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Peter BonczView profile

Professor

• Database Systems
• Column-Store Databases
• Vectorized Query Processing
• +5 more

Peter Boncz is a Professor in the special chair of Large Scale Analytical Database Systems at Vrije Universiteit Amsterdam and leads the Database Architectures (DA) research group at CWI (Centrum Wiskunde & Informatica), the Netherlands' national research institute for mathematics and computer science. He serves on the CWI management team and is actively involved in multiple research initiatives and industry collaborations. Professor Boncz is internationally recognized as a pioneer of column-store databases, introduced through his PhD project MonetDB. His research spans database architecture, query processing optimization, and analytical database systems. His work on vectorized query processing with his first PhD student Marcin Zukowski has become foundational in modern analytical databases including BigQuery, Databricks, Snowflake, and DuckDB, which has millions of monthly downloads. Current research focuses include GPU data processing, vector search optimization, confidential computing, and graph data management. Boncz's recent publications reveal strong trends toward optimizing database systems for modern hardware architectures, particularly GPUs and cloud CPUs. His work bridges theoretical database concepts with practical implementation, focusing on performance optimization through innovative data layouts, compression techniques, and hardware-aware processing. The research shows a clear trajectory from foundational database concepts toward specialized optimization for emerging hardware and application requirements. VLDB Test of Time Award 2025 (second time, previously won in 2009) CIDR Test of Time Award 2024 ACM Fellow (2022) Humboldt Research Award (2013) ICTRegie Award (2006) Boncz has co-founded six spin-off companies in data systems, including MonetDB BV, and serves as an advisor to ventures like Databricks Corp. His research is supported by multiple external funding projects including Actian Research Grants, Databricks research agreements, and Motherduck Service Agreements. He has advised numerous students, with Marcin Zukowski being notably mentioned as his first PhD student who co-developed vectorized query processing. As leader of the Database Architectures research group at CWI, Boncz oversees a team focused on pushing the boundaries of database technology. The group maintains close ties with industry through projects with Databricks, Motherduck, and RelationalAI, while continuing to develop open-source technologies like DuckDB. The team is particularly active in GPU acceleration, confidential computing, and graph data management through the Linked Data Benchmark Council (LDBC), which Boncz founded.

• Reinforcement Learning
• Stochastic Optimization

Daniil Nikolaevich Tyapkin is a Junior Research Fellow and Postgraduate Student at the National Research University Higher School of Economics (HSE), affiliated with the Faculty of Computer Science. He works within the Institute of Artificial Intelligence and Digital Sciences and the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis under the supervision of Alexey Alexandrovich Naumov. Education: 2021: Bachelor's degree in Applied Mathematics and Computer Science from HSE Currently in 2nd year of postgraduate studies with dissertation topic: "Randomized trial for reinforcement learning" Research Interests: His work focuses on reinforcement learning and stochastic optimization, including exploration strategies, generative flow networks, and theoretical analysis of learning algorithms. He develops efficient methods for complex decision-making problems with applications in AI and data science. Publications: His research appears in top venues (ICLR, NeurIPS, ICML, AISTATS) covering reinforcement learning, optimization, and probabilistic modeling. Recent works explore GFlowNets, entropy regularization, and high-probability bounds for learning algorithms. Awards & Honors: Letter of gratitude from HSE Faculty of Computer Science (2022) Publication bonus for List A journal articles (2023-2024) HSE Personnel Reserve - New Researchers category (2022-2023) Teaching & Advising: Teaches courses in Mathematical Statistics and Matrix Computations. No formal student advising mentioned. Laboratory Affiliation: Member of the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis, focusing on algorithmic development for machine learning challenges.

