Masaki Nishida is a Professor at the Faculty of Sport Sciences , Waseda University, and Vice President of the Health Support Center at the same institution. He also serves as Chief of the Sleep Research Institute . His research bridges Sleep Science , Sports Medicine , and Clinical Psychiatry , with a focus on the interplay between sleep, cognitive function, and athletic performance. Education: Tokyo Medical and Dental University, Faculty of Medicine (1996) His work has significantly advanced understanding of napping interventions for athletes, sleep quality in competitive divers, and neurophysiological mechanisms linking sleep spindles to memory consolidation. Current research projects include a 2024-2027 study on functional bedding effects funded by Japan Society for the Promotion of Science. Scientific awards include the 15th Japanese Society of Sleep Research Encouragement Award (2010) and the 88th Japanese Society for Occupational Health Award (2015). His publications span topics like REM sleep's role in emotional memory , sleep inertia , and motor adaptation , with recent emphasis on international sports psychiatry standards . He actively contributes to professional societies including the Japanese Association of Sports Psychiatry (Chairman) and Society for Neuroscience .
Stefan Helmreich is the Elting E. Morison Professor of Anthropology at MIT, where his research examines how scientists conceptualize fundamental phenomena—especially waves—across oceanography, biology, acoustics, and computing. His interdisciplinary work bridges anthropology, science studies, and media theory. Core research themes include: Cultural and scientific constructions of ocean waves Marine microbial ecologies and alien oceans Sound studies and transduction theories Multispecies ethnography Scientific visualization practices Helmreich's publications demonstrate consistent engagement with wave phenomena as both physical forces and cultural symbols, exploring how scientific representations of waves shape environmental understanding. His award-winning books trace connections between marine science, cosmology, and media technologies. He has received numerous prestigious awards including a Guggenheim Fellowship and multiple book prizes from anthropological associations. Helmreich maintains creative collaborations through projects like 'Wave Count'—a musical exploration of wave representations across genres.
Long Cai is a Professor at the California Institute of Technology, affiliated with the Biology and Biological Engineering department. He pioneered the field of spatial genomics and co-developed transformative technologies such as seqFISH and MEMOIR. Research Interests: His work focuses on decoding biological systems through spatial genomics, integrating molecular imaging with computational analysis to uncover cellular organization in tissues. Key areas include developmental biology, neuroscience, kidney regeneration, and cancer biology. Publications: Recent studies highlight applications of spatial transcriptomics in kidney disease, brain nuclear architecture, and multi-omics tissue mapping. His research emphasizes creating high-resolution atlases of cellular dynamics. Scientific Awards: NIH Director’s Pioneer Award (2022) Labs & Collaborations: He leads the Cai Lab, which develops cutting-edge imaging tools in collaboration with the Elowitz Lab and other interdisciplinary teams.
Nabil Alshurafa is an Associate Professor at Northwestern University, holding joint appointments in the McCormick School of Engineering (Computer Science and Electrical and Computer Engineering) and the Feinberg School of Medicine (Preventive Medicine). He directs the HABits Lab, which focuses on developing mHealth systems to address health behaviors such as overeating, stress, and UV exposure. His work integrates wearable sensors, machine learning, and behavioral science to create passive sensing solutions. Education: PhD in Computer Science and Wireless Health (UCLA), MS and BS in Computer Science (UCLA). Research Interests: Body sensor networks, activity recognition, embedded systems, and health informatics. His lab designs wearable devices (e.g., neck-worn sensors, UV patches) and AI frameworks to detect behaviors like eating patterns and stress levels. Collaborations include domain experts in medicine and engineering to translate technical innovations into clinical interventions. Recent Projects: Developing systems for stress monitoring via ECG-PPG patches, UV exposure tracking, and just-in-time interventions for overeating. The lab emphasizes ethical design, privacy preservation, and user-centered technology. Students and Lab Team: Supervises PhD, MS, and undergraduate researchers in areas like machine learning, embedded systems, and health data analytics. Notable advisees include Rawan Alharbi (PhD candidate), Shibo Zhang (PhD student), and Wilson Wang (MS student). Labs/Teams: HABits Lab collaborates with experts in Preventive Medicine, Psychiatry, and Dermatology to advance interdisciplinary health research. Current projects include predictive analytics for weight loss and interventions targeting maternal stress during pregnancy.
