جست‌وجوی استاد و پژوهشگر

• Medical Imaging
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Diana Mateus is a Researcher at Centrale Nantes, affiliated with the LS2N Laboratory. She holds a bachelor's in Electronic Engineering from the University of Javeriana (Colombia), a Master's in Control and Automation from Paul Sabatier University (France), and a PhD in Computer Vision from INRIA Grenoble. After postdoctoral work and eight years as an associate researcher at Technical University of Munich and Helmholtz Zentrum (Germany), she returned to France to join Centrale Nantes via the Connect Talent Program, securing a starting grant for interdisciplinary research in medical image analysis and machine learning. Her research focuses on Milcom —a project integrating multimodal imaging (MRI, CT, ultrasound, PET) with machine learning to advance computational medicine. Key objectives include computer-aided diagnosis, disease knowledge advancement, and improving imaging quality. Collaborations include the hospital's Nuclear Medicine Department, focusing on multiple myeloma, and companies Hera-Mi and Keosys for breast cancer research. She supervises six PhD students and two postdocs, emphasizing synergies between engineering and healthcare. At Centrale Nantes, she benefits from dynamic institutional support, quality students, and complementary team skills. Her lab recently acquired a research-dedicated ultrasound machine to develop machine learning-driven imaging optimization. Living in Nantes, she values its family-friendly environment and proximity to academic and medical networks.

Alejandro Laguna Sanzمشاهده پروفایل

استادیار

• Diabetes Technology
• Glucose Monitoring
• Artificial Pancreas Systems
• +۷ مورد دیگر

Alejandro Laguna Sanz is an Assistant Professor in the Department of Electronic Engineering at the School of Engineering, Universitat de València. His work bridges biomedical engineering and clinical diabetes care, focusing on technological solutions for glucose regulation. His research interests include diabetes technology, artificial pancreas systems, glucose monitoring accuracy, and the impact of exercise on glycemic control. He is particularly active in developing and evaluating predictive algorithms for glycemic management, especially in dynamic conditions such as physical activity and post-bariatric hypoglycemia. His work often combines control theory with clinical validation. The recent publications reveal a strong focus on real-world performance of continuous glucose monitors (CGMs), sex-specific responses in women with type 1 diabetes, and innovative interventions like elastic band resistance training and dual-hormone closed-loop systems. His research increasingly integrates population modeling, in silico studies, and virtual education platforms for artificial pancreas training. He is a member of the CLIDET research group (Clinimetry and Technological Development in Therapeutic Exercise), where he contributes to the design and clinical evaluation of advanced monitoring and control systems for metabolic diseases. Alejandro Laguna Sanz earned his PhD from Universitat Politècnica de València with a thesis on uncertainty in postprandial glucose modeling in type 1 diabetes, supervised by Dr. Paolo Rosseti and Dr. Jorge Bondía Company. He has not received any explicitly listed scientific awards in the provided text. He advises students within his research group, though no specific names are listed. His work is supported by collaborative projects in diabetes technology and engineering, particularly in the development of adaptive control systems and clinical validation of glucose monitoring devices. He is involved in both experimental and simulation-based studies.

Dimo Ivanovمشاهده پروفایل

استادیار

• Ultra-high field MRI
• Quantitative MRI techniques
• Brain microstructure imaging
• +۷ مورد دیگر

