جست‌وجوی استاد و پژوهشگر

• Cryptography
• Secure Multiparty Computation (MPC)
• Zero-Knowledge Proofs
• +۳ مورد دیگر

Aarushi Goel is an Assistant Professor of Computer Science at Purdue University's Department of Computer Science, part of the College of Science. She joined the department in Fall 2024 and is affiliated with Purdue's main campus in West Lafayette, Indiana. Her research focuses on cryptographic protocols, with a strong emphasis on secure multiparty computation (MPC), zero-knowledge proofs, and distributed systems security. Her work addresses challenges in privacy-preserving computation, Byzantine fault tolerance, and efficient cryptographic systems. Notable research directions include optimizing MPC protocols for dynamic environments, minimizing communication overhead in distributed systems, and developing practical zero-knowledge proof frameworks. Recent publications explore topics like homomorphic secret sharing, sublinear prover protocols, and Byzantine agreement with polylogarithmic communication complexity. Dr. Goel's contributions often bridge theoretical cryptography with real-world applications, such as secure machine learning training and verifiable computation. Her work has implications for decentralized systems, blockchain technology, and privacy-enhancing technologies. No awards or grants are explicitly listed in the provided materials, but her prolific publication record indicates active engagement in cutting-edge research. She holds no listed advisees or students in the current data. Lab and team affiliations are not specified in the provided text, though her research likely involves collaborations within Purdue's computer science department and external institutions. Her office is located in LWSN 2142D on the Purdue campus.

Dominik Kempaمشاهده پروفایل

استادیار

• String algorithms
• Data compression
• Compressed data structures
• +۳ مورد دیگر

Dominik Kempa is an Assistant Professor in the Department of Computer Science at Stony Brook University, NY. He previously held postdoctoral positions at Johns Hopkins University (2021), UC Berkeley (2020), and the University of Warwick (2019). His PhD (2015) and MS (Computer Science) are from the University of Helsinki, while his BS (Mathematics) and MS (Computer Science) are from Jagiellonian University, Kraków. Research focuses on string algorithms, data compression, compressed data structures, bioinformatics, and parallel/external-memory algorithms. Notable contributions include compressed indexing techniques for large-scale genomic data and theoretical advancements in LZ77 parsing and suffix array construction. He leads projects like the NSF-funded CAREER initiative on scalable compressed sequence indexing. Key awards include the NSF CAREER Award (2024), Junior Researcher Award, and Outstanding Doctoral Dissertation Award. His work bridges theoretical foundations and practical implementations, with over 30 publications in top venues like STOC, FOCS, and SODA. Education: PhD Computer Science (2015, University of Helsinki), MS Computer Science & BS Mathematics (Jagiellonian University) Selected Awards: NSF CAREER, Junior Researcher Award Professional Activities: Program committee member for STACS, SODA, DCC, and multiple conferences since 2020 Research highlights include resolving the Burrows-Wheeler Transform conjecture (2020) and developing sublinear-time LZ77 factorization (2024).

Aaron Bernsteinمشاهده پروفایل

دانشیار

• Graph Algorithms
• Dynamic Algorithms
• Sublinear Algorithms
• +۲ مورد دیگر

Aaron Bernstein is Charles S. Baylis Associate Professor in Computer Science at NYU Tandon School of Engineering. His research advances theoretical foundations of graph algorithms, with breakthroughs in dynamic algorithms, shortest path problems, and distributed computing. Research contributions: Pioneering work on near-linear time algorithms for shortest paths with negative weights Fundamental advances in dynamic graph algorithms for connectivity and matching Innovative frameworks for distributed load balancing and network optimization Breakthroughs in maximum flow and matching problems Publications demonstrate consistent theoretical innovation with numerous best paper awards at top theory conferences. Recent work resolves long-standing open problems in graph algorithms and establishes new algorithmic paradigms. Current research group includes PhD students working on graph algorithms and distributed computing. Recipient of prestigious awards including Presburger Award and Sloan Research Fellowship. Research funded by NSF CAREER grant and Google Research Scholar award.

