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.
Dr. Babak Taheri is a Full Professor at Texas A&M University's Department of Hospitality, Hotel Management & Tourism, affiliated with the College of Agriculture & Life Sciences. He holds an Honorary Professorship in Marketing at the University of Aberdeen and has held visiting professor roles at institutions like the University of Sassari and the University of Central Florida. His expertise spans consumer engagement, sustainability, and mixed-method research in tourism and hospitality. Education: BSc Industrial Engineering (Azad University), MSc Information Systems (Glasgow Caledonian University), PhD in Marketing (University of Strathclyde) with a focus on tourism. Additional credentials include a PhDip in Research Methods and MRes in Management Science. Research focuses on consumer behavior, co-creation value, CSR, and climate change impacts. He has published over 150 papers and serves as Associate Editor for The Service Industries Journal and International Journal of Contemporary Hospitality Management . Awards include Fellow of The Higher Education Academy and multiple Best Reviewer accolades. Leadership includes roles at Durham University, Strathclyde, and others. He secured grants exceeding $3M from Horizon 2020 and Innovate UK. Teaching spans BSc to PhD levels, emphasizing experiential learning. Media contributions include articles in The Conversation and The Irish Times. Grants & Leadership: Academic leadership in research and teaching, with grants focused on innovation and resilience in hospitality sectors.
Bryan Kian Hsiang Low serves as Associate Professor in the Department of Computer Science at the National University of Singapore's School of Computing, while simultaneously holding leadership positions as Director of AI Research at AI Singapore and Deputy Director of the NUS AI Institute. His academic journey includes a B.Sc. (2001) and M.Sc. (2002) in Computer Science from NUS, followed by a Ph.D. in Electrical & Computer Engineering from Carnegie Mellon University (2009). His research spans probabilistic machine learning, multi-agent systems, and trustworthy AI, with particular focus on Bayesian optimization , federated learning , and data-efficient methodologies . The Low Lab develops frameworks for collaborative AI, automated machine learning, and AI applications in scientific domains through the Group of Learning and Optimization Working in AI (GLOW.AI), which maintains a multi-disciplinary approach bridging computer science, mathematics, and engineering disciplines. Analysis of his recent publications reveals a consistent emphasis on data valuation , privacy-preserving collaborative learning , and robust optimization techniques , with increasing integration of large language models into his research framework. His work demonstrates strong theoretical foundations coupled with practical applications in computational sustainability and robotics. Andrew P. Sage Best Transactions Paper Award (2006) NUS Overseas Graduate Scholarship (2004-2009) Faculty Teaching Excellence Award (2017-2018) IEEE RAS Distinguished Lecturer (2019) World Economic Forum Global Future Councils Fellow (2016-2018) Dr. Low actively mentors PhD students including Rachael Sim, Quoc Phong Nguyen, and Zhongxiang Dai, while leading major initiatives like the AI Phenome Platform for plant breeding optimization. His research group GLOW.AI operates at the intersection of theory and practice, with strong industry engagement through AI Singapore. Current projects focus on scalable AI systems for scientific discovery and developing frameworks for equitable collaborative machine learning with robust privacy guarantees.
Brandon Weissbourd is an Assistant Professor in the Biology department at the Massachusetts Institute of Technology (MIT) and holds a joint appointment as an Investigator at the Picower Institute for Learning and Memory. He joined MIT in 2023 after completing a postdoctoral fellowship in the lab of David Anderson at the California Institute of Technology (Caltech). Prior to that, he earned his PhD in Biology from Stanford University in 2016 under the mentorship of Liqun Luo, and a BA in Human Evolutionary Biology from Harvard University in 2009. His research interests encompass systems neuroscience, evolutionary biology, and molecular biology. He uses jellyfish models, such as Clytia hemisphaerica, to study the evolution and functional mechanisms of nervous systems. His work combines computational techniques like single-cell RNA-seq and advanced microscopy with traditional genetic and anatomical approaches to dissect neural circuits and their roles in behaviors like feeding and social interaction. Additionally, he has explored serotonin and noradrenaline systems in mammals, focusing on their heterogeneity and functional connectivity. Recent publications emphasize the utility of non-traditional model organisms for evolutionary studies and underscore his expertise in computational methods for neurobiological analysis. Earlier work includes groundbreaking studies on the dorsal raphe serotonin system and basal forebrain circuits governing sleep-wake cycles. No scientific awards or honors have been explicitly mentioned in the provided text. Weissbourd’s academic trajectory reflects a strong emphasis on interdisciplinary research, merging evolutionary, molecular, and systems-level perspectives to understand neural systems across species. His advising record is not detailed here, though he has been affiliated with prestigious research labs during his training. Current affiliations include the MIT Biology department and the Picower Institute, where he likely contributes to collaborative projects in systems and evolutionary neuroscience. Weissbourd’s work is grounded in experimental models such as Clytia medusa and mouse brain studies, enabling him to investigate both ancient nervous system architectures and modern mammalian neural pathways. His lab’s focus on functional genomics and circuit mapping positions him at the forefront of studies on neural diversity and evolutionary innovation.
