Emmanuel J. Candès is the Barnum-Simons Chair in Mathematics and Statistics at Stanford University, with joint appointments in the Institute of Computational and Mathematical Engineering and as Professor of Statistics and Electrical Engineering (by courtesy). His research spans mathematical signal processing , high-dimensional statistics , and data science , focusing on compressive sensing, inverse problems, and applications to imaging sciences. 2021 IEEE Jack S. Kilby Signal Processing Medal 2020 Princess of Asturias Award for Technical and Scientific Research 2017 MacArthur Fellow His recent work on conformal prediction and uncertainty quantification has advanced machine learning reliability, particularly in high-dimensional settings. Publications include breakthroughs in medical imaging, gravitational wave detection, and AI validation frameworks. Key collaborations include Terence Tao (UCLA) and Justin Romberg (Georgia Tech) for IEEE Kilby Medal recognition. He serves as Co-chair of Stanford's Data Science Institute and previously as Statistics Department Chair (2016–2019).
Bryan A. Plummer is an Assistant Professor in the Department of Computer Science at Boston University, affiliated with the IVC Group and the Artificial Intelligence Research (AIR) initiative at the Rafik B. Hariri Institute. He holds a PhD from the University of Illinois at Urbana-Champaign, specializing in computer vision. His research focuses on multimodal machine learning, efficient neural architectures, explainable AI, and robust ML systems. Plummer's work bridges vision and language, addressing challenges in domain generalization, synthetic data utilization, and model efficiency. Notable contributions include the Flickr30K Entities dataset and advancements in vision-language model robustness against web artifacts. He has advised over 20 students, with several securing roles at top institutions like NVIDIA and Google. His recent awards include the 3M Foundation Fellowship and NSF GRFP honorable mention. Plummer actively serves on conference committees (NeurIPS, CVPR, ICCV) and leads initiatives like the 1st Findings Workshop at ICCV'25.
Reyhan Durmaz is an Associate Professor of Religious Studies at the University of Pennsylvania, affiliated with the School of Arts & Sciences and multiple interdisciplinary programs. Her research focuses on Syriac Christianity, medieval religious dynamics, and the intersection of Christianity and Islam in late antiquity. She holds a Ph.D. from Brown University (2019) and has received prestigious fellowships such as the Dumbarton Oaks Junior Fellowship and the Charlotte Newcombe Dissertation Fellowship. Her seminal book Stories between Christianity and Islam (2022) examines the transmission of saints' stories between religious traditions, earning Honorable Mention from the Middle East Medievalists. Current projects include a study of rural Christianity in the medieval Middle East through soldier-monk narratives and an analysis of the 19th-century Arabic newspaper Kawkab America . She co-leads the Digital Photograph Archive project Visualizing Countryside , documenting medieval religious sites. Dr. Durmaz teaches courses on global Christianity, material culture, and late antiquity, and serves as co-chair of the Philadelphia Seminar on Christian Origins. Her work bridges historical analysis, textual scholarship, and digital humanities, addressing themes of religious identity, cultural exchange, and marginalized communities.
John Kingston is a Professor of Linguistics and Director of the Phonetics Lab at the University of Massachusetts Amherst, where he has been since 1990. He holds a BA and MA from the University of Chicago (1976–1977) and a PhD from UC Berkeley (1985). His research focuses on the interplay between phonetics and phonology, particularly speech perception and its influence on phonological representations. He co-founded the Laboratory Phonology Conference series in 1987 and has conducted fieldwork on Otomanguean languages. His work emphasizes experimental methods to study phonological questions, including studies on vowel perception, tone systems, and cross-linguistic phonetic patterns. Kingston’s academic journey includes roles at the University of Texas, Austin (1984–1986) and Cornell University (1986–1990). His research explores how auditory processing and linguistic knowledge shape speech perception, with notable contributions to understanding tonogenesis, perceptual contrast effects, and vowel category learning in second languages. He collaborates on grants examining Ganong effects and phonological inventories, advocating for theories that bridge perceptual and structural aspects of language. His lab, the Phonetics Lab, supports experimental work on speech perception and production. Kingston is also the Honors Program Coordinator, mentoring students in linguistics and related fields. Despite no explicit awards listed, his extensive publications and conference leadership reflect his scholarly impact.
