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Daniel AlabiView profile

Assistant Professor

• privacy & cryptography
• information theory
• data mining & databases
• +2 more

Daniel Alabi is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign. His research focuses on addressing privacy and security concerns while studying the tradeoffs in computational and statistical resources. Previously, he was a postdoctoral researcher at Columbia University and a junior fellow in the Simons Society of Fellows. Dr. Alabi received his Ph.D. in Computer Science from Harvard University. His educational journey has positioned him at the forefront of research at the intersection of theoretical computer science and practical security applications. His primary research interests include privacy & cryptography, information theory, data mining & databases, and machine learning. Dr. Alabi investigates how to identify and address privacy and security concerns in computational systems, examining tradeoffs in computational resources (such as communication, randomness, time, memory, and parallelism) and statistical resources (including samples drawn from unknown distributions). His work bridges theoretical foundations with practical implementations in secure data systems. Analysis of Dr. Alabi's recent publications reveals a strong focus on privacy-preserving technologies, with particular emphasis on differential privacy techniques applied to databases, machine learning models, and data markets. His research spans theoretical foundations of privacy, practical implementations of secure systems, and educational initiatives to broaden participation in computer science, particularly through his NaijaCoder organization. Dr. Alabi is the president and co-founder of NaijaCoder, Inc., which focuses on early algorithms education in the Global South. His GitHub profile shows active contributions to open-source projects, including the FlyLatex collaborative editing platform which has garnered significant attention in the academic community. His laboratory work centers around developing secure data systems, with connections to the Coordinated Science Lab at UIUC where he maintains his office in room 118. His research group explores the theoretical and practical aspects of privacy-preserving computation across multiple domains.

• Analysis of Algorithms
• Data Analysis
• Massive Data Sets
• +10 more

Robert Krauthgamer holds The Harry Weinrebe Professorial Chair of Computer Science at The Weizmann Institute of Science, where he also serves as Department Head of the Department of Computer Science & Applied Mathematics within the Faculty of Mathematics and Computer Science. His distinguished career includes editorial leadership as Editor-in-Chief of SIAM Journal on Computing (2019-2025) and significant service on numerous program committees for major theoretical computer science conferences. Dr. Krauthgamer's primary research focuses on Analysis of Algorithms , with particular expertise in Data Analysis and Massive Data Sets, Combinatorial Optimization, Approximation Algorithms and Hardness of Approximation, Average-case Analysis and Heuristics, Embeddings of Finite Metrics, and Routing and Peer to Peer networks. His work also spans broader areas of Discrete Mathematics and High-Dimensional Geometry, reflecting his deep mathematical approach to algorithmic problems. His recent publications reveal a strong emphasis on sparsification techniques, high-dimensional computational geometry, streaming algorithms, and graph-theoretic problems. Throughout his career, Krauthgamer has supervised numerous graduate students at both MSc and PhD levels, with recent theses addressing cutting-edge topics in algorithm design and analysis. His service contributions include steering committee roles for major conferences like SODA, ESA, and HALG, as well as committee membership for the prestigious Gödel Prize (2019-2021). As an educator, he regularly teaches advanced courses including Randomized Algorithms and Sublinear Time and Space Algorithms, often in collaboration with other leading researchers. He also organizes Weizmann's TheoryLunch and has coordinated the Foundations of Computer Science seminar for many years, demonstrating his commitment to fostering academic community and intellectual exchange.

George AtiaView profile

Associate Professor

• Big Data Analytics
• Sparsity-Based Learning
• Controlled Sensing
• +5 more

George Atia is an Associate Professor at the Department of Electrical and Computer Engineering, University of Central Florida, directing the Data Science and Machine Learning Lab (DSML). Previously, he was a postdoc at the Coordinated Science Laboratory (CSL) at UIUC and earned his Ph.D. from Boston University, where he was affiliated with the Information Systems & Sciences Lab (ISS) and Center for Information & Systems Engineering (CISE). His research spans big data analytics, sparsity-based learning, controlled sensing, and verifiable planning , with applications in machine learning, cyberphysical systems security, and optical/neural signal processing. His work emphasizes robust algorithms for high-dimensional data, adversarial attacks in machine learning, and inverse problems in optical imaging. Recent projects include tensor completion for visual data recovery and multi-agent reinforcement learning with robustness guarantees. He has secured major funding from NSF, DOE, and ONR, including the NSF CAREER Award. Notable scientific contributions include Robust Tensor Completion for Visual Data Game-Theoretic Frameworks for Cloud Security Adversarial Sample Synthesis in Hierarchical Classifiers Steady-State Policy Synthesis in MDPs His teaching includes graduate courses in random processes and detection theory.

