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David BleiView profile

Professor

• Probabilistic Topic Models
• Bayesian Nonparametric Methods
• Approximate Posterior Inference
• +5 more

David Blei is a Professor of Computer Science and Statistics at Columbia University, holding dual appointments in Columbia Engineering and the Faculty of Arts and Sciences. He joined Columbia in 2014 after serving as an Associate Professor of Computer Science at Princeton University. His research focuses on probabilistic topic models, Bayesian nonparametric methods, and approximate posterior inference, with applications in text, images, social networks, and scientific data. Education: Bachelor’s in Computer Science and Mathematics, Brown University (1997) PhD in Computer Science, University of California, Berkeley (2004) Research Interests: Probabilistic modeling and Bayesian methods Machine learning applications in social and biological sciences Causal inference and fairness in AI Generative models and representation learning Awards: Sloan Fellowship (2010) Office of Naval Research Young Investigator Award (2011) Presidential Early Career Award for Scientists and Engineers (2011) Blavatnik Faculty Award (2013) Advising & Grants: Professor Blei has advised numerous students and led interdisciplinary research projects, though specific grant details are not explicitly listed. His work bridges theoretical foundations with practical applications in healthcare, economics, and social science. Labs & Affiliations: Affiliated with Columbia’s Foundations of Data Science, Computing Systems for Data-Driven Science, and Computational Social Science centers, advancing interdisciplinary data science initiatives.

Azalia MirhoseiniView profile

Assistant Professor

• AI Systems
• Chip Design Automation
• Deep Reinforcement Learning
• +3 more

Azalia Mirhoseini is an Assistant Professor of Computer Science at Stanford University and founder of the Scaling Intelligence Lab. She also serves as a Senior Staff Scientist at Google DeepMind. Her research focuses on scalable and self-improving AI systems, including advancements in chip design automation, large language models, and reinforcement learning applications. Prior roles include co-founding the ML for Systems team at Google Brain. Education: BSc in Electrical Engineering from Sharif University of Technology; PhD in Electrical and Computer Engineering from Rice University. Awards include MIT Technology Review’s 35 Under 35 and Best ECE Thesis Award. Research highlights include AlphaChip (deep RL for chip layout optimization), Mixture-of-Experts (MoE) architectures, and Constitutional AI frameworks for alignment. Her work bridges AI and systems, addressing real-world challenges in hardware design and scalable AI. Key contributions span over 50 publications in top venues like Nature , NeurIPS , and ICLR . Media coverage includes MIT Tech Review, IEEE Spectrum, and WIRED. Teaching includes CS229s: Systems for Machine Learning. Labs/Teams: Scaling Intelligence Lab (Stanford), collaborations with Google Research/DeepMind. Advising/Grants: Mentors students through Stanford; her work is funded via industry-academic partnerships.

David JurgensView profile

Associate Professor

• Natural Language Processing
• Computational Social Science
• Social Psychology
• +4 more

David Jurgens is an Associate Professor jointly appointed at the School of Information and the Department of Electrical Engineering and Computer Science at the University of Michigan. His research bridges Natural Language Processing (NLP), social psychology, and data science to study human behavior within social systems. He holds a PhD from UCLA and has held postdoctoral positions at Stanford University and McGill University. Education: PhD in Computer Science, University of California, Los Angeles Postdoctoral Scholar, Stanford University (with Dan Jurafsky and Jure Leskovec) Postdoctoral Scholar, McGill University (Network Dynamics Group) Research Interests: Focuses on sociolinguistics, computational social science, and ethical AI. Key themes include demographic inference from text, social relationship modeling, online behavior impacts, and mitigating incivility in digital spaces. His work has been published in venues like PNAS, WWW, ACL, and EMNLP. Publications: Recent work spans AI ethics frameworks, LLM fine-tuning for subjective perception, citation fidelity analysis, and multilingual moral reasoning. His research emphasizes interdisciplinary applications of NLP to societal challenges. Awards: Cozzarelli Prize (National Academy of Sciences) Cialdini Prize (Society for Personality and Social Psychology) Best Paper Awards at ICWSM and W-NUT Advising & Grants: Mentors students in NLP and computational social science. Teaches courses on NLP, information retrieval, and data mining. Active in conference organization (e.g., ACL, EMNLP) and journal editing (Frontiers special issue on Computational Sociolinguistics). Labs & Teams: Leads the Blablablab research group, focusing on interdisciplinary projects in NLP and social science. Collaborates with experts in psychology, linguistics, and sociology to advance data-driven methodologies.

