Andrew Zisserman is a Royal Society Research Professor at the University of Oxford's Department of Engineering Science, affiliated with the Visual Geometry Group (VGG). His research focuses on computer vision, artificial intelligence, and neural networks, with significant contributions to multimodal learning, video understanding, and 3D scene analysis. He leads projects exploring visual-language models, audio-visual synchronization, and clinical imaging applications. Key research areas include: Video analysis and temporal modeling Multimodal systems for sign language translation and action recognition 3D shape estimation and physical property inference Foundation models and cross-modal retrieval Recent work highlights: Developed Flamingo and Tapir models for video-language tasks Advancements in spinal MRI analysis and clinical imaging Leadership in EGO4D and VoxCeleb challenges Honors include Fellowship of the Royal Society (FRS) and the ISSLS Prize in Clinical Science 2023 for spinal analysis innovations. His lab collaborates globally, emphasizing real-world applications in healthcare and autonomous systems.
Jiatao Gu is an Assistant Professor in the Department of Computer and Information Science (CIS) at the University of Pennsylvania, with a part-time role as Staff Research Scientist at Apple (MLR). He holds a Ph.D. in Electrical and Electronic Engineering from the University of Hong Kong (2018) and a B.Eng. in Electronic Engineering from Tsinghua University (2014). His research focuses on generative machine learning and AI agent interaction with the physical world, emphasizing multi-modal systems spanning language, images, videos, and 3D. Key themes include efficient modeling , flexible architecture design , and scalable decision-making frameworks . 2025: ICLR paper on DART framework 2024: TMLR work on GFlowNet alignment 2023: NeurIPS research on diffusion stability 2022: ACL papers on speech translation Recent publications explore diffusion models for text-to-image synthesis, 3D reconstruction, and efficient sampling techniques. His work addresses fundamental challenges in attention mechanisms, entropy collapse, and multi-stage distillation while advancing non-autoregressive translation and vision-language reasoning . Prospective students can apply through his recruitment process at UPenn. Prior affiliations include Meta AI (FAIR Labs) and academic collaborations with institutions like New York University's CILVR Lab.
Philip S. Yu is a Distinguished Professor in the Department of Computer Science at the University of Illinois at Chicago and holds the Wexler Chair in Information Technology. Previously, he led the Software Tools and Techniques department at IBM Thomas J. Watson Research Center. Education: B.S. in Electrical Engineering, National Taiwan University M.S. and Ph.D. in Electrical Engineering, Stanford University M.B.A., New York University His research spans data mining , big data , social networks , privacy-preserving data publishing , graph/network mining , recommender systems , and deep learning . He has authored over 970 papers with 74,500+ citations and an H-index of 127. Recent work focuses on heterogeneous graph representation, quantum walks in network analysis, and federated unlearning. Scientific Honors: ACM SIGKDD 2016 Innovation Award IEEE Computer Society 2013 Technical Achievement Award IEEE ICDM 2003 Research Contributions Award IEEE Region 1 Award (1999) UIC Research of the Year (2013) IBM Master Inventor with 300+ patents AI 2000 Most Influential Scholar Honorable Mentions (2024-2025) He served as Editor-in-Chief for ACM Transactions on Knowledge Discovery from Data and IEEE Transactions on Knowledge and Data Engineering , and on steering committees for ACM KDD and IEEE Data Mining. His work bridges theoretical advances in graph neural networks , deep learning , and privacy-preserving systems with applications in healthcare, social media, and enterprise analytics.
Panos Ipeirotis is a Professor at the Leonard N. Stern School of Business at New York University, affiliated with the Department of Technology, Operations, and Statistics. He also serves as the George A. Kellner Faculty Fellow and is associated with the Center for Data Science and Computer Science departments at NYU. PhD in Computer Science (Columbia University, 2004) MSc in Computer Science (Columbia University, 2001) BSc in Computer Engineering & Informatics (University of Patras, 1999) His research spans crowdsourcing, machine learning, human-AI collaboration, online labor markets, and social media analytics. He pioneered human-machine loop systems that combine human and machine intelligence to achieve superior outcomes. His work has applications in data quality assurance, visual media search (e.g., Google Project Glass), and economic valuation of user-generated content. Recent publications focus on algorithmic fairness in hiring systems, occupational segregation analysis, and theoretical advancements in crowdsourcing consensus mechanisms. Earlier work includes foundational studies on data quality in crowdsourcing platforms, economic impacts of product reviews, and query optimization for text-centric tasks. 2015 Lagrange Prize in Complex Systems NSF CAREER Award SIGKDD Test of Time Award (2020) Multiple Best Paper awards (WWW 2011, KDD 2008, SIGMOD 2006) He has received significant grants, including a $1.5 million Google Research Grant (2013) for integrating crowdsourcing with machine learning algorithms. His work bridges computer science, economics, and social psychology, with implications for policy-making and business strategy.