• Robust AI
• Algorithmic Decision-Making
• Reinforcement Learning
• +3 more

Ilija Bogunovic is an Assistant Professor in the Department of Electronic and Electrical Engineering at University College London (UCL). His research focuses on robust AI, algorithmic decision-making, and reinforcement learning, with applications to large language models, safe RL, and human-AI interaction. He leads a team of PhD students and collaborates with institutions like UKAEA and Google DeepMind. His work has been recognized through the Google Research Scholar Program Award and EPSRC New Investigator Award for robust decision-making and RL. Research interests include non-stationary preference optimization, adversarial robustness in decision transformers, and group-robust preference alignment. He advises over a dozen PhD students and actively contributes to conferences like NeurIPS, ICML, and AISTATS. His methods address challenges in distribution shifts, class imbalance, and safe multi-agent systems. Key achievements include developing algorithms like REDUCR for robust data downsampling and AE-LSVI for active reinforcement learning. He organizes reading groups on adaptive experimental design and co-organized NeurIPS workshops on drug discovery and AI alignment.

David WoodruffView profile

Professor

• Algorithms and Complexity
• Machine Learning
• Theoretical Computer Science
• +5 more

David Woodruff is a Professor in the Computer Science Department at Carnegie Mellon University's School of Computer Science. His research focuses on algorithms, complexity theory, machine learning, and theoretical computer science. He is particularly known for contributions to communication complexity, data stream algorithms, graph algorithms, and numerical linear algebra. His work bridges foundational theory with practical applications in privacy, security, and efficient computation. Research Interests: Algorithms and Complexity Machine Learning Data Stream Algorithms Communication Complexity Sketching and Randomized Numerical Linear Algebra Recent publications emphasize adversarial robustness in sketching, space-efficient streaming algorithms for graph problems, and eigenvalue approximation techniques. His work on privacy-preserving GWAS and transformer optimizations showcases interdisciplinary impact. He advises three PhD students and teaches courses in algorithms (15451, 15651, 15851). Advising and Grants: Active mentor to Honghao Lin, Hoai-An Nguyen, and Madhusudhan Pittu. His research has been supported by grants focusing on streaming algorithms, communication complexity, and machine learning foundations.

Ronitt RubinfeldView profile

Professor

• Theory of Computation
• Algorithms
• Randomized Algorithms
• +3 more

Ronitt Rubinfeld is the Edwin Sibley Webster Professor of Electrical Engineering and Computer Science at MIT, affiliated with the Computer Science and Artificial Intelligence Laboratory (CSAIL). She holds a PhD from UC Berkeley (1991) and a BSE in Electrical and Computer Engineering from the University of Michigan. Prior to MIT, she was at Cornell University and NEC Research Laboratories. Her research focuses on randomized and sublinear-time algorithms, exploring how to understand data with minimal sampling. Notable roles include ONR Young Investigator, Sloan Fellow, and Radcliffe Fellow. She advises numerous PhD and master’s students, contributing to fields like algorithmic theory and data science. Education: PhD UC Berkeley (1991), BSE University of Michigan. Affiliations: MIT CSAIL, Theory of Computation Group, Algorithms Group. Research Interests: Sublinear algorithms, randomized algorithms, distribution testing, and complexity theory. Her work bridges theoretical foundations with practical applications in big data analysis. Awards: ONR Young Investigator, Sloan Research Fellowship, Cornell Teaching Award, Radcliffe Fellowship. Advising: Supervised 20+ PhD students (e.g., Funda Ergun, Badih Ghazi) and 10+ master’s students. Current advisees include Amartya Biswas and Ted Pyne. Grants include NSF funding for distribution testing and sublinear algorithms. Labs/Teams: Core member of MIT Theory of Computation group, CSAIL Algorithms Group, and collaborator on projects like WOLA (Workshop on Sublinear Algorithms).