Andrew Markham is a Professor of Computer Science at the University of Oxford , affiliated with Kellogg College . He leads a research group focusing on Cyber Physical Systems (CPS) , specializing in sensors, signal processing, and machine learning to enable machines to better perceive the physical world. His work emphasizes cross-disciplinary collaboration, notably in wildlife tracking and indoor positioning systems. He has held roles as a Postdoctoral Fellow (2008-2012), Associate Professor (2013), and Full Professor (2021). Education : PhD in Electrical Engineering (University of Cape Town, 2008), BSc (Hons) in Electrical Engineering (2004). Research Interests : Tracking and localization in GPS-denied environments (e.g., underground, indoors), magneto-inductive systems, physics-informed machine learning, and data-driven approaches for noisy sensor data. His projects include wildlife monitoring via wireless sensor networks and mmWave radar for human motion capture. Key Projects : CARACAL acoustic monitoring system, mmPoint dense human tracking, and RandLA-Net for large-scale point cloud segmentation. His work spans robotics, environmental sensing, and biomedical applications. Advising & Grants : Supervises over 30 students and collaborates with industrial partners. Research teams include Cyber Physical Systems, Autonomous Ubiquitous Sensing, and Wildlife Monitoring initiatives. Labs/Teams : Leads the CPS research group, focusing on sensor networks, inertial navigation, and multimodal fusion systems. Collaborates with zoology and earth science disciplines on applied projects.
Pengtao Xie is an Associate Professor (with tenure as of June 2025) in the Department of Electrical and Computer Engineering at the University of California San Diego. He also serves as Associate Adjunct Professor in the Division of Biomedical Informatics, Department of Medicine, and holds affiliate appointments with the Halıcıoğlu Data Science Institute, School of Biological Sciences, Shu Chien-Gene Lay Department of Bioengineering, Skaggs School of Pharmacy and Pharmaceutical Sciences, and multiple research institutes including the AI Group, Center for Machine-Intelligence, Computing and Security, Institute of Engineering in Medicine, and Institute for Genomic Medicine. Education: PhD in Machine Learning, School of Computer Science, Carnegie Mellon University Research Interests: His research focuses on machine learning inspired by human learning skills, such as self-explanation, small-group learning, and learning by teaching. He applies these techniques to large language models, foundation models, healthcare, and biomedicine. His work spans generative AI, medical imaging, protein modeling, and drug discovery. Recent Research Trends: His 2024–2025 publications emphasize generative AI for ultra-low-data medical image segmentation, multimodal large language models for biomedical applications, protein function prediction, and novel training strategies like task-adaptive pretraining and bi-level optimization for model adaptation. Scientific Awards: NIH MIRA Award (2025) NSF CAREER Award (2024) Best Graduate Teacher Award – UCSD ECE (2023) ICLR Notable-Top-5% Paper (2023) Global Top-100 Chinese Young Scholars in AI (2022) UCSD Faculty Career Development Award (2022) Tencent Faculty Award (2021) Outstanding Reviewer – ICLR (2021) AMIA Doctoral Dissertation Award Finalist (2020) Amazon AWS Research Award (2020) Tencent AI-Lab Faculty Award (2020) Innovator Award – Pittsburgh Business Times (2018) Siebel Scholarship (2014) Advising and Grants: He currently advises PhD students, postdocs, and master’s students. He has received major grants including the NIH MIRA and NSF CAREER awards, and actively mentors Schmidt AI in Science postdocs and graduate students. Teaching and Labs: He teaches ECE285 Deep Generative Models and ECE175B Probabilistic Reasoning and Graphical Models . His lab focuses on foundational and translational AI research with applications in biomedicine and healthcare.