Sophie Whitehouse is a Lecturer in Marketing (Education) at King's Business School, King's College London. She holds a PhD in Marketing, an MA in English Language and Linguistics, and a BA in Management with Marketing from the University of Leicester. She is a Fellow of the Higher Education Academy and has been teaching since 2013. PhD in Marketing, University of Leicester MA in English Language and Linguistics, University of Leicester BA in Management with Marketing, University of Leicester Sophie's research centers on marketing in the creative and cultural industries, especially music. She explores consumer culture, authenticity, nostalgia, and the materiality of consumption in a digital age. Her work employs qualitative methodologies and has examined why consumers remain committed to physical formats like vinyl records despite the dominance of digital streaming. Her recent publications reveal a consistent focus on the intersection of marketing, culture, and emotion. She investigates how nostalgia shapes consumption, how record artwork fosters cultural identity, and how pedagogical tools like crib sheets affect student well-being. Her upcoming work extends into collaborative arts research and societal inclusion in higher education. Her scientific contributions have been recognized with the Elsevier Peer Recommendation Prize at the University of Leicester’s Festival of Postgraduate Research. Elsevier Peer Recommendation Prize, University of Leicester’s Festival of Postgraduate Research Sophie has extensive experience supervising undergraduate, Master’s, and MBA dissertations. She is currently open to supervising PhD students whose projects align with her interests in marketing and nostalgia, consumer culture, and the creative industries. While no specific grants are mentioned, her research output and invited talks indicate active scholarly engagement and external collaboration. Sophie is involved in academic events and roundtables such as 'La Pensée Artistique: Artistic Thinking in Consumer Research' and 'The Future of Higher Education,' indicating her integration into research networks and thought leadership in her domain.
Máté Szabó is an Assistant Professor at the University of Debrecen , affiliated with the Faculty of Informatics and the Department of Information Technology . His email contact is szabo.mate@inf.unideb.hu . He works in areas such as Machine Learning , Smart Cities , and Mobile Computing . His research spans topics like microservice architecture for ensemble models, Markov modeling of traffic flows, and distributed machine learning on mobile platforms. He has explored neural models for conversational AI and gamification in programming education through Minecraft-based challenges. His work also addresses edge computing and data parallelism in mobile environments. His publications (2016–2024) reflect trends in machine learning deployment on Android platforms smart city traffic analytics gamified educational tools cognitive modeling of numerical understanding microservice-based model integration .
Andrew Miller is an Associate Professor in the Electrical and Computer Engineering department at the University of Illinois, specializing in Programming Languages, Formal Methods, Software Engineering, Security and Privacy, and Systems and Networking. His research focuses on blockchain technologies, cryptography, and secure systems. Professor Miller's research spans multiple critical areas in modern computer security. His work primarily focuses on blockchain technologies , where he has made significant contributions to understanding and improving the security, privacy, and performance of systems like Bitcoin and Ethereum. He has conducted empirical analyses of privacy in the Lightning Network and developed protocols for confidential smart contracts. His work in cryptography includes research on multiparty computation, zero-knowledge proofs, and formal methods for cryptographic protocol design. Miller also investigates security vulnerabilities in proof-of-stake systems and resource exhaustion attacks, contributing to the robustness of decentralized systems. His applied security research extends to privacy-preserving health applications, as evidenced by his work on the Safer Illinois platform for COVID-19 contact tracing. Miller's publication record demonstrates consistent contributions to top security and systems venues including IEEE Security & Privacy, ACM CCS, Financial Cryptography, and USENIX Security. His research shows a clear trajectory from foundational work in blockchain security to more applied systems addressing real-world privacy and security challenges. Recent work focuses on making multiparty computation services publicly auditable and developing decentralized identity solutions that maintain compatibility with existing systems. Distinguished Reviewer Award, IEEE Security & Privacy 2018 Professor Miller has advised students including Vivek Nair, who joined the prestigious Hertz Fellows program in 2022. He has taught various courses including Introduction to Algorithms & Models of Computation, Advanced Computer Security, Cryptography, Applied Cryptography, and Ideal Functionality in Cryptography. His research has received support through grants including the SaTC: CORE: Medium project on "Automated Support for Writing High-Assurance Smart Contracts" in 2018. Miller is actively involved in research groups focusing on blockchain security, cryptographic protocols, and privacy-preserving systems. His lab appears to collaborate extensively with researchers across multiple institutions, as evidenced by the diverse author lists on his publications. Current work seems to be focused on making decentralized systems more secure, privacy-preserving, and accessible for real-world applications.