Margaret Shih is the Neil H. Jacoby Chair in Management and a professor of management and organizations at UCLA Anderson School of Management. On July 1, 2025, she was appointed the school's interim dean. She has been a faculty member at UCLA Anderson since 2008 and previously served on the faculty at the University of Michigan for eight years and worked at the RAND Corporation. Her educational background includes a Ph.D. and M.A. in Social Psychology from Harvard University and a B.A. in Psychology with honors from Stanford University. Professor Shih's research focuses on the effects of diversity in organizations, particularly examining social identity and the psychological effects of stereotypes, prejudice, discrimination, and stigma in organizational contexts. Her work spans organizational behavior, social psychology, and diversity studies, with significant contributions to understanding how identity affects workplace dynamics, leadership, and decision-making. She has recently published research on the influence of political polarization on perceptions of threats to democracy. Her scholarly work demonstrates consistent exploration of how individuals navigate multiple identities in organizational settings, with particular attention to colorblind diversity policies, stereotype activation mechanisms, and strategies for reducing stigma in workplaces. Her research has evolved from foundational work on stereotype boost effects to more complex examinations of multiracial identity and organizational inclusion strategies. 2017 La Force Award for Leadership 2017 Niedorf Decade Teaching Award 2011 Fulbright Award 2006 Literature, Sciences and Arts Class of 1934 Memorial Teaching Award, University of Michigan 2006 Literature, Sciences and Arts Award for Educational Excellence, University of Michigan 2005 Outstanding Scholar Honor, National Science Council, Taiwan 2003 Martin E.P. Seligman Award for Outstanding Dissertation Research in Positive Psychology 1998-1999 Certificate of Distinction in Teaching, Derek Bok Center for Teaching and Learning, Harvard University Professor Shih has received substantial research funding from prestigious organizations including the National Science Foundation, National Institute of Mental Health, Social Sciences and Humanities Research Council of Canada, John Templeton Foundation, and the Robert Wood Johnson Foundation. Her administrative roles have included serving as Management and Organizations department chair, deputy dean of academic affairs (where she recruited five new ladder faculty and updated Anderson bylaws), and faculty special advisor in the UCLA Office of Equity, Diversity and Inclusion. She serves on the executive committee for the International Society for Self and Identity and is a consulting editor for the Journal of Personality and Social Psychology and Personality and Social Psychology Bulletin. Her laboratory work and research teams focus on understanding how systems contribute to different types of inequities and how bureaucratic obstacles impede equity, diversity, and inclusion initiatives. She has been instrumental in developing frameworks for identity management strategies in organizational contexts and examining how political polarization affects workplace dynamics.
Emily Cooper is an Associate Professor of Optometry & Vision Science at the Herbert Wertheim School of Optometry & Vision Science, University of California, Berkeley. She serves as the Chair of the Vision Science PhD Program and is a co-Director of the Center for Innovation in Vision & Optics. Additionally, she is a member of the Helen Wills Neuroscience Institute and a Visiting Faculty Researcher at Google. Dr. Cooper's research focuses on 3D vision, perceptual graphics, AR/VR, computational neuroscience, visual encoding, and display system design. Her work investigates how the visual system processes information to create our perception of the 3D world, with applications in computer graphics, virtual reality, and assistive technologies for people with low vision. Analysis of Dr. Cooper's recent publications (2023-2025) reveals a strong focus on the intersection of vision science and emerging technologies, particularly in augmented reality and assistive vision systems. Her work spans fundamental research on visual perception mechanisms to applied research developing practical technologies for low vision rehabilitation. A significant portion of her recent work addresses visual discomfort in XR displays, perceptual guidelines for AR/VR systems, and innovative approaches to assistive vision technologies that enhance mobility and independence for visually impaired individuals. Dr. Cooper leads an active research laboratory at UC Berkeley's 391 Minor Hall, where she mentors students and collaborators in vision science research. Her lab investigates both basic questions about how vision works and translational questions about improving visual technologies. She has developed perceptual guidelines for optimizing field of view in stereoscopic augmented reality displays and created assistive technologies such as an augmented reality sign-reading assistant for users with reduced vision. Dr. Cooper is also involved in professional activities including co-organizing the Computational Neuroscience: Vision summer course at Cold Spring Harbor Laboratory and working with Community Resources For Science to promote science education.