Richard McKay is a part-time Research Fellow in the Department of History and Philosophy of Science at the University of Cambridge and serves as Director of Studies for History and Philosophy of Science at Magdalene College. He has held prestigious fellowships from the Wellcome Trust and the ESRC, and he divides his professional life between academic research and private practice as an International Coach Federation-certified career, academic and life coach. Research Interests: Twentieth-century history of medicine and public health History of sexuality and sexually transmitted infections History of HIV/AIDS Use of visual sources in medical history His current Wellcome-supported project, ‘Before HIV: Homosex and Venereal Disease, c.1939–1984’ , explores how healthcare workers and gay men in Canada, the United States and England came to understand the role of men who have sex with men in STI transmission during the mid-twentieth century. Publications & Scholarly Impact: Across monographs, peer-reviewed articles and book chapters, McKay interrogates the social construction of the “Patient Zero” myth, the pre-history of HIV, and the visual cultures surrounding AIDS-related illnesses such as Kaposi’s sarcoma. His 2017 monograph Patient Zero and the Making of the AIDS Epidemic has become a key reference in HIV historiography. Awards & Fellowships: Wellcome Trust Research Fellowship (current) ESRC Postdoctoral Fellowship, King’s College London Teaching & Advising: As Director of Studies at Magdalene College, McKay guides undergraduate students in History and Philosophy of Science. He has also contributed pedagogical modules to the U.S. National Library of Medicine’s online exhibition Surviving and Thriving: AIDS, Politics, and Culture . Professional Activities: Aside from his academic appointments, McKay maintains an active coaching practice accredited by the International Coach Federation, supporting academic and professional clients worldwide.
Prof. Dr. Julia Vogt is an Assistant Professor at the Department of Computer Science at ETH Zürich, leading the Professur für Medizin. Datenwiss. Her research focuses on medical machine learning, data science, and AI applications in healthcare. She specializes in developing interpretable AI systems for clinical decision support, particularly in pediatric diabetes management, medical imaging analysis, and anomaly detection. Her work bridges translational gaps by emphasizing causal approaches and clinical validation. Her academic role includes teaching courses like the Data Science Lab (263-3300-00L/10L) and Topics in Medical Machine Learning (263-5100-00L). Her lab's research spans predictive modeling for nocturnal hypoglycemia, echocardiogram analysis for pulmonary hypertension detection, and multimodal learning in radiology. She also contributes to national pediatric data initiatives like SwissPedHealth. Key technical areas include concept bottleneck models, stochastic AI frameworks, and generative models for medical signal denoising. Her work often emphasizes model interpretability, fairness, and robustness to distribution shifts. She collaborates on projects involving wearable devices for pediatric monitoring and AI-driven rehabilitation tools for post-stroke gait analysis.
Chang S. (CS) Nam is a Professor of Industrial and Systems Engineering at North Carolina State University, with affiliations in the UNC/NCSU Joint Department of Biomedical Engineering and the Department of Psychology. He holds editorial roles as Editor-in-Chief of Brain-Computer Interfaces journal and is a Fellow of the Human Factors and Ergonomics Society. His research focuses on advancing human-centered technologies through neuroergonomics, brain-computer interfaces (BCIs), and rehabilitation engineering, supported by grants from NSF, AFRL, AFOSR, and the Laboratory for Analytic Sciences. Education: Ph.D. in Industrial Engineering, Virginia Tech (2003) MSIE in Industrial Engineering, State University of New York at Buffalo (2000) Research Interests: His work spans neurorehabilitation, haptic interaction design, and BCI applications, aiming to bridge human cognitive and neural processes with technological systems. Key areas include adaptive human-computer interaction, EEG-based emotion recognition, and spatial augmented reality for skill training. Grants & Awards: Recipient of NSF CAREER Award (2010), Air Force Summer Faculty Fellowship (2018), and the Leland S. Kollmorgen Spirit of Innovation Award (2019) Editor of Brain-Computer Interfaces Handbook: Technological and Theoretical Advances (CRC Press) Advising & Labs: Advises students like Lingchao Mao and Priscille Koutouan Leads the Brain-Computer Interface and Neuroergonomics Lab, developing BCI systems for communication in vegetative states and neurorehabilitation
Daan Christiaens is a tenure track lecturer at KU Leuven's Faculty of Medicine and Faculty of Engineering Sciences. He is affiliated with the Department of Electrical Engineering (ESAT) and Department of Imaging & Pathology, serving as a member of the Medical Imaging Division and the KU Leuven Brain Institute (LBI). His academic responsibilities include membership in the Faculty Councils of Engineering Sciences and Medicine. His research focuses on: Inverse problems in medical imaging reconstruction Neuroimaging techniques for brain analysis Advanced quantitative MRI methodologies Diffusion-weighted imaging for microstructural assessment Dr. Christiaens' recent publications (2023-2025) demonstrate a consistent focus on diffusion MRI innovations, including novel reconstruction algorithms, neonatal brain development mapping, and clinical applications for neurodegenerative disorders. Key technical themes include motion correction, multi-shell modeling, and AI-enhanced image processing, while clinical applications span Alzheimer's disease, cerebral palsy, and autism research. He leads significant research projects including: MRI reconstruction with dynamic field monitoring (2024-2028) Compressed sensing for microstructure imaging (2022-2026) Neonatal diffusion MRI network connectivity analysis (2024-2028) As a core developer of the MRtrix3 software framework for medical image processing, he contributes to essential tools in neuroimaging research.
