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Yi-Zhe SongView profile

Professor

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +5 more

Professor Yi-Zhe Song is a Professor of Computer Vision and Machine Learning at the University of Surrey's Centre for Vision, Speech and Signal Processing (CVSSP), one of Europe's largest AI research centers. He leads the SketchX Lab, focusing on understanding human sketching to advance computer vision and cognitive science. His roles include Co-Director of the Surrey Institute for People-Centred AI and Programme Lead of Surrey’s MSc in Artificial Intelligence. He holds a PhD (2008) and MSc (2004, Best Dissertation Award) from the University of Cambridge and Bath. Research interests span sketch-based systems, fine-grained visual classification, domain adaptation, and generative models. His lab has produced over 67 publications in top-tier conferences (CVPR, ICCV, ECCV) and journals, including a Best Paper Award at BMVC 2015. He is an Associate Editor for IEEE TPAMI and Frontiers in Computer Science, and has served as Programme Chair for BMVC 2021. Recent trends in his work emphasize explainable AI (SketchXAI), 3D reconstruction from sketches, and practical applications like virtual try-on and fashion-focused V+L models. His MSc AI programme integrates technical, ethical, and business aspects, preparing students for diverse AI roles. Awards: Best Paper Award (BMVC 2015), Senior Member of IEEE, Fellow of the Higher Education Academy. Lab Leadership: 3 academics, 2 postdocs, 14 PhD students in the SketchX Lab. Grants & Partnerships: Collaborations with industry and international funding bodies (e.g., EPSRC, São Paulo Research Foundation). Future Work: Expanding AI’s societal impact through inclusive education, human-centric AI, and democratizing generative AI tools.

Eng-Jon OngView profile

Research Fellow

• Computer Vision
• Machine Learning
• 3D Media and Animation
• +7 more

Eng-Jon Ong is a Research Fellow at the University of Surrey's Centre for Vision, Speech and Signal Processing (CVSSP) within the Faculty of Engineering and Physical Sciences. His work spans computer vision, machine learning, and human-computer interaction, with a focus on facial feature tracking, lip reading, sign language recognition, and biomedical applications of deep learning. University: University of Surrey Department: Centre for Vision, Speech and Signal Processing (CVSSP) Affiliations: CVSSP research group Research Interests: Ong specializes in advanced computer vision techniques, including real-time 3D reconstruction, facial expression analysis, and automated lip-reading systems. His work bridges theoretical machine learning with practical applications like medical imaging (protein localization) and media production (virtual studios). Notable contributions include: Development of robust facial feature tracking algorithms using linear predictors and AAMs Pioneering methods for sign language recognition via sub-unit analysis Deep learning architectures for single-cell protein localization (HCPL system) Real-time lip-reading systems with state-of-the-art accuracy Publications reflect a strong focus on temporal pattern recognition, ensemble learning, and cross-modal interaction analysis. Recent work emphasizes biomedical applications, such as improving protein localization accuracy through novel deep learning ensembles. Labs/Teams: Active contributor to CVSSP's multidisciplinary research projects in computer vision and machine learning.

Scott NiekumView profile

Associate Professor

• Artificial Intelligence
• Robotics
• Machine Learning
• +4 more

Scott Niekum is an Associate Professor in the Manning College of Information and Computer Sciences (CICS) at the University of Massachusetts Amherst, where he directs the Personal Autonomous Robotics Lab (PeARL). Previously, he held assistant and associate professor positions at the University of Texas at Austin (2015–2022). He earned his PhD in Computer Science from UMass Amherst in 2013 and completed a postdoctoral fellowship at Carnegie Mellon University's Robotics Institute (2013–2015). His research focuses on enabling robots to learn autonomously through interactions with humans and environments, emphasizing safe and efficient algorithms. Key areas include reinforcement learning, imitation learning, human-robot collaboration, and safety guarantees. His work bridges robotics and machine learning to address challenges like reward design, causal reasoning, and generalization from limited data. Notable contributions include the EMPATHIC framework for task learning via implicit human feedback, the PeARL lab's advancements in gaze-guided robotics, and scalable methods for offline reinforcement learning. He has been recognized with the NSF CAREER Award (2023), AFOSR Young Investigator Award, and teaching excellence honors. Dr. Niekum actively serves on conference committees (NeurIPS, ICML, RSS) and journals, promoting rigorous research standards. His lab's innovations aim to deploy autonomous systems in real-world settings like homes and workplaces with minimal expert intervention.