Dr. Dimo Ivanov is an Assistant Professor at the Department of Cognitive Neuroscience at Maastricht University in the Netherlands. Since 2025, he has also served as Head of the 7 Tesla MR Core structure at the Cooperative Brain Imaging Center and as an Independent Research Group Leader at the Max Planck Institute for Empirical Aesthetics in Frankfurt, Germany. His work bridges physics, neuroscience, and advanced imaging technology. Dr. Ivanov earned his PhD (Dr. rer. nat.) in Physics from the University of Leipzig/Max Planck Institute for Human Cognitive and Brain Sciences in 2012, followed by an M.Sc. in Physics from the International Physics Studies Program at the University of Leipzig in 2007. His educational foundation was completed with studies at the Foreign Language High School in Pleven, Bulgaria. Dr. Ivanov specializes in high-resolution structural, perfusion, and functional mapping of the human cortex and subcortical regions. His research focuses on quantitative MRI techniques to assess tissue vascularization, microstructure, myelination, and iron content. He develops advanced MRI sequences and analysis pipelines for both 7T and 3T systems, with translational applications in neurodegenerative disorders, psychiatric conditions, and neurodevelopmental studies. His work on resting-state and task-based fMRI provides critical insights into neural connectivity and brain dynamics. His publication record demonstrates consistent contributions to ultra-high field MRI methodology, with emphasis on 7T and 9.4T systems. His research spans technical MRI development, quantitative imaging methods, and clinical applications across multiple neurological conditions. Recent work shows increasing focus on translational applications connecting advanced imaging techniques with clinical outcomes. Dr. Ivanov has received numerous scientific awards and successfully secured competitive funding: ERC Marie Curie Program (2023) - 2-year postdoc position Maastricht University-Chinese Science Council PhD Program (2022) Maastricht University Center for Integrative Neuroscience PhD Program (2022) Magnetic Resonance in Medicine issue cover (2017) ISMRM merit award magna cum laude (2013) Trainee abstract travel award from the Organization for Human Brain Mapping (2011) Educational Stipends from the International Society for Magnetic Resonance in Medicine (2009-2011) Dr. Ivanov has successfully mentored postdoctoral researchers and PhD students through competitive funding mechanisms. His research group at the Max Planck Institute for Empirical Aesthetics and his position at Maastricht University support a collaborative environment that bridges physics, neuroscience, and clinical applications. His work with the 7 Tesla MR Core structure provides critical infrastructure for advanced neuroimaging research in Frankfurt. Dr. Ivanov leads the Structural and Physiological Imaging with High-Field MRI research group at the Max Planck Institute for Empirical Aesthetics. His team specializes in pushing the boundaries of ultra-high field MRI technology, developing novel acquisition and analysis methods that enable unprecedented insights into brain structure and function. The group maintains strong collaborations with multiple institutions across Europe and beyond.

Michael McTearمشاهده پروفایل

استاد

• Chatbot
• Artificial Intelligence
• Large Language Model
• +۳ مورد دیگر

Professor Michael McTear is affiliated with Ulster University in the Department of Computer Science . His research focuses on Artificial Intelligence and Large Language Models , with significant contributions to Chatbot Technology , Data Fusion , and Digital Mental Health interventions. Active in Computer Science with expertise in Information Retrieval , Human-Computer Interaction , and Multimodal Interfaces Recent work includes machine learning applications for depression detection and sensor technology in the e-VITA project His publications (102 total) demonstrate sustained academic activity since 1979, with notable research output in 2024-2025. Current projects emphasize generative AI , voice biomarkers , and elderly care technology . Scientific recognition includes: Best Journal Paper Award (ACM Transactions on Computing for Healthcare, 2025) Outstanding Interdisciplinary Research Team Award (2024)

• Biometrics
• Behavioral Biometrics
• Mobile Authentication
• +۱۲ مورد دیگر

Ruben Tolosana is a researcher at the Biometrics and Data Pattern Analytics Lab (BiDA Lab) in the School of Engineering at Universidad Autónoma de Madrid. His work centers on biometrics, with emphasis on behavioral and mobile authentication, face recognition, privacy-enhancing technologies, and deep learning applications in human-computer interaction. He actively contributes to major research initiatives such as the FRCSyn-onGoing challenge and the ChildCI framework. PhD in Computer Science or related field (inferred from publication volume and role) Advanced training in machine learning, computer vision, and biometrics His research interests include behavioral biometrics, mobile device security, synthetic data for AI training, privacy in biometric systems, and the application of Transformer models to user authentication. Tolosana's work often involves the development of novel datasets and benchmarking platforms to advance the field. He has co-authored influential surveys on privacy vulnerabilities in mobile sensors and privacy-preserving techniques in biometric recognition. The most recent articles highlight trends in using synthetic data for face recognition, applying Transformers to behavioral biometrics (keystroke, touchscreen, gait), and developing frameworks for child age detection via interaction patterns. His work consistently addresses real-world challenges such as privacy, bias, and system generalization. Publications in journals like Information Fusion , Pattern Recognition , and ACM Computing Surveys reflect the high impact and interdisciplinary nature of his research. Second FRCSyn-onGoing: Winning solutions and post-challenge analysis to improve face recognition with synthetic data (2025) ChildCI framework: Analysis of motor and cognitive development in children-computer interaction for age detection (2024) FRCSyn-onGoing: Benchmarking and comprehensive evaluation of real and synthetic data to improve face recognition systems (2024) SwipeFormer: Transformers for mobile touchscreen biometrics (2024) An Overview of Privacy-Enhancing Technologies in Biometric Recognition (2024) Ruben Tolosana collaborates extensively with researchers in the BiDA Lab, including Rubén Vera-Rodríguez, Aythami Morales, Julian Fierrez, and others. He has contributed to the development of public databases such as mEBAL and ChildCIdb, supporting open science. His work is supported by ongoing research grants (inferred from project scope and publications), and he plays a key role in organizing workshops such as WAMWB to advance the mobile and wearable biometrics community. He is a core member of the Biometrics and Data Pattern Analytics Lab (BiDA Lab), a leading research group in biometric technologies, where he contributes to multiple projects involving mobile authentication, face recognition, and privacy-preserving AI systems.

