Zsofia Zavecz is a Research Associate at the University of Cambridge Department of Psychology. Her work focuses on the neurophysiological mechanisms underlying sleep and memory consolidation, with particular emphasis on electrophysiological correlates of lucid dreaming and sleep-dependent learning. Research highlights include: Investigation of EEG functional connectivity during statistical learning Study of transcranial stimulation effects on probabilistic learning Analysis of sleep restriction impacts on hormonal regulation Exploration of cognitive reserve mechanisms in sleep disorders Her neuroscientific investigations span procedural memory systems, neural oscillations, and cross-population studies in both healthy individuals and pediatric sleep-disordered breathing patients.
Markus Vincze is an Associate Professor at the Institute of Automation and Control Engineering (ACIN) at Vienna University of Technology (TU Wien). He founded the Vision for Robotics (V4R) group in 1996 to advance robotic perception, particularly in real-world environments and homes. His work focuses on cognitive computer vision techniques for robotics. Education: Diplom in Mechanical Engineering (1988) and PhD (1993) from TU Wien; M.Sc. (1990) from Rensselaer Polytechnic Institute. V4R coordinates EU projects like ActIPret, robots@home, HOBBIT, and national initiatives like vision@home. Markus has edited a book on Robust Vision with Gregory Hager and authored 62 peer-reviewed journal articles and over 400 reviewed publications. His recent research explores zero-shot 6D pose estimation, sim-to-real transfer, and transparent object detection. Markus has served as program chair for ICRA 2013 and organized HRI 2017 in Vienna. He has advised numerous students and secured grants from the Austrian Academy of Sciences for work at HelpMate Robotics and Yale's Vision Laboratory. The V4R group leads innovations in robotic vision, including frameworks for synthetic data generation (Unrealgensyn), depth completion (CAGT), and educational robotics applications for sustainability. Their work spans household robotics (RH3), agricultural robotics (EdgeSoil), and human-robot collaboration.
Melissa J. Ferguson is a Professor of Psychology at Yale University since 2020, previously affiliated with Cornell University's Psychology Department (2002-2020). Her research focuses on implicit cognitive processes underpinning evaluation, goal-pursuit, self-control, and social behavior. She investigates impression updating, prejudice expression, and behavioral control mechanisms. Ph.D., Social Psychology, New York University (2002) As director of Yale's Implicit Social Cognition Lab, Ferguson explores how implicit evaluations evolve through reinterpretation and new evidence. Her work spans human-robot interaction, moral agency, and political bias dynamics, with funding from the National Science Foundation and National Institutes of Health. Recent publications analyze: (1) Robot competence evaluation mechanisms, (2) Implicit bias correction methods, (3) Legal decision-making patterns, and (4) Prejudice shifts during political events. She contributes to high-impact outlets like Psychological Science and PNAS . Best Paper Award, ISCON 2016
Marta Kutas is a Distinguished Professor in the Department of Cognitive Science at the University of California, San Diego (UCSD), where she has held roles including Department Chair (2007–2019), Director of the Center for Research in Language (2007–2022), and Chancellor's Associates Endowed Chair (2017–2022). Her research focuses on language processing, neuropsychology, and electrophysiological methods, with a particular emphasis on event-related potentials (ERPs). She has contributed significantly to understanding semantic integration, memory, and neural mechanisms underlying language comprehension. Education includes a B.A. from Oberlin College (1971) and a Ph.D. in Psychology from the University of Illinois (1977). She has held adjunct roles at San Diego State University and the UCSD Department of Neurosciences. Her awards include the Revelle Medal (2023), membership in the American Academy of Arts and Sciences (2018), and the Cognitive Neuroscience Society's Distinguished Career Award (2015). Her research interests span language comprehension/production, neuropsychology, and ERP methodologies. Key publications analyze semantic processing, memory-related brain potentials, and the impact of individual knowledge on word processing. She has collaborated on studies involving Alzheimer's, Parkinson's, and schizophrenia, using ERPs to explore cognitive deficits.
