Ferenc Huszár is an Associate Professor of Machine Learning at the University of Cambridge, affiliated with the Department of Computer Science and Technology. His research focuses on foundational aspects of deep learning, including optimization, generalization, representation learning, and causal reasoning. He co-founded Magic Pony Technology, where he contributed to super-resolution and compression techniques, later acquired by Twitter. Education: PhD in Bayesian Machine Learning from the University of Cambridge (supervised by Carl Rasmussen, Máté Lengyel, and Zoubin Ghahramani), followed by roles in tech/startups. Research Interests: Theoretical underpinnings of deep learning, neural network behavior analysis, LLM theory, causal inference, and AI safety. His lab explores algorithmic reasoning in neural networks and implicit Bayesian inference in LLMs. Selected Contributions: Co-authored influential papers on super-resolution (CVPR 2016) and GAN-based image enhancement (CVPR 2017). Active in advising 9 PhD students and mentoring research assistants. Grants & Collaborations: Collaborates with institutions like the Max Planck Institute and ELLIS. Supervises projects on causal representation learning, geometric deep learning, and federated learning.
Dr. Zheng Yuan is an Associate Professor (Senior Lecturer) in the School of Computer Science at the University of Sheffield. Previously, they held roles as an Assistant Professor at King's College London and a Research Associate at the University of Cambridge's Department of Computer Science and Technology. Their primary research focuses on machine learning and deep learning applications in natural language processing (NLP), particularly in educational technology, healthcare, creativity, and multilingual contexts. Key projects include computer-assisted language learning (CALL), human-centered NLP in education, computational code-switching, and creative AI. Education includes a PhD and MPhil in Natural Language Processing from the University of Cambridge, and a BSc(Eng) from Queen Mary University of London. They hold affiliated positions at the University of Cambridge, King's College London, and are a Fellow of Trinity College, Cambridge. They contribute to The Alan Turing Institute's Data-Centric Engineering Programme and hold FHEA status (2024-). Research interests span educational NLP, multilingual systems, transfer learning, and explainable AI. They actively organize workshops and serve on editorial boards (e.g., PeerJ Computer Science) and conference committees (ACL/EMNLP). Recent activities include co-organizing NLP workshops at ACL 2025 and NAACL 2024, alongside roles in professional societies like the ACL Professional Conduct Committee. Awards include Fellowship of the Higher Education Academy (2024-) and ASEFClassNet18 Faculty Collaboration (2025-). They welcome PhD applications in NLP and machine learning, emphasizing interdisciplinary applications.
Una-May O'Reilly is a Principal Research Scientist at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), leading the ALFA group. She holds a PhD in Computer Science from Carleton University (1995), with prior roles including a postdoctoral appointment at MIT's Artificial Intelligence Laboratory. Her research focuses on cybersecurity, adversarial AI, software security, and disinformation dynamics, applying evolutionary algorithms and machine learning to address arms races in cyber defense and societal challenges like climate change communication. Education: B.Sc., University of Calgary M.C.S., Carleton University Ph.D., Carleton University (1995) Research Interests: Adversarial machine learning for secure systems Coevolutionary algorithms in cybersecurity and healthcare Program comprehension via neuroscience and AI Climate disinformation mitigation on social media Large language model applications in code synthesis and threat hunting Key Projects: Adversarial Cyber Security : Modeling cyber attack-defense arms races GIGABEATS : AI-driven medical sensor data analysis for critical care MOOC Learner Project : Data science for online education insights Awards: EvoStar Award (2013) for contributions to evolutionary computation Fellow of ACM Sig-EVO Leadership & Service: Co-founder and Vice-Chair of ACM Sig-EVO Former Chair of GECCO (2005), major evolutionary computation conference Editorial roles in Evolutionary Computation and Genetic Programming and Evolvable Machines Labs & Groups: Leads the AnyScale Learning for All (ALFA) group at CSAIL, focusing on scalable AI for cybersecurity, healthcare, and education.