Hsiao-Wen Liao is an **Assistant Professor of Psychology** in the **Adult Development and Aging program** at the **Georgia Institute of Technology**. She holds a Ph.D. in Developmental Psychology from the University of Florida (2017) and completed postdoctoral training at Stanford University’s Center on Longevity. Her research focuses on **autobiographical memory**, **episodic future thinking**, and **meaningful aging**, leveraging **VR and NLP methods** to explore how life experiences shape adult development. She investigates how memory processes interact with sociocultural contexts to foster resilience and continuity across the lifespan. **Research Interests**: Autobiographical memory functions, cognitive aging, intergenerational connectivity, and the role of technology (VR/NLP) in memory reconstruction. Current projects examine VR applications for recollection enhancement and NLP analysis of life narratives. **Awards & Grants**: ISSI Mini Research Grant (2024), College of Sciences Travel Grant (2025), APS Travel Assistance (2025), and ImmerseGT Hackathon Best Use of Omniverse Track (2023). **Lab Members**: Supervises graduate students (DaEun Kim, Sarah Kim, Liam Hart) and collaborates with interdisciplinary teams in HCI and machine learning. Undergraduate assistants focus on neuroscience and psychology interfaces. **Lab Activities**: Janus Lab emphasizes mixed-methods approaches, combining qualitative content analysis with experimental designs. Recent projects address aging well, resilience, and technology-mediated memory interventions.
Ke Xu is an Assistant Professor at the Department of Finance, Faculty of Business and Economics, University of Victoria. His research bridges finance, econometrics, and cryptocurrency, focusing on market microstructure, high-frequency trading, and price discovery mechanisms. He has extensively studied Bitcoin ETFs, fractional cointegration models, and machine learning applications in financial markets. Key Research Areas: Market Microstructure High-Frequency Trading Cryptocurrency Dynamics Price Discovery Machine Learning in Finance Financial Econometrics Article Trends: Xu’s work spans empirical analyses of Bitcoin ETFs, volatility modeling (e.g., affine GARCH), and algorithmic trading strategies. His recent papers explore mini flash crashes using machine learning, regulatory impacts on market quality, and sustainable crypto portfolios.
Dr. Nour Moustafa is an Associate Professor and ARC DECRA Fellow at the School of Systems & Computing (SysCom) , University of New South Wales (UNSW) Canberra , Australia. He leads the Intelligent Security Group and focuses on developing AI/ML-driven cybersecurity frameworks for smart systems. Educated at Helwan University (BSc/MSc in Information Systems) and UNSW (PhD in Cybersecurity). Research Interests include intrusion detection, threat intelligence, privacy preservation, digital forensics, and cyber resilience, with methodologies spanning statistical analysis , machine learning , and deep learning applied to IoT , Edge/Cloud , and Industrial IoT environments. His work emphasizes federated learning for privacy preservation, blockchain for secure AI, and digital twins for network self-healing. Notable contributions include the TON-IoT , Bot-IoT , and UNSW-NB15 datasets for cybersecurity evaluation. Scientific Awards : 2020 Spitfire Memorial Defence Fellowship ACM Distinguished Speaker IEEE Senior Member He has served as guest associate editor for IEEE Transactions journals and held leadership roles in conferences like IEEE TrustCom . His research bridges academia and industry, with over 75 publications in top-tier venues.
Steve Luck is a Distinguished Professor at the University of California, Davis, holding appointments in the Department of Psychology and the Center for Mind and Brain (CMB). He served as CMB Director from 2009–2019 and is affiliated with the UC Davis MIND Institute and the Center for Neuroscience. His research focuses on attention, working memory, and cognitive dysfunction in psychiatric disorders (e.g., schizophrenia), employing ERP recordings, eye tracking, and behavioral methods. He is a leading developer of ERP methodologies, including the ERPLAB Toolbox and global ERP Boot Camp workshops. Education: Ph.D., Neurosciences, UC San Diego, 1993 M.S., Neurosciences, UC San Diego, 1989 B.A., Psychology, Reed College, 1986 Research Interests: Dr. Luck explores mechanisms of cognitive control, with a focus on working memory's role in guiding attention. His lab investigates ERP correlates of attentional deficits in schizophrenia and develops standardized ERP protocols. Recent work emphasizes multivariate decoding of EEG signals and transdiagnostic neurocognitive biomarkers. Awards: Troland Award (2001) APA Distinguished Scientific Award (1998) McGuigan Young Investigator Prize (2004) Elected Fellow, Society of Experimental Psychologists and AAAS Teaching & Leadership: Professor Luck pioneered hybrid course formats in Cognitive Science and teaches advanced topics in perception and cognitive neuroscience. He co-founded the UC Davis Cognitive Science major and advocates for innovative undergraduate education models. Labs & Collaborations: The Luck Lab integrates clinical and basic research, collaborating globally on ERP method development and schizophrenia biomarker studies. Key projects include ERP Core resources and the CNTRACS consortium for neurocognitive reliability studies.
