Dr. Michael Boyd is a prominent archaeologist affiliated with the Department of Archaeology at the University of Cambridge and serves as an Honorary Research Associate at the McDonald Institute for Archaeological Research and a Senior Research Affiliate at The Cyprus Institute . His work centers on the prehistoric Aegean, with expertise in material culture, mortuary studies, ritual practices, and landscape archaeology. Research Interests : Boyd's scholarship interrogates the theoretical and methodological foundations of archaeology, emphasizing the dynamic interplay between landscapes, tombs, and human agency. His studies span Early Bronze Age Cycladic cultures, environmental archaeology, and the emergence of ritualized spaces in the Aegean. Publications : Boyd has co-directed the Keros Seaways Project and authored seminal works on Aegean ritual deposits, including the Keros Sanctuary volumes (2016–2018) and Staging Death (2016). His 2021 edited volume Far from Equilibrium critiques contemporary archaeological theory and advocates for reorienting the discipline. Methodological Contributions : He pioneers interdisciplinary and geo-ethnoarchaeological approaches, integrating soil micromorphology and phytolith analysis to reconstruct degradable architectural elements in the Cyclades.
Lars Ulander is a Professor at Chalmers University of Technology specializing in radar remote sensing. His research focuses on synthetic aperture radar (SAR) signal processing, particularly for applications in forest biomass mapping and ground imaging using VHF/UHF-band systems. He is a key proposer for ESA's BIOMASS satellite mission (launching 2025) and leads the BorealScat project, utilizing a 50-meter tower-based tomographic radar to study boreal forest dynamics. His work spans radar system development, SAR tomography techniques, and environmental monitoring of forests and sea surface currents. Current research areas include vegetation water content estimation, bistatic radar configurations, and optimization of SAR data processing algorithms for multi-temporal analysis. Recent publications demonstrate expertise in P-band/L-band SAR for biomass retrieval, passive radar systems, and interferometric techniques. His articles investigate radar backscatter sensitivity to forest structure, moisture parameters, and seasonal changes, while contributing to mission design frameworks like SLAINTE and SESAME.
Mark Crowley is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, with a cross-appointment in the Cheriton School of Computer Science. He is a member of the Waterloo Artificial Intelligence Institute (WAII) and the Waterloo Institute for Complexity and Innovation (WICI), and serves as National Secretary of the Canadian Artificial Intelligence Association (CAIAC). His educational background includes a Ph.D. and M.Sc. in Computer Science from the University of British Columbia, where he worked in the Laboratory for Computational Intelligence, and a B.A. in Computer Science from York University. He completed a postdoctoral fellowship at Oregon State University working with Tom Dietterich's machine learning group. Crowley's research focuses on developing dependable and transparent algorithms to augment human decision-making in complex domains with multiple agents, spatial structure, or uncertainty. His work spans Reinforcement Learning , Deep Learning , Ensemble Methods , and Manifold Learning . He frequently collaborates with researchers in applied fields including Computational Sustainability, Sustainable Forest Management, Autonomous Driving, Medical Imaging, and Material Design. His research is motivated by both theoretical opportunities and real-world challenges such as forest fire management, automotive applications, and medical imaging. His recent publications demonstrate a strong focus on addressing challenges in reinforcement learning, particularly around observation costs, multi-agent systems, and causal representation learning. His work on ChemGymRL provides a significant contribution to digital chemistry and material design through reinforcement learning frameworks. The textbook Elements of Dimensionality Reduction and Manifold Learning represents a major contribution to the theoretical foundations of machine learning. Crowley actively supervises graduate students, with recent thesis completions including Shayan Shirahmadi Gale Bagi (PhD, Feb 2025) and Oleksandra Nahorna (MASc, Dec 2024). His lab, UWECEML (Waterloo ECE Machine Learning Lab), focuses on developing new algorithms at the intersection of Machine Learning, Optimization, and Probabilistic Modeling. He teaches courses including ECE 457C (Reinforcement Learning), ECE 657A (Data & Knowledge Modelling & Analysis), and ECE 457B (Fundamentals of Computational Intelligence). His blog Computationally Thinking explores AI, machine learning, and the societal impact of these technologies.