Keith LevinView profile

Assistant Professor

• Network analysis
• Dimension reduction
• Concentration inequalities
• +5 more

Keith Levin is an Assistant Professor in the Department of Statistics at the University of Wisconsin-Madison, part of the School of Computer, Data & Information Sciences. He holds a Ph.D. in Computer Science from Johns Hopkins University (2017) and completed a postdoctoral fellowship at the University of Michigan. His research focuses on network analysis, dimension reduction, and statistical inference in complex systems, with applications to neuroscience and speech processing. Levin teaches courses on computational tools for data science and has developed methodologies for network bootstrapping, latent space modeling, and spectral graph analysis. His work bridges theoretical statistics with practical machine learning challenges, particularly in high-dimensional data settings. Research Interests: - Network analysis and community detection - Dimensionality reduction techniques - Latent space models and graph embeddings - Applications in neuroscience and speech processing - Statistical methods for large-scale data Selected Contributions: - Developed bootstrap methods for network data analysis - Advanced spectral embedding techniques for graph inference - Authored tools for clustering and motif detection in stochastic blockmodels

Christopher MuscoView profile

Assistant Professor

• Algorithmic Foundations of Data Science
• Machine Learning
• Numerical Linear Algebra
• +5 more

Christopher Musco is an Assistant Professor in the Department of Computer Science and Engineering at New York University’s Tandon School of Engineering. His research focuses on the algorithmic foundations of data science and machine learning, integrating theoretical computer science, numerical linear algebra, and optimization. He holds a Ph.D. from MIT and B.S. degrees in Applied Mathematics and Computer Science from Yale University. Education: Ph.D. in Computer Science, MIT B.S. in Applied Mathematics & Computer Science, Yale University Research Interests: Scalable machine learning, numerical linear algebra, randomized algorithms, sketching/streaming methods, and matrix approximation. His work emphasizes efficient processing of large datasets through algorithmic innovation and theoretical analysis. Publications: Over 50 peer-reviewed articles, including contributions to NeurIPS, SODA, COLT, and ICML, focusing on matrix approximation, spectral methods, and graph algorithms. Recent work includes breakthroughs in hierarchical matrix approximation and provably accurate nearest neighbor search. Awards & Funding: NSF CAREER Award Google Research Scholar Award Funding from NSF, DOE, and NYU grants Labs & Groups: Member of NYU’s Theoretical Computer Science Group and Visualization Imaging and Data Analysis Center (VIDA). Active in organizing the CS Theory Seminar and mentoring over 15 Ph.D. students and postdocs.

Mikkel ThorupView profile

Professor

• Algorithms
• Data Structures
• Hash Functions
• +3 more

Mikkel Thorup is a Professor at the Department of Computer Science, University of Copenhagen, and a VILLUM Investigator heading the Basic Algorithms Research Copenhagen (BARC) Center supported by a €5.3 million grant from the VILLUM Foundation. Born in 1965, he holds a D.Phil. from Oxford University (1993). Prior roles include positions at the University of Copenhagen (1993–1998) and AT&T Labs-Research (1998–2013). His research focuses on algorithms, data structures, and hashing, with a particular emphasis on unifying theoretical and practical aspects. Awards include the ACM Fellowship, Villum Kann Rasmussen Award, and Fulkerson Prize. Education: D.Phil. in Computer Science, Oxford University (1993). Earlier positions: University of Copenhagen (1993–1998), AT&T Labs-Research (1998–2013). Research interests encompass algorithm design, data structure optimization, and hashing techniques. His work bridges theory and practice, addressing problems like graph clustering, optimization, and efficient sampling. He is known for contributions to correlation clustering, hash function analysis, and dynamic graph algorithms. Awards include the 2011 MAA Robbins Award, 2015 Villum Kann Rasmussen Award (Denmark’s top research prize), and 2021 AMS-MOS Fulkerson Prize. His Erdős number is 2, and he boasts an H-index above 60 with over 200 publications in top venues like STOC, FOCS, and SODA. Advising and grants: Leads BARC since 2017, funded by VILLUM Foundation. His research has influenced both academia and industry, particularly in telecommunications and algorithmic efficiency. Collaborates internationally, with a focus on foundational algorithmic challenges. Labs/Teams: Founder and head of BARC, fostering research in algorithms and their applications. Engages in interdisciplinary work combining theory with practical implementations.

