Find Academic Supervisors

• Network Science
• Machine Learning
• Theoretical Computer Science
• +5 more

Jennifer Chayes is Dean of the College of Computing, Data Science, and Society and a Professor at the University of California, Berkeley, with appointments in Electrical Engineering and Computer Sciences, Mathematics, Statistics, and Information. She co-founded Microsoft Research New England, New York City, and Montreal, leading interdisciplinary research for 23 years before joining Berkeley in 2020. Previously, she was a Professor of Mathematics at UCLA, where she received the Distinguished Teaching Award. Education PhD in Mathematical Physics (1983), Princeton University BA in Biology and Physics (1979), Wesleyan University Her research spans network science , machine learning , and theoretical computer science , focusing on phase transitions in networks, graphons for large-scale network modeling, and applications in cancer immunotherapy , ethical AI , and climate change . Her work on graph limits and exchangeable graphs has foundational implications for network analysis. Recent publications highlight trends in sparse graph theory , privacy-preserving algorithms , and fairness in AI . Awards include the Anita Borg Institute Women of Vision Leadership Award (2012), SIAM John von Neumann Lecture Prize (2015), and ACM Distinguished Service Award (2020). She is a member of the National Academy of Sciences and a Fellow of multiple academic societies. Chayes actively promotes Diversity in STEM and serves on advisory boards for institutions like MIT’s Schwarzman College of Computing, the Howard Hughes Medical Institute, and the National Science Foundation’s Institute for AI and Fundamental Interactions.

Manos AthanassoulisView profile

Associate Professor

• Data Systems
• Cloud Data Management
• Database Systems
• +7 more

Manos Athanassoulis is an Associate Professor in the Department of Computer Science at the College of Arts and Sciences, Boston University. He is the Founder and Director of the BU Data-intensive Systems and Computing (DiSC) lab and a member of the BU MiDAS group. His research focuses on data systems, particularly cloud data management, hybrid transactional/analytical workloads, and integration with emerging hardware such as non-volatile memory and heterogeneous computing. His educational background includes a PhD from EPFL (2014), an MSc in Computer Systems Technology, and a BSc in Informatics and Telecommunications from the University of Athens, Greece. Prior to BU, he was a Postdoctoral Researcher and Research Associate at Harvard University, supported by a SNSF Postdoc Mobility Fellowship. His research interests span data systems, database architectures, LSM trees, indexing, storage systems, and performance optimization. He explores how novel hardware can be leveraged to improve data management efficiency and scalability, especially in cloud environments. His recent publications (2021–2025) predominantly focus on LSM trees, covering topics such as compaction policies, Bloom filter tuning, DPU offloading, adversarial resilience, and sustainable caching. Earlier works include foundational contributions on access methods (RUM Conjecture) and optimal key-value stores (Monkey). The trend shows a consistent focus on data system efficiency, adaptability, and robustness under varying workloads and hardware constraints. Scientific Awards: NSF CAREER Award (2022) Facebook Faculty Research Award (2020) NSF CRII Award (2019) Best of VLDB 2017 and Best of SIGMOD 2017 SIGMOD Most Reproducible Paper Award (2017) Multiple ACM SIGMOD Distinguished PC Member recognitions (2018–2025) VLDB 2023 Best Demo Award RedHat Collaboratory Research Incubation Awards (multiple, 2021–2023) SNSF Postdoc Mobility Fellowship (2015–16) IBM PhD Fellowship (2011–12) Dr. Athanassoulis has advised numerous students and collaborators, evident from his co-authorship on works with researchers such as Niv Dayan, Stratos Idreos, and A. Ailamaki. His grants include major awards from NSF, Facebook, and RedHat, supporting research in robust data systems, hardware-software co-design, and learned cost models. He has also been recognized for teaching excellence at Harvard University. He leads the DiSC lab at Boston University, which focuses on data-intensive computing and systems research. The lab explores next-generation data architectures, particularly in cloud and hardware-aware environments. Collaborations with the BU MiDAS group enhance interdisciplinary research in data science and AI.