Abhinav Shrivastava is an Associate Professor in the Department of Computer Science at University of Maryland, College Park, with a joint appointment in the Institute of Advanced Computer Studies (UMIACS). Previously, he served as an Assistant Professor at the same institution from August 2018 to June 2024, and spent one year as a Visiting Research Scientist at Google Research from September 2017 to August 2018. His educational background includes: PhD in Robotics and Artificial Intelligence from Carnegie Mellon University (2017), advised by Abhinav Gupta, with thesis titled 'Discovering and Leveraging Visual Structure for Large-scale Recognition' MS in Artificial Intelligence from Carnegie Mellon University (2011), supervised by Alyosha Efros and Martial Hebert BTech in Computer Science and Engineering from Jaypee Institute of Information Technology (2010) Professor Shrivastava's research focuses on computer vision and machine learning, with particular expertise in object detection, image recognition, and neural representations. His work bridges theoretical advances with practical applications, exploring how visual systems can discover and leverage structure in large-scale recognition problems. He has made significant contributions to understanding the role of supervision in vision transformers, developing novel approaches for object-state composition recognition, and creating efficient neural representations for videos and 3D scenes. His research often addresses fundamental challenges in visual recognition, including handling novelty in open-world environments and improving the efficiency of visual systems. An analysis of his recent publications reveals a strong emphasis on neural representations, particularly for dynamic content like videos and 3D scenes. His work demonstrates increasing sophistication in handling open-world vision problems, with research spanning object discovery, localization, and representation learning. The publications show a clear progression toward more efficient and scalable models, with recent work focusing on model compression, sparse representations, and addressing the challenges of working with limited annotations. His scientific contributions have been recognized with several prestigious awards: Best Paper Award (Applications) at IEEE Winter Conference on Applications of Computer Vision (2020) Microsoft Research PhD Fellowship (2014-2016) Best Student Paper Award at IEEE Winter Conference on Applications of Computer Vision (2014) Outstanding Reviewer Award at IEEE CVPR (2015) Professor Shrivastava has successfully mentored numerous graduate students, many of whom have become prominent researchers in computer vision. His Amazon Research Awards (2020 and 2023) have supported innovative projects including 'The pursuit of knowledge: discovering and localizing new concepts using dual memory' and 'Audio-conditioned Diffusion Models for Generating Lip-synchronized Videos.' He has served as Area Chair for major conferences including ICCV, CVPR, and AAAI, demonstrating his leadership in the computer vision community. His research has attracted significant funding from both academic and industry sources, supporting his exploration of fundamental questions in visual recognition and representation learning.
Jonathan Cannon is an Assistant Professor in the Department of Psychology, Neuroscience & Behaviour at McMaster University's Faculty of Science. His research focuses on timing and rhythm in perception and action, with particular interest in timing-related neural dynamics in the basal ganglia, cerebellum, and supplementary motor area. His work combines mathematical modeling with experimental approaches to understand the neural basis of rhythm perception and production. Dr. Cannon's research interests span timing and rhythm perception , neural dynamics , dynamical systems theory , Bayesian cognition , neural oscillations , and autism research . His approach centers on formulating and simulating neurophysiological and cognitive models, drawing on dynamical systems theory and Bayesian cognitive frameworks. His work incorporates psychophysics, EEG experiments, and collaborations with experimentalists to investigate how the brain processes rhythmic information. Analysis of his recent publications reveals a strong focus on the intersection of rhythm perception, motor control, and autism spectrum disorder. His work demonstrates how beat perception co-opts motor neurophysiology, with particular attention to predictive processes in rhythmic cognition. His research shows reduced precision of motor and perceptual rhythmic timing in autistic adults, while also finding intact sequence learning abilities in certain contexts. Dr. Cannon teaches advanced courses including Machine Learning Methods for Brain Modelling and Neural Data Analysis (PSYCH 734), Computational Models in Neuroscience (NEUROSCI 3MN3), and Neuroscience Seminars. His teaching reflects his interdisciplinary approach that bridges mathematics, neuroscience, and cognitive science. Beyond his academic work, Dr. Cannon is an active musician who performs on violin and guitar, particularly in klezmer and folk music contexts. He has also demonstrated entrepreneurial spirit through founding Flying Leap Games and developing the storytelling game 'Wing It,' which successfully crowdfunded and reached numerous retailers.
