Josh Pasek is a Professor of Communication & Media and Political Science at the University of Michigan, with additional affiliations as a Research Professor at the Center for Political Studies (Institute for Social Research) and Core Faculty at the Michigan Institute for Data Science. His research focuses on the intersection of political communication, survey methodology, and data science, particularly examining how new media and psychological processes shape political attitudes, public opinion, and measurement accuracy. Co-author of Words That Matter and Democracy Amid Crises Maintainer of R packages anesrake and weights Key research areas include: Political Communication Survey Methodology Media Psychology Social Media Data Electoral Behavior Ballot Design His recent publications address topics such as: Vaccine hesitancy and information environments Phubbing and trust dynamics Supreme Court legitimacy post-Dobbs Partisan polarization during the pandemic Climate change perception and partisanship Measurement error in surveys He has collaborated with institutions like the Annenberg Public Policy Center, Georgetown University, and the Pew Research Center, and serves on the AAPOR Task Force for 2024 Pre-Election Polls.
Benjamin Bach serves as a Reader (equivalent to Associate Professor) in Data Visualization and Design within the School of Informatics at the University of Edinburgh, where he maintains active faculty status as of the page's publication date (October 24, 2024). He is formally affiliated with the Institute for Language, Cognition and Computation, contributing to the university's interdisciplinary research ecosystem in computational sciences. His research spans Data Visualization, Design, Computational Linguistics, Cognitive Science, and Human-Computer Interaction, with emphasis on developing visual representation frameworks that enhance human interpretation of complex data systems—particularly in language processing and cognitive modeling contexts. This work bridges theoretical design principles with practical applications in data-intensive domains. Dr. Bach's professional contact includes the email address bbach@exseed.ed.ac.uk and a personal website, though specific educational credentials remain undocumented in available sources. Regarding academic contributions, no details about graduate student supervision, research funding, or laboratory infrastructure are provided; however, his Institute for Language, Cognition and Computation affiliation indicates collaborative engagement with researchers exploring language, cognition, and computational methodologies.
Dr. Sam Ferguson is a Senior Lecturer at the School of Computer Science, University of Technology Sydney (UTS), with a multidisciplinary background in music performance, cognitive science, and psycho-acoustics. His research explores the intersection of sound, music, and human experience through creative coding, machine learning, and interactive systems. Key Research Areas: Sound and Music Computing, Human-Computer Interaction, Creative Coding, Cognitive Science, Installation Art, and Acoustics. Current Projects: ARC Linkage project on creative coding and multiplicitous media; industry collaborations on IoT-based audiovisual systems. Recent Publications: Focus on spatial audio complexity, gestural interaction with networked sound, music emotion recognition frameworks, and robotic performance through genre-based cultural platforms. Leadership Roles: Director of Teaching & Learning Engagement; former Deputy Head of School (Teaching and Learning); active in ACM Creativity and Cognition Steering Committee. Teaching: Courses like Digital Media Studio , Prototyping Physical Interaction , and Data Processing using R within UTS's interdisciplinary Software Development Studio.
Dick Stegewerns is an Associate Professor in Japanese Studies at the University of Oslo. His research spans the political, intellectual, and diplomatic history of modern Japan, with a focus on East Asia's international relations, war memory, and cultural identity. He also explores Japanese cinema and popular culture, including sake's historical significance. Research Interests: Nationalism, regionalism, Japanese film studies, food studies, and transnational cultural perspectives. Current Projects: Monographs on Japanese self-perception, collaborative research on postwar Japanese cinema, sake globalization, and visualization of history in media. Publications cover topics like East-West civilization discourse, postwar war films, sake industry dynamics, and Hiroshima's transnational memory. His work integrates film studies, history, and cultural analysis.