Antonio Machicao y PriemerView profile

Researcher

• Linguistics
• Syntax
• Register Variation
• +6 more

Dr. Antonio Machicao y Priemer is a researcher affiliated with the Humboldt-Universität zu Berlin's Institut für deutsche Sprache und Linguistik . His work focuses on formal syntax, register variation, and the application of Head-Driven Phrase Structure Grammar (HPSG). He contributes to the CRC 1412 project A04: Building register into the architecture of language , investigating how grammatical structures interact with situational-functional variation. Research Interests: His studies span German syntax (e.g., topic drop phenomena), Brazilian Portuguese agreement variation, and the formal modeling of register knowledge. He also explores psychological verb constructions in Spanish, emphasizing clitic reflexives and event boundaries. Recent work combines experimental methods (e.g., matched-guise studies) with computational modeling to analyze how social meanings shape syntactic choices. Publications: Key contributions include analyses of German nominal structures, register-driven variation in HPSG frameworks, and cross-linguistic studies of verb cliticization. His collaborative efforts with the CRC 1412 aim to bridge theoretical linguistics, psycholinguistics, and corpus-based methodologies. Labs/Teams: Active participant in the Sonderforschungsbereich 1412: Register , contributing to interdisciplinary investigations of language variation and cognitive modeling. Engaged in corpus development projects like the Berlin Dialogue Corpus (BeDiaCo) and the Corpus of Non-Native Addressee Register (CoNNAR).

• Computer Vision
• Deep Learning
• Biomedical Imaging
• +4 more

Meng Xu is a Visiting Professor at the School of Electronic Engineering and Computer Science, Queen Mary University of London. Her research focuses on computer vision, deep learning, and biomedical imaging applications within robotics and healthcare domains. She contributes to advancing localization systems, image processing techniques, and neural network architectures for real-world challenges. Her work bridges theoretical advancements with practical implementations, such as end-to-end visual localization networks (e.g., Bev-locator) and unsupervised methods for biomedical image enhancement. She also explores multi-modal fusion strategies (e.g., MCAPR) and optimization techniques for efficient deep learning models. Key research directions include improving camera pose estimation accuracy, developing robust feature matching algorithms, and applying deep learning to body shape classification and human pose estimation. Her publications reflect a strong emphasis on solving technical challenges in both robotics and medical imaging through innovative computational approaches.

Jacob SollView profile

Professor

• History of Information and Knowledge
• Political History
• Economic History
• +5 more

Jacob Soll is a University Professor and Professor of Philosophy, History, and Accounting at the University of Southern California. His research focuses on the history of information and knowledge, political and economic history, the history of books and libraries, and accounting's role in governance. He explores themes such as financial transparency, the Enlightenment, and the Renaissance, with a particular emphasis on how information systems shaped early modern states. His work spans the intersection of political thought, fiscal policy, and cultural history, analyzing figures like Jean-Baptiste Colbert and institutions such as early modern archives and libraries. He has written extensively on the development of accounting practices and their impact on democratic governance, arguing for greater transparency in public finance. Recent publications emphasize the importance of comprehensive balance sheets for understanding public wealth and liabilities, advocating for modern accounting methods to address fiscal challenges. His interdisciplinary approach bridges history, economics, and political science, offering critical insights into governance and accountability mechanisms.