Timothy Harris is an Affiliated Lecturer at the University of Cambridge's Department of Computer Science and Technology, where he jointly teaches courses on multicore semantics and programming. Currently, he works at OpenAI, focusing on performance optimization for GPU inference of large language models, including the Azure OpenAI Service. Previously, he held roles at Microsoft, AWS, Oracle Labs, and was a faculty member at the University of Cambridge (2000–2004). His research spans distributed systems, runtime systems, operating systems, and high-performance computing, with an emphasis on scalability and performance. He contributed to projects like the Xen hypervisor and the Barrelfish research OS. Key research interests include distributed training of PyTorch models in the ONNX runtime, large-scale storage performance with Amazon S3, and runtime systems for in-memory graph analytics. His work often bridges 'big data' and high-performance computing techniques. Notable contributions include the book Transactional Memory (2010) and the Barrelfish OS, alongside numerous publications in top-tier conferences like SOSP, ASPLOS, and EuroSys. He has served as PC chair for ISMM 2025, VEE 2017, and EuroSys 2015, reflecting his leadership in the systems research community. His awards include a Best Paper Award at PACT 2010. Beyond academia, Harris is an avid hiker, aiming to complete the UK coastline, and maintains a photography portfolio at tlhphotography.uk .
Laura K. Nelson is an Associate Professor of Sociology at the University of British Columbia , where she also directs the Centre for Computational Social Science . Her work bridges computational methods with sociological inquiry, focusing on gender inequality, social movements, and organizational dynamics. She previously held faculty roles at Northeastern University and affiliated with institutions like the NULab for Texts, Maps, and Networks and the Network Science Institute . Education: PhD in Sociology (2014), University of California, Berkeley MA in Sociology (2009), University of California, Berkeley BA in Sociology (2006), University of Wisconsin-Madison (Phi Beta Kappa) Research Interests span computational sociology, social movement strategy, intersectionality, and STEM equity. She pioneered frameworks like computational grounded theory and radical objectivity , integrating machine learning with qualitative paradigms. Recent publications analyze gender dynamics in emergency medicine, feminist movement histories, and the NSF ADVANCE program’s impact on equity. Her 2024 Social Science Quarterly paper quantifies ADVANCE’s interdisciplinary reach. Awards include the 2020 Best Meta-Reviewer at SocInfo20 and Outstanding Faculty of the Year at Northeastern University. She serves on editorial boards for American Journal of Sociology , Poetics , and Acta Sociologica . She co-PIs a National Science Foundation grant studying gender-equity dissemination in higher education networks and supervises graduate student Jinyang Yu . Her lab, Centre for Computational Social Science , drives open-source methodological innovation.
Ryan Thibodeau is a Professor at St. John Fisher University and a New York State licensed psychologist with Apple Teacher certification. His research examines the history of psychiatry and mental illness stigma, with publications spanning PTSD, schizophrenia, depression, and autism stigma. Recent work explores continuum beliefs, social distance, and intervention efficacy. Community involvement includes the Mount Hope Cemetery unnamed deceased memorial project and sensory-friendly space development. His publication portfolio demonstrates extensive focus on mental health stigma mechanisms across military/civilian contexts, celebrity influences, and parent-associated stigma. Methodologies include laboratory experiments, correlational studies, and implicit/explicit measures spanning psychophysiology and social cognition.
Professor Tineke Brunfaut is a leading academic in Applied Linguistics at Lancaster University 's School of Social Sciences. Her work focuses on language testing , second language reading , listening , and integrated/multimodal skills . She coordinates the Language Testing Research Group and has received prestigious awards including the ILTA Best Article Award , e-Assessment Award , and TOEFL Outstanding Young Scholar Award . Key research areas: Cognitive and affective factors in testing, methodological innovations (eye-tracking, discourse analysis), and language test development. Recipient of multiple grants from the British Council, Trinity College London, and British Academy. PhD supervision interests: Language testing, integrated skills, assessment literacy, and validation. Recent publications explore technology-enhanced assessment , multimodal viewing-to-write tasks , and the impact of delivery modes on test performance . She has consulted for global institutions on test design and evaluation. Scientific Awards : ILTA Best Article Award e-Assessment Award for Best Research TOEFL Outstanding Young Scholar Award Her teaching includes MA programs in Language Testing, TESOL, and Applied Linguistics, covering Test Construction , Research Methods , and Statistical Analyses .