• Machine Learning
• Statistics
• Causal Inference
• +4 more

Arthur Gretton is a Professor at the Gatsby Computational Neuroscience Unit and director of the Centre for Computational Statistics and Machine Learning at University College London (UCL). He is also a Research Scientist at Google DeepMind, focusing on causal inference, representation learning, generative models, and nonparametric hypothesis testing. Research Interests: Causal modeling with hidden confounders, distributional representations via kernel methods, gradient flows for statistical divergence optimization, and robustness in machine learning. Recent Publications: His 2025-2024 work includes causal effect estimation with proxies, kernel-based generative model training (e.g., MMD gradient flows), and foundational work in distributional reinforcement learning. Scientific Recognition: Includes Top 5% paper, ICLR 2023 Spotlight presentations at ICML 2024, NeurIPS 2023, and UAI 2023. Students: Current advisees include Jakub Wornbard, Zikai Shen, Zhe Wang, and others co-supervised in causal and statistical learning projects. Labs: Leads the Gatsby Unit and UCL Centre for Computational Statistics and Machine Learning, with software tools for kernel tests, gradient flows, and distribution embeddings.

Robert BamlerView profile

Professor

• Deep Generative Models
• Machine Learning
• Probabilistic Inference
• +3 more

Robert Bamler is a Professor of Data Science and Machine Learning at the University of Tübingen, Germany , and a member of the Cluster of Excellence "Machine Learning: New Perspectives for Science" and the Tübingen AI Center . Current affiliation: University of Tübingen (since November 2020) Prior roles: Postdoctoral Scholar at UC Irvine (with Stephan Mandt), Machine Learning Researcher at Disney Research (Pittsburgh/Los Angeles) Education: PhD in Theoretical Statistical and Quantum Physics from University of Cologne (2016), advised by Achim Rosch Research Focus : Algorithm development for deep generative models (including large language models) Resource-efficient inference and model compression Probabilistic machine learning applications in natural sciences Theoretical foundations in statistics, information theory, and physics Scientific Contributions : German Telekom Foundation PhD scholarship awardee Co-founder of the BamlerLab reading group on probabilistic modeling and compression Key Collaborations : Stephan Mandt (UC Irvine) Bernhard Schölkopf (MPI-IS) Cluster of Excellence "Machine Learning" (Tübingen) Teaching : Lecturer for "Data Compression With and Without Deep Probabilistic Models" (2021-2025) Organizer of weekly reading groups on function-space inference and compression algorithms

Rocky Tong ChenView profile

Senior Lecturer

• Data Mining
• Recommender Systems
• Predictive Analytics
• +2 more

Dr. Rocky Tong Chen is a Senior Lecturer and ARC DECRA Fellow at the Data Science Discipline within the School of Electrical Engineering and Computer Science at The University of Queensland. He maintains active affiliations with the Centre for Enterprise AI and the ARC Centre of Excellence for Children and Families Over the Lifecourse. His research focuses on developing accurate, efficient, and trustworthy data mining solutions to extract actionable patterns from large-scale user data. Chen's research interests span Data Mining, Recommender Systems, Predictive Analytics, Machine Learning, and Health Informatics. His work demonstrates a strong emphasis on practical applications, particularly in developing lightweight and efficient algorithms for recommendation systems that can operate on resource-constrained devices. His research trajectory shows a clear progression from traditional recommender systems toward more privacy-preserving, decentralized, and trustworthy approaches that address real-world deployment challenges. Chen has published over 70 peer-reviewed papers in top-tier venues including KDD, SIGIR, WWW, ICDM, ICDE, AAAI, and IJCAI, as well as leading journals like VLDBJ, IEEE TKDE, IEEE TNNLS, ACM TOIS, and WWWJ. His publication trends reveal a strong focus on federated learning, graph neural networks, and on-device recommendation systems in recent years. 3 Best Paper Awards 1 Best Paper Nomination 2 Travel Awards Dr. Chen has secured multiple ARC grants including the Discovery Early Career Researcher Award (2023-2026) for 'Scalable and Lightweight On-Device Recommender Systems,' and is actively supervising numerous PhD students across various projects related to recommendation systems, data analytics, and machine learning. His supervision portfolio includes both principal and associate advisory roles, demonstrating his collaborative approach to research mentorship. His laboratory work focuses on developing practical implementations of recommender systems with an emphasis on efficiency, privacy, and real-world applicability.