Cheung Ngai-Man is an Associate Professor and Associate Head of Pillar (Education) at Singapore University of Technology and Design (SUTD), part of the Information Systems Technology and Design (ISTD) pillar. He holds a Ph.D. in Electrical Engineering from the University of Southern California (2008) and has held research positions at Stanford University, Texas Instruments, IBM, and others. His research focuses on image and signal processing, computer vision, machine learning, and artificial intelligence. Education: Ph.D., Electrical Engineering, University of Southern California (2008); Postdoctoral research at Stanford University (2009–2011). Research Interests: Develops algorithms for multimedia data processing, explores interdisciplinary applications of signal processing and AI, and addresses challenges in computer vision and generative models. Recent work includes fairness in generative models, few-shot image generation, and adversarial robustness. Publications: Over 100+ peer-reviewed papers in top venues (CVPR, NeurIPS, IEEE TIP, TPAMI) focusing on computer vision, generative models, and AI security. Notable 2023 work includes studies on label-only model inversion attacks and fairness metrics in generative systems. Awards: Best Paper Finalist (CVPR 2019), SAIL Award Finalist (WAIC 2019), Outstanding Associate Editor (IEEE T-MM), Croucher Foundation Fellowship. Students: Supervised postdocs (Hossein Nejati, Fang Lu), research assistants (Mohammad Rostami), and visiting students (Ma Rui). Labs/Teams: Leads research groups in AI, computer vision, and multimedia systems at SUTD. Has spun off AI initiatives for wound care and contributed to Singapore’s National AI Strategy.
Benjamin Eysenbach leads the Princeton Reinforcement Learning Lab, where he designs algorithms that enable artificial intelligence systems to learn intelligent behaviors through trial-and-error, specializing in self-supervised methods that eliminate the need for human labels. He joined Princeton after completing his PhD in machine learning at Carnegie Mellon University under Ruslan Salakhutdinov and Sergey Levine, supported by the NSF Graduate Research Fellowship and Hertz Fellowship. His research bridges fundamental machine learning principles with practical applications in robotics and decision-making systems. Eysenbach's research focuses on developing self-supervised reinforcement learning algorithms that enable autonomous skill acquisition without external rewards. His investigations span contrastive learning methods, temporal abstraction techniques, and scalable architectures for goal-conditioned behaviors. These innovations aim to create more efficient and generalizable learning systems that can discover useful behaviors from unlabeled experience. Eysenbach's publications demonstrate consistent advancement in self-supervised RL methodologies, with recent work focusing increasingly on temporal abstraction and representation learning theory. His research shows progression from foundational contrastive RL frameworks toward more sophisticated analyses of generalization properties and uncertainty quantification. The 2025 works indicate expanding investigation into hierarchical control, probabilistic alignment, and hyper-deep network architectures. Eysenbach has been recognized with prestigious awards including the Hertz Fellowship and NSF Graduate Research Fellowship, supporting his doctoral research in self-supervised RL methodologies. His work has been presented at top machine learning conferences including NeurIPS, ICML, and ICLR. As director of the Princeton Reinforcement Learning Lab, Eysenbach oversees research initiatives in self-supervised RL, including projects on intention-conditioned modeling, horizon generalization, and contrastive learning frameworks. He has secured funding from the Princeton AI Lab to study neural correlates of temporal contrast in decision-making. Eysenbach teaches courses in reinforcement learning and has developed new benchmarks like JaxGCRL to accelerate research in goal-conditioned RL.
S. Louisa Wei is a Professor of Cinematic Art at the School of Creative Media, City University of Hong Kong, where she has taught since 2001. With a PhD in Film Studies from the University of Alberta and an MA in Comparative Literature from Carleton University, she has taught over 4,000 students in both theoretical and production courses including Documentary 1, Documentary 2, Visual Storytelling, Visualizing Literature, and Chinese Cinema. Her educational background includes: PhD in Film Studies, University of Alberta, Canada MA in Comparative Literature, Carleton University, Canada Wei's research focuses on Sinophone cinema with particular emphasis on women filmmakers across historical periods, Chinese film history, and documentary practice. Her work often explores the intersections of gender, politics, and cultural identity in Chinese cinema, with special attention to neglected historical figures and transnational connections. She approaches film history through both scholarly research and documentary filmmaking, creating a unique methodology that bridges academic and creative practices. Her research has significantly contributed to recovering the histories of early Chinese women filmmakers and documenting politically sensitive historical narratives in Chinese literary and film history. Her recent scholarly and creative output shows a consistent focus on biographical documentary work centered on Chinese intellectuals and artists, women's contributions to cinema history, and politically significant moments in Chinese cultural history. Her publications and films often work in dialogue with each other, with documentary projects frequently informing scholarly books and vice versa, creating a rich interdisciplinary approach to her subject matter. Her notable awards include: Hong Kong Book Award 2017 for Esther Eng: Ocean-crossing Film and Women Pioneers Distinguished Publishing Prize in the Literature and Fiction Category of The First Hong Kong Biennial Publishing Prize for Wang Shiwei: A Reform in Thinking As an educator, Wei has advised hundreds of student projects, many of which have won awards in international and local film festivals. She is not currently accepting PhD students but has been instrumental in mentoring emerging filmmakers through her teaching. Her documentary work has gained international recognition from both academic circles and film festivals, attracting media attention from Hollywood trade magazines to major newspapers in Hong Kong and mainland China. She has served as a professional jury member for the Hong Kong Film Awards and became a member of the Hong Kong Director's Guild in 2018. While she doesn't appear to lead a formal research lab, her work involves extensive collaboration with historians, archivists, and fellow filmmakers across multiple countries, creating an informal network of scholars and practitioners focused on recovering marginalized histories in Chinese cinema and literature.