• Urology
• Oncology
• Artificial Intelligence in Healthcare
• +3 more

Giovanni Cacciamani, MD, is an Associate Professor of Urology and Radiology Research at the University of Southern California (USC). He holds leadership roles, including Director of the Artificial Intelligence Center for Surgical and Clinical Applications in Urology, Chair of the European Association of Urology's UroTechnology and Digital Healthcare Group, and Associate Editor for Journal of Urology . His research focuses on integrating AI into urological care, oncological diseases, patient safety, and surgical innovation. Dr. Cacciamani has been recognized with awards such as the 2022 Citizen of the Year (Italy) and the Italian Matula Award for excellence in urology. His lab at USC explores Generative AI (GAI) applications, aiming to enhance clinical practice accessibility. He has authored over 350 publications and delivered 200+ global lectures on AI-driven healthcare and digital medicine. His work emphasizes bridging technology and clinical practice, shaping global standards for AI in surgery and medical research. Recent research trends include optimizing AI tools for clinical decision-making, improving patient education materials via AI, and advancing surgical techniques like robotic cystectomy and focal therapy. Dr. Cacciamani’s contributions extend to guideline development, such as the TRIPOD-LLM reporting standards for AI studies. He chairs initiatives to promote diversity in academic urology and leads global collaborations like the ICARUS project for standardized surgical complication reporting. His lab also pioneers projects such as robotic bladder auto-transplantation and infrared imaging for surgical guidance.

Yunus Can GültekinView profile

Researcher

• Wireless Communication
• Optical Communications
• Quantum Key Distribution
• +5 more

Yunus Can Gültekin is a Researcher at the Eindhoven University of Technology (TU/e), affiliated with the Signal Processing Systems department within the Electrical Engineering school. He holds a B.Sc. and M.Sc. from Middle East Technical University (Turkey) and a Ph.D. from TU/e (2020). His research focuses on future wireless/optical communication systems, quantum key distribution, and signal processing solutions using information theory tools. Key contributions include developing coded modulation techniques and mitigating nonlinear interference in optical systems. Education: B.Sc., Middle East Technical University, Ankara, Turkey (2013) M.Sc., Middle East Technical University, Ankara, Turkey (2015) Ph.D., Eindhoven University of Technology, Netherlands (2020) Research Interests: Quantum Key Distribution (QKD) systems Optical and free-space communication systems Probabilistic shaping and nonlinear mitigation Coded modulation design for high-speed transmission Notable Achievements: Best Paper Award at WIC/IEEE Symposium (2018) Optica Student Paper Award (2022) Quantum Delta NL Exchange Visit Grant (2023) International Excellence Fellowship (2024) Teaching: Current courses: Communication Theory, Digital Wireless Communication Exploration Lab Past courses: Analog Electronics Lab, Digital Design Lab Key Projects: LaiQa: Quantum Satellite Communications (2024–2026) BIT-FREE: Free-Space Optics (2024–2028) COCOLI: Optical Link Complexity (2022–2028) Labs/Teams: Active in the Signal Processing Systems group, collaborating on quantum and optical communication initiatives at TU/e.

• Monetary Policy
• Central Banking
• Payment Systems
• +4 more

Christopher Waller serves as a Governor of the Board of Governors of the Federal Reserve System in Washington, D.C., where he leads critical research in monetary policy and financial stability. His work spans central banking operations, payment systems innovation, and macroeconomic modeling, with frequent appearances at international policy forums including the OECD, IMF, and central bank conferences worldwide. Waller's research focuses on contemporary monetary policy challenges, particularly inflation dynamics, balance sheet normalization, and the implications of digital currencies. His publications reveal deep engagement with practical policy dilemmas such as zero-lower-bound constraints, optimal stabilization frameworks, and the fiscal implications of monetary policy implementation. Recent work increasingly addresses payment system innovations including stablecoins and central bank digital currencies. Analysis of his 15 most recent publications shows consistent thematic evolution from traditional monetary policy frameworks toward emerging financial technologies and crisis-era policy tools. His work demonstrates sophisticated integration of theoretical modeling with empirical analysis of real-world policy impacts, particularly regarding market reactions to communication strategies and the microfoundations of monetary phenomena. Top 5% Author on RePEc across multiple ranking dimensions including citation impact, publication volume, and network centrality Recognized for influential contributions to monetary theory and central banking practice Waller actively shapes monetary policy discourse through frequent speeches at major institutions including the Council on Foreign Relations, Brookings Institution, and international central banks. His work informs both academic scholarship and practical policy implementation at the highest levels of central banking, with particular emphasis on transparent communication and data-driven decision making. Though not explicitly documented in the source material, his position implies significant advisory responsibilities regarding Federal Open Market Committee deliberations and financial regulatory frameworks.