Snježana Babić serves as Associate Professor and Head of the Graduate Study Program Informatics - Teaching Major at the Faculty of Informatics in Pula , University of Pula. With a PhD obtained in 2016 and university affiliation since 2017, she teaches across undergraduate and graduate programs focusing on ICT fundamentals, web design, digital competencies, and informatics education methodology. Her educational background includes graduation in 1993 followed by doctoral studies culminating in 2016. Though specific institutions aren't detailed in the text, her career trajectory shows progression from foundational ICT teaching to specialized informatics education leadership. Babić's research centers on educational technology adoption with particular emphasis on programming pedagogy for young learners through the MEMA method. Her work investigates digital competencies across multiple educational contexts, from primary schools to higher education. Recent publications demonstrate rapid adaptation to emerging technologies, with 2025 studies examining ChatGPT integration in Croatian educational settings and cross-cultural applications of programming methods. Analysis of her 15 most recent publications reveals consistent focus on technology acceptance models applied to Croatian educators and students. The research trajectory shows evolution from foundational e-learning systems (2016-2020) to cutting-edge AI and robotics applications (2022-2025), maintaining strong methodological focus on teacher motivation , implementation barriers , and culturally-specific adoption factors . Her teaching portfolio demonstrates deep integration with research interests: Undergraduate: ICT Fundamentals, Web Design and Application Graduate: Digital Competencies in Education, Informatics Education Methodology, Practicum for IT Educators Cross-disciplinary: ICT in Nursing, Healthcare Informatics Babić actively contributes to institutional leadership as program head while maintaining robust research productivity. Her work bridges theoretical technology adoption frameworks with practical implementation challenges in Croatian educational contexts, particularly examining how emerging technologies like humanoid robots and AI assistants can be effectively integrated into teaching practices.
Kian-Lee Tan is a Tan Sri Runme Shaw Senior Professor and Professor of Computer Science at the School of Computing, National University of Singapore (NUS). He holds a Ph.D. (1994), M.S. (1992), and B.Sc. (1st Class Honours) from NUS. His academic career spans decades of contributions to database systems and data analytics. Ph.D. in Computer Science, National University of Singapore (1994) M.S. in Computer Science, National University of Singapore (1992) B.Sc. in Computer Science (1st Class Honours), National University of Singapore As a leading researcher in database systems, Tan focuses on query processing and optimization in multiprocessor/distributed systems, database performance, security, and multimedia information retrieval. His work extends to computational biology applications like genome databases and real-time influence analysis on social streams. His recent publications highlight trends in GPU-accelerated graph analytics, trajectory pattern mining, and computational journalism. These works emphasize parallel processing, performance optimization, and social/media data analysis. IEEE Technical Achievement Award (2013) President Science Awards, Singapore (2011) NUS Graduate School Excellent Mentor Award (2010/2011) Outstanding University Researchers Award (1997/1998) Tan has supervised numerous research projects and mentored students contributing to database systems. He secured significant grants including a US$1 million Ripple Foundation grant (2024) for financial technology education. His editorial roles include ACM Transactions on Database Systems and IEEE Transactions on Knowledge and Data Engineering. He leads the FinTech Lab at NUS Computing and has served on the VLDB Endowment Board (2012-2017). His work bridges database foundations with emerging applications in AI, fintech, and computational journalism.
Aishwarya Agrawal is an Assistant Professor at Université de Montréal in the Department of Computer Science and Operations Research (DIRO), affiliated with Mila – Quebec Institute of Artificial Intelligence and a Canada CIFAR AI Chair. She also serves as a research scientist at Google DeepMind, spending one day weekly there. Education: B.E. in Electrical Engineering (IIT Gandhinagar, 2014), Ph.D. in Computer Science (Georgia Tech, 2019). Her research focuses on multimodal learning , deep learning , natural language processing , and computer vision , particularly in developing AI systems that 'see' and 'communicate' effectively. Grants & Awards: Canada CIFAR AI Chair, 2020 Sigma Xi Best PhD Thesis Award, NVIDIA Fellowship (2018–2019), and multiple fellowships from Google and Facebook. She leads projects like Advancing Multimodal Vision-Language Learning (CRSNG-funded) and StarDoc: Document Structure Extraction (MITACS). Research Contributions: Pioneered benchmarks like CulturalVQA and UI-Vision , and frameworks such as PROGRESS for efficient VLM training. Her work emphasizes cross-modal alignment, robust evaluation, and cultural understanding in AI systems. Labs/Teams: Active in Mila’s core academic group and collaborates with Google DeepMind on multimodal and vision-language research. Supervises a dynamic team of PhD and master’s students in Montreal.