Mark Iwenمشاهده پروفایل

استاد

• Computational Harmonic Analysis
• Mathematical Data Science
• Signal Processing
• +۱۲ مورد دیگر

Mark Iwen is a Full Professor with a dual appointment in the Department of Mathematics and the Department of Computational Mathematics, Science and Engineering (CMSE) at Michigan State University. His research bridges theoretical mathematics and computational science, focusing on high-dimensional data analysis, signal processing, and numerical algorithms. Research Interests: His work centers on computational harmonic analysis, mathematical data science, and algorithms for large-scale data. Key areas include sparse spectral methods, phase retrieval, tensor decomposition, compressive sensing, sublinear-time algorithms, and Johnson-Lindenstrauss embeddings. He develops provably accurate and efficient algorithms for problems in high dimensions, such as solving PDEs, function approximation, and manifold learning. Publications and Trends: His recent publications (2023–2025) emphasize sparse and low-rank tensor methods, phase retrieval, and high-dimensional PDE solvers. There is a strong focus on algorithmic efficiency, provable guarantees, and applications in imaging and data science. Many works involve collaborative efforts and include open-source code. Scientific Awards: Scholarship established in honor of Benjamin (Ben) Segal, reflecting his mentorship and academic impact Advising and Grants: He advises numerous students, including undergraduates and graduate researchers, many of whom are co-authors on publications and code repositories. While specific grants are not listed, his sustained research output and collaborative projects suggest active funding. He co-founded the One World MINDS Seminar, demonstrating leadership in the academic community. Labs and Teams: He leads a research group focused on algorithm development for high-dimensional problems, with strong ties to computational mathematics and data science. His team develops and shares code for sparse FFTs, phase retrieval, tensor methods, and PDE solvers, indicating a collaborative and open research culture.

Jara Uittoمشاهده پروفایل

استادیار

• Massively Parallel Computation
• Distributed Computing
• Sublinear Computing
• +۳ مورد دیگر

Jara Uitto is an Assistant Professor in the Department of Computer Science. His research focuses on Massively Parallel Computation, Distributed Computing, and Sublinear Computing, with a particular emphasis on graph algorithms and distributed systems. Research Interests: Massively Parallel Computation (MPC) Distributed Algorithms and Symmetry Breaking Graph Theory and Edge Processing Algorithm Design for Sparse Graphs Approximation and Optimization in Streaming Models Key Project: Massively Parallel Algorithms for Large-Scale Graph Problems (2020-2024) , where he led research on optimizing algorithms for distributed and parallel computing environments. Publications highlight contributions to symmetry breaking, parallel coloring, and approximation algorithms in dynamic streams. His work bridges theoretical foundations with practical distributed computing challenges.

• Algorithms for Big Data
• Parallel and Distributed Systems
• Sublinear-Time Algorithms
• +۴ مورد دیگر

Krzysztof Onak is the Shibulal Family Career Development Assistant Professor in the Faculty of Computing & Data Sciences at Boston University. His research focuses on theoretical foundations of algorithms for big data, including applications in AI, parallel/distributed systems, and sublinear-time algorithms. He holds a PhD from MIT (2010) and a master's from the University of Warsaw (2005). Before academia, he worked at IBM T.J. Watson Research Center and was a Simons Postdoctoral Fellow at CMU. Education PhD in Computer Science, Massachusetts Institute of Technology (2010) Master of Science in Computer Science, University of Warsaw (2005) Bachelor of Science in Mathematics and Computer Science, University of Warsaw (2003-2004) Research Interests Dr. Onak specializes in algorithms for modern computational challenges, including: Sublinear-time algorithms and property testing Streaming and sketching techniques Parallel and distributed algorithms Graph algorithms and optimization Applications to machine learning and data science Recent Work His publications emphasize scalable solutions for large-scale data problems, with contributions to: Efficient parallel graph algorithms (STOC 2020, STOC 2018) Streaming algorithms for dynamic graphs (SODA 2019) Fair clustering methods (ICML 2019) Awards & Recognition Simons Postdoctoral Fellowship (2010-2012) World Champion in ACM ICPC (2003) Gold Medal in International Olympiad in Informatics (2000) Teaching & Service He teaches graduate and undergraduate courses on algorithms for data science and programming. Currently advises 4 PhD students. Active in organizing theory workshops (e.g., WOLA 2022, STOC 2023 PC member). Former research roles include IBM Research Staff Member (2012-2020) and visiting positions at Microsoft Research and Google Research.