Erik B. Sudderth is a Professor of Computer Science and Statistics and Chancellor's Fellow at the University of California, Irvine (UCI). He leads the Learning, Inference, & Vision Group and directs multiple research centers, including the UCI Center for Machine Learning and Intelligent Systems and the HPI Research Center in Machine Learning and Data Science. He previously served as an Associate Professor at Brown University. Education: B.S. (summa cum laude) in Electrical Engineering from UC San Diego (1999), M.S. and Ph.D. in EECS from MIT (2002, 2006). His research focuses on statistical methods for scalable machine learning, Bayesian nonparametrics, probabilistic graphical models, and applications in computer vision, AI, and environmental science. Key areas include nonparametric clustering, deep generative models, and particle-based inference algorithms. Research interests span diverse topics: advancing Bayesian nonparametric models for medical time series, scalable variational inference, and AI ethics. Notable contributions include the NET-VISA seismic monitoring system (ISBA Mitchell Prize, 2014), the BNPy toolbox (NSF CAREER Award), and work on diverse particle max-product algorithms for continuous inference. Scientific awards include the NSF CAREER Award, ISBA Mitchell Prize, and recognition as one of "AI's 10 to Watch" (IEEE). He has served as editor for top journals (JMLR, IEEE PAMI) and conference chairs (NeurIPS, CVPR). His work bridges theory and practice, with applications in robotics, climate science, and healthcare. Labs/Teams: UCI Learning, Inference, & Vision Group; UCI Center for Machine Learning; CREATE Technology Center. Grants include NSF funding for visually impaired collaboration tools and soil biogeochemical modeling.
Dr. Edyta Chlebowska is an Assistant Professor at the Cyprian Norwid Research Center, John Paul II Catholic University of Lublin. Specializing in the visual art and literary legacy of Cyprian Norwid, she explores intersections between Polish Romanticism, art history, and cultural studies. Her work extends to album art analysis and interdisciplinary connections with music (e.g., Pink Floyd, Czesław Niemen). Her research focuses on Norwid's self-portraits, eschatological themes in his drawings, and comparative studies with 19th-century Warsaw painting. She reconstructs lost artworks, documents private collections (e.g., Konstancja Górska, Victor Gomulicki), and examines Norwid's reception in Florence and Odessa. Key projects include translating Studia Norwidiana and critical editing of Norwid's complete works. As editor-in-chief of multiple On Cyprian Norwid. Studies and Essays volumes and organizer of conferences like Niemen Non Stop and Colloquia Norwidiana , she bridges academic rigor with public engagement. Her publications span peer-reviewed books, journal articles, and popular science pieces, emphasizing Norwid's unpublished works and symbolic programs.
Dr. Arno Solin is a tenured Associate Professor in Machine Learning at Aalto University's Department of Computer Science and an Academy of Finland Research Fellow . He leads the Aalto machine learning research group and serves as Director of the Finnish Doctoral Program Network in AI (AI-DOC) . His work bridges probabilistic modeling with practical applications in sensor fusion and real-time inference. ELLIS Scholar (European Laboratory for Learning and Intelligent Systems) Adjunct Professor at Tampere University Member of Young Academy Finland (2021–2025) Research Interests focus on data-efficient machine learning with probabilistic methods for real-time inference and sensor fusion. Key areas include Gaussian processes, diffusion models, stochastic differential equations, and uncertainty quantification in deep learning. His group develops methods that combine structural constraints with adaptive learning for deployment on resource-limited hardware. Publication Trends show consistent output in top venues (NeurIPS, ICML, ICLR, AISTATS) with emphasis on diffusion models , 3D scene reconstruction , and real-time probabilistic modeling . Recent works explore physics-informed learning, heterophily-aware graph models, and compressed representations for world models in reinforcement learning. Scientific Recognition : Awarded AI Researcher of the Year 2024 by AI Finland Teacher of the Year 2023 at Aalto CS ISIF Jean-Pierre Le Cadre Best Paper Award (2018) MLSP Schizophrenia Classification Challenge Winner (2014) NeurIPS/ICML Reviewer Awards Research Leadership includes coordinating Finland's AI Center of Excellence program and directing the Finnish Doctoral Program Network in AI (AI-DOC). He supervises 15+ doctoral/postdoctoral researchers and has spun off Spectacular AI , a company commercializing sensor fusion technology.
