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Aphrodite GalataView profile

Lecturer

• Computer Vision
• Machine Learning
• Human-Robot Interaction
• +3 more

Aphrodite Galata is a Lecturer in Advanced Interfaces at the Human Computer Systems department. She holds a Doctor of Philosophy from the University of Leeds (2001) in Learning Variable Length Markov Models of Behaviour. Her research focuses on Computer Vision and Machine Learning, with special emphasis on sign language processing, human-robot interaction, and embodied learning systems. She has contributed to advancements in artificial agents learning sign language through imitation, phonological property recognition, and dataset development for American Sign Language analysis. Her research interests also span robotics, 3D pose estimation, and real-time tracking systems. Key topics in her work include gesture analysis, multimodal communication, and neural network architectures for feature representation. She has published widely in top-tier conferences including IEEE ICASSP and ACL, with notable contributions to datasets like WLASL-LEX. Notable projects include developing algorithms for hand tracking from monocular RGB data and exploring anisotropic cost functions for facial landmark detection. She has collaborated with researchers like Angelo Cangelosi on embodied sign language systems. Her work bridges computer vision techniques with cognitive modeling to enhance human-agent communication systems. Her research is aligned with the Digital Futures research beacon, emphasizing innovation in human-computer systems. While no specific awards are noted, her work has garnered significant readership on platforms like Mendeley, reflecting its academic impact. She has supervised 8 research works, contributing to the training of future scholars in advanced interface technologies.

• Machine Learning
• Biomedical Data Analytics
• Deep Learning
• +3 more

Dr. Amir Joudaki is a Researcher affiliated with the Department of Biomedical Informatics at ETH Zürich. His role is part of the Professorship for Data Analytics, focusing on interdisciplinary research at the intersection of machine learning and biomedicine. He is stationed at CAB F 53.1, Universitätstrasse 6, Zurich. His research interests span machine learning, biomedical data analytics, neural network theory, genomics, and bioinformatics algorithms. Notable contributions include work on deep neural network dynamics, batch normalization techniques, and graph-based genomic sequence analysis. Though not explicitly listed in the provided texts, his work suggests involvement in projects related to alignment-free bioinformatics methods, optimization in deep learning, and theoretical aspects of neural networks. Lab/Team Affiliation: Part of the Biomedical Informatics group at ETH Zürich, likely contributing to interdisciplinary data science initiatives in healthcare and genomics.

Thomas PockView profile

Professor

• Computer Vision
• Image Processing
• Mathematical Optimization
• +7 more

Thomas Pock is a Professor of Computer Science at Graz University of Technology, holding the AIT Stiftungsprofessur for Mobile Computer Vision. He is affiliated with the Institute for Computer Graphics and Vision (ICG) within the Faculty of Computer Science and serves as a principal scientist at the Austrian Institute of Technology (AIT), Center for Vision, Automation & Control. He leads the Vision, Learning and Optimization (VLO) research group, which focuses on mathematical modeling and optimization in computer vision. His research interests lie at the intersection of computer vision, image processing, and mathematical optimization. Specifically, he develops mathematical models for computer vision and efficient convex and non-smooth optimization algorithms , particularly for mobile scenarios. His recent work increasingly integrates variational methods with deep learning, especially in solving inverse problems in imaging such as medical reconstruction and deblurring. The trends in his recent publications show a strong emphasis on deep learning for inverse problems , variational networks , and learned optimization . His group explores how to combine classical mathematical models with data-driven deep learning approaches to achieve stable, interpretable, and high-performance solutions in image reconstruction and processing. His scientific achievements have been recognized with several prestigious awards: START Prize, Austrian Science Fund (FWF), 2013 German Pattern Recognition Award, DAGM, 2013 ERC Starting Grant, European Research Council, 2014 Thomas Pock actively mentors students and leads a research group of 10 PhD students and 2 postdocs. He has secured significant research grants, including the ERC Starting Grant, which supports his foundational work. He is also engaged in scientific communication, giving invited talks at international venues such as SIAM and co-organizing the IMAGINE One World seminar series to foster global collaboration in imaging and inverse problems. He leads the Vision, Learning and Optimization (VLO) group at the Institute for Computer Graphics and Vision. The group develops mathematical models and efficient algorithms for computer vision and image processing, with a focus on mobile applications. The team includes multiple PhD students and postdoctoral researchers and has produced notable software and publications in top venues.

