial intelligence. His work is supported by NSERC, CFI, and Mitacs. Research interests include mind wandering measurement via EEG, AI ethics, human-AI interaction, and neurophysiological impacts of digital technologies. He explores topics such as virtual teacher perception, privacy calculus in AI systems, and cognitive state awareness in human-AI collaboration. His recent studies address challenges in remote work ergonomics, digital transformation during crises, and legal/ethical aspects of brain-computer interfaces. His publications analyze behavioral responses to cybersecurity notifications, virtual influencer trust dynamics, and adaptive online learning systems. Current projects investigate the cognitive effects of AI-generated media and the neurophysiological foundations of attention in digital environments. Colin’s research is funded through grants emphasizing interdisciplinary innovation and societal impact. He collaborates with industry partners to translate neuroscientific insights into practical applications in education and workplace design.

Peter K. AllenView profile

Professor

• Robotics
• Computer Vision
• Medical Robotics
• +6 more

Peter K. Allen is a Professor of Computer Science at Columbia University's School of Engineering and Applied Science, with a career spanning over three decades in robotics research. His work focuses on robotic grasping , 3D vision and modeling , and medical robotics , where he has made significant contributions to autonomous manipulation and sensor integration. Current affiliation: Columbia University Robotics Lab Academic rank: Professor Key research areas: Robotics, Computer Vision, Artificial Intelligence Education A.B. in Mathematics-Economics from Brown University M.S. in Computer Science from University of Oregon Ph.D. in Computer Science from University of Pennsylvania (recipient of CBS Foundation Fellowship, Army Research Office Fellowship) Research Interests Allen's research bridges fundamental robotics challenges with applied domains. His work on robotic grasping explores low-dimensional subspaces and semantic task suitability, while 3D vision contributions include illumination coherence and texture registration methods. In medical robotics , he develops surgical imaging tools and BCI-enabled grasping systems. Recent publications show trends in: Deep learning for robotic manipulation (2017-2022) Human-robot interaction through BCI and augmented reality Deformable object manipulation (garments, thin shells) Multi-modal sensing (vision-tactile fusion) Scientific Recognition NSF Presidential Young Investigator Award Best Student Paper Award (2007) for collaborative work Over 30 years of continuous funding from NSF, Army Research Office, and medical grants Teaching and Mentorship He has taught graduate courses in robotics (COMS 4733/6731) since 2010, emphasizing hands-on projects with advanced platforms like Baxter, PR2, and Fetch robots. His lab provides immersive training in: 3D photography Humanoid robotics Autonomous navigation Grasp planning

Christine KingView profile

Associate Professor

• Engineering Education
• STEM Education
• Active Learning
• +6 more

Dr. Christine King is an Associate Professor of Teaching in the Department of Biomedical Engineering at UC Irvine's Samueli School of Engineering. Her research focuses on innovative pedagogical approaches including active learning, VR clinical immersion, and STEM education reform. She directs the BioENGINE program integrating innovation and entrepreneurship into biomedical education. Education includes: Ph.D. in Biomedical Engineering, UC Irvine (2014) M.Sc. in Biomedical Engineering, UC Irvine (2010) M.Sc. in Mechanical Engineering, Manhattan College (2009) B.Sc. in Mechanical Engineering, Manhattan College (2008) Her research develops novel educational frameworks using active learning techniques, virtual reality simulations, and real-world clinical applications. Current projects investigate VR clinical immersion platforms for needs-finding training, AI integration in biomedical curricula, and culturally responsive engineering pedagogy. She creates hands-on learning experiences connecting engineering principles to healthcare challenges. Her publications demonstrate strong emphasis on educational innovation (65%), neurorehabilitation technology (20%), and medical device development (15%), with recent work focusing on VR/AI applications in education. Award recognition includes AIMBE Fellowship and best paper awards for educational innovation and medical technology. She leads the BioENGINE program fostering industry-academia partnerships and medical innovation. Laboratory facilities include VR clinical simulation environments and engineering education research labs supporting her pedagogical innovations and medical device prototyping.

Jian KangView profile

Professor

• Biostatistics
• Bayesian Methods
• Gaussian Processes
• +11 more

Jian Kang is a Professor and Associate Chair for Research at the University of Michigan School of Public Health , specializing in Biostatistics . His work focuses on developing advanced statistical methods for large-scale biomedical data , with applications to precision medicine , neuroimaging , and genomics . Education: PhD in Biostatistics, University of Michigan (2011) MS in Mathematics (Statistics), Tsinghua University (2007) BS in Statistics, Beijing Normal University (2005) Research Interests include Bayesian nonparametric methods , deep learning for medical imaging , ultra-high-dimensional variable selection , and graphical models for network inference . His 2025-2023 publications demonstrate expertise in Bayesian hierarchical modeling , spatial statistics , and machine learning for healthcare . Scientific Awards : Michigan SPH Excellence in Research Award (2025) ICSA President's Citation Award (2024) Statistics in Biopharmaceutical Research Best Paper (2023) Best Paper in Biometrics by IBS Member (2022) Fellow, American Statistical Association (2021) Grants include NSF-IIS (2021-2025) for BCI statistical learning , NIGMS (2020-2022) for metabolomics biomarker selection , NIDA (2020-2025) for imaging data analysis , and NIMH (2014-2025) for multidimensional neuroimaging methods . Labs and Teams develop Bayesian computational tools for neuroimaging and spatial transcriptomics , collaborating with institutions like Emory University and University of North Carolina.