Hans MoosmüllerView profile

Research Professor

• Atmospheric Sciences
• Aerosol Physics
• Climate Science
• +7 more

Hans Moosmüller is a Research Professor in Atmospheric Sciences at the Desert Research Institute (DRI), focusing on aerosol physics, optical spectroscopy, and climate interactions. His work emphasizes developing measurement techniques for aerosol light absorption, scattering, and remote sensing. He holds a Ph.D. from Colorado State University (1988) and an M.S. from Ludwig-Maximilians University in Munich (1982). His research spans aerosol optics, biomass burning emissions, and mineral dust properties. He has contributed to studies on black carbon, fractal-like particle aggregates, and the radiative effects of aerosols. Moosmüller has led major projects, including NASA-funded investigations into aerosol optical properties and NSF-backed soil-impact studies post-wildfires. His recent work explores novel mixing rules for aerosol refractive indices and hyperspectral reflectance analysis of post-fire soils. Collaborations include FMI (Finland) and Antarctic field campaigns, advancing understanding of light-absorbing particles in polar regions.

Mark LescroartView profile

Assistant Professor

• Neuroscience
• Cognitive Psychology
• Visual Perception
• +3 more

Mark Lescroart is an Assistant Professor in the Department of Psychology at the University of Nevada, Reno, where he leads the Pieces of Mind Lab. He joined the Cognitive & Brain Sciences group in 2018 after completing a Ph.D. in Neuroscience at the University of Southern California (2011) under Irving Biederman and postdoctoral research with Jack Gallant at UC Berkeley. His research focuses on how the brain transforms visual stimuli into cognitive representations of objects, spatial relationships, and human actions. He teaches Measuring the Human Brain (PSY 413) . Education Ph.D. in Neuroscience, University of Southern California, 2011 B.S. in Psychobiology, University of Southern California, 2002 Research Interests Dr. Lescroart’s lab develops computational models of fMRI responses to decode brain activity during visual perception. Key areas include object identity/shape encoding, spatial structure interpretation, and action recognition. Techniques involve voxel-wise encoding models and decoding algorithms to predict neural responses to stimuli like movies and images. Awards & Grants Ruth L. Kirschstein National Research Service Award (2012–present) USC Travel Award (2010) Rockwell Dennis Hunt Scholastic Award (2006) Lab & Collaborations The Pieces of Mind Lab integrates neuroscience, computational modeling, and psychophysics to study high-level vision. Recent work includes the Visual Experience Dataset , a multimodal recording of naturalistic visual experiences.

• Distribution Matching
• Probabilistic Shaping
• Coding for Multi-user Scenarios
• +3 more

M.Sc. Constantin Runge is a doctoral researcher at the Institute for Communications Engineering (Chair of Communications Engineering under Prof. Gerhard Kramer) at the Technical University of Munich (TUM) since October 2021. He holds a M.Sc. (2019-2021) and B.Sc. (2015-2019) in Electrical Engineering and Information Technology from TUM. Research Interests Probabilistic shaping and distribution matching for optimizing channel input distributions. Coding schemes for multi-user communication scenarios (e.g., broadcast channels). Advancements in polar coding theory and applications, including list decoding and shaping. Information-theoretic foundations and their practical implementations in modern communications. Awards & Recognition Walter Gademann thesis award for the thesis Quantized Enumerative Sphere Shaping . Ralf Kötter memorial award for the Master's thesis Higher-Order Polar-Coded Modulation for Asymmetric Channels . Teaching & Supervision Has taught courses in digital communications, wireless systems, and multi-user information theory. Supervised multiple theses on polar codes, probabilistic shaping, and error-correcting coding techniques. Affiliations Part of the TUM School of Computation, Information and Technology, collaborating with leading researchers in communications engineering including Prof. Kramer and Prof. Wachter-Zeh.