Steve CarrView profile

Senior Lecturer

• Moving Image Art
• Sculpture
• Photography
• +3 more

Steve Carr is a Senior Lecturer in Moving Image at the Ilam School of Fine Arts, University of Canterbury, where he has been teaching since January 2016. His academic appointment is specifically as Senior Lecturer Above the Bar within the Faculty of Arts. Carr maintains dual representation in the art world through STATION galleries in Melbourne and Sydney, Australia, and Michael Lett in Auckland, New Zealand. Carr's educational background includes: MFA with 1st Cass Honours from University of Auckland's Elam School of Fine Arts (2001-2002) BFA with 1st Class Honours from Dunedin School of Art (1995-1998) Diploma in Teaching from Dunedin College of Education (1999) Carr's artistic practice centers on material transformation through controlled experiments that manifest primarily in sculpture, moving images, and photography. His work explores seemingly banal events—such as paint-filled balloons popped with a needle or bubbles meeting their demise on cactus tips—captured in real-time, slow motion, loops, and time-lapse. This approach challenges detached spectatorship in our era of visual saturation, encouraging sustained observation and reflection. His fascination with magical moments of material transformation spans a career of over two decades, seeking possibilities beyond linear narratives with subtle, humorous, and sometimes intense transformations as culmination points. Analysis of Carr's recent 15 exhibitions reveals consistent exploration of material transformation across diverse media. His work demonstrates a sophisticated understanding of time-based media, particularly through slow-motion and time-lapse techniques that reveal hidden dimensions in ordinary phenomena. The 'In Bloom' series represents a significant sculptural investigation using bronze-cast tires arranged as vases with bespoke plantings, examining the tension between artificial and organic forms. His exhibitions consistently engage with perception, temporality, and the unexpected beauty in controlled destruction or transformation. Carr co-founded the Blue Oyster Gallery in Dunedin and served on Auckland's Artspace board (2002-2004). His work is held in major institutional collections including Christchurch Art Gallery, Dunedin Public Art Gallery, Museum of New Zealand Te Papa Tongarewa, and National Gallery of Victoria. He has exhibited extensively across New Zealand and Australia at venues including Christchurch Art Gallery (2021), Dunedin Public Art Gallery (2021), Museum of New Zealand Te Papa Tongarewa (2020), and National Gallery of Victoria (2019). Carr maintains an active studio practice alongside his academic responsibilities, with his work represented by STATION in Australia and Michael Lett in New Zealand. His creative process involves collaboration with gardeners, botanists, and other specialists, particularly evident in the 'In Bloom' series where customized plantings interact with his sculptural forms. His teaching in moving image at the Ilam School of Fine Arts informs and is informed by his ongoing artistic investigations into time, materiality, and perception.

Kai GaoView profile

Associate Professor

• Software Supply Chain
• Mining Software Repositories
• AI for Software Engineering
• +3 more

Kai Gao is a Pre-Tenure Associate Professor in the School of Computer & Communication Engineering at University of Science and Technology Beijing (USTB), China. He received his Ph.D. in Software Engineering from Peking University (2019-2024) under advisors Prof. Minghui Zhou and Prof. Bing Xie, and holds a B.S. in Computer Science and Technology (major) and B.A. in Economics (minor) from PKU (2015-2019). His research spans software engineering with primary focus on Software Supply Chain , Mining Software Repositories , and Empirical Software Engineering . He actively explores intelligent software engineering applications and program analysis techniques, particularly investigating open-source ecosystems like PyPI. Current projects analyze license compliance in LLM-generated code (LiCoEval), repository validation for package managers (PyRadar), and deep learning supply chain characteristics. Dr. Gao has served on program committees for ASE (2024 Research Track, 2025 FOSS Award Track) and ICSE (2023 Artifact Evaluation), and reviewed for top journals including IEEE TSE, ACM TOSEM, and EMSE. His work has earned recognition including the ACM SIGSOFT Distinguished Paper Award (ICPC 2022) and multiple Academic Excellence Awards from Peking University. ACM SIGSOFT Distinguished Paper Award (ICPC 2022) Award for Academic Excellents (Peking University 2022, 2020) National Encouragement Scholarship (Peking University 2018) Founder Scholarship (Peking University 2018) He actively mentors students with co-authored publications at ASE, ICSE, and FSE, and maintains active GitHub repositories for research tools. His work bridges theoretical software engineering with practical challenges in modern open-source ecosystems and AI development pipelines.