• Theoretical Computer Science
• Design and Analysis of Algorithms
• Discrete Optimization
• +7 more

Nikhil Bansal holds the prestigious Patrick C. Fischer Professorship of Theoretical Computer Science in the Department of Computer Science & Engineering at the University of Michigan's College of Engineering. His research program has established him as a leading figure in theoretical computer science, with significant contributions to algorithm design and analysis, particularly in discrete optimization problems. Bansal's research focuses on theoretical computer science with emphasis on design and analysis of algorithms for discrete optimization problems. His work spans multiple areas including discrepancy theory, approximation algorithms, randomized algorithms, combinatorial optimization, complexity theory, machine learning theory, and probability. He has made significant contributions to understanding the limits of approximation algorithms and developing novel techniques for combinatorial optimization problems. Analysis of Bansal's recent publications reveals a strong focus on discrepancy theory, online algorithms, and combinatorial optimization. His work often bridges theoretical computer science with discrete mathematics and probability theory. A recurring theme across his publications is the development of novel algorithmic techniques for solving NP-hard problems with provable guarantees. His research has evolved from foundational work in approximation algorithms to more recent contributions in quantum computing complexity and stochastic optimization. Patrick C. Fischer Professor of Theoretical Computer Science Bansal has advised numerous PhD students including Marek Elias, Shashwat Garg, and Greg Koumoutsous, as well as mentoring several postdoctoral researchers. He has served on editorial boards for top journals including Journal of the ACM, Theory of Computing, and Stochastic Models, and has been active on program committees for major conferences such as STOC, FOCS, SODA, and ICALP, including serving as chair for ICALP 2021. Bansal has organized multiple academic workshops including the STOC 2020 Workshop on Recent Advances in Discrepancy and Applications, several SDP Days at CWI Amsterdam, and the Semester on Bridging Continuous and Discrete Optimization at UC Berkeley in Fall 2017.

Nikhil BansalView profile

Professor

• Theoretical Computer Science
• Design and Analysis of Algorithms
• Discrete Mathematics
• +3 more

Nikhil Bansal is a Professor in Theoretical Computer Science at the University of Michigan, Ann Arbor. He earned his PhD from Carnegie Mellon University and previously worked at IBM Research, TU Eindhoven, and CWI Amsterdam. His research focuses on algorithm design, discrepancy theory, and combinatorial optimization. Education: PhD, Carnegie Mellon University Bansal's work bridges classical and quantum computing, with recent publications exploring k -Forrelation, vector balancing, and stochastic scheduling. His algorithmic approaches often combine geometric insights and probabilistic methods. Scientific Awards: Patrick C. Fischer Professor of Theoretical Computer Science NSF Career Award (2023) He has advised numerous PhD and postdoctoral researchers, including Marek Elias, Shashwat Garg, and Makrand Sinha. Bansal actively contributes to program committees (ICALP 2021, STOC 2020, FOCS 2018) and organizes workshops on discrepancy theory and optimization.

Ciriaco D'AmbrosioView profile

Researcher

• Combinatorial Optimization
• Heuristics
• Metaheuristics
• +4 more

Ciriaco D'Ambrosio is a Research Fellow at the Department of Mathematics, University of Salerno, specializing in combinatorial optimization and its applications to wireless sensor networks. He teaches courses in operations research and maintains regular reception hours for students on Tuesdays (3:00-5:00 PM) and Wednesdays (4:00-5:00 PM), conducted both in-person and via Microsoft Teams. Education: PhD in Computer Science, University of Salerno (2015) - Thesis: models and algorithms for coverage in Wireless Sensor Network Laurea cum laude in Computer Science, University of Salerno (2011) Research Focus: D'Ambrosio specializes in combinatorial optimization, developing heuristics, metaheuristics, and math-heuristics for mixed integer linear programming problems. His work addresses challenging optimization problems in wireless sensor networks, particularly network lifetime maximization under coverage, connectivity, and interference constraints. His research bridges theoretical computer science with practical applications in sensor network design, seismic monitoring systems, and resource allocation problems. His methodology often combines exact approaches with sophisticated heuristic techniques to solve computationally difficult problems. Publication Trends: Analysis of D'Ambrosio's publications (2017-2025) reveals a progression from foundational work on sensor network lifetime problems toward increasingly sophisticated algorithmic approaches for combinatorial optimization. His recent work shows expansion into seismic monitoring applications while maintaining strong focus on knapsack problem variants and network optimization. His publications appear in high-quality journals including Soft Computing, Computers & Operations Research, and Networks, demonstrating both theoretical rigor and practical relevance of his research. Professional Activities: Member of the Italian Operations Research Society (AIRO) Associate Editor for Soft Computing, A Fusion of Foundations, Methodologies and Applications Active collaborator with researchers including Andrea Raiconi, Raffaele Cerulli, and Francesco Carrabs Research Infrastructure: D'Ambrosio works within the Department of Mathematics at University of Salerno's Fisciano Campus (Building F2, Room 040). His research contributes to the university's growing expertise in computational optimization and has practical applications in environmental monitoring systems like SEISMONOISY.