Bei Wang Phillips is an Associate Professor in the School of Computing and a faculty member at the Scientific Computing and Imaging (SCI) Institute at the University of Utah. She holds a Ph.D. in Computer Science from Duke University and an undergraduate degree from the University of Bridgeport. Her research focuses on Topological Data Analysis (TDA), data visualization, computational topology, and machine learning, with applications in scientific data exploration and analysis. She has received prestigious awards including the NSF CAREER Award (2022) and the PECASE Award (2025). Her work spans projects funded by NSF, NIH, and DOE, including multiparameter TDA and topology-aware data compression. She advises numerous students and collaborates on interdisciplinary initiatives in astrophysics, climate science, and AI fairness. Education: Ph.D. in Computer Science, Duke University (2010) B.S. in Computer Science and Mathematics, University of Bridgeport (2003) Research Interests: Topological techniques for large-scale data analysis Integration of topological, geometric, and machine learning methods Applications in visualization, bioinformatics, and network analysis Key Projects: NSF-funded TDA research (DMS-2301361, OAC-2313124) DOE project on topology-preserving data compression Collaborations with NASA, Argonne National Lab, and Carnegie Institution of Washington Awards: Presidential Early Career Award for Scientists and Engineers (2025) NSF CAREER Award (2022) DOE Early Career Research Program (2020) Advising and Grants: Mentored over 30 students and postdocs Recipient of multiple NSF and DOE grants totaling millions
Christopher Potts is Professor and Chair of Linguistics at Stanford University, with a courtesy appointment as Professor of Computer Science. He serves as Director Emeritus of the Stanford Center for the Study of Language and Information (CSLI) and leads the Pragmatic Enrichment & Contextual Interface Lab. His work bridges theoretical linguistics and computational approaches to language understanding. Education: B.A. in Linguistics from New York University (1999) Ph.D. in Linguistics from University of California, Santa Cruz (2003) Potts' research focuses on how computational methods can illuminate linguistic phenomena, particularly in the areas of semantics, pragmatics, and sentiment analysis. His work explores how emotion is expressed in language and how linguistic production and interpretation are influenced by context. He has made significant contributions to understanding conventional implicatures, sentiment analysis frameworks, and the application of neural networks to linguistic problems. His recent work has increasingly focused on the interpretability of large language models and the development of frameworks like DSPy for building reliable AI systems. An analysis of Potts' recent publications reveals a strong trend toward the intersection of linguistic theory and practical AI applications. His work spans theoretical linguistics (e.g., compositionality, preposing constructions), neural network interpretability, and practical NLP systems (e.g., ColBERT, DSPy). The research demonstrates consistent focus on making language models more transparent, controllable, and linguistically informed, with particular attention to how context shapes meaning. Scientific Awards: Best Paper Award at 2024 ACL for 'Mission: Impossible Language Models' Outstanding Paper Award at 2024 ACL for 'CausalGym' ACL Test of Time Award 2023 Dean's Award for Distinguished Teaching (2015-2016) Best New Data Set or Resource Award at 2015 EMNLP Potts has secured numerous research grants as PI or Co-PI from major organizations including Google, Amazon, NSF, Office of Naval Research, and Stanford's HAI institute. His current projects focus on evaluation of retrieval-augmented generation systems, LLM-mediated communication in organizations, interpretability techniques for language models, and frameworks like DSPy for building next-generation AI systems. He has mentored numerous researchers who have gone on to make significant contributions in NLP and computational linguistics. As Director of CSLI (2013-2020) and current Chair of Linguistics at Stanford, Potts has played a key leadership role in shaping interdisciplinary research at the intersection of language, computation, and cognition. His Pragmatic Enrichment & Contextual Interface Lab continues to be a hub for innovative research combining formal linguistic theory with cutting-edge computational methods.
Andreas Vlachos is a Professor of Natural Language Processing and Machine Learning at the Department of Computer Science and Technology, University of Cambridge, and holds the Dinesh Dhamija Fellowship at Fitzwilliam College. His research spans dialogue modeling, automated fact-checking, imitation learning, semantic parsing, biomedical text mining, and trustworthiness in AI systems. PhD in Computer Science, University of Cambridge (supervised by Ted Briscoe and Zoubin Ghahramani) Lecturer at University of Sheffield Postdoctoral roles at UCL, University of Cambridge (NLIP group, Stephen Clark), and University of Wisconsin-Madison (Mark Craven) Current research focuses on evaluating and mitigating biases in language models, advancing fact-checking methodologies, and improving model robustness through interpolation, reinforcement learning, and causal reasoning. His work integrates natural logic, knowledge graphs, and multimodal evidence for verification tasks. Recent publications address uncertainty quantification, temporal planning benchmarks, and ethical framing of NLP artifacts. Grants from ERC, EPSRC, Facebook, Google, and the Alan Turing Institute fund his research team. Collaborations include Sebastian Riedel, Stephen Clark, and Mark Craven. Key projects explore disinformation detection, long-form generation, and confidence calibration in AI systems.