• Nephrology
• Chronic Kidney Disease
• Acute Kidney Injury
• +5 more

Lisbet Brandi is a Clinical Associate Professor in the Department of Clinical Medicine at the University of Copenhagen, specializing in Internal Medicine with a focus on Nephrology. Her dual institutional affiliations at Blegdamsvej 3, 2200 København N and Dyrehavevej 29, 3400 Hillerød reflect her integration of academic research with clinical practice within the Danish healthcare system (Region Hovedstaden). Her research spans multiple dimensions of kidney disease management with particular emphasis on: Chronic Kidney Disease treatment optimization Acute Kidney Injury diagnosis and prevention strategies Implementation of clinical guidelines in real-world settings Mathematical modeling of dialysis processes Healthcare provider perspectives on patient management Analysis of her publication record reveals a strategic focus on translating evidence into practice, particularly through innovative approaches like electronic nudges to improve guideline adherence. Her work bridges clinical medicine, health services research, and biomedical engineering, addressing critical gaps between theoretical recommendations and actual patient care. Dr. Brandi maintains extensive national collaborations across Danish medical institutions, as evidenced by her multi-author publications in high-impact journals including Circulation, American Heart Journal, and PLoS ONE. Her research demonstrates growing influence in both nephrology and cardiology communities, reflecting the significant cardiovascular comorbidities associated with kidney disease.

Riadh MunjyView profile

Professor

• Photogrammetry
• Unmanned Aircraft Systems (UAS) Mapping
• Remote Sensing
• +5 more

Riadh Munjy serves as a Professor in Geomatics Engineering at California State University, Fresno's Lyles College of Engineering, where he teaches core courses including CE 205 (Computing in Engineering Analysis), GME 123 (Stereo-Photogrammetry), and GME 125 (Analytical Photogrammetry). His academic foundation includes a Ph.D. in Civil Engineering (1982), M.S. in Applied Mathematics (1981), and M.S.C.E. (1979) from the University of Washington, complemented by a B.S. in Civil Engineering from the University of Baghdad (1976). Ph.D., Civil Engineering, University of Washington (1979-1982) M.S., Applied Mathematics, University of Washington (1980-1981) M.S.C.E., Civil Engineering, University of Washington (1978-1979) B.S., Civil Engineering, University of Baghdad (1972-1976) Professor Munjy's research centers on advancing photogrammetric methodologies with particular emphasis on Unmanned Aircraft Systems (UAS) mapping accuracy, sensor calibration, and novel processing techniques for LIDAR and IFSAR data. His work bridges theoretical mathematics with practical applications in transportation infrastructure, disaster response, and terrain modeling, consistently addressing industry pain points like rolling shutter effects in UAV imagery and LIDAR strip adjustment challenges. He has pioneered standards for GPS-controlled photogrammetry adopted by Caltrans and contributed foundational chapters to the ASPRS Manual of Photogrammetry. Analysis of his 15 most recent publications (2010-2020) reveals a strategic evolution from traditional photogrammetric techniques toward UAS-centric workflows, with increasing focus on automated processing, accuracy validation, and integration of multi-sensor data. His research consistently targets real-world implementation barriers in transportation mapping and corridor projects, demonstrating strong industry relevance through Caltrans collaborations and ASPRS leadership. American Society of Photogrammetry and Remote Sensing Fellow Award (2020) ASPRS Fairchild Award (2014) Caltrans Research Innovation Award (2004) Five School of Engineering Research Excellence Awards (1992, 1996, 1998, 2002, 2003) ASPRS Meritorious Service Awards (1992, 1997) Halliburton Research Award (1992) Professor Munjy's research program has been consistently supported through Caltrans-funded projects including GPS Photogrammetry (2005), UAS Research (2018), and corridor mapping standards development. His advisory impact extends through co-authoring the ASPRS Manual of Photogrammetry and mentoring numerous conference presentations, though formal student lists aren't documented. Current initiatives focus on sUAS data accuracy validation and rolling shutter effect mitigation, positioning his work at the forefront of evolving UAV mapping regulations.

Ehsan AdeliView profile

Assistant Professor

• Translational Artificial Intelligence
• Medical Imaging
• Computational Neuroscience
• +3 more

Ehsan Adeli is an Assistant Professor of Psychiatry & Behavioral Sciences at Stanford University, with courtesy appointments in Computer Science and Biomedical Data Science. He leads the Stanford Translational AI (STAI) Lab, focusing on developing AI algorithms for healthcare and mental health. His research integrates 3D motion analysis from video data with neuroimaging techniques like MRI to study neurodegenerative and neuropsychiatric diseases. He holds academic roles including Director of AI/Innovation in Precision Mental Health and membership in institutes like HAI and Wu Tsai Neurosciences. His work spans digital health, computational neuroscience, and explainable machine learning. Education: Ph.D. in Artificial Intelligence & Computer Vision (Carnegie Mellon University), Postdoc in Biomedical Imaging & Computational Neuroscience (UNC Chapel Hill). Research interests include translational AI for mental health, digital humans (3D motion analysis), and human neuroscience (MRI-based brain function exploration). His lab develops tools like the Short Physical Performance Battery (SPPB) app for objective assessments. Notable honors include the Chairman’s Award for Educational Excellence (2023) and Jaswa Innovator Award (2022-2024). He advises over 20 graduate and postdoctoral students across multiple disciplines. Labs/Teams: STAI Lab, Computational Neuroscience Lab, Stanford Vision and Learning Lab.