• Quantum Physics
• Quantum Optics
• Quantum Information
• +7 more

Professor John Jeffers is a leading academic in the Department of Physics at the University of Strathclyde, Faculty of Science, and a member of the Scottish Universities Physics Alliance (SUPA). His research lies at the forefront of quantum optics and quantum information science, with a focus on quantum illumination, quantum lidar, quantum retrodiction, and quantum key distribution. He is actively involved in both theoretical and experimental quantum technologies. His educational background is not explicitly detailed in the text, but his extensive research output and professorial rank suggest a strong foundation in physics, likely including a PhD in a quantum-related field. Jeffers' research interests span quantum sensing, quantum communication, and quantum foundations. He explores how quantum states can enhance detection and ranging capabilities beyond classical limits, particularly in noisy or adversarial environments. His work integrates theoretical modeling with practical implementations, often in collaboration with experimental groups. He is especially known for contributions to quantum retrodiction and the optimization of quantum illumination protocols using simple detection schemes. The most recent articles show a strong trend toward applied quantum technologies, particularly quantum-enhanced sensing and secure communication. His work frequently appears in high-impact journals like Philosophical Transactions A and Physical Review series, reflecting both theoretical depth and experimental relevance. Keywords across his publications include quantum optics, photonics, quantum information, and quantum metrology, with subfields such as quantum lidar, finite-key QKD, and entanglement-based sensing dominating recent efforts. Fellow of the Institute of Physics (2008) Award for Outstanding Refereeing (2008) Professor Jeffers has supervised multiple PhD students and postdoctoral researchers, including Richard Murchie and Roberto Gonzalez Pousa. He has led and co-led numerous research projects funded by EPSRC and NPL, such as the DTP 2224 studentship and the NPL iCASE project on quantum transduction. These grants support doctoral training and interdisciplinary research in quantum photonics. He also contributes to dataset creation and open science, supporting reproducibility in quantum experiments. He is actively involved in research labs and teams at the University of Strathclyde, particularly those working on quantum technologies. He collaborates with the Quantum Optics group and participates in the QuantIC hub. His projects often involve multidisciplinary teams combining theory and experiment, focusing on quantum sensing, communication, and imaging. He regularly presents at major conferences and hosts visiting researchers, indicating a vibrant and collaborative research environment.

Dan B GoldmanView profile

Adjunct Associate Professor

• Computer Graphics
• Computer Vision
• Artificial Intelligence
• +3 more

Dan B Goldman is an affiliate associate professor at the University of Washington's Computer Science and Engineering department, while serving as a Principal Engineer and AI/ML architect for Lucasfilm & ILM. His career bridges academia and industry, with prior roles at Adobe's Creative Technologies Lab (2007-2015) and Google (2015-2024), where he led computer vision R&D for Project Starline. He is a member of the Visual Effects Society and holds a PhD in Computer Science (2007) from the University of Washington. Research Focus: Computer graphics, computer vision, artificial intelligence, and human-computer interaction Notable Projects: Content-Aware Fill (Photoshop), Project Starline (Google), HyperNeRF (neural radiance fields), and Nerfies (deformable 3D modeling) Academic Legacy: Mentored by David Salesin, with an Erdős-Bacon number of 6 (via Rose Byrne and David Salesin's academic lineage). His work appears in top venues like SIGGRAPH, CVPR, and ECCV, covering topics from neural rendering to 3D reconstruction. Industry Impact: Pioneered technologies used in major films (Star Wars, Jurassic Park) and Adobe Photoshop features. His recent work at Google Labs (2022-2024) focused on generative media, reflecting his dual expertise in creative tools and computational methods.

Kyung Taek LimView profile

Assistant Professor

• Nuclear Engineering
• Radiation Detection
• Gamma-ray Spectroscopy
• +4 more

Kyung Taek Lim is an Assistant Professor in the Department of Quantum and Nuclear Engineering at Sejong University. He received his Ph.D. in 2019 from KAIST and has held postdoctoral positions at KAIST's Radiation and Nuclear Medical Engineering Research Center (2019-2020) and the Korea Institute of Nuclear Nonproliferation and Control (2020-2022). His research focuses on radiation detection technologies, nuclear nonproliferation, and silicon photomultiplier applications. Education: B.S. (2012) - Davidson College B.S. (2012) - Columbia University M.S. (2013) - Columbia University Ph.D. (2019) - KAIST Lim's research interests include: Radiation detector development Retrospective dosimetry techniques Gamma-ray detection systems Photosensor applications Nondestructive assay methods Recent publications highlight his work in machine learning for radiation measurement (2024-2025), including: ANN-based pulse shape discrimination for neutron depth profiling Bayesian localization methods for radioactive sources Optimization of backscatter X-ray detection systems Spent-fuel verification in heavy water reactors His technical contributions involve commercializing radiation detection modules using low-light imaging solutions and developing large-area plastic scintillation detectors for radioisotope identification.