Chandrajit BajajView profile

Professor

• Computational Biology
• Imaging Sciences
• Computational Geometry
• +5 more

Chandrajit Bajaj is a Professor of Computer Science and Computational Applied Mathematics Chair in Visualization at the University of Texas. He leads the Center for Computational Visualization (CVC) within the Oden Institute of Computational Engineering and Sciences. His research focuses on actionable intelligence, machine learning, computational biology, imaging sciences, and visualization. He holds a B.Tech. from IIT Delhi and a Ph.D. from Cornell University. Professional Experience: Professor at University of Texas since 1997; Director of CVC and Image Analysis & Visualization Center. Previously held roles at Purdue University and visiting positions at Cornell. Awards: AAAS, ACM, IEEE, and SIAM Fellowships; Distinguished Alumnus of IIT Delhi; ICES-Sid Moncreif Awards; multiple best paper awards. Research: Develops algorithms for geometric modeling, bioinformatics, and predictive data sciences. Current projects include microscopy optimization, generative design, and nature-inspired models. Sponsors include NSF and NIH. Education: B.Tech. in Electrical Engineering (IIT Delhi), M.S./Ph.D. in Computer Science (Cornell).

Stuart AgnewView profile

Associate Professor

• Criminology
• Youth Studies
• Educational Technology
• +3 more

Stuart Agnew is an Associate Professor in Criminology and Director of the Centre for Academic & International Partnerships (CAIP) at the University of Suffolk since 2008. Previously, he served as Associate Dean in the School of Social Sciences & Humanities and Course Leader for Criminology. He holds a 1st Class Honours degree and MSc Criminology from the University of Teesside, alongside a PGCE and is a Fellow of the Higher Education Academy. His expertise spans course design, validation, and delivery at undergraduate and postgraduate levels. Research interests include youth unemployment, technology-enhanced learning, and crime prevention strategies. Notable projects include evaluations of urban street gangs in Birmingham, youth unemployment in Ipswich, and the effectiveness of video doorbells in enhancing community security. He has published extensively on criminological theory, digital methods in education, and youth policy, with contributions to peer-reviewed journals and edited volumes. Professionally, he serves on the British Society of Criminology’s Learning & Teaching Network and has been an External Examiner for higher education quality standards. His work has been recognized internationally, such as through a case study on technology in postgraduate learning published by the Scottish Higher Education Enhancement Committee. He also evaluates initiatives like the 'Challenge for Change' program and collaborates with organizations like the Marie Collins Foundation and Suffolk Constabulary. Agnew’s contributions extend to policy and community engagement, including roles in shaping educational opportunities for youth and enhancing crime prevention strategies. His interdisciplinary approach bridges criminology, education, and social policy, emphasizing practical solutions to societal challenges.

Ramesh JohariView profile

Professor

• Online Platforms Design
• Economic Analysis
• Machine Learning
• +3 more

Ramesh Johari is a Professor of Management Science and Engineering and Professor (by courtesy) of Electrical Engineering at Stanford University. He holds affiliations with the Institute for Human-Centered Artificial Intelligence (HAI), Precourt Institute for Energy, and the Institute for Computational and Mathematical Engineering (ICME). As Associate Director of Stanford Data Science and Co-Director of the Stanford Causal Science Center, he leads initiatives in data-driven research and causal inference. His research focuses on online platforms, statistical/machine learning, and healthcare technology, with contributions to diabetes care optimization and algorithmic decision-making. Education: PhD in Electrical Engineering and Computer Science from MIT (2004). His work integrates economics, operations research, and computer science to address challenges in platform design and healthcare systems. Notable achievements include developing the Timely Interventions for Diabetes Excellence (TIDE) platform, improving outcomes for type 1 diabetes patients through remote monitoring and algorithmic prioritization. Key research areas include: design of digital health platforms, economic analysis of online markets, and scalable healthcare solutions. His recent studies emphasize equitable access to CGM technology and precision medicine in diabetes care. Scientific Awards: Over 11 honors, including the George E. Nicholson Student Paper Competition (2003), ACM Doctoral Dissertation Award (2004), and NSF CAREER Award (2007). Advising: Mentors doctoral and master’s students in healthcare technology, data science, and operations research. Serves on editorial boards and program committees for top conferences in his field. Current roles include partial leave from January 2025 to June 2025. Labs/Teams: Active in the Society and Algorithms Lab (SOAL) and collaborates with Stanford’s interdisciplinary healthcare initiatives. His work bridges academic research and real-world applications in technology and medicine.