• Information and Communication Technology
• Electrical and Electronic Engineering
• Artificial Intelligence
• +۳ مورد دیگر

Paweł Tarnowski is a researcher at the Institute of the Theory of Electrical Engineering, Measurement and Information Systems , Faculty of Electrical Engineering, Warsaw University of Technology. His work focuses on biomedical signal processing, emotion recognition, and machine learning applications in affective computing and human-computer interaction. Research areas include Information and Communication Technology (ICT) Electrical and Electronic Engineering Artificial Intelligence Neuroscience Human Factors His recent publications highlight trends in emotion recognition using multimodal physiological signals (EEG, EOG, GSR), driver fatigue detection, and deep learning techniques like 1D-CNN for medical diagnostics. Articles also address urban traffic monitoring and neuromarketing research via EEG analysis. At Warsaw University of Technology, he supervises thesis projects and contributes to interdisciplinary research in biomedical engineering and signal processing. Labs/Teams: Institute of the Theory of Electrical Engineering, Measurement and Information Systems .

Rada Mihalceaمشاهده پروفایل

استاد

• Natural Language Processing
• Text Mining
• Computational Linguistics
• +۵ مورد دیگر

Rada Mihalcea is a Professor at the University of Michigan in the Computer Science and Engineering department. She actively promotes Natural Language Processing (NLP) applications for social good and founded the ACL Year Round Mentorship program. University: University of Michigan School: College of Engineering Department: Computer Science and Engineering Academic Rank: Professor Research Interests include: Natural Language Processing for social good applications Text Mining and Sentiment Analysis Deception Detection and Multimodal Analysis Cross-Cultural Computational Linguistics Educational Technology and Gender Studies Recent research trends focus on: Fake news detection using multimodal features Lexicon-based cross-cultural value analysis Thermal imaging for affective computing Deception detection through linguistic and physiological cues Applications in healthcare and counseling analysis Scientific Awards : Sarah Goddard Power Award (2019) Best Paper Award at CICLING (2019) Best Paper Award at SocInfo (2018) Additional Contributions : Co-authored textbooks on Text Mining and Graph-based NLP Developed FunPython tutorial for children Collaborative work with the School of Music on the Ada Lovelace Opera Active in diversity initiatives in computing

• Decision-Making Neurobiology
• Reward Processing
• Psychopathology
• +۶ مورد دیگر

Jessica A. Cooper is an Assistant Professor in the Department of Psychiatry and Behavioral Sciences at Emory University School of Medicine, where she serves as Assistant Director of the Translational Research in Affective Disorders (TReAD) Laboratory under Dr. Michael Treadway. Her research focuses on decision-making neurobiology and reward processing , examining how stress, aging, individual differences, and inflammatory processes impact reinforcement learning and motivational circuits. Key areas include computational phenotyping of effort-based decision-making in depression and psychosis, inflammation-depression interactions, and sex-specific drug effects on reward processing. Analysis of her recent publications reveals a strong emphasis on translational immunopsychiatry , particularly TNF-alpha antagonism trials for motivational deficits in high-inflammation depression, alongside innovative work on spontaneous thought dynamics and social learning across the lifespan. While no specific awards are listed in available sources, her work demonstrates significant contributions to understanding corticostriatal circuitry in affective disorders through multimodal approaches including fMRI, computational modeling, and pharmacological challenges. As Assistant Director of the TReAD Lab, Dr. Cooper mentors research staff and contributes to studies investigating neural mechanisms of depression, with particular focus on stress-induced alterations in glutamatergic signaling and inflammatory pathways. The laboratory maintains active collaborations across Emory's neuroscience and immunology departments. The TReAD Lab operates within Emory's robust neuroscience ecosystem, utilizing advanced neuroimaging facilities and maintaining partnerships with clinical departments for patient recruitment in mechanistic treatment trials.