Stephen Bach is an Assistant Professor in the Computer Science Department at Brown University, where he leads the BATS (Bach's Awesome Team of Students) research group. His research focuses on improving how humans teach computers through programmatic weak supervision and methods for learning from fewer examples like zero-shot and few-shot learning. His primary research interests include weak supervision, data programming, probabilistic soft logic (PSL), statistical relational learning (SRL), information extraction, zero-shot learning, and few-shot learning. Bach's work often focuses on exploiting high-level, symbolic or semantically meaningful domain knowledge, with applications in information extraction, image understanding, scientific discovery, and data science. Bach's recent publications show a strong focus on language models, weak supervision techniques, and multimodal learning, particularly examining the capabilities and limitations of models like CLIP. His research has increasingly emphasized practical applications in low-resource settings and cross-lingual scenarios. Best Paper Award at NeurIPS Workshop on Socially Responsible Language Modelling Research (SoLaR) 2023 Larry S. Davis Doctoral Dissertation Award Selected for oral presentation at ICLR 2024 Best of VLDB 2018 paper selection Bach advises numerous Ph.D., Master's, and undergraduate students, many of whom have gone on to positions at leading tech companies, research institutions, and graduate programs. His research group has developed several influential frameworks including Snorkel (for weak supervision), PSL (Probabilistic Soft Logic), T0 (for zero-shot task generalization), ZSL-KG (for zero-shot learning with knowledge graphs), TAGLETS (for semi-supervised learning with auxiliary data), and WISER (for programmatic weak supervision in sequence tagging).
Professor Saskia Goes is a Professor of Geophysics at Imperial College London's Department of Earth Science & Engineering within the Faculty of Engineering. She specializes in geodynamics, subduction dynamics, and seismic hazard analysis using numerical modeling and geophysical data interpretation. Her affiliations include the Dynamic Earth and Hazards groups at the Imperial Centre for Geohazards Dynamics. Education: PhD in Geophysics from UC Santa Cruz (1995), Drs (BSc/MSc equivalent) from Utrecht University (1990). Prior roles include SNF Professor of Tectonophysics at ETH Zurich (2003-2005), Visiting Assistant Professor at the University of Michigan (1995-1996), and postdoctoral research at Utrecht University (1996-1999). Research focuses on mantle dynamics, lithosphere structure, and subduction zone processes. Her work integrates seismic imaging, machine learning, and numerical simulations to study phenomena like slab dynamics, mantle plumes, and fluid migration. Key themes include the interplay between tectonic forces and geochemical processes in continental and oceanic settings. Publications emphasize subduction zone processes, seismic tomography, and induced seismicity. She has led projects like the VoiLA initiative studying volatile recycling in the Lesser Antilles. Awards and recognition include invited lectures at leading conferences (AGU, EGU) and universities worldwide. Teaching includes undergraduate geodynamics, geohazards courses, and advanced MSc modeling modules. Active in promoting geohazard research through interdisciplinary collaboration and public engagement.
Prof. Willemijn van Dolen is a Professor in the Section of Marketing at the Faculty of Economics and Business, University of Amsterdam. Her research focuses on consumer behavior, marketing strategies in digital environments, and the psychological impacts of communication in service encounters. She has extensively studied topics such as visual influence in consumer decisions, corporate greenwashing detection, and the role of humor in service interactions. Her academic career includes notable contributions to understanding online consumer behavior through multimodal datasets and AI frameworks. She has published widely on social media analytics, CSR communication, and child helpline effectiveness, bridging psychological insights with practical marketing applications. Prof. van Dolen’s work emphasizes empirical investigations into customer engagement, ethical consumption, and the interplay between digital platforms and human decision-making. Her research demonstrates a consistent focus on real-world applications, from optimizing brand posts on Instagram to analyzing CEO communication during global crises. Her studies often employ interdisciplinary methods, combining marketing theory with data science and behavioral economics. Despite her prolific output (over 50+ publications), no specific scientific awards are highlighted in the provided texts.
