Pouya Bashivan is an Assistant Professor in the Department of Physiology at McGill University's Faculty of Medicine. His research focuses on developing computational models to explain and regulate neural responses during visual tasks requiring memory, combining machine learning, neuroscience, and cognitive science. Education : Ph.D. in Computer Engineering (2016), Postdocs in Machine Learning (2020) and Computational Neuroscience (2016-2020) His lab investigates: Topographical neural networks for visual cortex simulation Massively-multitask models for prefrontal cortex Saccade-driven visual exploration models Predictive hippocampus models for episodic memory Recent publications explore adversarial robustness, memory-augmented networks, and brain-state decoding. Current projects emphasize causal models, brain-AI alignment, and translating computational neuroscience into therapeutic applications. The lab is located in the McIntyre Medical Sciences Building, Room 1117, Montreal, Quebec.
Lee M. Miller is a Professor and Vice Chair of Academic Affairs in the Department of Neurobiology, Physiology and Behavior at the University of California, Davis, affiliated with the Center for Mind and Brain. His research focuses on neuroengineering, computational neuroscience, and neural mechanisms underlying attention, speech processing, and multisensory integration. Research interests include the development of neural prosthetics, decoding of neuromuscular signals for prosthetic control, and understanding how auditory and visual systems interact during speech perception and attentional processes. His work bridges clinical applications (e.g., cochlear implants) with fundamental neuroscience, leveraging tools like electrophysiological recordings, EEG/MEG, and advanced signal processing techniques. Recent publications highlight innovations in electromyographic speech neuroprosthetics, the topology of neuromuscular signals, and the neural basis of speech-in-noise processing. Miller’s studies emphasize translational potential, such as improving speech synthesis from brain signals and designing haptic feedback systems for motor coordination. His contributions have advanced understanding of neural mechanisms in sensory integration, auditory attention, and the impact of cognitive factors on perception. Miller maintains a lab dedicated to these interdisciplinary efforts, with a focus on both basic science and clinical applications.
Prof. Sophie Schwartz is a leading neuroscientist at the University of Geneva , where she heads the Sleep & Cognition Lab within the Faculty of Medicine . Her research integrates neuroimaging (fMRI, hd-EEG, MEG) , behavioral testing , and computational modeling to unravel the neural mechanisms underlying memory consolidation , emotion processing , and dreaming during sleep, while also developing clinical interventions to enhance sleep in neurological and psychiatric disorders.
Kelly Bijanki is an Associate Professor of Neurosurgery, Director of Intracranial Monitoring Research, and holds joint appointments in Psychiatry and Neuroscience at Baylor College of Medicine. Her work bridges clinical neurosurgery and neuroscience, focusing on understanding the neural basis of affective disorders and developing neuromodulation therapies. She directs the Translational Neuromodulation Lab, where she leverages stereotactic electroencephalography (sEEG) to study deep brain structures critical to emotional functioning. Dr. Bijanki's research explores the electrophysiological, neurobiological, and behavioral correlates of neuromodulation of affective neural circuits. Her lab primarily works with patients undergoing intracranial monitoring for epilepsy or depression, using this unique platform to conduct in-vivo studies of neural correlates to affective function. Her work has identified novel stimulation-based strategies for evoking positive affect and anxiolysis, including the discovery that stimulation to the cingulum bundle evokes changes in anxiolysis, mirth, and euphoria, which was featured as a cover article in the Journal of Clinical Investigation and highlighted in the NIH Director's Blog. Analysis of her recent publications reveals a consistent focus on mapping neural circuits involved in emotion processing, particularly using stereo-EEG informed deep brain stimulation approaches. Her work spans multiple psychiatric conditions including depression, obsessive-compulsive disorder, and anxiety disorders, with a strong emphasis on translating electrophysiological findings into therapeutic applications. The integration of computational approaches, particularly machine learning for decoding neural activity related to mood states, represents a growing trend in her research program. Her scientific achievements include: United States Patent (US:11,241,575) for a