Michael Dinitzمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Approximation Algorithms
• Online Algorithms
• +۵ مورد دیگر

Michael Dinitz is an Associate Professor in the Department of Computer Science at Johns Hopkins University with a secondary appointment in the Department of Applied Mathematics and Statistics . He is a member of the Algorithms and Complexity group and the Mathematical Institute for Data Science . His research focuses on Theoretical Computer Science , with emphasis on approximation algorithms , online algorithms , and distributed algorithms , particularly in applications to computer networking , distributed systems , and machine learning . His recent work involves differential privacy , dynamic networks , and resilient network design . His publications cover diverse topics in graph theory , privacy-preserving algorithms , and online optimization , with a focus on practical applications of theoretical results. He has received multiple NSF grants including a CRII award and Algorithms in the Field funding, and was honored with the Professor Joel Dean Excellence in Teaching Award . He advises a research group with PhD, MSE, and undergraduate students , and serves on program committees for leading theory conferences like FOCS , STOC , and ICALP . Education PhD in Computer Science, Carnegie Mellon University (2010), advised by Anupam Gupta AB in Computer Science, Princeton University (2005), advised by Sanjeev Arora Grants & Awards NSF CRII Grant (2015) NSF Algorithms in the Field Grant (2016) Multiple NSF Algorithmic Foundations grants (2019, 2022, 2025) NSF Graduate Research Fellowship (2005-2010) ARCS Foundation Scholarship (2005-2010) Best Paper Awards at ICDCS (2014) and DISC (2017)

• Algorithms
• Computational Complexity
• Graph Theory
• +۳ مورد دیگر

Jacob Holm is a Tenure Track Assistant Professor in the Department of Computer Science at the University of Copenhagen, specializing in the Algorithms and Complexity research section. His work focuses on theoretical computer science with emphasis on graph algorithms and data structures. Dr. Holm's research interests span multiple areas of theoretical computer science: Dynamic graph algorithms, particularly for planar graphs Biconnectivity and triconnectivity in dynamic settings Efficient data structures for graph problems Parallel and distributed algorithms for graph processing Computational geometry and pursuit-evasion problems His publication record shows 25 research outputs including 17 article in proceedings, 6 journal articles, 1 book chapter, and 1 Ph.D. thesis. His work demonstrates consistent contributions to theoretical computer science, with numerous publications in top venues like the ACM-SIAM Symposium on Discrete Algorithms (SODA). Analysis of his recent publications reveals a strong focus on worst-case performance guarantees for dynamic graph problems, particularly in planar graph settings where maintaining efficiency during updates presents significant theoretical challenges. Dr. Holm maintains an active research profile with an ORCID identifier (0000-0001-6997-9251) and collaborates extensively with researchers in the theoretical computer science community, particularly with Eva Rotenberg as evidenced by multiple co-authored publications. His work bridges theoretical computer science with practical applications, developing algorithms that maintain efficiency even as graphs dynamically change.

Artur Czumajمشاهده پروفایل

استاد

• Theoretical Computer Science
• Randomized Algorithms
• Parallel and Distributed Computing
• +۴ مورد دیگر

Artur Czumaj is a Professor of Computer Science and Director of the Center for Discrete Mathematics and its Applications (DIMAP) at the University of Warwick, United Kingdom. He is a member of the Global Faculty at the University of Cologne and serves as President of the European Association for Theoretical Computer Science (EATCS). PhD and Habilitation from University of Paderborn Recipient of the IBM Award and Fellow of EATCS His research focuses on theoretical computer science, particularly in the design of randomized algorithms, analysis of large data, and applications to parallel/distributed computing, property testing, sublinear algorithms, optimization algorithms, and algorithmic game theory. His work has been funded by major institutions including EPSRC, NSF, Royal Society, European Union, Simons Foundation, and IBM. The 15 most recent publications reflect expertise in graph algorithms, distributed systems, approximation techniques, and algorithmic complexity. His research spans foundational algorithm design to practical applications in large-scale data processing. Fellow of the European Association for Theoretical Computer Science (EATCS) IBM Award recipient Czumaj has held leadership roles in prominent conferences, including Program Committee Chair for STOC, ICALP, SODA, and HALG. His academic career is supported by grants from multiple international research councils and organizations.

Leonid Barenboimمشاهده پروفایل

دانشیار

• Distributed Algorithms
• Communication Networks
• Dynamic Algorithms
• +۵ مورد دیگر

Leonid Barenboim is an Associate Professor in the Department of Mathematics and Computer Science at The Open University of Israel. His research focuses on distributed algorithms, communication networks, dynamic algorithms, and graph theory, with applications in big data analysis. Prior to his current position, he was a postdoctoral fellow at the Simons Institute for the Theory of Computing at UC Berkeley and I-CORE Israel at Weizmann Institute, hosted by Prof. David Peleg. He completed his Ph.D. in Computer Science at Ben-Gurion University of the Negev under Prof. Michael Elkin's supervision. His research interests include distributed computing, sublinear-time algorithms, network security, and the design of efficient algorithms for dynamic and faulty networks. He has authored influential works such as the book Distributed Graph Coloring: Fundamentals and Recent Developments (2013) and numerous journal and conference papers in top venues like the Journal of the ACM, SIAM Journal on Computing, and PODC. Notable awards include the PODC 2018 Best Paper Award for work on distributed backup placement and the ICALP 2012 Best Student Paper Award for research on locality in distributed symmetry breaking. His contributions span theoretical foundations and practical applications in distributed systems, with a focus on optimizing synchronization, coloring, and fault-tolerant algorithms. His publications highlight advancements in edge-coloring, vertex-coloring, and self-stabilization in distributed systems, alongside contributions to network security and algorithmic efficiency in dynamic environments. His work often bridges theoretical computer science with real-world distributed computing challenges.