Sarah Ita Levitan is an Assistant Professor in the Department of Computer Science at Hunter College, CUNY, and a member of the doctoral faculty in both Computer Science and Linguistics PhD programs at the CUNY Graduate Center. She previously served as a Postdoctoral Research Scientist at Columbia University, where she completed her PhD in Computer Science in 2019 under Dr. Julia Hirschberg. Research Focus: Spoken Language Processing Natural Language Processing Paralinguistic Analysis Trustworthiness and Deception Detection Acoustic-Procedic and Lexical Feature Extraction Online Radicalization and Misinformation Recent Publications demonstrate expertise in analyzing speech and text for trust cues, deception detection, and mental health prediction. Her awards include grants from NSF, Google, and Columbia University fellowships. She leads the Hunter Speech Lab , mentoring PhD, MS, and undergraduate students in computational linguistics research. Scientific Awards and Grants: NSF EAGER Grant (2023) Google Cyber NYC Grant (2023) NSF AI Institute Grant (2023) Air Force Office of Scientific Research Grant (2020) Brown Institute Seed Grant (2020) Knight News Innovation Fellowship (2018) Teaching: Courses include Natural Language Processing (undergraduate/graduate), Computational Linguistics, Computer Theory, and advanced topics in spoken language processing at both Hunter College and Columbia University.
Guillaume Lajoie is an Associate Professor in the Department of Mathematics and Statistics at Université de Montréal and a Core Academic Member of Mila – Quebec Artificial Intelligence Institute. He holds a Canada CIFAR AI Research Chair and a Canada Research Chair in Neural Computation and Interfacing. His research focuses on the intersection of AI and neuroscience, particularly in understanding neural network dynamics and developing brain-machine interfaces for clinical and scientific applications. He is affiliated with the Centre de recherches mathématiques (CRM), the Interdisciplinary Center for Research on the Brain and Learning (CIRCA), and the UNIQUE initiative. Education: PhD in Applied Mathematics from the University of Washington (Seattle), postdoctoral fellowships at the Max Planck Institute for Dynamics and the University of Washington Institute for Neuroengineering. Awards include the FRQS Scholar designation and leadership roles in strategic research initiatives like UNIQUE and CIRCA. Research interests include neural computations, recurrent neural networks, neurotechnology, and responsible AI development. Supervised students include François Paugam (PhD), Giancarlo Kerg (PhD), and others. Key grants include projects on adaptive neuroprosthetics, neural decoding, and Canada Research Chairs funding.
Nasir M. Rajpoot is a Professor in the Department of Computer Science at the University of Warwick, UK. His research focuses on computational pathology, medical image analysis, and deep learning applications in histology. He leads interdisciplinary projects integrating artificial intelligence with healthcare, particularly in cancer diagnostics and pathology workflows. Rajpoot’s work emphasizes developing robust algorithms for histology image analysis, including nuclear segmentation, tumor classification, and domain generalization in computational pathology. His contributions include the TIAToolbox, an open-source framework for tissue image analytics, and the CoNIC Challenge to advance nuclear detection and counting in histology images. He collaborates with clinicians and biologists to translate AI models into clinical practice, addressing challenges like tumor heterogeneity and staining variability. Rajpoot’s research spans colorectal, lung, and oral cancers, with a focus on predicting clinical outcomes via histological features and genomic data integration. Notable projects include the development of Handcrafted Histological Transformer (H2T) for unsupervised representations of whole slide images and the SAFRON framework for histology image synthesis. His work addresses domain adaptation, robustness evaluation, and explainability in AI-driven pathology systems.
Zaiqiao Meng is a Lecturer (Assistant Professor) at the University of Glasgow's School of Computing Science, affiliated with the Information Retrieval Group and IDA section. He also holds an Affiliated Lecturer position at the University of Cambridge's Language Technology Lab. His research focuses on the intersection of machine learning, knowledge graphs, and NLP, particularly in biomedical applications. Key areas include AI agents, large language models, and healthcare informatics. Current roles include co-leading the Glasgow AI4BioMed Lab, which develops AI solutions for biomedical knowledge extraction. He has extensive postdoctoral and visiting research experience, including at KAUST's MINE lab. Meng has published widely in top conferences like ACL and EMNLP, with over 40 publications since 2019. His work spans topics such as drug-target interaction prediction, clinical summarization, and knowledge graph construction. Teaching includes courses on Recommender Systems and Data Science at both undergraduate and graduate levels. He advises multiple PhD students on projects involving LLMs, biomedical entity representation, and conversational agents.