novel stimulation-based strategy for evoking positive affect and anxiolysis Journal of Clinical Investigation cover article (March 2019) on cingulum stimulation enhancing positive affect NIH Director's Blog feature highlighting her groundbreaking work Multiple NIH grants including R01, R21, and K01 awards Dr. Bijanki mentors a diverse team including graduate students, postdoctoral fellows, and undergraduate researchers. Her research program is generously funded by multiple NIH grants (R01-MH127006, R01-MH130597, K01MH116364, R21NS104953, UH3NS103549), as well as support from the ARCO Foundation, Caroline Wiess Law Fund, American Foundation for Suicide Prevention, and NARSAD. She maintains strong collaborations with researchers at institutions including UTSW, Iowa, Duke, UCLA, Brown, UPenn, and WashU. The Translational Neuromodulation Lab operates at the intersection of clinical neurosurgery, neuroscience, and engineering, utilizing stereo-EEG as a research platform to study deep brain structures involved in emotional processing. The lab employs multiple methodologies including advanced surgical neuroimaging, affective electrophysiology, autonomic surveillance, facial motor analysis, and pulse-evoked potentials to comprehensively characterize mood-relevant neural circuits. Their current flagship project involves using explainable artificial intelligence to map the relationship between mood and intracranial neural activity, with the goal of developing naturalistic patterns of intracranial stimulation for therapeutic applications.
Gabriele Gratton is a Professor in the Department of Psychology and Neuroscience Program at the University of Illinois Urbana-Champaign, and serves as a Theme Lead at the Beckman Institute for Advanced Science and Technology. With over 178 research outputs, Gratton has established a distinguished career in cognitive neuroscience with a focus on brain function, aging, and neuroimaging techniques. Gratton's research interests center around brain function and activity , particularly using Event-related Optical Signal (EROS) and other neuroimaging methods. Key areas include optical imaging , aging effects on cognition , event-related brain potentials , and cerebrovascular health . Much of their work investigates how vascular health, fitness, and aging interact to affect brain structure and cognitive performance across the lifespan. The research portfolio shows a strong trend toward multimodal neuroimaging approaches, with recent work focusing on trimodal brain imaging techniques that simultaneously investigate human brain function. There's also significant emphasis on understanding how physical activity and fitness impact cognitive aging, white matter integrity, and cerebrovascular health. Association for Psychological Science Fellow (2006) Foundation of Augmented Cognition Award (2005) President of Society for Psychophysiological Research (2009) SPR Award for Distinguished Contributions to Psychophysiology (2019) SPR Early Career Award (1997) Gratton has received extensive recognition for their work, with publications being picked up by news outlets and shared across social media platforms. Their research has been referenced by numerous scholars, with several papers accumulating significant reader attention on academic platforms like Mendeley. Much of this work is conducted through the Beckman Institute, where Gratton leads research themes focused on advanced brain imaging and cognitive neuroscience.
Ramana Vinjamuri is an Associate Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). He holds a secondary appointment as Visiting Professor at the Indian Institute of Technology, Hyderabad, India. His academic journey includes a Ph.D. in Electrical Engineering from the University of Pittsburgh (2008), M.S. in Bioinstrumentation from Villanova University (2004), and B.Tech. in Electrical and Electronics Engineering from Kakatiya University (2002). Dr. Vinjamuri's research focuses on Brain-Machine Interfaces (BMIs) for upper-limb prostheses control , neuroprosthetics and exoskeletons , machine learning in motor control , and neurophysiological signal processing . His work extends synergy-based models to control 37-dimensional hand movements, addresses human-robot interaction through emotionally intelligent systems, and develops neurotechnologies for substance use disorder using wearable sensors and AI. NSF CAREER Award (2019) NSF IUCRC BRAIN Center Planning Grant (2020) Harvey N Davis Distinguished Teaching Assistant Professor Award (2018) His publications demonstrate expertise in EEG and EMG signal analysis , deep learning for motor decoding , synergy modeling , and humanoid robot control . The Vinjamuri Lab at UMBC involves graduate, undergraduate, and high school researchers, with international collaborations in India and the US.