Jeffrey M. Chungمشاهده پروفایل

دانشیار

• Epilepsy
• Neural Signal Processing
• Neurosurgery
• +۴ مورد دیگر

Jeffrey M. Chung, MD, FAAN is an Associate Professor of Neurology at Cedars-Sinai Medical Center. He serves as Director of both the Epilepsy Program and the Neurophysiology Laboratory since 2013. His clinical practice focuses on epilepsy and neurophysiology, with 26 years of experience in neurology and clinical neurophysiology. Northwestern University Medical School (1999) - MD Evanston Hospital (2000) - Internship UCLA (2003) - Residency UCLA (2005) - Fellowship Dr. Chung's research explores epilepsy mechanisms, neural signal processing , multi-modal probabilistic mapping for surgical outcomes, and device-oriented therapies . His recent publications focus on theta phase precession in memory , single-neuron correlates of seizures , and neuroimaging in post-stroke conditions . He leads clinical trials on novel anti-seizure agents and neuromodulation techniques. Circle of Friends – Grateful Patient Donation Honoree (Cedars-Sinai) Southern California Super Doctors (Los Angeles Magazine) Falks Medical Scholar (Arthritis Foundation) Golden Apple Teaching Award (Cedars-Sinai) Dr. Chung directs the Epilepsy Program and Neurophysiology Laboratory at Cedars-Sinai, while also mentoring through journal clubs and clinical teaching. He has received multiple industry grants from companies like Marinus Pharmaceuticals and Vertex Pharmaceuticals for trials on treatment-resistant epilepsy. His work bridges clinical care with neurophysiological research and advanced neural signal processing .

Ren, Leiمشاهده پروفایل

استاد

• Image-guided Radiation Therapy
• Artificial Intelligence in Radiotherapy
• Medical Imaging Physics
• +۶ مورد دیگر

Lei Ren, PhD is a Professor and Associate Chief of Physics Research in the Department of Radiation Oncology at the University of Maryland School of Medicine. Previously, he held faculty positions at Duke University Medical Center from 2011-2021, progressing from Assistant Professor to Professor and Director of Physics Research. A clinical medical physicist certified by the American Board of Radiology (ABR), Dr. Ren leads a research program focused on advancing precision radiation therapy through innovative imaging and AI technologies. Dr. Ren's research centers on image-guided radiation therapy (IGRT) and AI applications in radiotherapy. His seminal contributions include developing digital tomosynthesis (DTS) for fast target localization, creating the limited-angle intrafraction verification (LIVE) system, and pioneering cone-beam CT (CBCT) scatter correction methods. Current work focuses on deep learning for deformable image registration, virtual MRI/CBCT generation, XMAN phantom development, and proton dose verification using prompt gamma imaging. His research integrates biomechanical modeling with AI to improve soft tissue contrast and motion management. His 15 most recent publications (2023-2025) reveal a strong trend toward clinical translation of AI in radiation oncology, with emphasis on interpretable AI for clinical decision making, radiomics for cancer prognostics, and novel imaging techniques for proton therapy verification. Approximately 70% of recent work involves deep learning applications, while 30% focuses on advanced imaging physics for motion management and dose verification. Award highlights include: Fellow of the American Association of Physicists in Medicine (AAPM), 2020 Thomas Gorrie Clinical Leadership Impact Award, Duke University, 2019 Top 5 team in AAPM Grand Challenge for 4D-CBCT reconstruction, 2018 Multiple Excellence in Research/Mentorship awards from Duke Medical Physics Program Distinguished Graduate Award from Tsinghua University, 2003 Dr. Ren actively mentors postdoctoral fellows and has secured substantial NIH funding as Principal Investigator for projects including 3D prompt gamma imaging (R01CA279013), XMAN phantoms (R01EB032680), and hybrid virtual-MRI/CBCT systems (R01EB028324). His lab collaborates with industry partners including Varian Medical Systems and ScandiDos AB on clinical implementation of imaging technologies. He serves on NIH study sections and as Deputy Editor for Medical Physics journal. His research group operates within the Radiation Oncology Department's imaging physics division, developing clinical tools for liver SBRT, proton therapy verification, and AI-driven treatment planning. Current projects include FLASH proton therapy dosimetry and virtual clinical trials using XMAN phantoms.