Soumya Dutta is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur (IITK), where he leads the INSIGHT: Intelligent Scientific and Visual Computing of Big Data Research Group. He joined IIT Kanpur in October 2022 after working as a Scientist II at Los Alamos National Laboratory (LANL) from July 2019 to August 2022, and previously as a Postdoctoral Research Associate at LANL from June 2018 to July 2019. His educational background includes a Ph.D. and M.S. in Computer Science and Engineering from The Ohio State University (2011-2018), where he was part of the GRAVITY research group, and a B.Tech. in Electronics and Communication Engineering from West Bengal University of Technology, India (2005-2009). Research Interests: Dr. Dutta's research focuses on the intersection of machine learning, visual computing, big data, and high-performance computing. His primary research areas include Machine Learning for Visual Computing and Image Analysis, Big Data Visualization and Analytics, Data Science and HPC, Machine Learning for Scientific Computing, and Explainability and Interpretability of AI Models. His work addresses various big data characteristics including the 5 Vs: Volume, Velocity, Variety, Veracity, and Value. He develops techniques that make complex machine learning models more interpretable and explainable, enabling their effective adoption in real-life applications across scientific domains, social media, IoT, healthcare, and industry applications. Dr. Dutta's research group has secured multiple funded projects including: DAVi: An Intelligent Data Analytics and Visualization Framework (funded by ISRO), Intelligent Visual Computing of Extreme-scale Data for Accelerating Scientific Discovery (IIT Kanpur Initiation Grant), Enabling Interactive Big Data Analytics and Visualization at Exascale (SERB), Development of AI-Enabled National Portal for Efficient Search of Missing People (C3iHub), and Proactive and Generalized Deepfake Defense Mechanisms (C3iHub). Best Reviewer, Honorary Mention Award for IEEE Transactions on Visualization & Computer Graphics (TVCG), 2021 Best Paper Award at ISAV 2021, co-located with Supercomputing (SC) LAAP Award at Los Alamos National Laboratory, 2021 Best Paper Award at TopoInVis 2019 Best Paper Award at ISAV 2018, co-located with Supercomputing (SC) Best Poster Award in 12th Annual CSE Student Poster Exhibition, The Ohio State University, 2018 Best Poster Award in 11th Annual CSE Student Poster Exhibition, The Ohio State University, 2017 Best Paper Honorable Mention Award at IEEE Visualization Conference (IEEE VIS) 2016 Dr. Dutta actively mentors a large group of students including Ph.D., M.Tech., and B.Tech. students. His current Ph.D. students include Shanu Saklani, Sankhadeep Bhowmick, Ananya Chaturvedi, Arpita Santra, Anubhav Dixit (co-supervised), and Robin Shah. He has supervised numerous M.Tech. students with thesis topics ranging from uncertainty-aware neural networks to deepfake detection. Dr. Dutta currently teaches courses including CS360 - Introduction to Computer Graphics and CS661 - Big Data Visual Analytics. The INSIGHT research group collaborates internationally with researchers from Meta, Oak Ridge National Laboratory, and National Taiwan Normal University. The group's work focuses on building machine learning and data science-based solutions to analyze large-scale multifaceted data in a scalable way, enabling interactive and interpretable analytics of complex data from scientific simulations, social media, IoT, healthcare, and other application domains.