• Human-Robot Interaction
• Rehabilitation Robotics
• Socially Assistive Robotics
• +7 more

Carlos Cifuentes is an Associate Professor in Human-Robot Interaction at the Bristol Robotics Laboratory (BRL) , part of the University of the West of England (UWE Bristol) . He also serves as the Deputy Director of the VIVO Hub , a £13.4M UK-funded research initiative (2024-2030) focused on robotics for rehabilitation and healthcare. His research spans Human-Robot Interaction , Rehabilitation Robotics , Healthcare Robotics , and Socially Assistive Robotics , with applications for conditions such as cardiac diseases , post-stroke recovery , spinal cord injuries , cerebral palsy , Parkinson's disease , musculoskeletal disorders , and autism spectrum disorder (ASD) . Carlos has over 15 years of experience and 200+ publications in robotics for rehabilitation and assistive technologies. His recent work explores smart walkers with multimodal feedback, soft prosthetics using polymeric optical fiber sensors , and machine learning models for fatigue and stress estimation. He also investigates inclusive design for social robots in global contexts , including a CASTOR Robot for ASD therapy and collaborative design processes with Colombian communities. Carlos serves as an Associate Editor for IEEE Robotics and Automation Magazine (since 2021), ICRA , and IROS (since 2023). He leads projects integrating wearable sensors , deep learning , and haptic feedback to enhance mobility, autonomy, and quality of life for individuals with disabilities. As Deputy Director of the VIVO Hub, he focuses on long-term deployment of robotic systems in real-world healthcare settings, emphasizing collaboration with clinicians , caregivers , and neurodiverse communities . His work bridges robotics , biomedical engineering , and human-centered design .

Tuukka RuotsaloView profile

Associate Professor

• Brain-Computer Interfaces
• Human-Information Interaction
• Recommender Systems
• +4 more

Tuukka Ruotsalo serves as Associate Professor in the Machine Learning Section at the Department of Computer Science, University of Copenhagen. His research bridges human cognition with computational systems through brain-computer interfaces and physiological computing. As Academy Research Fellow at University of Helsinki (2019-2024), he maintained dual institutional affiliations while leading cutting-edge work in neuro-linguistic modeling and affective relevance. His research focuses on brain-computer interfaces for information retrieval , where he pioneers methods to decode cognitive states from neural signals to improve search systems. Key areas include affective relevance modeling that integrates emotional states into search algorithms, and neuro-linguistic reconstruction that translates brain activity into language. His work on fairness-relevance tradeoffs in recommender systems established Pareto frontier evaluation frameworks now widely adopted in ethical AI research. Recent publications demonstrate how physiological signals like EEG and galvanic skin response can create more adaptive human-information interaction systems. Ruotsalo's scientific recognition includes the prestigious Academy Research Fellow position. His publications in IEEE Transactions on Human-Machine Systems , Journal of the Association for Information Science and Technology , and Communications Biology reveal growing interdisciplinary impact. His advising spans cognitive neuroscience and machine learning students, with notable collaborations across the SCIENCE AI Centre. Current projects include the TreeSense initiative for remote sensing of global tree resources and development of quantum-inspired neural architectures. His lab leverages the department's powerful compute cluster for large-scale physiological data analysis.

Ali YousefiView profile

Associate Professor

• Neuroscience Data Analysis
• Biomedical Engineering
• Brain-Computer Interfaces (BCI)
• +6 more

Dr. Ali Yousefi is an Associate Professor in the Department of Biomedical Engineering at the University of Houston's Cullen College of Engineering. His research focuses on developing statistical and computational methods for analyzing neuroscience data, particularly in linking neural activity to biological/behavioral signals. Key areas include model identification, Bayesian analysis, and real-time neural decoding for applications like brain-computer interfaces and closed-loop stimulation systems. Education: B.S. (Electrical Engineering, Iran University of Science & Technology, 1998), M.S. (Electrical Engineering, Sharif University of Technology, 2000), Ph.D. (Electrical Engineering, University of Southern California, 2014). Postdoctoral training at Harvard Medical School (2019) and Boston University (2019). Research Interests: Neural data analysis frameworks Dynamic neural ensemble modeling Closed-loop brain stimulation systems Bayesian statistical inference High-dimensional data decoding Labs/Teams: Principal Investigator of Yousefilab, focused on neurotechnology and BCI development. Active in interdisciplinary collaborations combining engineering, neuroscience, and machine learning. Key Contributions: Developed methodologies for neural signal decoding, including Bayesian Gaussian process models and latent variable techniques. Pioneered real-time cognitive state prediction and closed-loop systems for enhancing cognitive control in humans.