Kostas DaniilidisView profile

Professor

• Computer Vision
• Robotics
• Geometric Deep Learning
• +4 more

Kostas Daniilidis is the Ruth Yalom Stone Professor of Computer and Information Science at the University of Pennsylvania, where he has been a faculty member since 1998. A distinguished IEEE Fellow, he has held leadership roles such as Director of the GRASP Laboratory (2008–2013), Associate Dean for Graduate Education (2012–2016), and Faculty Director of Online Learning since 2016. His academic journey includes an undergraduate degree in Electrical Engineering from the National Technical University of Athens (1986) and a PhD in Computer Science from the University of Karlsruhe (1992). Education : National Technical University of Athens (BEng), University of Karlsruhe (PhD) Daniilidis is renowned for his contributions to geometric deep learning, data association, event-based cameras, and vision-based manipulation and navigation. His work spans visual odometry, omnidirectional vision, 3D pose estimation, and structure from motion, with a focus on integrating deep learning with geometric principles for real-time applications. His publications highlight advancements in event camera datasets, spherical CNNs for rotation-equivariant representations, and unsupervised motion learning. These works bridge robotics, computer vision, and neural networks, emphasizing dynamic scene analysis and robust perception systems. Scientific Awards : IEEE Fellow Best Conference Paper Award at ICRA 2017 Best Student Paper Finalist at Robotics Science and Systems 2018 Daniilidis has contributed to key academic roles, including Associate Editor of IEEE Transactions on Pattern Analysis and Machine Intelligence (2003–2007) and co-chairing conferences like IEEE 3DPVT 2006 and ECCV 2010. He leads research initiatives in the GRASP Laboratory, focusing on cutting-edge robotics and perception technologies.

• Natural Language Processing
• Computational Linguistics
• Language Model Interpretability
• +7 more

Anna Rogers is a leading researcher in Natural Language Processing (NLP), known for her foundational and critical work on language model interpretability, dataset quality, and the ethics of AI systems. She has published extensively at top-tier venues including ACL, EMNLP, and NAACL, often collaborating with prominent researchers like Anna Rumshisky and Isabelle Augenstein. Her research interests center on data curation , model generalization , responsible data use , and transparency in large language models . She has conducted influential surveys on community perspectives regarding intelligence in AI and has contributed to improving peer review practices in NLP conferences. Her work on BERTology, including papers like A Primer in BERTology and Revealing the Dark Secrets of BERT , has become key reading in the field. Recent publications highlight her focus on data transparency (e.g., the ROOTS Search Tool), synthetic content detection , and temporal annotation frameworks like NarrativeTime. She also investigates the robustness of models, probing techniques, and the limitations of current benchmarks. Her work consistently emphasizes methodological rigor and ethical considerations in NLP research. She has advocated for better data practices and more thoughtful evaluation paradigms across the community. Natural Language Processing Computational Linguistics Language Model Interpretability Dataset Curation and Quality Ethics in NLP Peer Review in Academic Conferences Anna Rogers has played a key role in shaping meta-discussions in the NLP field, including peer review reform and the societal impact of language technologies. She has co-organized workshops on negative results and contributed to tutorials on reviewing and data use. Her work bridges technical depth with critical reflection on the direction of the field. She has advised or collaborated with numerous researchers, though specific students are not listed in the available data. She has not received any explicitly mentioned scientific awards in the provided text. Anna Rogers leads or contributes to several open-source initiatives and shared resources, including the DECAF framework, the ROOTS Search Tool, and various datasets such as TimeBankNT and RuSentiment. Her work often includes released code and tools to promote reproducibility and further research.

