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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.

Laura GrigoriView profile

Professor

• High-Performance Scientific Computing
• Numerical Linear Algebra
• Communication-Avoiding Algorithms
• +6 more

Laura Grigori is a Professor of Applied and Computational Mathematics at EPFL and Head of the Laboratory for Simulation and Modeling at the Paul Scherrer Institute (PSI), Switzerland. She holds a Ph.D. in Computer Science from Université Henri Poincaré (2001) and has been a leading figure in high-performance numerical algorithms and communication-avoiding methods. Her research focuses on numerical linear algebra, randomized algorithms, and scalable scientific computing for applications in astrophysics, molecular simulations, and exascale systems. Affiliations: EPFL Chair of High-Performance Numerical Algorithms & Simulation; Head, PSI Laboratory for Simulation and Modeling. Education: Ph.D. in Computer Science, Université Henri Poincaré (2001); Postdoctoral Researcher, UC Berkeley/LBNL (2001–2003); Senior Researcher at INRIA (2004–2023). Research Interests: Communication-avoiding algorithms, randomized numerical linear algebra, parallel preconditioners, tensor computations, and exascale computing challenges. Awards: SIAM Fellow (2020), ERC Synergy Grant (2018), SIAM Supercomputing Career Prize (2024), and Best Paper Awards (2016, 2023). Grants & Leadership: PI of the ERC Synergy Project EMC² (Extreme-scale Computational Chemistry); Chair of PRACE Scientific Steering Committee (2021–2022); SIAM SIAG Supercomputing Chair (2016–2017). Labs/Teams: Alpines Group (INRIA/Sorbonne University), EMC² Collaboration (Cancès, Maday, Piquemal).

Michael KapralovView profile

Associate Professor

• Theoretical Computer Science
• Sublinear Algorithms
• Big Data Analysis
• +9 more

Michael Kapralov is an Associate Professor in the School of Computer and Communication Sciences at École Polytechnique Fédérale de Lausanne (EPFL) , where he leads research at the Theory Group . His work spans multiple departments including the Laboratory of Theory of Computation 4 and the Doctoral Program in Computer Science and Communications . Kapralov's research focuses on theoretical computer science , particularly sublinear algorithms for big data analysis , with applications in streaming , sketching , sparse recovery , and Fourier sampling . University: EPFL School: School of Computer and Communication Sciences Department: Theory Group Academic Rank: Associate Professor Education: Kapralov earned his Ph.D. in Computer Science from Stanford iCME under the supervision of Ashish Goel . He subsequently held postdoctoral positions at the Mit CSAIL Theory of Computation Group with Piotr Indyk and as a Herman Goldstine Postdoctoral Fellow at IBM T. J. Watson Research Center . Ph.D.: Stanford iCME (2012), advisor: Ashish Goel Postdoctoral: MIT CSAIL (2012-2014), IBM Watson (2014) Research Interests: Kapralov's work addresses fundamental challenges in processing large-scale data through rigorous mathematical models. His contributions include advancements in sublinear algorithms , streaming complexity , spectral sparsification , sparse Fourier transforms , and differential privacy . He has developed techniques for dimension-independent signal processing , kernel ridge regression , and graph spanners , with theoretical guarantees and practical implications for machine learning and data analysis. Scientific Awards: Kapralov received the ERC Starting Grant SUBLINEAR (2018-2023) and the Gene H. Golub Dissertation Award (2012). Advising: He has supervised numerous Ph.D. students and postdoctoral researchers, including Ekaterina Kochetkova , Grzegorz Gluch , Kshiteej Sheth , and Amir Zandieh , many of whom have taken academic or industry positions at institutions like UC Berkeley, National University of Singapore, and Google Zurich. Collaborations and Teaching: Kapralov co-organizes the Turing Course for high school students, leads the Reading Group on Foundations of Deep Learning , and contributes to academic initiatives such as Theory Coffee and the Swiss Winter School on Theoretical Computer Science . He teaches courses like Sublinear Algorithms for Big Data Analysis and Algorithms II , focusing on advanced algorithm design and analysis.