• Brain-Computer Interface
• Biomedical Engineering
• Neuroprosthetics
• +5 more

Prof. Dr. Gernot R. Müller-Putz is Head of the Institute of Neural Engineering and the Graz Brain-Computer Interface Lab at Graz University of Technology. He serves as Dean of the Faculty of Computer Science & Biomedical Engineering and holds editorial roles at Frontiers in Human Neuroscience IEEE Transactions in Biomedical Engineering Brain-Computer Interface Journal . With over 212 peer-reviewed publications and an h-index of 80, his research focuses on Brain-Computer Interfaces , Neuroprosthetics , and EEG-based Motor Control . His work investigates: Neural signal decoding for spinal cord injury rehabilitation Hybrid BCI systems with error processing Artificial sensory feedback mechanisms Machine learning applications in neural engineering VR-based neurofeedback environments Non-invasive multimodal biosignal recording Research trends show strong emphasis on EEG signal processing , BCI clinical applications , and neurotechnology integration . Scientific Awards : ERC Consolidator Grant (2015) Ludwig-Guttman Award (2017) CYBATHLON Best Paper (2019) State of Styria Research Award (2019) Förderstipendium (2013-2014) He advises 21 PhD students and has managed major projects like MoreGrasp (EU Horizon 2020) , Feel Your Reach (ERC) , and INTRECOM (EU EIC Pathfinder) . The Institute hosts the BCI Racing Team Mirage91 and offers international thesis opportunities.

• Biomedical Engineering
• Neuroscience
• Signal Processing
• +3 more

Andres Soler is a Lecturer at NTNU's Department of Engineering Cybernetics within the Faculty of Information Technology and Electrical Engineering. His research focuses on EEG signal processing for applications in brain-computer interfaces (BCI), stress/health monitoring, and low-density electrode systems. He has published extensively on topics including EEG source imaging, artifact removal, and optimized channel selection techniques. His work bridges biomedical engineering and machine learning, with notable contributions to driver alcohol detection systems and motor imagery classification for neurorehabilitation. Teaching roles include serving as Guest Lecturer for Biomedical Instrumentation and Control (TTK4270) and Adaptive Data Analysis (TTK7), while acting as main lecturer for Industrial Electrotechnics (TTK4240). His research group collaborates internationally on projects like FlexEEG and has presented at conferences such as IEEE EMBC and Brain Informatics. Key research directions include advancing EEG-based systems for clinical and automotive applications, developing algorithms for real-time brain activity decoding, and optimizing EEG hardware configurations for cost-effective implementations. Current trends show focus on enhancing signal quality through artifact mitigation strategies and improving BCI communication systems for locked-in patients.

Yuan YangView profile

Associate Professor

• Neural engineering
• Biomedical imaging
• Physiological systems
• +4 more

Yuan Yang is an Associate Professor at the University of Illinois Urbana-Champaign (UIUC) in the Department of Bioengineering and an adjunct Associate Professor at Northwestern University's Feinberg School of Medicine. He also holds a part-time role as Associate Professor at Carle Foundation Hospital. His research focuses on neural engineering, neurorehabilitation, and biomedical imaging, particularly in stroke recovery and Alzheimer's disease biomarkers. Yang leads the Illinois-Carle Joint Neural Engineering and Rehabilitation Laboratory (JNERL), collaborating on innovative solutions for neurological conditions using advanced imaging and neurotechnology. Education: PhD in Signal and Images from Telecom ParisTech (2013), MS in Biomedical Engineering from Shanghai Jiao Tong University (2010), and BS from Central South University (2008). He has held faculty positions at UIUC, the University of Oklahoma, and Northwestern University since 2017. Research interests include cortical connectivity, non-invasive brain stimulation (e.g., HD-tDCS), functional MRI analysis, and electrophysiological signal processing. His work bridges engineering and clinical applications, addressing motor impairment in stroke and cognitive decline in Alzheimer's. Key contributions include studies on sex differences in brain connectivity and neuroplasticity, as well as developing targeted interventions using EEG and neuroimaging. Yang has received prestigious awards such as the NSF CAREER Award and serves as an IEEE Distinguished Lecturer. Professional service includes roles in grant review for NIH, AHA, and international bodies, along with editorial positions in major journals like Frontiers in Neuroscience and Medical & Biological Engineering & Computing.

Lee MillerView profile

Professor

• Motor Control
• Neural Networks
• Brain-Machine Interfaces
• +3 more

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.