• Computer Vision
• Deep Learning
• Robotics
• +5 more

Hao Yang is a Professor at Southeast University's School of Computer Science and Engineering, specializing in computer vision, deep learning, and robotics. His research spans multiple domains including medical imaging, infrastructure inspection, and multimodal AI systems. His research interests focus on advancing computer vision techniques for practical applications. He develops innovative deep learning architectures for object detection, image segmentation, and quality assessment, with particular emphasis on solving challenges in complex real-world scenarios including rotated object detection, ancient character recognition, and 3D point cloud analysis. His work often integrates novel transformer variants and Mamba architectures to address domain-specific challenges. His publication record shows significant contributions across multiple IEEE and ACM journals, with a strong emphasis on practical applications of AI in engineering contexts. His recent work demonstrates expertise in adapting cutting-edge neural network architectures to solve domain-specific problems in civil engineering, medical imaging, and transportation systems. Hao Yang actively collaborates across disciplines, with publications spanning computer science, electrical engineering, civil engineering, and biomedical applications. His work frequently appears in top-tier conferences including CVPR, AAAI, and ACL, demonstrating his broad impact across multiple AI subfields.

Georgios TriantafyllouView profile

Associate Professor

• Criminal Law
• Criminal Procedure
• European Criminal Law
• +3 more

Georgios Triantafyllou is an Associate Professor of Criminal Law and Criminal Procedure at the University of Athens Law School. He specializes in European and International Criminal Law, with expertise in judicial cooperation mechanisms, human rights in criminal proceedings, and transnational legal frameworks. Education: Bachelor's degree, University of Athens (1987) PhD in Criminal Law, University of Athens (1992) Research focuses on: Criminal procedure reform EU legal harmonization in criminal matters Defense rights in cross-border cases Antitrust criminal liability Key contributions include analysis of European Arrest Warrants, exclusionary rules for illegally obtained evidence, and procedural safeguards under the European Convention on Human Rights. His work bridges national criminal systems with international legal frameworks. Professional activities: Legislative committee member for EU directive transpositions Single point of contact for the EU judicial cooperation study Board member, Hellenic Association of Criminal Law Member, European and International Criminal Law Institute

Kimberly ClevengerView profile

Assistant Professor

• Exercise Physiology
• Physical Activity Measurement
• Accelerometry
• +7 more

Kimberly Clevenger is an Assistant Professor in the Department of Kinesiology and Health Science at Utah State University, within the College of Agriculture and Applied Sciences. Her research focuses on advancing the measurement and understanding of physical activity in children and adolescents, particularly through the integration of accelerometry and GPS technologies. She earned her PhD in Kinesiology (Exercise Physiology) from Michigan State University (2019), an MPH in Quantitative Methods from Harvard University (2020), and both her MS and BS in Exercise Physiology from Ohio University. Her research interests center on ensuring valid, reliable, and sensitive measurement of physical activity, the influence of the built environment on children's free-play behavior, and the impact of recess policies on physical activity and health outcomes. She investigates how behavior varies spatially and temporally, and how such variations affect environmental exposures like noise and nature. A key goal is ensuring equitable access to free-play opportunities in schools from pre-K to 12th grade. Her recent publications reveal a strong focus on methodological rigor in physical activity assessment, including calibration of accelerometers, comparison of open-source versus proprietary data processing, fragmentation analysis, and the development of consensus standards for activity intensity classification. Her work often leverages nationally representative datasets such as NHANES and addresses critical public health and policy issues related to youth physical activity. Surveillance and measurement of youth physical activity Recess policy and implementation Accelerometer data processing and validation GPS and spatial analysis of schoolyard activity Childhood obesity and cardiometabolic health Open science and reproducible research methods Dr. Clevenger has mentored several graduate students, including Benjamin Verdejo, Katherine McKee, and Cassie Webster. She teaches KIN 4400 - Measurement and Evaluation in Kinesiology and contributes to the training of future kinesiology researchers. She actively maintains a research blog where she shares technical tutorials on R, accelerometry, NHANES data analysis, and open-source methods, promoting transparency and accessibility in her field.