Shi LiView profile

Professor

• Theoretical Computer Science
• Design and Analysis of Algorithms
• Combinatorial Optimization
• +11 more

Shi Li is a Professor in the Theory Group at the Department of Computer Science and Technology, School of Computer Science, Nanjing University. He previously held faculty positions at the University at Buffalo (2015–2023) as Assistant and Associate Professor, and was a Research Assistant Professor at Toyota Technological Institute at Chicago (2013–2015). He earned his Ph.D. from Princeton University in 2014 under Moses Charikar and completed his B.S. in Computer Science and Technology at Tsinghua University, where he was part of Andrew Chi-Chih Yao’s Special Pilot Class. His research lies at the intersection of theoretical computer science and combinatorial optimization, with a focus on the design and analysis of algorithms for problems in clustering, scheduling, network design, facility location, and online algorithms. He also explores learning-augmented algorithms and differential privacy in algorithmic contexts. His work combines deep theoretical insights with practical algorithmic frameworks, often leveraging linear programming relaxations, iterative rounding, and randomized techniques. The recent publications highlight a consistent trend in approximation algorithms, particularly in clustering (e.g., correlation clustering, fair k-set selection), scheduling (e.g., unrelated machine scheduling, load balancing), and robust optimization. His work frequently appears in top-tier theoretical venues such as STOC, FOCS, SODA, and ICALP, with increasing emphasis on fairness, privacy, and efficiency in algorithm design. Best Paper Award of Track A, ICALP 2011 Co-winner of Best Paper Award, FOCS 2012 Invited to Special Issue of SICOMP (FOCS 2017 paper) Best Paper Award, COCOON 2018 Invited to Special Issue of SICOMP (STOC 2019 paper) Best Paper Award of Track A, ICALP 2024 Outstanding Paper Award, SPAA 2024 Shi Li has advised several PhD and master’s students, including Yuda Feng, Han Dai, Zihao Liang, and Jia Ye, and has mentored postdoctoral researcher Ruilong Zhang. He has served on numerous program committees (e.g., STOC, SODA, ICALP) and is an Editorial Board Member of ACM Transactions on Algorithms . He teaches core algorithm courses such as Design and Analysis of Algorithms and Advanced Algorithms , and actively collaborates with researchers worldwide. His lab focuses on theoretical foundations of efficient and fair algorithm design, with applications in large-scale data analysis and distributed systems.