Dr. Adrian Nestor is an Associate Professor at the University of Toronto Scarborough (UTSC) and director of the Visual Recognition Laboratory. His work focuses on neurocomputational aspects of visual processing, particularly face and object recognition, utilizing fMRI, EEG, and computational modeling. He completed his PhD in Cognitive Science at Brown University and held postdoctoral and research scientist roles at Carnegie Mellon University. Research Interests : Neural representations in high-level visual cortex Development of neuroimaging analysis methods Computational modeling of visual cognition Neuropsychological investigations of perception Collaborative Involvement : Active participant in the TRIDENT Preclinical Trials project, a $24M federal research initiative targeting neurodegenerative disease treatments.
Professor Usha Goswami is a leading academic in Cognitive Developmental Neuroscience at the University of Cambridge, serving as Professor of Cognitive Developmental Neuroscience and Director of the Centre for Neuroscience in Education. She is also a Fellow at St John's College, Cambridge. Her work focuses on understanding the neural and cognitive foundations of language acquisition, reading development, and developmental disorders such as dyslexia. Key areas of interest include the role of rhythm perception, speech processing, and EEG-based studies of neural entrainment in typical and atypical development. Her research integrates insights from neuroscience, cognitive psychology, and education, emphasizing translational approaches to improve literacy interventions. She has conducted longitudinal studies on infants and school-aged children, examining predictors of language outcomes and the impact of rhythmic training on executive function. Her work bridges developmental linguistics, auditory neuroscience, and educational policy, with a focus on universal speech structures in child-directed communication. Professor Goswami’s contributions include pioneering studies on the neural basis of phonological awareness and the application of machine learning to decode speech information from EEG data in infants. She has also explored cross-linguistic patterns in poetry and infant-directed speech, highlighting universal acoustic features underlying language acquisition. Her research has significant implications for understanding and remediating developmental disorders, particularly dyslexia and developmental language disorder. Her lab, the Centre for Neuroscience in Education, focuses on translating neuroscientific findings into practical educational strategies. Current projects include investigating rhythm-based interventions for children with language difficulties and analyzing the acoustic properties of speech in diverse linguistic contexts.
Dr. Adrian Owen is a Professor of Cognitive Neuroscience & Imaging at Western University's Brain and Mind Institute, holding the Canada Excellence Research Chair. His work focuses on consciousness disorders, neurodegenerative diseases, and functional neuroimaging applications. He pioneered techniques like detecting residual cognition in vegetative patients using fMRI and fNIRS. Research Focus: Owen's research bridges cognitive neuroscience and clinical practice, emphasizing disorders of consciousness, Alzheimer's/Parkinson's mechanisms, and neurorehabilitation. He develops brain-computer interfaces for communication in non-responsive patients and investigates anesthesia effects on neural connectivity. Key Contributions: Pioneered covert cognition detection in vegetative patients, advanced fNIRS applications in ICU settings, and established international clinical cohorts for consciousness assessment. His work has appeared in Nature , Science , and The Lancet . Labs/Teams: Leads the Owen Lab at Western University, collaborating with clinicians, engineers, and neuroscientists to translate neuroimaging innovations into clinical tools. Grants/Industry Links: Receives funding for interdisciplinary projects linking brain imaging, neurology, and biomedical engineering, fostering industry partnerships for neurotech development.
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 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.