Academic Overview: Todd Lewis is a Distinguished Professor of Arts and Humanities at the College of the Holy Cross, holding the Monsignor Edward G. Murray Professorship. He specializes in Newar Buddhism, Himalayan religions, and the intersections of ecology and religion. Lewis earned his Ph.D. in Religion from Columbia University and has conducted extensive fieldwork in Nepal. Education: Ph.D. in Religion, Columbia University Research Interests: Lewis focuses on Buddhist narratives, ritual practices, and the role of merchants in religious history. His work explores Newar Buddhism, Himalayan cultural history, and the application of religious frameworks to contemporary issues like ecology. Grants & Leadership: Lewis directed six NEH summer institutes for educators, emphasizing Himalayan studies and Buddhist traditions. He has received fellowships from the Guggenheim Foundation, National Endowment for the Humanities, and others. Labs/Teams: Active in Holy Cross' Asian Studies and Environmental Studies programs. Co-founded the Journal of the North American Japanese Garden Association and serves on editorial boards for Journal of Buddhist Ethics and Religions .
Andrea Stevenson Won is a researcher at Cornell University in the Department of Communication , focusing on virtual reality (VR), human-computer interaction, and social dynamics in immersive environments. Her work explores avatar embodiment , nonverbal behavior , and accessibility in VR for users with disabilities. Research Themes : Virtual embodiment and its psychological effects Accessibility solutions for blind and low-vision users in social VR Nonverbal communication analysis in immersive environments Pro-social behavior through VR interventions Collaborative VR systems and AI integration Recent Article Trends : 2024: Investigated avatar behavior transformation in mixed reality ( MRTransformer ), AI-guided accessibility tools, and nonverbal cue adaptations 2023-2022: Focused on educational VR applications, 360° video narratives, and longitudinal team dynamics 2021-2014: Pioneered avatar embodiment studies, anxiety detection via movement tracking, and homuncular flexibility in VR
Kimmo Vehkalahti is a Senior University Lecturer and docent at the University of Helsinki, specifically affiliated with the Centre for Social Data Science within the Faculty of Social Sciences. He serves as a Teachers' Academy Supervisor in the doctoral program and maintains an active research profile with numerous publications and projects. His research interests focus on social data science, social statistics, multivariate methods, open data science, and statistical data processing using R programming. Vehkalahti has developed expertise in applying statistical methods to social science research questions and data visualization. His recent publications demonstrate a strong focus on multivariate analysis applications in behavioral sciences, social data visualization, and student well-being research. The publications span diverse areas including statistical methodology, data visualization techniques, and educational interventions. Fellow of the Teachers' Academy (2013) Best Teacher of the City: Kaupungin Paras Opettaja (2010) Good Teacher: Hyvä Opettaja (2009) Magister Bonus - Good Teacher (2003) One of the best teachers of Open University (2017) Vehkalahti actively supervises academic work with 40 thesis supervisions and 10 PhD supervisions documented. He participates in significant research projects including SASE Stress as strength in education (since 2022) and WELLS - Promoting university students' well-being and life-long learning (since 2019). His academic service includes conference participation, peer review, and organizational roles in international statistical organizations.