Isabel ValeraView profile

Professor

• Machine Learning
• Robust Machine Learning
• Interpretable AI
• +10 more

Isabel Valera is a full Professor in the Department of Computer Science at Saarland University in Saarbrücken, Germany, and an Adjunct Faculty member at the Max Planck Institute for Software Systems (MPI-SWS). She is also a fellow of the European Laboratory for Learning and Intelligent Systems (ELLIS), contributing to the Robust Machine Learning Program and the Saarbrücken AI & ML (Sam) Unit. Department of Computer Science, Saarland University Adjunct Faculty, MPI for Software Systems ELLIS Fellow, Robust ML Program Sam Unit, Saarbrücken AI & ML She obtained her PhD and MSc from Universidad Carlos III de Madrid, followed by postdoctoral research at the University of Cambridge and MPI for Software Systems. She previously led an independent research group at MPI for Intelligent Systems in Tübingen and held the Humboldt Post-Doctoral Fellowship and Minerva Fast Track Fellowship. PhD in Machine Learning, Universidad Carlos III de Madrid, 2014 MSc in Multimedia and Communications, Universidad Carlos III de Madrid, 2012 Telecommunications Engineering, Technical University of Cartagena, 2009 Her research centers on developing machine learning methods that are flexible, robust, interpretable, and fair, particularly for heterogeneous, temporal, and high-stakes decision-making systems. She emphasizes applications in medicine, psychiatry, and social domains such as hiring, bail, and lending. Her methodological contributions include Bayesian nonparametric models, latent feature modeling, and temporal point processes. Her recent publications reflect a strong focus on fairness, robustness, and interpretability in machine learning. Key themes include latent feature modeling for mixed data types, clustering temporal event streams, source separation, and fair classification. Her work bridges theoretical innovation with practical applications across healthcare, social networks, and policy-relevant domains. Scientific awards and recognitions include: Humboldt Post-Doctoral Fellowship Minerva Fast Track Fellowship (Max Planck Society) ELLIS Fellow She has been actively involved in teaching and dissemination, delivering tutorials at NIPS and MLSS on temporal point processes and social network analysis. She has also supervised research assistants and mentored junior researchers. Her research has been supported through prestigious fellowships and institutional affiliations. She leads the development of open-source tools such as GLFM, HDHP, and iFDM, promoting reproducibility and accessibility in machine learning research. She is affiliated with the following labs and research groups: Max Planck Institute for Intelligent Systems (former group leader) Max Planck Institute for Software Systems (adjunct, postdoctoral) ELLIS Sam Unit (Saarbrücken AI & ML) Robust Machine Learning Program (ELLIS)

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

Deng Cai is a Professor at Zhejiang University's College of Computer Science, working in the State Key Laboratory of CAD&CG in Hangzhou, China. He also maintains an affiliation with Tencent AI Lab, demonstrating his strong connection between academic research and industry applications in artificial intelligence. His academic background includes a PhD from the University of Illinois at Urbana-Champaign, Department of Computer Science (2009). Professor Cai's research spans multiple domains within artificial intelligence, with particular emphasis on computer vision, deep learning, and their applications. His work shows strong focus on 3D object detection, lane detection for autonomous vehicles, and the application of large language models to various vision tasks. He has made significant contributions to traffic forecasting, trajectory prediction, and CAD generation systems. His recent work increasingly integrates large language models with computer vision tasks, demonstrating the evolving nature of his research interests toward multimodal AI systems. The trajectory of Professor Cai's publications reveals a clear progression from foundational computer vision and machine learning research toward increasingly complex and applied systems. His work shows strong emphasis on practical applications in autonomous driving, with numerous papers on 3D object detection, lane detection, and trajectory prediction. More recently, his research has expanded to include generative models for CAD systems and video customization, often leveraging large language models in innovative ways. The consistent publication output across top-tier venues including CVPR, ICCV, AAAI, and NeurIPS demonstrates sustained research productivity and impact. Professor Cai has established significant research collaborations, particularly with Xiaofei He (161 joint publications), Haifeng Liu (50), Zhou Zhao (42), Wenxiao Wang (41), and Binbin Lin (39). His work appears across diverse publication venues including IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing, and proceedings of major AI conferences. The breadth of his publication venues reflects the interdisciplinary nature of his research spanning theoretical machine learning to applied computer vision systems. Professor Cai leads research activities within Zhejiang University's College of Computer Science, particularly focusing on the State Key Laboratory of CAD&CG. His work bridges academic research with practical industry applications through his affiliation with Tencent AI Lab. The laboratory environment supports research in computer vision, machine learning, and their applications to real-world problems in autonomous systems, content generation, and intelligent transportation.