V. Arvindمشاهده پروفایل

استاد

• Computational Complexity Theory
• Structural Complexity
• Randomized Computation
• +۵ مورد دیگر

V. Arvind is a Professor in the Theoretical Computer Science faculty at the Institute of Mathematical Sciences (IMSc) , Chennai. His research is centered on computational complexity theory, with a focus on structural complexity, randomized and algebraic computation, and quantum information and computation. He explores the deep connections between theoretical computer science and mathematics. Institution: Institute of Mathematical Sciences (IMSc), Chennai School: Theoretical Computer Science Academic Rank: Professor Arvind's research interests include computational complexity, structural complexity theory, algebraic computation, derandomization, and quantum computing. He is particularly interested in the interplay between mathematical structures and computation. His work often bridges theoretical computer science with algebra, combinatorics, and logic. His recent publications, primarily expository articles in the EATCS Bulletin’s Computational Complexity Column, cover a wide range of topics such as robust oracle machines, the Alon-Roichman theorem, noncommutative arithmetic circuits, graph isomorphism, and quantum computation. These works reflect trends in foundational complexity theory, algebraic methods in computation, and the exploration of quantum models. The articles emphasize structural insights, lower bounds, and connections to mathematical disciplines. Professional Service and Editorial Roles: Associate Editor, ACM Transactions on Computation Theory Editor, EATCS Computational Complexity Column (since June 2011) Editorial Board Member, International Journal of Computer Mathematics (2009–2013) Co-organizer, ICM Satellite Conference on Algebraic and Probabilistic Aspects of Combinatorics and Computing Program Committee Member for WALCOM 2014, STACS 2012, COCOON 2009, FSTTCS (multiple years, including chair roles), CCC 2006, INDOCRYPT (2002, 2005), and others Teaching: Arvind has taught advanced courses including Computational Complexity, Algorithms, Algebra and Computation, and Discrete Mathematics, often based on foundational texts and notes from leading experts. Lecture notes from his courses have been compiled by students and collaborators. Collaborations: He has an extensive list of co-authors, including prominent researchers such as Manindra Agrawal, Eric Allender, Johannes Köbler, Meena Mahajan, Jacobo Torán, and Ramprasad Saptharishi, indicating strong collaborative research networks in complexity theory and algorithms.

Graham Cormodeمشاهده پروفایل

استاد

• Data Sketching
• Streaming Algorithms
• Differential Privacy
• +۷ مورد دیگر

Graham Cormode is a Professor in the Department of Computer Science at the University of Warwick. He has held research positions at Bell Laboratories and AT&T Labs-Research and leads major research projects in algorithms for big data. He is affiliated with the Centre for Discrete Mathematics and its Applications (DIMAP), the Warwick Institute for the Science of Cities (WISC), and the Alan Turing Institute (ATI). His research focuses on the data lifecycle , particularly data sketching , streaming algorithms , and privacy-preserving data analysis . He develops efficient summaries for massive datasets, enabling scalable analytics under constraints of space, time, and privacy. His work spans theoretical algorithm design and practical deployment, especially in differential privacy and distributed systems. His recent publications show a strong trend in private data release , quantile estimation , and interactive proofs for outsourced computation. He frequently publishes in top venues like PODS, VLDB, and STOC, demonstrating sustained impact in database theory and algorithms. Adams Prize, University of Cambridge (2017–2022) Royal Society Wolfson Research Merit Award - Small Summaries for Big Data (2014–2019) Cormode supervises PhD students and postdoctoral researchers, including Pavel Veselý and Michael Shekelyan. He has secured substantial funding from EPSRC, ERC, GCHQ, and industry partners like Microsoft and AT&T. His projects include Small Summaries for Big Data (ERC), FAIR (EPSRC), and ComPaTrIoTs . He is actively involved in organizing workshops and giving keynote talks, such as at PODC and DISC. He leads a research group focused on algorithms for big data , particularly within the ERC-funded “Small Summaries for Big Data” project. The team works on sketching, streaming, and privacy-preserving techniques, often in collaboration with DIMAP, WISC, and the Alan Turing Institute.