Yan HuangView profile

Professor

• Spatio-Temporal Databases
• Geo-Stream Data Processing
• Smart Transportation
• +3 more

Yan Huang is a Regents Professor in the Department of Computer Science and Engineering at the University of North Texas (UNT). She holds ACM Distinguished Member and UNT Decker Scholar titles. Her roles include Associate Dean for Research and Graduate Studies (2016-2019), Interim Dean of the College of Engineering (2018), Senior Associate Dean (2019-2021), and Interim Chair of Computer Science and Engineering (2020-2021). She has served as a visiting scholar at Microsoft Research Asia and Fudan University. Education: B.S. in Computer Science from Peking University (1997) and Ph.D. in Computer Science from the University of Minnesota (2003). Research interests focus on spatio-temporal databases, geo-stream data processing, smart transportation, and location-based social networks. Her work is funded by NSF, DoD, Texas Department of Transportation, and Oak Ridge National Lab. Notable achievements include a $3.76M grant from the Army Research Lab (2023). Key awards include the Best Paper Award at IEEE IRI 2023 (student work) and recognition as a Regents Professor (2021). She advises multiple Ph.D. students and oversees a lab (DP F205).

Eli UpfalView profile

Professor

• Algorithms
• Randomized Algorithms
• Probabilistic Analysis
• +5 more

Eli Upfal is the Rush C. Hawkins Professor of Computer Science at Brown University, where he has been a faculty member since 1998 and served as department chair from 2002 to 2007. Prior to Brown, he held positions as a researcher and project manager at IBM Almaden Research Center and a professor at the Weizmann Institute of Science. He earned his undergraduate degree in mathematics and statistics and a doctorate in computer science from the Hebrew University in Jerusalem, Israel. Education: B.Sc./M.Sc. in Mathematics & Statistics, Ph.D. in Computer Science Previous Institutions: IBM Almaden Research Center, Weizmann Institute Upfal's research focuses on the design and analysis of algorithms, particularly randomized algorithms and probabilistic analysis. His work spans combinatorial and stochastic optimization, routing and communication networks, computational biology, and computational finance. He has also contributed to applying Rademacher Averages and VC-dimension to sampling-based algorithms for graph analysis and pattern mining. Recent publications emphasize algorithmic applications in graph theory, machine learning, and handling distribution drift in data analysis. His work includes practical implementations of theoretical concepts, such as Abra for betweenness centrality approximation and frameworks for secure data exploration like VizCertify and VizRec . Scientific Awards IEEE Fellow ACM Fellow IBM Outstanding Innovation Award IBM Research Division Award Upfal has advised PhD student Matteo Riondato and received funding from the National Science Foundation (NSF), Defense Advanced Research Projects Agency (DARPA), Office of Naval Research (ONR), and National Institute of Health (NIH). He co-authored the textbook Probability and Computing: Randomized Algorithms and Probabilistic Analysis with M. Mitzenmacher.

• Data systems
• Knowledge graphs
• Large-scale data analysis
• +4 more

Zoi Kaoudi is an Associate Professor at the IT University of Copenhagen, affiliated with the Data, Systems, and Robotics school and the Data-intensive Systems and Applications department. Her research focuses on advancing data systems, knowledge graphs, and large-scale data analysis. She leads the Rank4QO project (2024–2027), funded by the Carlsberg Foundation, which explores query optimization using ranking algorithms. Her work emphasizes machine learning integration in data management systems, including frameworks like Apache Wayang, which unifies diverse data analytics platforms. Key collaborations include projects with Volkswagen Group and SAP, addressing dynamic graph processing and knowledge graph embeddings. Notable contributions include innovative approaches to parameter management (e.g., Good Intentions ), automated data science pipelines ( DORIAN ), and efficient graph processing algorithms. Her recent publications (2023–2025) highlight advancements in machine learning systems, distributed data processing, and adaptive optimization techniques. Current projects aim to bridge machine learning and data management through frameworks like Wayang and Dorian, with applications in air cargo revenue management and semantic web systems.