Leo J. Garofalo is the Virginia Eason Weinmann '51 Professor of History at Connecticut College, where he joined in 2002. He coordinates the Social Justice and Sustainability Pathway and co-directs the Mellon Mays Undergraduate Fellowship program. His scholarship focuses on colonial Latin America with emphasis on racial dynamics and cultural exchange. Education: B.A. in History and Hispanic Studies from Brown University M.A. from Boston College Ph.D. from University of Wisconsin at Madison Research Focus: Garofalo investigates the intersection of African and Indigenous cultures in colonial Peru, examining how marketplaces, taverns, and religious practices shaped Afro-Andean identities. His current work traces West African diaspora routes to the Andes and analyzes Afro-Iberian influences on witchcraft traditions. He utilizes Inquisition records and Spanish imperial archives to reconstruct transatlantic cultural networks. Awards: Virginia Eason Weinmann '51 Professorship (endowed chair) Social Science Research Council Grant Fulbright-Hays Research Grant Academic Leadership: Garofalo has organized campus events featuring Afro-Peruvian performers (Negrura Peruana) and activists like former Colombian Governor Luis Murillo. In 2010, he co-led a Study Away Teach Away program in Oaxaca, Mexico, examining immigration and indigenous rights movements.
David W. Jacobs is a Professor in the Department of Computer Science at the University of Maryland, with a joint appointment at the University of Maryland Institute for Advanced Computer Studies (UMIACS). He also served as the interim Director of the University of Maryland Center for Machine Learning starting in 2018. University: University of Maryland School: College of Computer, Mathematical, and Natural Sciences Department: Department of Computer Science Academic Rank: Professor Education: He received his B.A. from Yale University, and M.S. and Ph.D. in Computer Science from MIT. Research Interests: His research primarily focuses on computer vision and machine learning, particularly visual object recognition, lighting variation modeling, 3D reconstruction, perceptual organization, motion understanding, and the integration of vision with graphics and human-computer interaction. A major applied contribution is the development of Leafsnap , an electronic field guide app for plant identification, which has been downloaded over 1.5 million times and used in biodiversity and educational contexts. Publication Trends: His recent scholarly output centers on deep learning, convolutional networks, residual architectures, generative models (especially GANs), and interpretability. His work often bridges theoretical insights with practical applications in vision and AI. Scientific Awards: Honorable Mention, Best Paper Award, CVPR 2000 Best Student Paper Award, UIST 2003 Best Paper Award, Eurographics 2016 2011 Edward O. Wilson Biodiversity Technology Pioneer Award for Leafsnap Teaching and Advising: He has taught advanced courses such as CMSC 422 (Introduction to Machine Learning) and CMSC 828L (Deep Learning). He mentors students through course projects and research, though specific advisees are not listed. He has collaborated with institutions like Columbia University and the Smithsonian on impactful interdisciplinary projects. Labs and Teams: He is affiliated with UMIACS and leads research efforts in vision and learning, contributing to the University of Maryland Center for Machine Learning. His team has developed several mobile applications including Leafsnap, Birdsnap, and Dogsnap, demonstrating a strong focus on real-world deployment of vision technology.
Rui Ni is an associate professor in the Department of Mechanical Engineering at Johns Hopkins University, directing the Fluid Transport Lab. His research focuses on experimental fluid mechanics, turbulence, multiphase flows, and their applications in energy systems, environmental engineering, and physiological processes. He holds a PhD in Physics from the Chinese University of Hong Kong (2011), followed by postdoctoral work at Yale and Wesleyan Universities. Before joining JHU, he held the Kenneth Kuan-Yun Kuo Early Career Professorship at Penn State University. His research interests include dusty flows, Lagrangian particle tracking, and animal collective behaviors. Notable projects include collaborations with NASA on plume-surface interaction and the development of advanced diagnostic tools like physics-informed machine learning and 3D particle tracking. He has received prestigious awards, including the NSF CAREER Award and ACS-PRF New Investigator Award, and leads studies on turbulence modulation by deformable bubbles, fish schooling efficiency in turbulent environments, and interfacial mass transfer dynamics. Key Projects: Plume-Surface Interaction (NASA collaboration), Fish Aquarium with Turbulent Environment (FATE) facility, V-ONSET multiphase flow facility. Grants: Gordon and Betty Moore Foundation’s Experimental Physics Investigators Initiative Grant. Lab Focus: Experimental and computational studies of multiphase flows, physiological flows, and complex systems. Ni’s work bridges fundamental fluid dynamics with practical applications, such as improving energy efficiency and understanding biological systems like fish schooling and nasal drug delivery mechanisms.