Christopher A. Baldassano is an Associate Professor in the Department of Psychology at Columbia University, maintaining offices in Schermerhorn Hall (370 for office, 312 for lab). Contact is available via email c.baldassano@columbia.edu or phone +1 212 854 1902 by appointment. Education Ph.D., Stanford University, 2015 Research Focus Dr. Baldassano leads the Dynamic Perception and Memory Lab investigating how humans process and recall complex real-world experiences through event segmentation, temporal/spatial structure modeling, and neural representation formation. His work integrates cognitive neuroscience with machine learning approaches to analyze fMRI data during narrative, movie, and virtual reality experiments. Key research themes include event cognition dynamics, memory summarization mechanisms, and how prior knowledge shapes mental representations of everyday experiences. Scientific Awards No awards or fellowships were documented in the provided materials. Advising and Grants While specific student advisees and grant details weren't listed, his lab structure implies active mentorship of graduate researchers in cognitive neuroscience methodologies. Funding likely supports fMRI experimentation and computational modeling infrastructure. Laboratory Operations The Dynamic Perception and Memory Lab employs functional MRI combined with data-driven machine learning techniques to model neural representation variations across stimuli and individuals. Current projects examine event boundaries in continuous experiences using ecologically valid paradigms like movies and virtual environments, with emphasis on how temporal/spatial world structures influence cognitive processing.
Kristen Pleil is an Associate Professor at Weill Cornell Medicine in the Graduate School of Medical Sciences, affiliated with the Department of Pharmacology and Neuroscience. She serves as Co-director of the NIGMS-sponsored Training in Pharmacological Sciences (TIPS) predoctoral T32 program. Academic Appointments: Assistant Professor (2016), Associate Professor (2021) Education: BA in Psychology (Emory University, 2005), PhD in Neuroscience (Duke University, 2010), Postdoc at University of North Carolina School of Medicine Research Focus: The Pleil lab investigates how sex and stress hormones regulate alcohol/substance use, stress responsivity, and affective behavior through neuropeptidergic brain circuits and hormone-neuropeptide signaling. Key projects include estrogen's role in alcohol drinking and anxiety, opioid receptor signaling in reward/aversion learning, and sex-dependent thalamic control of stress responses. The lab uses in vivo and ex vivo mouse models to study synaptic/epigenetic plasticity across developmental stages. Publications Trends: Her work spans molecular pharmacology, neurophysiology, and addiction research with a focus on sex differences in neuropsychiatric diseases. Articles cover topics like estrogen signaling in binge drinking, opioid-context memory, thalamic circuits in stress response, and optogenetic tools for receptor imaging. Scientific Awards: President’s Young Investigator Award, ISBRA (2013) Elizabeth Young New Investigator Award, OSSD (2015) K99/R00 Pathway to Independence Award, NIAAA (2015) Kellen Foundation Junior Faculty Fellowship (2016) NARSAD Young Investigator Award (2017) Early Career Investigator Award, NIDA-NIAAA (2017) Training and Collaborations: As Co-director of the TIPS T32 program, she mentors graduate students in pharmacological sciences. Her collaborations include researchers at UNC, Duke, and Weill Cornell, with grants from NIAAA, NIMH, and NIGMS.