Thomas BonaldView profile

Professor

• Data Analysis
• Machine Learning
• Graph Theory
• +4 more

Thomas Bonald is a Professor at Telecom Paris, part of the Institut Polytechnique de Paris, and heads the DIG (Data, Intelligence and Graphs) team within the LTCI laboratory. His research focuses on data analysis, machine learning, graph theory, knowledge bases, and natural language processing. He leads projects like the YAGO knowledge base and develops open-source tools such as scikit-network and torch-kge for graph analysis and knowledge graph embedding. Key achievements include advancing link prediction, knowledge base refinement using LLMs, and contributions to fair resource allocation in networks. His work integrates theoretical insights with practical applications, including enhancing adaptive streaming performance in mobile networks. Scientific awards include recognition of his former students Céline Comte (Telecom Paris award) and Mathieu Feuillet (Gilles Kahn award). Bonald advises numerous PhD students and has supervised over 20 doctoral candidates. His research spans algorithmic fairness, network science, and large-scale data integration. Labs/Teams: DIG team at LTCI, contributing to projects like YAGO and NetSet datasets. His teaching includes probability, statistics, graph learning, and reinforcement learning, with lecture notes on topics like PageRank and spectral embedding.

• Artificial Intelligence
• Automated Planning
• Machine Learning
• +7 more

Dimitris Vrakas is an Assistant Professor at the Department of Informatics, School of Informatics, Aristotle University of Thessaloniki (AUTh). He holds a PhD in Intelligent Planning Systems and has conducted post-doctoral research focusing on Planning for the Semantic Web. His professional experience includes roles as a Lecturer (per Greek law 407/80), Informatics Instructor in multiple educational institutions, and IT consultant for medium-sized enterprises through the Go-online Project. Education: Bachelor of Science (1995–1999): Informatics, Aristotle University of Thessaloniki PhD (2000–2004): Intelligent Planning Systems, Dept. of Informatics, AUTh Postdoctoral Research (2005–2007): Planning for the Semantic Web, AUTh Research Interests: Automated Planning, Heuristic Functions, Intelligent Autonomous Systems, Machine Learning Applications (Energy Management, NILM), Semantic Web Services, and Smart Environment Technologies. His work bridges theoretical AI advancements with practical implementations in energy systems and emergency response. Publications: Over 45 papers (including 15 journal articles) and 5 book chapters, with a focus on energy disaggregation, evacuation algorithms, and semantic web composition. His work demonstrates expertise in applying advanced techniques like GANs for data generation and reinforcement learning for adaptive planning. Awards: 2025 ACM SIGMOD Test-of-Time Award for contributions to time-series clustering Advising & Grants: Active in guiding PhD candidates (as per recent calls) and coordinating projects like the IRIS RISK PARADIGM for industrial safety. His work involves developing smart university platforms for energy monitoring and collaborating on EU-funded initiatives. Labs & Teams: Member of the Intelligent Systems Lab at AUTh, contributing to projects like the BOnSAI ontology for smart buildings and the IRISPortal for risk management. He also coordinates the Hellenic Artificial Intelligence Society and chairs conferences like SETN 2016.

Ola SvenssonView profile

Associate Professor

• Approximation Algorithms
• Combinatorial Optimization
• Computational Complexity
• +3 more

Ola Svensson is an Associate Professor at the School of Computer and Communication Sciences, EPFL. His research focuses on approximation algorithms, combinatorial optimization, computational complexity, and scheduling. He has been supported by grants including the ERC Starting Grant "OptApprox" (2014-2019), SNF grants, and the ERC Consolidator Grant "POTCO" (2023-). He teaches courses such as Advanced Algorithms and Approximation Algorithms and Hardness of Approximation. Education: PhD from IDSIA - Universita della Svizzera italiana (2009) and Master's from Uppsala University (2005). Research Interests: Design and analysis of approximation algorithms for NP-hard problems, scheduling, and computational complexity. He explores limitations of approximation techniques through hardness results and contributes to theoretical computer science. Publications span clustering, scheduling, and graph problems like the Traveling Salesman Problem. Recent work includes learning-augmented algorithms and robust optimization. Awards: I&C teaching award and best paper awards at FOCS (2017) and STOC (2018). Over a dozen PhD students advised, many entering postdocs or industry roles. Labs/Teams: Part of the theory group at EPFL, collaborating on academic projects and course development.