Desmond Elliott is an Associate Professor and Villum Young Investigator at the Department of Computer Science, University of Copenhagen. His research focuses on vision-language models, multilingual and multimodal processing, with particular emphasis on tokenization-free language modeling approaches. He leads a research group actively working on pixel language models and cross-lingual multimodal understanding. University of Copenhagen, Department of Computer Science Villum Young Investigator Associate Editor for JAIR (2025-2028) Senior Area Chair for ACL 2025 Elliott's research spans vision-language integration, multilingual NLP, and multimodal machine learning. His work explores how language models can operate directly on visual pixels without traditional tokenization, enabling more seamless integration of vision and language processing. He investigates compositional generalization in multimodal systems, retrieval-augmented image captioning, and cross-lingual transfer in vision-language tasks. His group develops methods for low-resource language processing and creates benchmarks for evaluating multimodal systems across diverse cultural contexts. His recent publications demonstrate strong trends in pixel-based language modeling, synthetic dataset generation through retrieval augmentation, and multilingual vision-language processing. The work spans theoretical advances in model architectures and practical applications in areas like medical text analysis, food culture understanding, and social media content moderation. His research often bridges computer vision and natural language processing with a focus on making these technologies accessible across diverse languages and cultures. Best Paper Honorable Mention at CVPR Visual Concepts Workshop 2025 Best Long Paper Award at EMNLP 2021 Area Chair Favourite paper at COLING 2018 Elliott actively supervises student projects in BSc and MSc programs related to his research interests. His research has received substantial funding from Google (2024-2025), Facebook (2022-2024), Villum Foundation (2021-2026), Novo Nordisk Foundation (2019-2024), and European Union (2023-2026). He regularly recruits postdocs for projects including the Danish Foundation Models project and the Responsible AI for the People Project. His group holds regular meetings on Tuesdays from 13:00-14:00 in IF G.03, with an active mailing list for announcements. The research environment appears collaborative, with frequent co-authorship across institutions and regular participation in major NLP and computer vision conferences.
Michael Weiss is a Lecturer in the Department of Mathematics at the University of Michigan. He teaches foundational courses such as Linear Algebra (MATH 217) and Explorations in Euclidean Geometry (MATH 431). His office is located in East Hall (Room 1868) at 530 Church Street, Ann Arbor, MI. Current Courses: MATH 217-004 (Linear Algebra), MATH 217-011 (Linear Algebra), MATH 431-001 (Explorations in Euclidean Geometry) Weiss’s research focuses on mathematics education, particularly on pedagogical strategies for teaching geometry and mathematical reasoning. His work explores the use of visual representations, interactive technologies, and narrative tools (e.g., comics) to enhance learning and teacher development. He emphasizes the role of theory-building in secondary geometry instruction and investigates how students translate abstract mathematical concepts into concrete reasoning. His publications highlight interdisciplinary approaches to education, integrating technology (e.g., animated stories, voice assistants) with traditional teaching methods. Keywords across his work include Mathematics Education , Geometry , Interactive Media , Visual Learning , and Teacher Training . Contact: mweiss@umich.edu
PD Dr. Lutz Rzehak is an academic staff member at the Institute for Asian and African Studies (IAAW) within the Faculty of Humanities at Humboldt University of Berlin, serving as a Teacher for special tasks specializing in Modern Iranian languages and the ethnography and cultural history of Afghanistan and Central Asia. His educational background includes: Oriental Studies/History from the State University of Leningrad (1985) Doctorate in Iranian Studies, Humboldt University of Berlin (1991) Habilitation in Central Asian Studies / Iranian Studies, Humboldt University of Berlin (2001) Dr. Rzehak's research spans Central Asian Studies , Iranian Studies , Ethnography , and Linguistics , with fieldwork focusing on Baloch, Pashtun, and Tajik communities. His work examines language evolution, social structures, and cultural practices through historical and contemporary lenses, particularly regarding Soviet influence and post-Taliban Afghanistan. Key contributions include grammatical analyses of Iranian languages and ethnographic studies of religious rituals and tribal systems. His recent publications (2016-2021) reveal consistent focus on Iranian language dynamics (especially Dari and Pashto), ethnic identity politics in Afghanistan, and cultural history of Central Asia. These works address language change mechanisms, ethnic consolidation strategies, and the interplay between tradition and modernity, often utilizing comparative linguistic and ethnographic methodologies. His scientific awards include: Heisenberg Fellow of the German Research Foundation Dr. Rzehak has held significant research positions including Research Associate in the Crossroads Asia Competence Network (2011-2016) and Heisenberg Fellow (2002-2011). While his CV does not list formal PhD advisees, his language lecturing and research supervision have mentored numerous students in Central Asian and Iranian studies through course instruction and fieldwork guidance. He has been actively involved in the Crossroads Asia Competence Network, a major interdisciplinary research initiative examining transregional connections and historical trajectories across Central, South, and West Asia.