René Vidalمشاهده پروفایل

استاد

• Machine Learning
• Computer Vision
• Biomedical Data Science
• +۳ مورد دیگر

René Vidal is the Rachleff & Penn Integrates Knowledge (PIK) University Professor at the University of Pennsylvania, with appointments in the Departments of Electrical and Systems Engineering, Radiology, Computer and Information Science, and Statistics and Data Science. He also serves as Director of the Center for Innovation in Data Engineering and Science (IDEAS) and the NSF-Simons Collaboration on the Mathematical Foundations of Deep Learning (THEORINET). A dual faculty member at Johns Hopkins University in Biomedical Engineering, Computer Science, and other departments, Vidal is an Amazon Scholar and Affiliated Chief Scientist at NORCE. PhD, Electrical Engineering and Computer Sciences, University of California, Berkeley (2003) M.S., Electrical Engineering, University of California, Berkeley (2000) B.S. (valedictorian), Electrical Engineering, Pontificia Universidad Catolica de Chile (1997) His research spans the mathematical foundations of deep learning, focusing on non-convex optimization , learning dynamics , and overparametrization . Key contributions include Sparse Subspace Clustering , Kernel GPCA , and Low-Rank Matrix Factorization , with applications in autism diagnosis , robotic surgery , and cardiac imaging . Recent work explores continual learning , adversarial robustness , and trustworthy AI in biomedical contexts. His 15 most recent publications highlight advances in medical imaging , language models , and robust computer vision , spanning topics from chest X-ray analysis to motor imitation tasks in autism . Articles like Geometric Analysis of Nonlinear Manifold Clustering underscore his theoretical contributions, while works on KDA: Knowledge-Distilled Attacker and Conformal Information Pursuit address practical AI safety and interpretability. Scientific accolades include: 2022 ACM Fellow 2021 IEEE McCluskey Technical Achievement Award 2017 Jean D’Alembert Fellowship 2012 J.K. Aggarwal Prize 2009 Sloan Research Fellow 2005 NSF CAREER Award His lab mentors 11 current PhD students across Johns Hopkins and University of Pennsylvania , with alumni contributing to institutions like Meta , Amazon , and GE Research . Vidal’s interdisciplinary work bridges mathematics , engineering , and healthcare , supported by grants from the DoD , NSF , and ONR .

• Computer Vision
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Simone Bianco is an Associate Professor at the Department of Informatics, Systems and Communication (DISCo) of the University of Milano-Bicocca, Italy. His academic and research contributions span computer vision, artificial intelligence, machine learning, and optimization algorithms applied to multimodal and multimedia systems. His educational background includes a PhD in Computer Science (2010) and BSc/MSc degrees in Mathematics (2003/2006), both from the University of Milano-Bicocca. Bianco’s research focuses on color constancy, deep learning for video restoration, neural architecture search, and computational color imaging, with a strong emphasis on practical applications like biometric recognition, medical imaging, and environmental monitoring. The 15 most recent articles (2025–2020) highlight trends in computer vision, including uncertainty estimation in color constancy, portable material appearance modeling, temporal consistency in low-light videos, and advanced deep learning architectures for image and video processing. His work often integrates photogrammetry, sensor technology, and multimodal data analysis. Scientific accolades include recognition on Stanford University’s World Ranking Scientists List for achievements in artificial intelligence and image processing. Bianco also serves as R&D Manager for the University of Milano-Bicocca spin-off Imaging and Vision Solutions and contributes to international conferences and workshops.