Dr. Robin Laycock is a Senior Lecturer in the Department of Health and Biomedical Sciences at RMIT University. He leads the Social and Cognitive Neuroscience (SoCoNeuro) Lab, focusing on behavioral and neural mechanisms of social perception in neurotypical and neurodivergent populations, including autism spectrum disorders and the neurocognitive effects of concussion. His research integrates methodologies such as eye-tracking, EEG, and fNIRS to study visual perception, face processing, and the impact of stress/anxiety on cognition. Dr. Laycock holds a PhD from La Trobe University, where he investigated visual processing pathways. His work also includes the BabyFace study, exploring social perception in pre-term infants using fNIRS. He is affiliated with RMIT’s Healthy Foundations Research Group and supervises research projects on topics like concussion neurocognition and social media’s neurobiological effects. Research interests span visual neuroscience, social neuroscience, and clinical applications of neuroimaging. He teaches undergraduate courses in Biological Psychology and supervises postgraduate research in vision science, neuropsychology, and affective neuroscience. His lab’s recent studies examine sex differences in sports-related concussion neuroimaging and the role of deepfakes in emotion perception research. He actively collaborates on projects addressing autism traits, stress effects on visual processing, and neuroimaging advancements.
Habib Ullah is an Associate Professor in Data Science at the Norwegian University of Life Sciences (NMBU), Norway, where he conducts research at the intersection of computer vision and machine learning. He is affiliated with the Institute of Data Science under the Faculty of Science and Technology. He has previously held academic positions at COMSATS University Islamabad, Pakistan, and the University of Ha'il, Saudi Arabia, and served as a postdoctoral researcher at The Arctic University of Norway. Educational Background: PhD in Information and Communication Technology (Computer Vision), University of Trento, Italy (2011–2015) MSc in Electronics and Computer Engineering, Hanyang University, South Korea (2007–2009) BSc in Computer Systems Engineering, NWFP University of Engineering and Technology, Pakistan (2002–2006) Habib Ullah's research is primarily focused on computer vision and machine learning, with applications in aquaculture, agriculture, and human behavior analysis. He investigates underwater fish feeding sounds using audio classification, develops zero-shot learning models for recognizing unseen classes, and applies deep learning to detect stress in salmon via skin dot patterns. He also explores AI-driven controlled environment agriculture, leveraging sensors and automation for optimal crop growth. His work emphasizes practical AI solutions for real-world challenges in environmental and biological domains. The recent publications highlight a strong trend in leveraging deep learning for zero-shot and semi-supervised learning, particularly in computer vision tasks such as sea ice classification, crowd anomaly detection, and agricultural monitoring. His research spans remote sensing, biomedical signal processing, and human activity recognition, demonstrating interdisciplinary versatility. The keywords reflect a focus on robust feature representation, knowledge transfer, and model generalization. Scientific Awards and Funding: Industrial PhD grant 'Advancing Controlled Environment Agriculture AI' from The Research Council of Norway (Project number 354125, 2 million NOK, 2024) Team member (Coordinator-Participant) in the Battery Cell Assembly Twin (BatCAT) project funded by Horizon Europe (7 mEuro, 2023–2027) Development of an AI-Based Image Analysis System for Monitoring Plant Status (Funding: 1.8 mNOK, starting 2025) Habib Ullah actively supervises PhD projects and contributes to academic service through editorial and organizational roles. He has served as an Associate Editor for IEEE Access, Guest Editor for MDPI Remote Sensing, and Editor of the Springer book Machine Learning Techniques and Sensor Applications for Human Emotion, Activity Recognition, and Support (ML-SHEARS) . He has also been a Track Chair and Program Committee Member for several international conferences, reflecting his leadership in the academic community. His research is supported by significant grants and collaborative projects, indicating strong institutional and international engagement. He is involved in multiple research teams and projects, including the BatCAT project on battery manufacturing and AI applications in controlled environment agriculture with RIFT LABS AS. His lab work integrates deep learning, sensor fusion, and data analytics for environmental and biological monitoring systems.