• Combinatorics
• Algebraic Combinatorics
• Log-Concave Polynomials
• +13 more

Jonathan Leake is an Assistant Professor in the Department of Combinatorics and Optimization at the University of Waterloo. His research lies at the intersection of combinatorics, optimization, and theoretical computer science, with a focus on log-concave and Lorentzian polynomials and their applications in discrete and continuous settings. Assistant Professor, University of Waterloo (2022–present) Dirichlet Postdoctoral Fellow, TU Berlin (2020–2022) Postdoctoral Fellow, Institut Mittag-Leffler, Stockholm (Spring 2020) Postdoctoral Fellow, KTH, Stockholm (Fall 2019) James H. Simons Fellow, Simons Institute, UC Berkeley (Spring 2019) His research explores the deep connections between algebraic structures and combinatorial phenomena, particularly through polynomial capacity and Lorentzian polynomials. He applies these tools to problems in optimization, sampling, and representation theory. His work often involves developing new algebraic and analytic techniques to tackle longstanding conjectures and algorithmic challenges. The recent publications highlight a consistent focus on Lorentzian polynomials, capacity bounds, and their applications in combinatorics, optimization, and theoretical computer science. Key themes include matroid theory, log-concavity, sampling algorithms, volume approximation, and connections to Lie theory and representation theory. The research spans both theoretical developments and algorithmic applications, often in collaboration with leading researchers in the field. Dirichlet Postdoctoral Fellowship, TU Berlin Postdoc Fellowship in Algebraic and Enumerative Combinatorics, Institut Mittag-Leffler James H. Simons Fellowship, Simons Institute, UC Berkeley Jonathan Leake has advised or collaborated with several researchers, though formal advisees are not listed in the provided text. His work has been supported by prestigious fellowships and collaborations with institutions such as the Simons Institute and TU Berlin. He has taught courses including CO 250: Introduction to Optimization, MATH 239: Introduction to Combinatorics, and CO 739: Lorentzian Polynomials at the University of Waterloo and TU Berlin. While specific lab or research group names are not mentioned, Leake's collaborative work with researchers like Petter Brändén, Nisheeth Vishnoi, and Leonid Gurvits suggests active participation in research teams focused on algebraic combinatorics, optimization, and theoretical computer science. His publicly shared code for sampling from HCIZ densities and verifying positivity in Lie-theoretic contexts indicates an active computational research component.

Christian TominskiView profile

Professor

• Data Visualization
• Visual Analytics
• Human-Computer Interaction
• +5 more

Christian Tominski serves as an apl. Professor (non-tenured) at the University of Rostock, holding the außerplanmäßige Professur for Human-Data Interaction within the Institute for Visual and Analytic Computing. His academic work spans teaching in Visual Computing and Computer Science programs, with active research contributions in data visualization and visual analytics. His research focuses on multi-variate data visualization, time-series and geo-visualization, graph visualization, and coordinated multiple views. He investigates interaction techniques including interactive lenses, visual comparison, navigation, and guidance mechanisms, alongside computational aspects such as efficient algorithms and asynchronous processing for visualization systems. Recent work emphasizes task-driven approaches and analytic support for interactive exploration. Analysis of his publication trends reveals strong emphasis on visual analytics for complex data structures, particularly in process mining and multivariate graphs. His work consistently explores guidance frameworks, progressive computation models, and novel interaction paradigms for large high-resolution displays, bridging theoretical foundations with practical applications in visual data analysis. Tominski holds professional roles as a member of the Faculty Council of IEF and the System Technical Group of Computer Science Institutes at the University of Rostock. He actively participates in the Informatik-Forum Rostock (INFO.RO), contributing to the regional computer science community through collaborative initiatives and knowledge sharing.

Hedyeh BeyhaghiView profile

Assistant Professor

• Algorithmic Game Theory
• Mechanism Design
• Machine Learning Theory
• +3 more

Hedyeh Beyhaghi is an Assistant Professor in the Department of Computer Science at the University of Massachusetts Amherst , affiliated with the Manning College of Information and Computer Sciences. She holds a PhD in Computer Science from Cornell University and completed postdoctoral research at the Toyota Technological Institute at Chicago , Northwestern University , and Carnegie Mellon University . Research Interests : Her work focuses on algorithmic game theory , mechanism design , machine learning theory , and algorithms under uncertainty . She investigates strategic agent behavior, fairness in algorithmic systems, revenue maximization in auctions, and optimization under stochastic constraints. Recent Publications address topics like the Strategic Perceptron , Pandora’s Box Problem , and Fair Incentive Design , reflecting trends in strategic learning , multi-agent optimization , and fairness-aware algorithms . These studies often intersect economics , machine learning , and theoretical computer science . Teaching : She teaches COMPSCI 611 - Advanced Algorithms , covering randomized algorithms, approximation techniques, and computational complexity. Weekly quizzes and biweekly assignments emphasize collaboration policies and academic integrity in algorithm design. PhD Advisee : Amirmahdi Mirfakhar. No scientific awards are currently documented.