Brent W. Roberts is a Professor of Psychology at the University of Illinois , affiliated with the Social-Personality-Organizational Division. He serves as Chair of the Social and Behavioral Sciences Research Initiative and holds the Edward William and Jane Marr Gutgsell Professorship. Education: Ph.D. in Personality Psychology (1994), University of California, Berkeley Research focuses on personality development across adulthood, personality assessment (especially conscientiousness ), and personality-health relationships . Methodologically, he emphasizes IRT and contextualized assessments. Scientific contributions include: Over 235 research outputs Highly Cited Researcher (Thomson Reuters 2016-2017) Key publications on BESSI, CONIC model, and longitudinal personality analysis Award-winning scholar: J. S. Tanaka Dissertation Award (1995) Carol & Ed Diener Mid-Career Award Theodore Millon Mid-Career Award Henry Murray Award Honorary Doctorate, University of Basel As academic advisor, he has mentored numerous graduate students and postdoctoral fellows in personality psychology, with lab alumni now at institutions like University of Houston and Carleton University.
Rachel Leow is an Associate Professor in Modern East Asian History and Fellow of Murray Edwards College at the University of Cambridge. She holds a PhD and MPhil from St Catharine’s College, Cambridge, and a BA from Warwick University. Her research critiques rigid nationalisms in East Asia, focusing on transregional cultural and intellectual histories. Her book Taming Babel: Language in the Making of Malaysia (2016) won the 2018 Harry J. Benda Prize for Southeast Asian studies. Current projects include an anti-diasporic history of Chinese migration and early 20th-century Asian thought-worlds. Leow emphasizes public engagement, contributing to blogs, literary reviews, and collaborative manifestos. She teaches World History courses, including Part IA’s Outline 11 and specialized modules on East/Southeast Asia. She supervises postgraduate students exploring transnational East Asian history from 1850 to the present. Professional affiliations include the Royal Historical Society, Center for History and Economics, and editorial roles at The Historical Journal and Global Intellectual History . Awards include the Harry J. Benda Prize and RHS Fellowship. Her digital work includes a visualization of Afro-Asian Cold War networks and a short film on diasporic identity.
Olga Fink is a Tenure Track Assistant Professor at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Department of Intelligent Maintenance and Operations Systems (IMOS) within the School of Architecture, Civil and Environmental Engineering (ENAC). She also holds roles in PhD program committees for Civil and Environmental Engineering (EDCE) and Robotics, Control, and Intelligent Systems (EDRS). Her research focuses on machine learning for infrastructure monitoring, predictive maintenance, and physics-informed AI models. She teaches courses on machine learning, data science for infrastructure, and advanced deep learning topics. Fink advises multiple PhD students and is involved in interdisciplinary projects such as ThermoNeRF (multimodal 3D thermal modeling) and physics-informed neural networks for fault diagnostics. Her work bridges AI and engineering with applications in smart infrastructure, energy systems, and industrial IoT. Education: PhD in Engineering (inferred from role) Affiliations: IMOS Lab, ENAC-SGC, EPFL PhD Committees (EDCE, EDRS) Key Research Themes: Explainable AI, Digital Twins, Structural Health Monitoring, Domain Adaptation Her publications (2023–2025) emphasize robust AI for industrial systems, including fault detection in high-voltage equipment, multimodal data fusion, and physics-consistent models. She collaborates on EU and industry-funded projects, focusing on real-world applications like predictive maintenance and energy efficiency.