Hachem Kadriمشاهده پروفایل

استاد

• Machine Learning
• Kernel Methods
• Functional Data Analysis
• +۵ مورد دیگر

Hachem Kadri is a Professor of Artificial Intelligence in the Department of Computer Science at Aix-Marseille University and Head of the Data Science Department at LIS Lab since 2023. He was previously an Assistant Professor at the same institution from 2012 to 2019 and conducted postdoctoral research at INRIA Lille and LAGIS. He holds a PhD in Electrical Engineering from the National Engineering School of Tunis (2008) and completed his HDR (Habilitation) at Aix-Marseille University in 2019 on operator-valued kernel methods. His research spans machine learning with core expertise in kernel methods, functional data analysis, statistical learning theory, deep learning, and quantum machine learning, bridging theoretical foundations with applications in signal processing and quantum computing. Recent publications (2022-2024) demonstrate a strategic shift toward quantum machine learning and C*-algebraic frameworks, with significant contributions in implicit regularization for deep tensor factorization, quantum perceptron algorithms, and operator-theoretic kernel extensions. Key works appear in ICML, NeurIPS, and AISTATS, highlighting interdisciplinary innovation at the intersection of algebra, functional analysis, and ML. Professor Kadri leads the ANR Starting Grant QuantML (2019-2024) as Principal Investigator and co-led the Franco-Tunisian OPERA project (2019-2023). He was nominated to the National Universities Council (CNU) for 2024-2027 and elected Head of LIS Data Science Department in 2023, reflecting his leadership in French academia. As a core member of the QARMA machine learning group at LIS Lab, he co-organized the inaugural Quantum Machine Learning workshop at ECML-PKDD 2022 and serves as Vice-president of SSFAM (Société Savante Francophone d’Apprentissage Machine), actively shaping the European ML research landscape.

• Affective Computing
• Explainable AI
• Data Privacy
• +۴ مورد دیگر

Dr. Olya Hakobyan is a Researcher at the University of Bielefeld, working in the Faculty of Engineering within the Human-Centered Artificial Intelligence Group at the Center for Cognitive Interaction Technology (CITEC). She has been with the university since June 2021 as a research associate in the Multimodal Behavior Processing lab. Dr. Hakobyan's educational background includes: PhD in Computational Neuroscience (2016-2020) from the Institute for Neural Computation at Ruhr University Bochum Master's degree in Cognitive Science (2014-2016) from Ruhr University Bochum Bachelor's degree in Linguistics (2010-2014) from Yerevan Brusov State University of Languages and Social Sciences Her research focuses on the intersection of artificial intelligence, cognitive science, and human-computer interaction. Dr. Hakobyan specializes in affective computing, explainable AI systems, and the development of privacy-preserving data donation platforms. Her work on the Dona platform represents a significant contribution to ethical data collection methods for social interaction analysis. She investigates how AI systems can interpret human emotions through audio and visual channels while maintaining transparency in decision-making processes. Her research also extends to cognitive modeling, particularly in understanding memory processes and recognition mechanisms. Dr. Hakobyan's publication record demonstrates a strong trajectory in developing methods for analyzing social interactions through digital footprints while addressing critical privacy concerns. Her work bridges technical innovation with ethical considerations in data collection. Her scientific contributions have focused on: Privacy-preserving data donation frameworks Explainable AI for emotion recognition systems Analysis of social interactions through digital communication Cognitive modeling of memory processes Ethical considerations in AI and data science

• Brain-Computer Interfaces
• Speech Neuroprosthetics
• Neural Decoding
• +۳ مورد دیگر

David Moses is an Assistant Professor in the Department of Neurological Surgery at the University of California, San Francisco (UCSF) School of Medicine. His research focuses on developing advanced brain-computer interfaces (BCIs) and speech neuroprostheses to restore communication in individuals with paralysis or speech impairments. Moses works at the intersection of neuroscience, biomedical engineering, and artificial intelligence, utilizing cortical activity mapping to create real-time decoding systems. Research Areas: Brain-Computer Interfaces Speech Neuroprosthetics Neural Signal Decoding Artificial Intelligence in Medicine Cortical Activity Analysis Cognitive Neuroscience Publication Trends: Moses' recent work (2024-2025) emphasizes streaming brain-to-voice neuroprostheses, bilingual speech decoding, and user agency in neuroprosthetic design. Earlier publications (2016-2022) established foundational methods for continuous phoneme decoding, real-time dialogue systems, and AI-driven cortical signal translation. Key Collaborations: Moses frequently collaborates with Edward Chang (UCSF), Gopala Anumanchipalli (UCSF), Karunesh Ganguly (UCSF), and Adelyn Tu-Chan (UCSF) on high-impact neuroprosthetic research. His work is cited extensively in medical and neuroscience literature, with significant social media and policy influence.