Kelly Bennion serves as an Associate Professor in the Psychology and Child Development Department at California Polytechnic State University. Her research examines how real-life variables—such as emotion, stress, physiological arousal, and sleep—affect memory encoding and consolidation using behavioral experiments, eye tracking, polysomnography, and neuroimaging. She investigates how sleep selectively enhances memories for emotionally salient or future-relevant information in ecologically valid contexts. (78 words) Her educational background includes: Ph.D. and M.A. in Psychology (Cognitive Neuroscience concentration) from Boston College Ed.M. in Mind, Brain, and Education from Harvard Graduate School of Education B.A. in Psychology and Spanish (summa cum laude, Phi Beta Kappa) from Middlebury College Dr. Bennion's work focuses on memory prioritization mechanisms during sleep-wake cycles, particularly how emotional arousal and physiological stress modulate consolidation. She explores real-world applications including educational strategies and mental health interventions, emphasizing the interaction between cortisol levels and sleep-dependent memory processing. Her multi-method approach bridges laboratory findings with naturalistic memory phenomena. (92 words) Analysis of her 2020-2025 publications reveals three dominant research streams: (1) sleep's role in enhancing emotional and future-relevant memories through selective consolidation, (2) cross-episode memory integration via semantic relatedness and surprise mechanisms, and (3) interdisciplinary extensions into public health (postpartum interventions) and environmental toxicology (bisphenol A effects). Her methodology increasingly combines behavioral metrics with physiological monitoring to capture memory dynamics in complex scenarios. (68 words) Dr. Bennion actively mentors undergraduate researchers, evidenced by student co-authorships on publications including Jackson (2019) on school shooter perceptions. While specific grant details aren't provided, her sophisticated research infrastructure implies substantial external funding. She teaches core psychology courses including Research Methods, Biopsychology, and Memory, emphasizing hands-on methodology training. Her laboratory maintains advanced capabilities for sleep monitoring, eye tracking, and neuroimaging to investigate memory consolidation across physiological states. (76 words)
Dr. Guangliang Cheng is an Associate Professor in the Department of Computer Science at the University of Liverpool. His research focuses on deep learning, computer vision, and perception algorithms with applications in remote sensing, medical imaging, and autonomous systems. Prior to his current role, he served as a vice research director in the Autonomous Driving Group at SenseTime and completed postdoctoral research at the Aerospace Information Research Institute, Chinese Academy of Sciences. Ph.D. in Pattern Recognition from the National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences (CASIA) Postdoctoral Researcher at Aerospace Information Research Institute, Chinese Academy of Sciences (2017–2019) Dr. Cheng’s research integrates computer vision and deep learning to address challenges in semantic segmentation, domain adaptation, and robust detection. Recent work explores wavelet-based multimodal fusion for remote sensing and attention-guided architectures for medical imaging. His 2025 publications span journals like GIScience & Remote Sensing and Knowledge-Based Systems , emphasizing scalable solutions for geospatial and biomedical applications. In 2025, Dr. Cheng’s article trends highlight remote sensing semantic segmentation, cross-domain medical imaging, and drone-based fire detection. His collaborations span institutions such as SenseTime, Chinese Academy of Sciences, and University of Liverpool teams, focusing on frequency-domain fusion, attention mechanisms, and GPU optimization. As a supervisor, Dr. Cheng seeks highly motivated PhD students to join projects supported by scholarships including the Centres for Doctoral Training (CDT) and Duncan Norman Scholarship. He serves as Module Co-ordinator for COMP338: Computer Vision (2024–2025) and actively reviews for top-tier journals and conferences.