Anthony D. Wagner is the Lucie Stern Professor in the Department of Psychology at Stanford University and Deputy Director of the Wu Tsai Neurosciences Institute. He holds affiliations with Stanford's Center for Cognitive and Neurobiological Imaging, Symbolic Systems Program, Human Biology Program, and Stanford Center on Longevity. His PhD (1997) is from Stanford University in Psychology. Research focuses on the neurobiology of learning, memory, and executive function across ages, with translational studies on aging, Alzheimer’s disease, multitasking, and neuroscience applications in law. Key themes include biomarker development for neurodegeneration, cognitive resilience, and the impact of attention on memory. Labs/teams include collaborations at the Wu Tsai Institute and neuroimaging centers. Recent work emphasizes tau PET imaging, proteomics of aging, and fluid biomarkers for Alzheimer’s prediction. He also explores societal impacts of neurotechnology and media on cognition.
Matthew Lakin is an Associate Professor with tenure in the Department of Computer Science at the University of New Mexico, with a courtesy appointment in the Department of Chemical & Biological Engineering. He is affiliated with the UNM Center for Biomedical Engineering and the School of Engineering, and collaborates extensively with the UNM Health Sciences Center and external institutions. Education: Ph.D., Computer Science, University of Cambridge, 2010 M.A. (Cantab), University of Cambridge, 2009 B.A. (Hons), Computer Science, University of Cambridge, 2005 Dr. Lakin's research focuses on molecular computing, DNA nanotechnology, synthetic biology, and formal verification of biomolecular circuits. He develops computational models and experimental systems for programmable biological devices, especially using heterochiral DNA to enhance stability in living cells. His work spans software tools for biodesign and experimental validation in mammalian systems, with applications in nanomedicine and biosensing. The recent publications highlight a strong trend in engineering robust, intelligent biomolecular systems. His work integrates machine learning concepts into chemical reaction networks, advances geometric modeling of DNA systems, and pioneers L-DNA-based circuits for intracellular applications. The research spans theoretical foundations, software tools, and wet-lab experimentation, emphasizing interdisciplinary innovation. Scientific Awards: Presidential Early Career Award for Scientists and Engineers (PECASE), 2025 NSF CAREER Award, 2021 UNM School of Engineering Junior Faculty Research Excellence Award, 2021 Multiple student awards under his mentorship, including the Outstanding Graduate Student Award and DNA28 Best Student Presentation recognition Dr. Lakin has advised numerous graduate and undergraduate students, including Ph.D. graduates in Biomedical Engineering and Computer Science. He leads major funded projects such as the NSF CAREER grant on heterochiral molecular computing, an EPSCoR Research Fellowship, and a $3M NSF grant on heavy metal biosensing in collaboration with Native American communities. He is also PI on multiple NSF grants related to synthetic cells and nucleic acid technologies. He directs the Lakin Lab for Programmable Biology, which operates within the Department of Computer Science and collaborates with Chemical & Biological Engineering and the Center for Biomedical Engineering. The lab emphasizes both computational modeling and experimental molecular biology, and runs an NSF-funded biotechnology summer camp in partnership with ¡Explora! science museum to strengthen STEM education in New Mexico.
George H. Chen is an Associate Professor at Carnegie Mellon University , with dual affiliations in the Heinz College of Information Systems and Public Policy and the Machine Learning Department . His research focuses on trustworthy machine learning methods for temporal reasoning , particularly in health applications such as time-to-event prediction (survival analysis) and electronic health records analysis . He has extensive experience in nonparametric methods requiring minimal data assumptions. Educational Background PhD in Electrical Engineering and Computer Science, MIT (2015) SM in Electrical Engineering and Computer Science, MIT (2012) BS in Electrical Engineering and Computer Sciences & Engineering Mathematics and Statistics, UC Berkeley (2010) His work spans survival analysis , deep learning , and time series modeling , with applications in neurological prognostication , medical adherence , and health equity . He has developed self-contained educational resources including a 2024 monograph on deep survival analysis and tutorials at CHIL and SIGMETRICS. His 2025 course 95-865: Unstructured Data Analytics focuses on practical unstructured data analysis techniques. Notable projects include advising the AgriTech startup CoolCrop , which provides cold storage and market forecasts for Indian farmers serving 9,000+ farmers across 7 states. His Google Scholar publications reveal a strong focus on temporal modeling in healthcare, with recent advancements in neural survival analysis and fairness-aware temporal prediction.