Sampath Kannanمشاهده پروفایل

استاد

• Algorithmic Fairness
• Combinatorial Algorithms
• Program Reliability
• +۳ مورد دیگر

Sampath Kannan is the Henry Salvatori Professor in the Department of Computer and Information Science at the University of Pennsylvania's School of Engineering and Applied Science. His research spans Algorithmic Fairness Combinatorial Algorithms Program Reliability Streaming Computation Computational Biology . His work focuses on theoretical computer science and its applications, particularly in ensuring correctness through program analysis, developing algorithms for massive data sets, and addressing bioinformatics challenges like evolutionary tree reconstruction. Recent publications highlight interdisciplinary efforts in oncology (2025) , game theory (2024) , and fairness in machine learning (2023) . Notable awards include Fellow of the AAAS (2019) Test of Time Award, Runtime Verification (2019) Penn India Research and Engagement Fund (2018) Fellow of the ACM (2013) ACM SIGACT Distinguished Service Award (2012) . He has also received advising recognitions like the Outstanding Faculty Advising Award (2005) and Ford Foundation 'Best Advisor' (2004). He has advised PhD students Eshwar Ram Arunachaleswaran Yiqiao Bao Anubhav Baweja and taught courses including CIS 677 - Advanced Topics in Algorithms & Complexity and GCB 537 - Advanced Computational Biology .

Andrew McGregorمشاهده پروفایل

استاد

• Theoretical Computer Science
• Algorithms
• Data Streams
• +۵ مورد دیگر

Andrew McGregor is a Professor in the Department of Computer Science at the University of Massachusetts Amherst, affiliated with the Manning College of Information and Computer Sciences. He is a core member of the Theory Group and directs the TRIPODS Institute for Theoretical Foundations of Data Science. His research focuses on algorithms for massive data sets, data streams, and information theory, with applications in clustering, approximation algorithms, and coding theory. Education: PhD, Computer Science, University of Pennsylvania (2007) MEng, Computer Science, University of Pennsylvania (2002) BA, Mathematics, University of Cambridge (2000) Research Interests: Design and analysis of algorithms for data streams and massive datasets Clustering algorithms and approximation techniques Coding theory and information-theoretic approaches Algorithmic fairness and combinatorial optimization Recent Article Trends: Advances in streaming algorithms for dynamic graphs and parameterized complexity Graph reconstruction and allocation problems under noisy conditions Quantum communication and scheduling policies for satellite networks Entropy estimation and probabilistic methods in data analysis Awards: NSF CAREER Award (2010) ACM PODS Test-of-Time Award (2020) Lilly Teaching Fellowship (2012) Outstanding Teacher Award (2016) Grants & Labs: Director, TRIPODS Institute for Theoretical Foundations of Data Science Collaborations with Microsoft Research and UCSD's Information Theory & Applications Center Active in Center for Data Science and Theoretical Computer Science Group Future Work: Expanding research on quantum algorithms, scalable causal inference, and fair data diversification techniques.

Mark A Iwenمشاهده پروفایل

دانشیار

• Computational Harmonic Analysis
• Mathematical Data Science
• Signal Processing
• +۴ مورد دیگر

Mark A Iwen is an Associate Professor at Michigan State University, holding dual appointments in the Department of Mathematics and the Department of Computational Mathematics, Science and Engineering (CMSE) . His research focuses on computational harmonic analysis, mathematical data science, signal processing, and algorithms for analyzing high-dimensional datasets. He has contributed to advancements in sparse Fourier transforms, compressive sensing, and sublinear-time algorithms for large-scale data. Key research themes include: Efficient algorithms for high-dimensional PDEs and spectral methods Phase retrieval and inverse problems in imaging Optimization of tensor decompositions and dimensionality reduction techniques Development of sparse approximation methods with theoretical guarantees His recent work emphasizes applications in: Sublinear-time algorithms for function approximation Terminal embeddings for manifold data Fast JL embeddings with bi-Lipschitz properties Tensor completion and low-rank approximations Notable contributions include: Development of Sparse Harmonic Transforms for functions of many variables Advancements in distributed SVD algorithms for large networks Empirical and theoretical analysis of phase retrieval techniques Efficient sparse FFT implementations (e.g., DMSFT, GFFT) Iwen collaborates on open-source code projects, including sparse FFT libraries and phase retrieval tools. His work bridges mathematical theory with practical applications in engineering and computational science.