Vijay K GargView profile

Professor

• Distributed Algorithms
• Fault Tolerance
• Multicore Computing
• +7 more

Vijay K Garg is a Professor at the University of Texas at Austin , holding positions in both the Department of Electrical and Computer Engineering and the Department of Computer Sciences . He serves as the Director of the Parallel and Distributed Systems Laboratory and has made significant contributions to distributed computing. Ph.D. in Electrical Engineering and Computer Science from University of California at Berkeley (1988) M.S. in Electrical Engineering and Computer Science from University of California at Berkeley (1985) B.Tech. in Computer Science & Engineering from Indian Institute of Technology, Kanpur (1984) His research focuses on Distributed Algorithms , Fault Tolerance , Multicore Computing , and Lattice Theory applied to computing problems. He has also contributed to areas like Distributed Debugging , Simulation , and Supervisory Control of Discrete Event Systems . His publications show a consistent focus on applying Lattice Theory to Distributed Systems , with recent work on Predicate Detection algorithms, Lattice Agreement , and applications to problems like Stable Marriage and Matching . His work bridges theoretical computer science with practical implementations in distributed systems. Among his scientific awards are: IEEE Fellow (2004) UT Outstanding Inventor (2011) Best Paper Award at 17th International Conference on Runtime Verification (RV'2017) Best Paper Award at 12th International Symposium on Stabilization, Safety, and Security of Distributed Systems (2010) Dean’s Fellowship at UT Austin (2002) Faculty Research Award at UT Austin (2000) Vijay has served on numerous program committees including ICDCS 2022 , SRDS 2021 , ICDCN 2020 , and DISC 2019 . He has also delivered keynote and invited talks at major conferences.

Mauricio BustamanteView profile

Assistant Professor

• Astroparticle physics
• Neutrino physics
• Ultra-high-energy astrophysical neutrinos and cosmic rays
• +5 more

Mauricio Bustamante is an Assistant Professor at the Niels Bohr Institute , University of Copenhagen, specializing in theoretical high-energy astrophysics, astroparticle physics, and neutrino phenomenology. His research bridges cosmic phenomena with fundamental particle physics, focusing on ultra-high-energy neutrinos, cosmic rays, gamma-ray bursts, and new physics beyond the Standard Model. PhD in Physics (2012-2014) M.Sc. in Physics (2007-2010) B.Sc. in Physics (2001-2006) His work explores neutrino oscillations, self-interactions, and decay in extreme astrophysical environments. He contributes to major international collaborations like GRAND (Giant Radio Array for Neutrino Detection) and IceCube-Gen2, developing simulation pipelines and forecasting detection methods for EeV-scale neutrinos. Recent publications highlight energy-dependent flavor transitions, Lorentz invariance testing, and constraints on long-range neutrino interactions via DUNE and T2HK experiments. He actively participates in peer review for journals such as Physical Review D , Physical Review Letters , and Astrophysical Journal , and has attended conferences like TeV Particle Astrophysics (2017). His research emphasizes detector design, cosmic ray reconstruction via graph neural networks, and multi-messenger astronomy.

Rolf WankaView profile

Professor

• Algorithms
• Combinatorial Optimization
• Swarm Intelligence
• +3 more

Prof. Dr. Rolf Wanka is a Professor at the Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), specializing in efficient algorithms and combinatorial optimization. His research focuses on swarm intelligence, discrete optimization algorithms, and scheduling problems, particularly in timetabling and robotics applications. Education : Sc.D. (Dr. rer. nat.) in Computer Science His work includes theoretical and experimental analyses of particle swarm optimization (PSO) algorithms, addressing runtime complexity, stagnation behavior, and convergence properties. He has developed novel heuristics for timetabling and sorting problems, with applications in multi-robot systems and medical imaging. Notable collaborations include studies on Markov chain-based PSO and fairness in academic scheduling. Key trends in his recent publications span swarm intelligence , discrete optimization , and scheduling heuristics , with a focus on robust timetabling , runtime analysis , and stochastic algorithm behavior . While no explicit scientific awards are listed, his mentorship in the Max Weber-Programm highlights his advisory role in academia. His publications demonstrate interdisciplinary applications of algorithms in robotics , medical imaging , and parallel computing , leveraging both theoretical rigor and practical experimentation. The full description below provides exhaustive details on his academic contributions and affiliations.