Martin Kern is the Joanna and Greg Zeluck ’84 P13 P18 Professor in Asian Studies at Princeton University. He specializes in ancient Chinese literature, philology, and textual studies, with a focus on poetry, ritual, and cultural memory. Kern co-edits T'oung Pao and directs the "International Center for the Study of Ancient Text Cultures" at Renmin University, Beijing. Education: Ph.D. in Sinology, Cologne University M.A. in Sinology, Cologne University His research spans early Chinese rhetoric, authorship debates, manuscript analysis, and the intersection of writing/orality. He has led Princeton's "Comparative Antiquity" initiative (2018–2022) and co-directs the Princeton-Venice Summer School in Classical Chinese. Recent publications include studies on the Classic of Poetry , Chuci , and Zuozhuan , emphasizing newly discovered manuscripts and their impact on understanding ancient Chinese textuality. His work often bridges philological rigor with comparative theoretical frameworks. Scientific Awards: Member of the American Philosophical Society President of the American Oriental Society (2023–2024) Kern's editorial roles include co-editing the monograph series Studies in the History of Chinese Texts and the Handbook of Oriental Studies: Section 4 China . He has held leadership positions in major academic initiatives on ancient China.
Dr. Mark B. Smith is an Associate Professor in Modern European History at the University of Cambridge’s Department of History. His research focuses on Soviet and European history, particularly the Soviet Union’s social structures, welfare policies, urban development, and cultural dynamics from Stalinism through late socialism. He teaches courses on welfare states, Soviet life, and 20th-century European violence. Key research areas include Soviet urban housing programs, welfare state evolution, and Cold War-era cultural exchanges. His publications analyze topics like Khrushchev’s housing reforms, post-Stalin urban welfare systems, and Soviet pension policies. Smith has co-edited special issues on European urban societies and authored books such as *The Russia Anxiety* (2019) and the forthcoming *Exit Stalin* (2026). He advises doctoral students on Soviet history, welfare states, and Cold War topics. His work bridges legal history, social policy analysis, and cultural studies, emphasizing how everyday life reflected broader ideological and structural changes in Soviet society.
Andrew Spakowitz is a Professor of Chemical Engineering, Materials Science and Engineering, and by courtesy, Applied Physics and Chemistry at Stanford University. He currently serves as the Senior Associate Dean for Research and Faculty Affairs and holds the Tang Family Foundation Chair of the Department of Chemical Engineering. His academic career at Stanford spans from Assistant Professor (2006-2014) to Associate Professor (2014-2020) and now Professor since 2020. Dr. Spakowitz earned his PhD in 2004, MS in 2001 from the California Institute of Technology, and his BS in Chemical Engineering from the University of Wisconsin, Madison in 1999. He completed postdoctoral training in Molecular and Cell Biology and Biophysics at UC Berkeley from 2004-2006. His research focuses on theoretical and computational approaches to understanding biological processes and complex materials. The Spakowitz lab addresses fundamental chemical and physical phenomena through four main research themes: chromosomal organization and dynamics, protein self-assembly, polymer membranes, and charge transport in conducting polymers. His group employs diverse theoretical and computational methods including analytical theory of semiflexible polymers, polymer field theory, continuum elastic mechanics, Brownian dynamics simulation, equilibrium and dynamic Monte Carlo simulations, and reaction-diffusion modeling. Analysis of his recent publications reveals a strong emphasis on epigenetics and chromatin dynamics, with significant work on DNA methylation patterns, nucleosome clustering, and chromosome organization. His research also extends to polymer physics applications in biological systems, particularly in respiratory diseases, water purification membranes, and bacterial phage interactions with human mucus. Tang Family Foundation Chair of the Department of Chemical Engineering Professor Spakowitz mentors several graduate students and postdoctoral scholars in the Chemical Engineering and Materials Science departments. His lab members work on diverse projects spanning from chromatin dynamics to polymer membranes for water purification. He teaches multiple courses including CHEMENG 120B (Energy and Mass Transport), CHEMENG 340 (Molecular Thermodynamics), CHEMENG 466 (Polymer Physics), and CHEMENG 467 (Physics of Biomacromolecules). The Spakowitz lab operates from Clark S295 at Stanford University, conducting theoretical and computational research that bridges chemistry, physics, biology, and engineering disciplines to address complex problems across multiple length and time scales.