Sivaraman Balakrishnan is a Professor at Carnegie Mellon University with joint appointments in the Department of Statistics and Data Science and the Machine Learning Department. His research bridges statistical machine learning, algorithmic statistics, and robust inference. Education: Ph.D. in Computer Science from Carnegie Mellon University (Language Technologies Institute, advised by Jaime Carbonell); postdoctoral work at UC Berkeley (Department of Statistics, advised by Martin Wainwright and Bin Yu). Research Interests: Spanning robust statistics, domain adaptation, minimax hypothesis testing, assumption-light inference, causal inference, statistical optimal transport, non-parametric statistics, ranking, crowdsourcing, optimization, and topological data analysis. Key Research Trends: Recent work focuses on domain adaptation under label/misingness shifts, robust gradient estimation, smooth optimal transport maps, and conditional independence testing. He explores minimax optimal methods, univariate mean estimation, and high-dimensional regression with missing data. Scientific Awards: IMS Lawrence D. Brown Student Award (2021, 2020) NVIDIA Pioneer Award (2018) Franklin V. Taylor Memorial Best Paper Award (2018) Grants and Editorial Roles: NSF grants (CCF-1763734, DMS-1713003, DMS-2113684, DMS-2310632), Amazon Research Award (2021), Google Research Scholar Award (2021). Associate Editor for JASA and JRSSB ; Editorial Board member for Foundations and Trends in Statistics . Collaborative Groups: Co-organizes the Statistics and Machine Learning Reading Group and participates in the Causal Inference Working Group at CMU.
Jeff Zhang is an Assistant Professor in the School of Electrical, Computer and Energy Engineering at Arizona State University. He joined ASU in January 2023 after completing a postdoctoral fellowship at Harvard University. His research spans deep learning, computer architecture, embedded systems, and EDA, with particular emphasis on energy-efficient and fault-tolerant design for AI/ML systems and hardware accelerators. Education: Ph.D., New York University M.Eng., B.Eng., Hunan University Dr. Zhang's research bridges theoretical machine learning with practical hardware implementation, developing novel architectures that optimize performance, power consumption, and reliability. He has pioneered approaches for hardware acceleration of large language models, efficient sparse matrix operations, and novel memory technologies for AI workloads. His work has received multiple awards including IEEE Top Picks in Test and Reliability (2023) and IEEE Micro Best Paper Award (2022). His recent publications demonstrate a strong trend toward heterogeneous computing, with significant work in chiplet-based AI accelerators, photonic computing for AI, and 2.5D/3D integration techniques. The research spans from high-level compiler frameworks to circuit-level innovations, with a consistent theme of co-designing algorithms and hardware for optimal AI performance. His work on the SODA toolchain has been particularly influential in bridging Python to silicon. Scientific Awards: IEEE Top Picks in Test and Reliability, IEEE ITC, 2023 Best Paper Award, IEEE Micro, 2022 Best Paper Award Candidate, IEEE DATE, 2022 Best Presentation Award Nomination, ACM SIGDA DATE PhD Forum, 2020 Best Paper Award Nomination, IEEE VLSI Test Symposium, 2018 Ernst Weber Ph.D. Fellowship, New York University, 2015, 2016 Dr. Zhang actively mentors a diverse group of graduate and undergraduate students, with several alumni now working at leading technology companies including Apple, TSMC, and Ansys. His research is supported by prestigious grants from NSF, Sandia National Labs, and industry partners. He serves on technical program committees of numerous top conferences and has organized special sessions on emerging topics like Gen AI for Chip Design and LLM-Aided Design. Dr. Zhang leads a vibrant research group that collaborates extensively with industry partners and national laboratories. Current projects focus on next-generation AI hardware, including chiplet-based systems, photonic accelerators, and novel memory technologies for large language models. His group has developed several open-source tools and frameworks, including the SODA toolchain for bridging Python to silicon.
Abolfazl Asudeh is an Associate Professor in the Department of Computer Science at the University of Illinois Chicago and director of the Innovative Data Exploration Laboratory (InDeX Lab) . He is a Senior Member of ACM and IEEE , serving as Associate Editor for IEEE Transactions on Knowledge and Data Engineering , VLDB Ambassador , and VLDB Endowment Liaison to NSF . His research focuses on Algorithm Design for Data and AI problems , emphasizing efficient, accurate, and responsible solutions through Approximation Algorithms , Randomized Methods , and Computational Geometry . Recent work explores LLM optimization ( Needle ), fair data structures ( FairHash ), and responsible AI frameworks ( Chameleon ). Scientific awards include Communications of the ACM Research Highlight Google Research Scholar Award SIGMOD 2019 Research Highlight Best of VLDB 2020 SIGMOD 2017 Reproducibility Award Grants: NSF IIS-2348919 (2024-2027): Fairness-aware Data Structures NSF IIS-2107290 (2021-2024): Collaborative Fairness Research The InDeX Lab develops systems like Needle (image retrieval) and RSR (matrix multiplication). His work integrates fairness , reliability , and computational efficiency across data structures , LLMs , and responsible AI implementations.