Filip Sondej is a Researcher at the Department of Cognitive Science within the Faculty of Philosophy at Jagiellonian University in Krakow, Poland. His work bridges cognitive science and artificial intelligence, focusing on critical safety aspects of modern language models and multi-agent systems. His primary research interests include AI safety, LLM unlearning techniques, Chain-of-Thought faithfulness, AI conflict resolution, and digital sentience. Sondej's work addresses fundamental challenges in ensuring that increasingly powerful language models behave safely and align with human values. Analysis of Sondej's publication record reveals a strong interdisciplinary focus combining cognitive neuroscience methodologies with AI safety research. His recent work demonstrates a clear trajectory from traditional cognitive neuroscience investigations toward cutting-edge AI safety mechanisms, particularly in developing methods for removing unsafe behaviors from language models while maintaining functionality. The publications show sophisticated integration of neural network analysis with human cognitive processes. Sondej collaborates extensively with researchers including Anna Grabowska and Magdalena Senderecka, appearing as co-author on multiple publications in high-impact journals such as NeuroImage, Cerebral Cortex, and Journal of Cognitive Neuroscience. His research program bridges theoretical cognitive science with practical AI safety applications.
Alexey Evgenievich Osadchiy is a Professor at the National Research University Higher School of Economics (HSE University), where he serves as Director of the Center for Bioelectric Interfaces at the Institute of Cognitive Neuroscience. He has been working at HSE since 2013 with 21 years of scientific and teaching experience. His academic appointments include Professor at the Faculty of Computer Science in the Department of Data Analysis and Artificial Intelligence. 2023 - Doctor of Science: National Research University Higher School of Economics 2003 - PhD: University of Southern California, specialty "Physical and Mathematical Sciences" and "Neurobiology" 1997 - Specialty: Bauman Moscow State Technical University, major in Autonomous Information and Control Systems Professor Osadchiy's research focuses on digital signal processing, magnetoencephalography (MEG), electroencephalography, inverse problems, synchronization, non-invasive detection, and brain mapping. His work bridges neuroscience, computer science, and medical applications, with particular emphasis on brain-computer interfaces, neurofeedback systems, and precision medicine applications for neurological disorders. He has pioneered methods for real-time brain activity monitoring and developed novel approaches for functional connectivity estimation in neural networks. His recent publications demonstrate a strong trend toward developing hardware-enabled low-latency systems for brain-state dependent stimulation, improving MEG technology with optically pumped magnetometers, and advancing speech mapping techniques for neurosurgical applications. His work increasingly integrates AI and deep learning approaches with traditional neuroimaging techniques to create more precise and accessible brain measurement and modulation systems. Scientific Awards and Recognition HSE University "Recognition - 10 Years of Successful Work" Medal (July 2025) Letter of Gratitude from the Higher School of Economics (September 2021) Letter of Gratitude from the Faculty of Computer Science at HSE (August 2018) Allowance for defending a doctoral dissertation (2023–2026) Bonuses for publications in international peer-reviewed journals (2015–2029) Professor Osadchiy has successfully advised numerous graduate students and doctoral candidates, with eight dissertation research projects currently under his supervision. His research has been supported by significant grants including a Russian Ministry of Education and Science contract for "System for registration and decoding of human brain bioelectric activity" (2014-2017), RFBR grants for "New non-invasive experimental-mathematical paradigm for preoperative magnetoencephalographic mapping of speech cortex" (14-02-00917, 16-04-01863), and projects on "Endogenous enhancement of brain-computer interface efficiency." As Director of the Center for Bioelectric Interfaces at the Institute of Cognitive Neuroscience, Professor Osadchiy leads a multidisciplinary team working on cutting-edge neurotechnology. His center collaborates with the Federal Brain and Neural Technology Centre at the Federal Medical and Biological Agency, where they established the Laboratory of Medical Neural Interfaces and Artificial Intelligence for Clinical Applications. The center is actively involved in developing brain-computer interfaces for rehabilitation, particularly for stroke patients and those with locomotor function disorders, and has created Russia's first neurointerface for controlling exoskeletons using imagined lower limb movements.