Inés Valdez is a Professor in the Department of Political Science at Johns Hopkins University's Krieger School of Arts & Sciences. Her research bridges critical race and feminist theory with analyses of migration, transnationalism, empire, and racial capitalism. She has held fellowships from the Humboldt Stiftung, Global Arts & Humanities Society of Fellows, and the Princeton University Center for Human Values. Her 2023 book Democracy and Empire rethinks popular sovereignty through the Black radical tradition, winning the T.V. Paul Best Book Award. Earlier work includes Transnational Cosmopolitanism (2019), which redefines cosmopolitanism through global structures of domination. Her scholarship critiques empire's entanglement with democratic systems, emphasizing ecological responsibility and transnational solidarity. Recent publications explore anti-imperial sovereignty, Du Bois's ecological critiques, and Latinx political thought. She has contributed to journals like Contemporary Political Theory and Review of Politics , and her blog post Undoing Empire's Hold on Democracy (2024) addresses contemporary political crises. Valdez co-authored influential articles on migration politics and Kantian philosophy's racial dimensions. Her work integrates political theory with anticolonial praxis, advocating for transformative frameworks that challenge global hierarchies. She advises on transnational solidarity strategies and serves as a leading voice in decolonial and critical race studies.
Yi Fang is an Associate Professor of Computer Engineering and an affiliated Associate Professor of Computer Science at New York University Abu Dhabi (NYUAD), and a Global Network Associate Professor at NYU Tandon. He is a core faculty member in the Division of Engineering, specializing in Electrical and Computer Engineering. His research is centered at the intersection of Embodied AI, Robotics, and AI-driven assistive technologies, with strong support from agencies such as the US NSF, UAE ADEK, and ASPIRE. PhD, Purdue University Yi Fang's research interests span 3D Computer Vision, Multimedia Processing, Machine Learning, Deep Learning, and Embodied AI . He focuses on AI-driven perception, learning, and real-world applications, particularly in engineering, medicine, and accessibility. His lab, the Embodied AI and Robotics (AIR) Lab, develops intelligent robotic systems that integrate perception, learning, and decision-making to solve complex societal challenges. His work emphasizes large-scale visual computing, deep visual learning, and cross-domain/multimodal foundation models , with recent innovations in assistive AI for the Deaf and Hard-of-Hearing community. The 15 most recent publications reflect a consistent focus on 3D vision, sketch-based 3D retrieval, point cloud learning, and assistive computer vision . His work leverages deep learning, adversarial training, metric learning, and generative models to bridge modalities such as sketches, depth images, and 3D models. There is a clear trend toward cross-modal understanding, unsupervised representation learning, and real-world assistive applications , especially for visually impaired individuals. Yi Fang actively contributes to the academic community as an Area Chair for top-tier conferences including CVPR, ECCV, ICCV, IJCAI, and IROS. He also serves in peer review and mentoring roles, shaping the future of AI and robotics research. As a dedicated educator, he teaches foundational and advanced courses such as Computer Vision, Applied Machine Learning, Data Structures, and Capstone Design . He mentors students through research seminars and honors projects, fostering innovation and technical excellence. His research is supported by major grants from US NSF, UAE ADEK, and ASPIRE, enabling high-impact interdisciplinary collaborations. He founded and directs the Embodied AI and Robotics (AIR) Lab at NYU Abu Dhabi, a dedicated research space for developing intelligent systems that seamlessly integrate perception, learning, and decision-making. The lab promotes interdisciplinary collaboration across engineering, medicine, and social sciences, advancing the frontiers of Embodied AI.
Georgia Gkioxari is an Assistant Professor in the Division of Computing and Mathematical Sciences at Caltech , with a part-time affiliation at Meta AI . Her work focuses on extending visual perception models through advanced 2D and 3D representation learning, spatial reasoning, and generative models. Education: Not explicitly mentioned in the text Research interests span 3D perception , spatial reasoning , and vision-language integration , with projects like Visual Agentic AI for Spatial Reasoning and Token-by-Token Multimodal Alignment . Her publications emphasize 3D object detection , reconstruction , and generative modeling techniques including diffusion models and transformers . Scientific recognition includes the Meta LLM Evaluation Research Grant , Okawa Research Grant , Google Faculty Scholar Award 2024 , and Amazon Research Award . She teaches courses like Large Language & Vision Models (EE/CS 148) and Learning & 3D (CS 101) at Caltech. Labs & Teams: Leads Glab with members including Ilona Demler, Ziqi Ma, and Damiano Marsili