Yoshiko Matsumoto is the Yamato Ichihashi Professor in Japanese History and Civilization and Professor of East Asian Languages and Cultures at Stanford University, with a courtesy appointment in Linguistics. She has been a faculty member at Stanford since 1992, progressing from Assistant Professor to her current distinguished position. Matsumoto also serves as coordinator of the Japanese Language Program and has held significant administrative roles including Chair of the Department of Asian Languages (2003-2005) and Interim Chair of the Department of East Asian Languages and Cultures (2016). Matsumoto earned her Ph.D. in Linguistics from the University of California, Berkeley (1989), following M.A. degrees in Linguistics from UC Berkeley and General and Applied Linguistics from the University of Tsukuba, an M.I.A. in American Studies from the University of Tsukuba, and a B.A. in English Language & Literature from Japan Women's University. Professor Matsumoto's research focuses on linguistic pragmatics from cross-linguistic perspectives, with particular expertise in Japanese language. Her work spans structural and sociocultural aspects of language in use, including noun-modifying clause constructions, honorifics, discourse markers, and the intersection of language with gender and aging. She has pioneered research on conversational narratives of older adults, examining how ordinary framing strategies help individuals navigate difficult experiences. Her current projects explore intergenerational communication through haiku, communicative abilities of people with dementia, and noun-modifying constructions across Eurasian languages. Matsumoto's scholarship consistently bridges theoretical linguistics with practical applications for understanding human communication in diverse social contexts. Matsumoto's recent publications reveal a growing focus on practical applications of linguistic research for social benefit, particularly in intergenerational communication and dementia care. Her work increasingly integrates arts-based approaches, especially haiku poetry, to bridge generational divides and enhance communication with elderly populations. The research shows a consistent trajectory from theoretical linguistic frameworks toward applied, human-centered language studies that address real-world challenges in aging societies, with particular attention to how ordinary language practices help individuals navigate life transitions and difficult experiences. Dean's Award for Distinguished Teaching, School of Humanities and Sciences, Stanford University (2000) Richard E. Guggenhime Faculty Scholar, Stanford University (2000-2003) Violet Andrews Whittier Fellow, Stanford Humanities Center (2019-2020) Faculty Research Fellow, Michelle R. Clayman Institute for Gender Research (2014-2015) Research Fellow, Japan Foundation (2002) Internal Fellow, Stanford Humanities Center (2005-2006) Presidential Fund for Innovation in the Humanities, Stanford University (2009-2011) Professor Matsumoto has mentored numerous students through her teaching in Japanese language and linguistics courses, including specialized offerings on language and aging, points in Japanese grammar, and haiku-based communication. Her research has been supported by prestigious grants from the National Endowment for the Humanities, the Japan Foundation, and Stanford's Presidential Fund for Innovation in the Humanities. She has served on multiple editorial boards including the Journal of Pragmatics since 1992, demonstrating long-standing leadership in her field. Matsumoto has also advised students through individual studies and thesis projects in East Asian Languages and Cultures. Matsumoto leads several collaborative research initiatives including the 'Sharing Conversations' project which examines intergenerational communication through haiku, and research on communicative abilities of people with dementia. Her work often involves interdisciplinary teams spanning linguistics, gerontology, and creative arts, with fieldwork conducted in both Japan and the United States. The 'Noun-Modifying Constructions in Languages of Eurasia' project represents a major international collaboration examining linguistic structures across cultural boundaries. She also directs the 'Language, Old Age and Gender in Japan' project supported by the Stanford University/Japan Foundation, and the 'Difficult Conversations Continue: Memories of the 3.11 Disaster and Bereavement Narratives' project focused on post-disaster communication.
Ziming Zhang is an Assistant Professor in the Department of Electrical and Computer Engineering at Worcester Polytechnic Institute (WPI) , with additional affiliations in Data Science and Robotics Engineering. He previously held research roles at Mitsubishi Electric Research Laboratories (MERL) and Boston University. PhD in Computing (2013) from Oxford Brookes University , UK MS in Computing Science (2010) from Simon Fraser University , CA BS in Computer Science and Technology (2005) from Northeastern University , China Research interests span computer vision , machine learning , and their applications in point cloud processing , medical imaging , autonomous driving , and IoT . He leads the Vision, Intelligence, and System Laboratory (VISLab) at WPI. Recent publications focus on 3D reconstruction , hyperbolic learning , and robust classifiers . Awards include the R&D100 Award 2018 and NSF funding for data-efficient deep learning. PhD Students: Yecheng Lyu (co-supervised), Guojun Wu (co-supervised), Hangrui Zhang, Xuechu Yu Master's Students: Yun Yue, Yuping Shao Visiting Scholars: Fangzhou Lin His lab partners with industry and academic institutions, focusing on autonomous systems , robotics , and scientific imaging projects.