Ruqi ZhangView profile

Assistant Professor

• Machine Learning
• Deep Learning
• Probabilistic Methods
• +5 more

Ruqi Zhang is an Assistant Professor in the Department of Computer Science at Purdue University. Previously, she was a postdoctoral fellow at the Institute for Foundations of Machine Learning, UT Austin (2021-2022), and earned her PhD in Statistics from Cornell University (2016-2021). Her research focuses on scalable probabilistic methods for machine learning, including alignment of foundation models, uncertainty quantification, and Bayesian deep learning. She has received awards such as the Ross-Lynn Research Scholar Fund and ICML Best Reviewer recognition. Education: PhD in Statistics, Cornell University (2021) MS in Computer Science, Cornell University (2021) Bachelor of Science in Mathematics, Renmin University of China (2012-2016) Research Interests: Trustworthy AI: Safety of LLMs/VLMs, alignment mechanisms Probabilistic Inference: Bayesian methods, MCMC, variational inference Generative Models: Diffusion models, energy-based models Uncertainty Estimation: Calibration, out-of-distribution detection Notable Contributions: Developed the Discrete Langevin Sampler for high-dimensional discrete spaces Pioneered Low-Precision SGLD for efficient Bayesian neural networks Designed DP-Fast MH for privacy-preserving Bayesian inference Teaching: CS57800 - Statistical Machine Learning (2022-Present) CS37300 - Data Mining and Machine Learning (2024) CS59200 - Probabilistic Machine Learning (2022)

Tianyi ZhouView profile

Assistant Professor

• Machine Learning
• Natural Language Processing
• Computer Vision
• +6 more

Tianyi Zhou is an Assistant Professor of Computer Science at the University of Maryland, College Park, affiliated with UMIACS. His research focuses on machine learning, optimization, and natural language processing, with notable contributions to multimodal AI, curriculum learning, and generative models. He has published extensively in top venues like NeurIPS, ICML, and CVPR. Education: Ph.D. in Computer Science from the University of Washington (2013–2021), supervised by Prof. Jeff A. Bilmes. Prior research at UTS and NTU under Prof. Dacheng Tao, and internships at Microsoft Research and Yahoo! Labs. Research Interests: Human-inspired learning strategies, multimodal reasoning, vision-language models (e.g., BLIP3-o), federated learning, and embodied AI. Current projects include TurningPoint AI for trustworthy embodied agents and Tianyi Lab for hybrid intelligence research. Awards: 2020 IEEE TCSC Most Influential Paper Award (GoDec), 2013 ICDM Best Student Paper Award (Divide-and-Conquer Anchoring). Teaching: Instructs CMSC 421 (Introduction to AI) and CMSC 828A (Machine Learning paradigms).曾担任华盛顿大学机器学习课程助教。 Service: Area Chair for NeurIPS 2025, ICLR 2025, IJCAI 2025. Reviewer for TPAMI, JMLR, and multiple conferences.

Lei YouView profile

Assistant Professor

• Mathematical Optimization
• Distributed Data Analytics
• Data-Driven Optimization
• +9 more

Lei You is an Assistant Professor in Applied Mathematics at the Department of Engineering Technology, Technical University of Denmark (DTU), located in Ballerup, Denmark. With a Ph.D. in Computer Science specializing in Mathematical Optimization from Uppsala University (2019), Dr. You has established expertise in distributed data analytics, data-driven optimization, and machine learning interpretability. Dr. You's research focuses on the theories of interpretability and efficiency of machine learning models toward On-Device AI. Key areas of investigation include: Strategies to streamline complex models without performance loss Unraveling the intricate mechanisms of decision-making models Understanding the synergy between model simplification and explainability Distributed data analytics and federated learning systems Optimization techniques for wireless communication and edge computing Dr. You's recent publications demonstrate a strong focus on counterfactual explanations, optimal transport theory, neural network pruning, and multi-objective optimization for federated learning. The research spans applications from explainable AI to 6G-satellite systems and energy-efficient base station management. A notable trend is the integration of causal inference and distributional analysis in machine learning optimization problems, with significant contributions to federated learning efficiency and robustness. Scientific awards and recognition: Doctoral Dissertation Award for Operations Research in Telecommunications and Network Analytics awarded by INFORMS in 2020 Dr. You actively supervises student projects and research activities, with recent supervisions including work on remote sensing assessment, network pruning techniques, Microsoft Graph API optimization, and applications of large language models in business contexts. Current research projects include 'Machine Learning for Beehives Monitoring' and 'InnoTech - TaskForce,' focusing on practical applications of machine learning in environmental monitoring and digital transformation. Dr. You maintains active industry connections through previous roles at Bolt, Wolt (Doordash), and The Boston Consulting Group, ensuring research relevance to real-world challenges.