Paul Nuyujukian serves as an Assistant Professor of Bioengineering and Neurosurgery, with courtesy appointment in Electrical Engineering at Stanford University. He is a Faculty Scholar of the Wu Tsai Neurosciences Institute, directing the Brain Interfacing Laboratory where his team develops neural interface technologies for clinical applications in stroke and epilepsy. Education: MD, Stanford University (2014) PhD in Bioengineering, Stanford University (2012) BS, UCLA (2006) Dr. Nuyujukian's research integrates motor systems neuroscience with neuroengineering to decode brain activity during movement and recovery from injury. His laboratory pioneers brain-machine interface (BMI) platforms that translate neural signals into communication and control systems, with particular emphasis on intracranial EEG recording and real-time neural decoding. Current work focuses on developing clinically viable BMI solutions for neurological conditions through both preclinical models and human trials, advancing our understanding of neural population dynamics in health and disease. Recent publications reveal strong trends in intracranial EEG acquisition systems, seizure detection algorithms using information theory, and closed-loop BMI applications for ambulatory neuroscience. His work bridges fundamental neuroscience with clinical translation, particularly in epilepsy monitoring, chronic pain management, and neural prosthetics for paralysis. A notable emphasis exists on creating scalable, minimally invasive recording platforms that reduce clinical burden while maintaining high-fidelity neural data. Scientific Awards: No specific awards listed in provided materials As director of the Brain Interfacing Laboratory, Dr. Nuyujukian mentors students and collaborators in neural engineering research while securing grant funding for BMI development. His group maintains active collaborations with Stanford's Department of Neurosurgery and Neurology for clinical translation, with current projects including real-time decision-state decoding and personalized network mapping for pain management. The laboratory operates advanced facilities for both animal and human neural recording, emphasizing seamless integration of engineering innovation with clinical neuroscience. The Brain Interfacing Laboratory comprises multidisciplinary scientists and engineers developing next-generation neural interfaces. Current initiatives include the LiCoRICE platform for ambulatory neuroscience, seizure detection systems using compression-enabled entropy estimation, and ketamine's effects on hippocampal connectivity. The team actively participates in clinical trials for BMI applications in stroke rehabilitation and epilepsy, with strong partnerships across Stanford's medical and engineering schools to accelerate technology translation.
Peter J. Kohler is an Assistant Professor in the Department of Biology within the Faculty of Science at York University, Toronto. He is eligible to supervise graduate students in the Biology Graduate Program and leads the Kohler Visual Neuroscience Lab, which is part of the Centre for Vision Research at York University. His research lies at the intersection of cognitive neuroscience and visual perception, focusing on mid-level visual processing. This involves understanding how the brain, within the first few hundred milliseconds of visual input, constructs representations of shape, motion, location, and perceptual organization—including figure-ground segregation, grouping, and constancy. His work integrates functional MRI (fMRI) , electroencephalography (EEG) , and visual psychophysics to probe the neural mechanisms underlying these processes in humans. Recent publications reveal a strong focus on symmetry processing, perceptual grouping, numerical estimation, and multisensory integration. His work often involves advanced neuroimaging techniques and collaborative international research, particularly with teams in Belgium and Luxembourg. The articles span high-impact journals such as PNAS , Nature Communications , Current Biology , and Journal of Vision , indicating a robust and influential research program in visual neuroscience. Scientific Awards and Funding: NSERC Discovery Grant (awarded April 2020) VISTA Research Grant (funded June 2023) Prof. Kohler actively mentors students, including graduate students such as Rachel Moreau, Sara Chaparian, Yara Iskandar, Shaya Samet, and Shenoa Ragavaloo, as well as undergraduate researchers. His lab has presented at major conferences including the Vision Sciences Society (VSS) and the Lake Ontario Visionary Establishment (LOVE), where he joined the organizing committee in 2024. The Kohler Visual Neuroscience Lab also develops experimental tools, as evidenced by GitHub repositories for stimulus generation and behavioral testing using jsPsych.