Felix NaumannView profile

Professor

• Data Profiling
• Data Quality
• Data Integration
• +4 more

Professor Felix Naumann is Chair for Information Systems at the Hasso Plattner Institute (HPI) at the University of Potsdam in Germany, where he leads the Information Systems research group. He is also Coordinator for MSc. Data Engineering and for the Data and AI track for MSc. Computer Science, and Speaker of the Research School on Data Science and Engineering. His extensive academic career includes visiting positions at CIRES Centre in Brisbane (2024-2025), SAP's Innovation Center (2020), AT&T Research (2016), and QCRI (2012). Professor Naumann's research focuses on data profiling, data cleansing, data integration, and data quality assessment with over 200 scientific publications. His work spans theoretical foundations and practical applications, with significant contributions to data quality metrics, metadata extraction, and AI-driven data preparation techniques. His research group develops prominent systems like Metanome for data profiling and Metis for data quality assessment. His recent publications demonstrate continued innovation in data management, with a growing intersection between traditional database research and AI applications. The 15 most recent papers show increasing focus on data quality for AI applications (KITQAR), multimodal data analysis (MELArt), and practical data cleaning frameworks that bridge database systems with machine learning pipelines. GI Dissertationspreis 2000 for best computer science PhD thesis IBM Research Division Award, 2002 Distinguished ACM member since 2021 Distinguished Reviewer Award - SIGMOD 2023 Best paper award at EDBT 2024 for Tasheeh paper Professor Naumann has successfully advised over 30 PhD students who now hold prominent positions at institutions like MIT, Google, Snowflake, and universities worldwide. His research has been funded by major grants including DFG Nachwuchsforschergruppe (2003-2008), IBM SUR Grant (2007), and DFG Forschergruppe Stratosphere (2010-2016). He leads the Information Systems research group at HPI, which includes PostDocs, PhD students, and student assistants working on projects like Metanome, Metis, KITQAR, and Janus, focusing on data profiling, quality assessment, and change exploration in data systems.

Pedro ParedesView profile

Lecturer

• Theoretical Computer Science
• Spectral Graph Theory
• Pseudorandomness
• +3 more

Pedro Paredes is a Lecturer in the Department of Computer Science at Princeton University . He completed his PhD in 2022 at Carnegie Mellon University under Ryan O'Donnell , following undergraduate and master's degrees from the University of Porto where he was advised by Pedro Ribeiro . He received the SEAS Excellence in Teaching Award in 2025. Education PhD, Carnegie Mellon University (2022) MSc & BSc, University of Porto (2017) Research Interests Specializes in Theoretical Computer Science , particularly Spectral Graph Theory , Pseudorandomness , Coding Theory , and Quantum Information Theory Contributes to Subgraph Analysis and Network Science through collaborative works Publication Trends Focuses on Expander Graphs and their applications in Quantum Computing , with recent works on Quantum LDPC Codes and Approximate Unitary Designs Develops algorithms for Spectral Graph Operations and Graph Expansion with mathematical rigor Engages in Interdisciplinary Research connecting Time Series Analysis with network theory Awards SEAS Excellence in Teaching Award (2025) Teaching & Outreach Teaches Algorithms and Data Structures at Princeton Organizes Competitive Programming Club at Princeton Active in Computer Science Education and Math Olympiads

Andrea SimonettoView profile

Research Professor

• Optimization
• Control Theory
• Quantum Computing
• +5 more

Andrea Simonetto is a Research Professor at the Applied Mathematics Unit (UMA) , ENSTA Paris, Institut Polytechnique de Paris. His work spans optimization, control theory, and learning algorithms for large-scale and streaming data , with applications in smart grids, intelligent transportation, personalized health, and quantum computing. Current research focuses on online algorithms for time-varying optimization , personalized optimization for cyber-physical systems , and variational quantum algorithms . Past contributions include theoretical and algorithmic advances in convex/non-convex optimization, distributed optimization (robotic networks, smart grids), and signal processing for sparse reconstructions and parallel computing in particle filtering. Key application domains include renewable energy integration , quantum state preparation , and human-in-the-loop control systems . His research is published in journals like ACM Transactions on Quantum Computing , IEEE Control Systems Letters , and Automatica .