• Computer Vision
• Artificial Intelligence
• Video Anomaly Detection
• +2 more

Dr. Jing Liu is a researcher at Fudan University's Academy for Engineering and Technology specializing in computer vision and AI-driven video analysis. Their work focuses on developing advanced deep learning frameworks for video anomaly detection in surveillance systems. Research explores diffusion models, causal representation learning, and privacy-preserving techniques. Primary research interests include video understanding through spatial-temporal modeling, unsupervised anomaly detection paradigms, and multimodal fusion techniques. Recent publications demonstrate innovative approaches to feature consistency learning and network architecture design. Publications emphasize practical applications in security surveillance while addressing theoretical challenges in temporal modeling and causal inference. Collaborations include international researchers working on computer vision benchmarks and industrial applications.

Mario BukalView profile

Assistant Professor

• Partial Differential Equations (PDEs)
• Fluid Dynamics
• Nonlinear Systems
• +3 more

Dr. Mario Bukal is an Assistant Professor at the University of Zagreb’s Faculty of Electrical Engineering and Computing, Department of Applied Mathematics. He specializes in Partial Differential Equations (PDEs), particularly evolution equations and systems, with current involvement in the Croatian Science Foundation-funded project Mathematical Analysis of Multi-Physics Problems Involving Thin and Composite Structures and Fluids (MAMPITCoStruFl) . His research focuses on fluid-structure interaction, nonlinear systems, and mathematical modeling. He has taught courses in Mathematics, Probability & Statistics, and Stochastic Processes at both undergraduate and PhD levels, including roles at Vienna University of Technology. Education & Positions: Holds a PhD and has taught at multiple institutions, including TU Wien. Research Interests: Nonlinear PDEs, fluid dynamics, quantum mechanics, and applied mathematical analysis. His recent publications emphasize rigorous derivation of reduced models for complex systems, entropy analysis in quantum diffusion, and numerical schemes for fourth/sixth-order equations. Bukal actively contributes to seminars and conferences on nonlinear analysis and differential equations.

• Biomedical Engineering
• Clinical Sciences
• Ultrasonics
• +6 more

Dr. James J. Choi is an Associate Professor in the Department of Bioengineering at Imperial College London, part of the Faculty of Engineering. He leads the Noninvasive Surgical and Biomedical Technologies (NSBLab) laboratory, focusing on developing noninvasive medical devices for microsurgery and wearable virtual/augmented reality (VR/AR) controllers. His primary research areas include noninvasive focused ultrasound therapies for drug delivery across the blood-brain barrier (BBB), targeting diseases like Alzheimer's, glioblastoma, and diffuse midline glioma. He also pioneers smart wristband technologies for hand tracking in VR/AR environments with haptic feedback. Choi holds a PhD in Biomedical Engineering from Columbia University (2010), an MS from the same institution (2006), and a BSE in Computer Engineering from the University of Michigan (2004). His academic appointments include Assistant Professor at Imperial College London (2013–2018) and Postdoctoral Researcher at the University of Oxford (2010–2013). He is affiliated with the CRUK Convergence Science Centre, Cancer Technology Network, Centre for Advanced Therapeutics, and Centre for Neurotechnology. His research emphasizes five key pillars in noninvasive microsurgery: hardware development, algorithm design, acoustic-tissue physics, biological response analysis, and clinical translation. Current therapeutic targets include BBB-penetrant drug delivery for neurodegenerative and oncological conditions. Wearable technologies explore tactile feedback and motion tracking for immersive technologies. His work bridges engineering and clinical practice through innovative device design and preclinical studies. Awards: 2022 Frederic Lizzi Award, 2011–2012 Frederick V. Hunt Postdoctoral Research Fellowship in Acoustics Labs: NSBLab (www.nsblab.org) Grants & Funding: Not explicitly listed in text; inferred from research scope and affiliations. Choi's publications span over two decades, with recent focus on microbubble dynamics, transcranial ultrasound therapy, and wearable sensor systems. His work often appears in journals like Ultrasound in Medicine and Biology and Small , and he holds multiple patents related to ultrasound-mediated drug delivery and device optimization.