Di Francesco MarioView profile

Professor

• Internet of Things
• Wireless Networking
• Mobile and Ubiquitous Computing
• +5 more

Professor Mario Di Francesco is a faculty member in the Department of Computer Science at Aalto University, School of Science. He holds the academic rank of Professor and leads research in Internet of Things (IoT), wireless networking, and edge computing. His work contributes to UN Sustainable Development Goals related to innovation and infrastructure. Education: PhD in Information Engineering from the University of Pisa (2009), followed by research fellowships at Scuola Superiore Sant'Anna and adjunct faculty roles at the University of Texas at Arlington (2012-2014). Research interests include IoT systems, mobile computing, virtual reality, and distributed network architectures. He has led projects such as ASPECT (mobile video optimization), MeXICO (mobile cross-reality), and DNN-ReCoDe (edge computing for AI). Recent publications focus on VR privacy, Kubernetes security, and cloud-based network measurement frameworks. Notable awards include the Google IoT Technology Research Award (2016) and a MobileHCI Honorable Mention (2020). Active in academic service, he serves on program committees for IEEE conferences and chairs workshops in networking and pervasive computing domains. Current projects explore virtual reality systems and resource-constrained AI computation.

Sinho ChewiView profile

Assistant Professor

• Optimal Transport
• Log-concave Sampling
• Machine Learning Theory
• +3 more

Sinho Chewi is an Assistant Professor of Statistics and Data Science at Yale University, where he conducts research at the intersection of mathematics, statistics, and machine learning. His work focuses on theoretical aspects of computational statistics, particularly leveraging optimal transport theory for solving complex problems in sampling and inference. Chewi earned his B.S. in Engineering Mathematics and Statistics from the University of California, Berkeley in 2018, followed by a PhD in Mathematics and Statistics from the Massachusetts Institute of Technology in 2023 under the supervision of Philippe Rigollet. Prior to joining Yale, he was a postdoctoral researcher at the Institute for Advanced Study during the 2023-2024 academic year. His research interests span optimal transport theory , log-concave sampling , variational inference , and theoretical foundations of machine learning . Chewi is currently authoring a comprehensive book on the complexity of log-concave sampling, building on his extensive publication record. His work bridges theoretical mathematics with practical applications in statistical computing and artificial intelligence. Analysis of Chewi's publication record reveals a strong focus on developing rigorous mathematical frameworks for sampling algorithms, with particular emphasis on complexity analysis, convergence guarantees, and connections between functional inequalities and optimization. His recent work extends into diffusion models, neural network theory, and parallel sampling algorithms, demonstrating both depth in core statistical theory and breadth across machine learning applications. ICLR 2023 (Notable Top 5%) ALT 2023 (Best Student Paper) NeurIPS 2021 (Spotlight) ICLR 2025 DeLTA Workshop (Best Short Paper Award) Chewi has received significant research support through his postdoctoral position at the Institute for Advanced Study and collaborations with leading researchers at institutions including MIT, NYU, and Microsoft Research. His teaching portfolio includes advanced courses in optimization techniques and sampling methods, reflecting his expertise in theoretical foundations of machine learning algorithms.

Todd ZicklerView profile

Professor

• Computer Vision
• Computer Graphics
• Machine Learning
• +8 more

Todd Zickler is the William and Ami Kuan Danoff Professor of Electrical Engineering and Computer Science at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS). His research focuses on modeling light-material interactions and developing computational techniques for visual data interpretation, with applications in autonomy, augmented reality, and computational imaging. He leads the Harvard Computer Vision Laboratory and is part of the Graphics, Vision, and Interaction Group. Education: B.Eng. (Honours Electrical Engineering), McGill University, 1996 Ph.D. (Electrical Engineering), Yale University, 2004 Research Interests: Computer Vision, Computer Graphics, Machine Learning Optical and Computational Imaging, Human Perception Applications in Robotics, AR, and Autonomous Systems Key Contributions: Developed novel depth sensors inspired by jumping spiders Advanced shape-from-texture and shading techniques Contributed to neural radiance fields (NeRF) and boundary detection algorithms Awards: NSF Career Award Alfred P. Sloan Research Fellowship Grants & Labs: Director of Harvard Computer Vision Lab Recipient of NSF AI Institute funding ($20M) for physics-driven AI research

• Pediatric Critical Care Medicine
• Anesthesiology
• Neurophysiological Monitoring
• +3 more

Alireza Akhondi-Asl is an Assistant Professor of Anesthesia at Harvard Medical School and Boston Children's Hospital, specializing in pediatric critical care medicine. His work focuses on integrating advanced analytical techniques with clinical practice to improve outcomes in critically ill children. He leads research in neurophysiological monitoring, machine learning applications for clinical decision support, and optimizing protocols for pediatric ICU care. His affiliations include Harvard Catalyst, where he contributes to translational research initiatives. Research interests include EEG-based sedation monitoring, causal inference methodologies in healthcare, and predictive modeling for organ dysfunction. He has developed automated clinical tools like the Pediatric Sequential Organ Failure Assessment (PSOFA) calculator and explored in silico models for cerebral hemodynamics in sepsis. His studies often address practical challenges in nutrition support, fluid management, and surgical outcomes for congenital disorders. Key contributions include advancing understanding of brain connectivity through dynamic Bayesian models and evaluating the impact of emerging evidence on clinical practices. His work bridges computational methods with real-world pediatric critical care challenges, aiming to enhance both diagnostic accuracy and patient management strategies.