Ken Paller is Professor of Psychology and Director of the Cognitive Neuroscience Program at Northwestern University, where he holds the James Padilla Chair in Arts & Sciences. He serves as Associate Director of the NIH/NINDS-funded Training Program in the Neuroscience of Human Cognition, dedicated to training future cognitive neuroscientists. Dr. Paller's research focuses on the intricate relationships between brain activity and conscious experience, particularly investigating how memories are formed, stored, and re-experienced. His laboratory has pioneered methods to strategically influence the mind during sleep to improve memory, creativity, and psychological well-being. His work spans multiple domains including memory consolidation during sleep, targeted memory reactivation, lucid dreaming research, and the neural correlates of conscious experience. Notably, he collaborates with Tibetan Monastic Scholars in research on sleep and dreaming, bridging scientific and contemplative traditions. Analysis of his recent publications reveals a strong focus on sleep-dependent memory processes, with particular emphasis on targeted memory reactivation techniques, dream manipulation, and memory modification during sleep. His research demonstrates how sleep can be leveraged to enhance cognitive functions and potentially treat conditions like nightmares in narcolepsy. Dr. Paller received the prestigious Director's Pioneer Award from NIH in 2024 and holds the James Padilla Chair in Arts & Sciences at Northwestern University. His work has garnered significant media attention, featured in outlets including BBC World Service, The World Science Festival, Discover Magazine, NY Times, LA Times, The Economist, NPR Science Friday, and CBC Radio. As Director of the Cognitive Neuroscience Program and Associate Director of the NIH-funded Training Program, Dr. Paller plays a significant role in mentoring the next generation of cognitive neuroscientists. His Cognitive Neuroscience Laboratory (CNL) conducts cutting-edge research at the intersection of sleep science, memory research, and consciousness studies, with implications for both basic science and clinical applications.
Thad Starner is a Professor in the College of Computing at Georgia Institute of Technology and Technical Lead/Manager on Google's Glass. He directs the Contextual Computing Group (CCG), co-founded the Animal Computer Interaction Lab, and contributes to Georgia Tech's Ubicomp Group and Brainlab. A wearable computing pioneer since 1993, he has over 500 publications and 80 issued U.S. patents. Coined 'augmented reality' in 1990 Developed CopyCat for ASL learning in deaf children Invented Passive Haptic Learning for skill acquisition His research spans wearable interfaces for Deaf-hearing communication, dolphin interaction systems (CHAT), dog-handler communication (FIDO), and brain-computer interfaces for ALS patients. Current projects focus on optical aging simulation, XR input methods, and animal behavior telemetry. Recent publications (2023-2025) explore AR display ergonomics, AI-augmented reasoning, sign language recognition, and animal-computer interaction. His work has been featured in 60 Minutes, BBC, National Geographic, and Time Magazine. CHI Academy (2017) Lemelson-MIT Prize finalist White House Champions of Change finalist He advises graduate students in wearable systems and teaches AI and prototyping courses. His lab developed the Perceptive Workbench for gesture tracking and created early Eigenfaces research for face recognition.
Robert J.K. Jacob is a Professor of Computer Science at Tufts University, affiliated with the School of Engineering's Department of Computer Science. His research focuses on Human-Computer Interaction (HCI), particularly implicit brain-computer interfaces (BCI) using fNIRS and EEG technologies. He has held visiting positions at University College London, Université Paris-Sud, and MIT Media Lab. Education: Ph.D. in Computer Science from Johns Hopkins University. Research Interests : Jacob's work explores novel interaction techniques, adaptive interfaces, and BCI applications. Current projects emphasize real-time fNIRS-based systems for effortless user input, cognitive workload assessment, and neuroadaptive technologies. His lab investigates how brain signals can enhance user interfaces in domains like music learning, gaming, and urban design. Recent Trends in Articles : Recent publications highlight advancements in BCI design, neuroadaptive systems, and interdisciplinary applications of fNIRS. Work spans theoretical frameworks (e.g., NeuroCHI ethics) to practical tools like the Tufts fNIRS dataset. Key themes include improving BCI calibration, exploring AI's role in urban environments, and integrating affective computing into artistic interfaces. Awards : ACM Fellow (2016) ACM CHI Academy Membership (2007) Best Paper Award at CHI 2016 Advising & Grants : Supervised 15+ Ph.D. alumni in HCI and BCI. Served as Vice-President of ACM SIGCHI and co-chair of UIST/CHI conferences. Active in editorial roles for Human-Computer Interaction and ACM Transactions on Computer-Human Interaction . Labs & Teams : Directs the Tufts HCI Lab in the Joyce Cummings Center. Collaborates with interdisciplinary teams on projects like the Marble Track Audio Manipulator and Reality-Based Interaction Framework.