Dale Shuger is a Professor of Spanish & Portuguese in the School of Liberal Arts at Tulane University. His academic career includes roles as Visiting Assistant Professor (2012-2013), Assistant Professor (2013-2023), and Associate Professor (2018-2023) before his current position since 2023. He holds a Ph.D. from New York University (2008) and a B.A. from Harvard University (2001). Shuger’s research focuses on early modern Spanish literature, heterodox religions, and popular culture in Spain and colonial Latin America. His work bridges literary analysis with cultural history, exploring topics such as mysticism, the Inquisition, and symbolic practices in early modern texts. His publications include God Made Word: An Archaeology of Mystic Discourse in Early Modern Spain (2021) and articles on Cervantes, puppetry, and religious symbolism. His research has been supported by grants like the COR International Travel Fund and the Lurcy Award. Shuger has led Tulane’s Program in Medieval and Early Modern Studies (2014-2017) and currently serves on the School of Liberal Arts Curriculum Committee. He advises Spanish majors and has supervised one thesis in the past five years.
Tian Li is an Assistant Professor of Computer Science at the University of Chicago. She holds a Ph.D. in Computer Science from Carnegie Mellon University and undergraduate degrees in Computer Science and Economics from Peking University. Her research focuses on distributed optimization, federated learning, and trustworthy machine learning, emphasizing algorithm design that addresses accuracy, scalability, and privacy concerns in practical systems. Key areas of expertise include federated learning systems, privacy-preserving technologies, and scalable distributed algorithms. She has contributed to foundational work on tilted empirical risk minimization and decentralized knowledge propagation. Notable achievements include winning the Best Paper Award at the ICLR Workshop on Secure Machine Learning Systems and First Place in the U.S. Privacy-Enhancing Technologies Pandemic Challenge (2023). Her academic trajectory includes recognition as a Rising Star in Machine Learning/Data Science and participation in prestigious workshops like the EECS Rising Stars Program. Her work bridges theoretical advancements with practical applications, aiming to enhance both the robustness and accessibility of machine learning systems.
Dr. Frank Heitmuller is an Associate Professor at the University of Southern Mississippi, affiliated with the Department of Geology and Geography within the College of Arts and Sciences. His research focuses on fluvial and coastal geomorphology, sedimentology, and hydrology, particularly along the northern Gulf Coast. He collaborates across disciplines to address ecosystem dynamics and land-use policies. He holds a PhD (2009), MA (2002), and BS (1998) from the University of Texas at Austin and Florida State University, respectively. His teaching includes courses like Physical Geology, Geomorphology, and Hydrology. Research interests include river dynamics, sediment transport, coastal ecosystems, and the interplay between abiotic factors and environmental policies. His work emphasizes fluvial systems in Texas and Louisiana, with studies on flood impacts, channel adjustments, and sedimentologic processes. Publications span topics such as flood sedimentation, lithologic controls on river channels, and policy-related sediment transport modeling. He is affiliated with organizations like the Geological Society of America and Association of Environmental and Engineering Geologists. Languages: English (native), Spanish (limited working). His expertise includes environmental science and soils.
Wenhu Chen is an Assistant Professor at the University of Waterloo's Computer Science Department and a CIFAR AI Chair at the Vector Institute. He also holds a part-time role as a Senior Research Scientist at Google DeepMind (20% allocation). His research focuses on natural language processing, deep learning, and multimodal reasoning, with contributions to models like MAmmoTH, OpenCoderInterpreter, and VISTA. He received awards including the Canada CIFAR AI Chair (2022) and the UCSB CS Outstanding Dissertation Award (2021). Education: PhD in Computer Science from the University of California, Santa Barbara (under William Wang and Xifeng Yan). Research interests include complex reasoning, controllable GenAI, and multimodal benchmarks like MEGABench and MMMU. Grants include CIFAR AI Chair Funding (2022-2027), NSERC Discovery Fund (2023-2028), and multiple NRC Canada grants. He directs the TIGER Lab, advancing generative models in text, images, videos, and music. Recent talks include presentations on multimodal reasoning at Apple and NeurIPS workshops.