Almut Sophia Koepke is a junior research group leader at the Technical University of Munich and University of Tübingen, focusing on multimodal learning problems integrating sound, vision, and text. Her work bridges foundational research in audio-visual understanding with practical applications in few-shot learning, zero-shot translation, and cross-modal attention mechanisms.
Daniel Dominic Kaplan Sleator is a Professor of Computer Science at Carnegie Mellon University's School of Computer Science. He maintains an office in the Gates-Hillman Center (7205 Gates-Hillman) and teaches various courses in algorithms and theoretical computer science. Professor Sleator's research spans several areas of theoretical computer science and algorithms. His primary interests include: Algorithms and Data Structures Amortized Analysis and Competitive Analysis Persistent and Self-Adjusting Data Structures Computational Geometry and Combinatorial Optimization Combinatorial Game Theory and Mathematical Games Music Analysis using Computational Methods His extensive publication record shows a consistent focus on efficient data structures and algorithms. Over the years, his work has evolved from foundational data structures like splay trees and skew heaps to applications in diverse areas such as music analysis and combinatorial games. A notable trend in his work is the development of self-adjusting data structures that achieve excellent amortized performance without maintaining explicit structural constraints. His papers on splay trees, skew heaps, and persistent data structures have become classics in the field. Professor Sleator has made significant contributions across multiple domains of computer science. His work on competitive algorithms for paging and list update problems has been particularly influential, establishing fundamental results in online algorithms. His research extends beyond traditional computer science into interdisciplinary areas like computational music theory, demonstrating the broad applicability of algorithmic thinking. He teaches a variety of courses including Algorithms 15-451/651, Competition Programming 15-295, and specialized topics like mathematical games.
Ajit Rajwade is a Professor at the Department of Computer Science and Engineering , Indian Institute of Technology Bombay. His research spans Artificial Intelligence , Compressed Sensing , and Medical Imaging , with affiliations to the Centre for Machine Intelligence and Data Science and the Koita Centre for Digital Health . Education : PhD in Computer and Information Science and Engineering, University of Florida (2010) MSc in Computer Science, McGill University (2004) BTech in Computer Engineering, University of Pune (2002) His research interests focus on intelligent data acquisition, particularly in neural network analysis , graph signal processing , and medical imaging . He develops compressed sensing algorithms for inverse problems like tomography and MRI reconstruction , alongside applying group testing to pandemic response. Scientific awards include the Prof. S. P. Sukhatme Award (2024) and Departmental Teaching Excellence (2019). His publications demonstrate expertise in image restoration , noise modeling , and epidemiological algorithms . He has advised PhD students like Sabyasachi Ghosh and Jerin Geo James , with a focus on computationally efficient methods in medical imaging and machine learning .
Christoph Koch is a Full Professor in the School of Computer and Communication Sciences at EPFL (Ecole Polytechnique Federale de Lausanne) , Switzerland. He has held academic positions at Cornell University (2007-2010, 2006), Saarland University (2005-2007), and TU Vienna (2001-2005). His research focuses on database systems, logic, programming languages, and data management.
Sarah Dean is an Assistant Professor in the Computer Science Department at Cornell University, affiliated with the College of Engineering. Her research focuses on the interplay of machine learning, optimization, and dynamics in real-world systems, particularly in control theory, recommendation systems, and ethical AI. Education: PhD in EECS, University of California, Berkeley (2021) Postdoctoral Research, University of Washington (2021-2022) Research Interests: Data-driven control systems, reinforcement learning, recommendation systems, user dynamics, algorithmic fairness, and the societal impacts of AI. She emphasizes foundational understanding of how learning systems interact with human and social processes. Recent Work Trends: Her articles explore topics like bilinear system identification, user participation dynamics in recommendation platforms, and ethical considerations in AI development. Recent work includes harm mitigation strategies and mathematical modeling of AI-human feedback loops. Awards: AI2050 Early Career Fellow (2024) Best Paper at ICML 2018 (Delayed Impact of Fair Machine Learning) Best Student Paper in Imaging Systems (OSA Congress 2018) Advising & Labs: Advises over 15 graduate and undergraduate students. Leads research on interactive ML systems, with contributions to projects like the 'MSGD' repository for streaming data learning. Active in the GEESE group, promoting socially responsible computing.