Frederick A. A. Kingdom is a Professor in the Department of Ophthalmology at McGill University's Faculty of Medicine, focusing on Perception, Cognition and Cognitive Neuroscience . His research explores the interplay between early visual feature detection (edges, bars) and intermediate stages forming contours, textures, and surfaces through spatial vision, color vision, stereopsis, texture perception, brightness/lightness perception, and transparency studies . Email: fred.kingdom@mcgill.ca Key research domains include: Perceptual Mechanisms : Lateral inhibition, contrast normalization, spatial bandpass filters, and their role in brightness/lightness perception and illusions like simultaneous brightness contrast. Color Vision : Red-green vs blue-yellow system distribution, chromatic contrast requirements for stereopsis, color-based depth processing limitations, and color-shading effects that parse surfaces vs illumination. Texture Analysis : Detection thresholds for orientation/frequency/contrast modulated textures, co-circularity in texture perception, and texture statistical sensitivity (e.g., kurtosis importance). Shape Processing : Shape-frequency/shape-amplitude aftereffects, global vs local shape coding, and contour inflection adaptation. His work combines psychophysics , fMRI , image processing , and computational modeling to dissect visual system architecture, particularly how color and luminance signals are integrated/separated in early cortical processing.
Carlos Alvarez Martinez is a faculty member at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Computer Architecture at the Barcelona School of Informatics (FIB). He is a key member of the Programming Models (PM) research group and collaborates closely with the Barcelona Supercomputing Center (BSC). His research focuses on high-performance computing, FPGA acceleration, task-based programming models like OmpSs, and hardware-software co-design for heterogeneous systems. His research interests center on advancing parallel computing through innovative programming models and hardware acceleration. He investigates efficient task scheduling, resource management in multicore and FPGA-based systems, and runtime support for dataflow models. His work enables high-performance execution of complex applications in domains such as scientific computing and cyber-physical systems. He actively contributes to European initiatives like TEXTAROSSA and AXIOM, aiming to develop next-generation exascale supercomputing technologies. The trend in his recent publications shows a strong focus on leveraging FPGAs for HPC, optimizing SpMV operations, improving task scheduling with hardware support, and developing frameworks for multi-FPGA clusters. His work consistently bridges theoretical models with practical implementations, emphasizing performance, scalability, and energy efficiency in heterogeneous computing environments. Scientific Awards: Premi UPC al Compromís Social 2019 Premi Disseny per al Reciclatge 2013 Alvarez Martinez has advised or collaborated with several doctoral students, including Jaume Bosch, Xubin Tan, and Fahimeh Yazdanpanah. He has been involved in numerous competitive R&D projects, often related to high-performance computing and parallel programming models. His work includes significant contributions to educational innovation, particularly in active learning methodologies and formative assessment using interactive systems. He leads and participates in research labs and teams focused on programming models and computer architecture, notably the PM group at UPC/BSC. These teams develop runtime systems, compilers, and hardware accelerators to push the boundaries of parallel computing efficiency and programmability.
David Salesin is an Affiliate Professor in the Department of Computer Science & Engineering at the University of Washington and a Principal Scientist/Director at Google Research since 2019. He has held academic roles at Cornell University (Visiting Assistant Professor, 1991-92) and guest professorships at Zhejiang University. His career spans academia and industry, including leadership at Adobe's Creative Technologies Lab (2005-17) and Microsoft Research (1999-2005). PhD, Stanford University (1991) Sc.B., Brown University (1983) His research focuses on computer graphics, particularly non-photorealistic rendering, digital typography, color science, and adaptive document layout. He pioneered techniques in image-based rendering, pen-and-ink illustration, and facial animation, with applications in multimedia and user interface design. Article Trends : His work bridges procedural content generation, 3D visualization, and artistic computing, emphasizing user-driven tools for creative industries. Key subfields include texture advection, multiresolution modeling, and real-time camera control for virtual cinematography. Scientific Awards : ACM Fellow (2002) ACM SIGGRAPH Achievement Award (2000) Carnegie Foundation Professor of the Year (1998) NSF Presidential Faculty Fellow (1995-98) Alfred P. Sloan Research Fellowship (1995-97) Numerous industry grants and lab donations He has advised over 30 PhD and Master's students, including leaders at Microsoft, Pixar, and Google. His labs at UW and Adobe focused on graphics, imaging, and creativity tools.