Lin FanView profile

Assistant Professor

• Applied Probability
• Data-Driven Operations
• Multi-Armed Bandits
• +3 more

Lin Fan is an Assistant Professor of Operations at the Kellogg School of Management, Northwestern University (2024–present). Previously, he was a Postdoctoral Scientist at Amazon's Supply Chain Optimization Technologies (2023–2024). He holds a PhD in Management Science and Engineering from Stanford University (2023), alongside MS degrees in Statistics (2017) and Mechanical Engineering (2015), and a BS in Mechanical Engineering from Georgia Tech (2012). His research bridges applied probability and data-driven operations, focusing on multi-armed bandits, reinforcement learning, stochastic processes, and simulation. He has received awards including the George Nicholson Student Paper Competition (2nd place, 2022) and an NSF Graduate Fellowship. His work addresses theoretical foundations and practical challenges in sequential decision-making systems, with applications to supply chain optimization and dynamic resource allocation. Ongoing research explores robustness in bandit algorithms and statistical inference for Markov processes.

Simone RossiView profile

Assistant Professor

• Machine Learning
• Deep Learning
• Generative Models
• +3 more

Simone Rossi is an Assistant Professor in the Data Science department at EURECOM. His research focuses on Bayesian methods, generative models, and deep learning, with particular emphasis on uncertainty quantification, diffusion processes, and scalable probabilistic models. He has contributed to foundational work on functional priors for Bayesian neural networks, conformal prediction for in-context learning, and optimization of diffusion models. Education: Not explicitly mentioned in the provided text. His research interests span machine learning, probabilistic modeling, and theoretical computer science. He explores topics like score-based generative models, continuous-time diffusion processes, and efficient Bayesian inference techniques. Recent work includes analyzing scaling laws for uncertainty in deep learning and improving LLM reasoning capabilities for Text2SQL tasks. His publications reflect contributions to top conferences like NeurIPS, ICML, and AABI, with a focus on both theoretical advancements and practical applications. Notable achievements include a Runner-up Best Paper Award at PAM 2024 for work on data augmentation in traffic classification. Scientific Awards: Runner-up Best Paper Award at PAM 2024 Dr. Rossi collaborates actively with industry and academia, contributing to open-source tools and foundational research in probabilistic deep learning. His work bridges theory and practice, addressing challenges in scalable Bayesian methods and generative model efficiency.

Li AngView profile

Assistant Professor

• Machine Learning
• Edge Computing
• Federated Learning
• +3 more

Dr. Li Ang is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Maryland, College Park. He joined UMD in August 2023 as a tenure-track faculty member. Prior to this, he was a Research Associate at Qualcomm AI Research and completed his Ph.D. in Electrical and Computer Engineering at Duke University in 2022, alongside another Ph.D. in Computer Science from the University of Arkansas. His research focuses on the intersection of machine learning and edge computing, emphasizing large-scale networked and trustworthy intelligent systems. Dr. Li has received notable awards, including the IEEE TCCPS Outstanding Ph.D. Dissertation Award, ACM KDD Best Student Paper Award, and the Cisco Research Award. Ph.D. in Electrical and Computer Engineering, Duke University (2022) Ph.D. in Computer Science, University of Arkansas MS in Management of Innovation and Venture Capital, Peking University Bachelor's in Computer Science, Henan University, China His research interests are centered at the intersection of machine learning and edge computing. He focuses on developing large-scale networked and trustworthy intelligent systems that address practical challenges in a collaborative, scalable, secure, and ubiquitous manner. Key areas include federated learning, distributed machine learning, edge computing, AI for IoT, and ensuring the trustworthiness of machine learning systems through fairness and robustness. Dr. Li has been recognized with several prestigious awards and honors: IEEE TCCPS Outstanding Ph.D. Dissertation Award ACM KDD Best Student Paper Award (2020) Duke ECE Department Outstanding Dissertation Award (2022-2023) Cisco Research Award (2025) Invited as an Associate Editor of IEEE Transactions on Neural Networks and Learning Systems (TNNLS) Distinguished Paper Award at ACM CCS’24 (2024) Dr. Li advises several doctoral and master’s students, including notable advisees Guoheng Sun and Shwai He, who have received the Qualcomm Innovation Fellowship. He actively seeks motivated students to join his research group. His work has been supported by grants such as the Cisco Research Award and he has been involved in various collaborations, including with Qualcomm AI Research and academic institutions. Dr. Li leads a research group focused on edge intelligence and federated learning. His work often involves interdisciplinary collaborations, leveraging expertise in machine learning, systems, and distributed computing to advance practical AI solutions. Key projects include developing efficient federated learning frameworks and exploring trustworthy AI systems for edge devices.