Paige BrooksView profile

Associate Professor

• Machine Learning
• Probabilistic Programming
• Generative Models
• +5 more

Paige Brooks is an Associate Professor at the University College London AI Centre and a Turing Fellow at the Alan Turing Institute. She specializes in interpretable machine learning, probabilistic programming, and generative models with applications in environmental science and chemistry. Her work includes seasonal Arctic sea ice forecasting and molecular synthesis pathway modeling. Brooks holds a DPhil from the University of Oxford and has contributed to foundational research in Bayesian inference and sequential Monte Carlo methods. Research highlights include collaborations on Arctic ice prediction with the British Antarctic Survey and development of the Molecule Chef framework for generating novel chemical compounds. She is also a statistical ambassador for the Royal Statistical Society, emphasizing ethical and transparent AI practices. Education: DPhil in Machine Learning at University of Oxford (under Frank Wood) Key Contributions: Probabilistic programming frameworks, interpretable ML, environmental AI Affiliations: Alan Turing Institute (Turing Fellow), Royal Statistical Society Her research spans theoretical advances in generative models and practical applications in domains like climate science and drug discovery. Recent work emphasizes model reliability and generalization in dynamic environments. Scientific Awards: Turing Fellowship, Royal Statistical Society Statistical Ambassador Labs/Teams: UCL AI Centre, Turing Institute's Environmental AI group

Yun YangView profile

Associate Professor

• Machine Learning
• Bayesian Inference
• High-Dimensional Statistics
• +4 more

Yun Yang is an Associate Professor in the Department of Mathematics at the University of Maryland, College Park . Previously, he held positions as Associate Professor (2022–2022) and Assistant Professor (2018–2022) at the University of Illinois at Urbana-Champaign, and Assistant Professor at Florida State University (2016–2018). He earned a B.S. in Mathematics from Tsinghua University (2011) and a Ph.D. in Statistics from Duke University (2014). His research focuses on Bayesian inference , high-dimensional statistics , machine learning , and optimal transport . Recent work emphasizes applying optimal transport and Wasserstein gradient flows to statistical problems, including regression, clustering, and generative modeling. He also investigates algorithmic scalability and theoretical guarantees for modern statistical methods. Key contributions include advancements in diffusion models for manifold structures, minimax-optimal distribution estimation, and Bayesian model selection via variational approximations. His work bridges theoretical foundations with practical applications in data science and optimization. Service roles include Associate Editorships at Bayesian Analysis (2025–present) and Journal of Computational and Graphical Statistics (2023–present), and Area Chair for AISTATS (2022–present).

• Robust Statistics
• Data Mining
• Outlier Identification
• +4 more

Dr. Xin Dang is a Professor of Mathematics at the University of Mississippi, affiliated with the Department of Mathematics within the College of Liberal Arts. His research focuses on robust statistics, data mining, outlier identification, and high-dimensional statistical analysis, with a particular emphasis on developing novel methods like Gini correlations and robust statistical computing techniques. He has contributed extensively to areas such as correlation analysis, classification, and clustering algorithms. Dr. Dang holds a B.S. in Applied Mathematics from Chongqing University (1991), an M.S. in Natural Sciences from the University of Texas at Dallas (2003), and a Ph.D. in Statistics from the University of Texas at Dallas (2005). He teaches a wide range of courses including Elementary Statistics, Calculus, and Advanced Statistics, reflecting his expertise in both theoretical and applied statistical methodologies. His recent work emphasizes methodological advancements in Gini-based correlations, high-dimensional feature screening, and computational statistics. Notable contributions include the development of the Maximum Projection Gini Correlation (MaGiC) and the Gini covariance matrix. His research bridges theoretical statistics with practical applications in economics, data science, and computational algorithms. Dr. Dang’s publications span over two decades, with a focus on robust statistical techniques, dependence modeling, and algorithmic efficiency. He has collaborated on projects involving economic growth prediction, nonparametric testing, and optimization methods. His work often addresses challenges in handling complex data structures and improving predictive accuracy through novel statistical frameworks.