Yu Sun is an assistant professor in the Department of Electrical and Computer Engineering at Johns Hopkins University with a joint appointment at the Data Science and Artificial Intelligence (DSAI) Institute. His research integrates machine learning, computer vision, optimization, and physics to advance computational imaging frameworks for reliable AI-driven imaging systems. He earned a BEng in electronics and information from Sichuan University (2015) and a PhD in computer science from Washington University in St. Louis (2022), where his dissertation received the Turner Dissertation Award. His academic journey includes a postdoctoral fellowship at Caltech's Department of Computing and Mathematical Sciences. Dr. Sun's research spans biomedical imaging, computational imaging, inverse problems, and machine learning, focusing on interpretable AI integration for next-generation imaging. His work bridges theoretical foundations with practical applications in medical and scientific imaging domains. Recent publications reveal a dominant trend in diffusion models for scientific imaging problems, including plug-and-play priors for reconstruction (NeurIPS 2024) and benchmarks for diffusion-based scientific problem-solving (ICLR 2025 Spotlight), demonstrating cross-disciplinary impact from biomedical engineering to cell biology. Key honors include: Turner Dissertation Award for doctoral contributions Rising Star Award from the Conference on Parsimony and Learning (CPAL, 2025) He serves as a consultant associate editor for the IEEE Open Journal of Signal Processing and actively participates in the IEEE Signal Processing Society’s Computational Imaging Technical Committee. His research is supported by institutional funding through the Hopkins Computational Imaging Group. The Hopkins Computational Imaging Group, which he leads, unites AI, mathematics, and data science to develop principled algorithms for imaging systems, with emphasis on biomedical applications and novel computational frameworks.
Dr. Alex S Clark is an Associate Professor in Quantum Technologies at the University of Bristol's School of Physics, where he serves as a Senior Lecturer and Royal Society University Research Fellow. He is a key member of the Quantum Engineering Technology Labs (QETLabs) and leads the Interfaces Work Package in the EPSRC Programme Grant 'Quantum Science with Ultracold Molecules (QSUM).' Additionally, he holds a Visiting Academic position at Imperial College London and serves as Honorary Secretary for the QQQ Group at the Institute of Physics. His research focuses on Solid State Quantum Nanophotonics, exploring the use of atoms, molecules, and solid state defects to develop quantum technologies. Dr. Clark's work spans quantum imaging, quantum sensing, and quantum information processing, with particular emphasis on creating on-demand photon sources, quantum memories, photonic quantum gates, and hybrid interfaces to link disparate quantum systems. His research integrates experimental and theoretical approaches across quantum photonics, nanophotonics, and quantum technology. Analysis of his recent publications reveals a strong trend toward practical quantum applications, particularly in quantum sensing and imaging using undetected light. His work demonstrates increasing focus on real-world applications including methane sensing, medical diagnostics, and environmental monitoring, while maintaining fundamental research in quantum optics and nanophotonics. The interdisciplinary nature of his research bridges physics, engineering, and materials science. Among his notable achievements is the prestigious Royal Society University Research Fellowship, recognizing his significant contributions to quantum technology research. His work has resulted in numerous publications and patents in quantum photonics and related fields. Dr. Clark leads multiple major research initiatives, including the Quantum Positioning, Navigation, and Timing Hub (2024-2029) and the Integrated Quantum Networks project. His research has secured substantial funding through EPSRC grants and other sources, supporting a vibrant research group focused on advancing quantum technologies from fundamental principles to practical applications. Within the Quantum Engineering Technology Labs (QETLabs), Dr. Clark's research group works at the intersection of quantum optics, nanophotonics, and quantum information science. His team develops novel photonic platforms for quantum applications, with particular expertise in quantum imaging with undetected photons, quantum sensing, and integrated quantum photonics.