• Theoretical Computer Science
• Combinatorics
• Discrete Mathematics
• +5 more

Dr. Heidi Gebauer is a researcher at the ZHAW School of Engineering, specializing in Scientific Computing & Algorithmics. She is based at the Winterthur campus and actively contributes to theoretical computer science and discrete mathematics research. Her research interests span Theoretical Computer Science , Combinatorics , Random Graphs , Algorithm Design , Advice Complexity , and Graph Theory . Her work combines mathematical rigor with computational applications, particularly in algorithmic game theory and combinatorial structures. The recent publications reflect a strong focus on theoretical analysis of algorithms and combinatorial models, with applications in computational complexity and discrete optimization. Her work often appears in high-quality peer-reviewed venues in combinatorics and computing. Dr. Gebauer has contributed to completed research projects including the development of an intelligent scheduling system for a 14-axis laser welding machine and a new end-point controller. These projects suggest applied algorithmic work alongside theoretical contributions. No scientific awards listed in the provided text. She has collaborated with researchers such as Dennis Clemens, Anita Liebenau, Dennis Komm, and others. There is no mention of student supervision or grant leadership in the available information. Dr. Gebauer is part of the research team in the Research Focus Scientific Computing & Algorithmics at ZHAW, which likely involves collaboration on algorithm development, mathematical modeling, and computational problem-solving.

• Machine Learning
• Online Learning
• Computational Game Theory
• +3 more

Andrea Celli is an Assistant Professor in the Department of Computing Sciences at Bocconi University, where he is a member of the theory group and the Bocconi Institute for Data Science and Analytics. He previously spent one year as a postdoctoral researcher with the Core Data Science team at Meta (London). He obtained his Ph.D. in Computer Science from Politecnico di Milano, where he was advised by Nicola Gatti, and was a Visiting Researcher at Carnegie Mellon University working with Tuomas Sandholm. His educational background includes: Ph.D. in Computer Science from Politecnico di Milano, advised by Nicola Gatti Visiting Researcher at Carnegie Mellon University with Tuomas Sandholm Postdoctoral researcher with the Core Data Science team at Meta (London) Andrea Celli's research focuses on problems at the intersection of computer science, machine learning, and economics, with particular interest in computational problems arising from strategic interactions in online settings where learning processes and incentives are fundamentally intertwined. His work spans machine learning theory, online learning algorithms, computational game theory, and algorithmic economics. His research has been published in top venues including NeurIPS, ICML, STOC, and EC, with a best paper award at NeurIPS 2020. Analysis of his recent publications shows a strong focus on online learning algorithms with constraints, bandit problems, game-theoretic aspects of machine learning, and algorithmic game theory. His work often bridges theoretical foundations with practical applications in economics and decision-making systems. Key themes include no-regret learning, Bayesian persuasion, knapsack problems in online settings, and equilibrium computation in extensive-form games. His scientific achievements include: Best paper award at NeurIPS 2020 ERC Starting Grant (2024) for the project PLA-STEER MUR-PRIN grant (2022) Andrea Celli actively mentors students and postdocs, currently supervising Riccardo Poiani, Sarah Sachs, and Martino Bernasconi as postdocs, and Annalisa Barbara, Emanuele Coccia, Davide Drago (now at Bending Spoons), and Antonio Preiti (now at Amazon) as MSc students. His research is supported by competitive grants including an ERC Starting Grant and a MUR-PRIN grant, indicating strong recognition of his work's potential impact. He is also a member of the ELLIS Society, connecting him to a broader European network of excellence in AI research. He is affiliated with the theory group and the Bocconi Institute for Data Science and Analytics at Bocconi University, contributing to a vibrant research environment focused on theoretical foundations of data science and their applications.

• Graph Algorithms
• Theoretical Computer Science
• Approximation Algorithms
• +2 more

Tianyi Zhang is a Researcher at the Professorship for Theoretical Computer Science, ETH Zurich, located at OAT Z 29, Andreasstrasse 5. His research focuses on advancing fundamental algorithms in graph theory, with particular expertise in dynamic graph problems, efficient spanner constructions, edge coloring optimizations, and shortest-path computations. Dr. Zhang develops both theoretical frameworks and practical implementations for complex computational challenges. His core research areas include the design of near-linear and subquadratic time algorithms for graph optimization problems, fault-tolerant network structures, streaming-optimized graph coloring, and geometric graph embeddings. Recent work emphasizes breakthroughs in Vizing's theorem implementations, dynamic set cover deamortization, and space-efficient distance oracles. Dr. Zhang's publications demonstrate consistent innovation in algorithm efficiency for planar graphs, Euclidean spaces, and dynamic network settings. His 2023-2025 articles reveal concentrated efforts on: 1) Optimizing edge coloring through multi-step Vizing chains and streaming adaptations, 2) Enhancing spanner constructions for doubling metrics and planar environments, and 3) Developing failure-resistant path algorithms with improved time/space complexity. These contributions address scalability challenges in large-scale network processing. He collaborates within the Theoretical Computer Science research group at ETH Zurich, contributing to the institution's leadership in algorithmic innovation. No information about awarded grants, supervised students, or educational background is available in the source materials.