Linus FrankeView profile

Researcher

• Computer Graphics
• Perceptual Rendering
• Computer Vision
• +3 more

Linus Franke is a postdoctoral researcher at Inria Sophia Antipolis in France, affiliated with the GraphDeco research group under George Drettakis. He previously completed his PhD at the Chair of Visual Computing , Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) in Germany under Marc Stamminger. His research focuses on Computer graphics Perceptual rendering Computer vision Machine learning Neural rendering Novel view synthesis Key trends in his recent publications include neural rendering , 3D Gaussian splatting , radiance fields , and real-time rendering with applications in virtual reality and light field displays. His work often integrates point clouds , differentiable rendering , and GPU optimization techniques. His personal website at lfranke.github.io highlights projects like VET (Visual Error Tomography) and TRIPS (Trilinear Point Splatting for Real-Time Rendering).

Andre BorrmannView profile

Professor

• Building Information Modeling
• Point Cloud Engineering
• Digital Twin
• +4 more

Andre Borrmann is Professor and Chair of Computing in Civil and Building Engineering at the Technical University of Munich (TUM), where he leads cutting-edge research at the intersection of computer science and construction engineering. His work spans over two decades with consistent publication output since 2003, demonstrating sustained academic leadership in computational methods for the built environment. His research focuses on: Building Information Modeling (BIM) and its advanced applications Digital Twin development for infrastructure management Point cloud processing and semantic enrichment Artificial intelligence integration in construction workflows Construction automation and robotics systems Analysis of his 2023-2025 publications shows a clear trend toward sophisticated AI applications in construction engineering, with increasing emphasis on graph neural networks, transformer models, and large language models. His work bridges theoretical computer science with practical construction challenges, particularly in model automation, code compliance checking, and digital documentation of existing structures. Scientific recognition includes: Konrad Zuse Medal (2024) Professor Borrmann leads multiple significant research initiatives including AM2PM (Additive to Predictive Manufacturing for Multistorey Construction), AI4CADCAM (AI-based CAD processing), and BauPuls360 (a public service platform for the construction industry). His research group maintains extensive international collaborations across academia and industry, contributing to UN Sustainable Development Goals related to sustainable cities, industry innovation, and climate action through their technological advancements in construction engineering.

• Machine Learning
• High-Dimensional Statistics
• Reinforcement Learning
• +4 more

Martin J. Wainwright is the Cecil H. Green Professor at the Massachusetts Institute of Technology (MIT) , affiliated with the Department of Electrical Engineering and Computer Science (EECS) and the Department of Mathematics . He is also associated with the Statistics and Data Science Center , the Laboratory for Information and Decision Systems , and the Institute for Data, Systems and Society . His research bridges machine learning , high-dimensional statistics , and information theory , with a focus on theoretical guarantees for algorithms in reinforcement learning, optimization, and graphical models. Books : High-Dimensional Statistics: A Non-Asymptotic Viewpoint (2019, Cambridge University Press), Statistical Learning with Sparsity: The Lasso and Generalizations (2015, CRC Press). Research Themes : Statistical and computational trade-offs, robustness in adaptive learning, posterior contraction rates, and decentralized estimation. His recent work explores non-asymptotic analysis , stochastic approximation , and instance-dependent guarantees in reinforcement learning and optimization. Key contributions include minimax optimality in value estimation, variance-reduced Q-learning , and adaptive inference under elliptical constraints. Awards : IMS Medallion Lecturer , COPSS Presidents' Award , Loève Prize in Probability , Fellow of the Institute of Mathematical Statistics , NIPS Outstanding Paper Award .