جست‌وجوی استاد و پژوهشگر

Tomasz Kociumakaمشاهده پروفایل

استاد

• String Algorithms
• Pattern Matching
• Edit Distance
• +۵ مورد دیگر

Tomasz Kociumaka is a Professor and Group Leader in the Algorithms and Complexity Department at the Max Planck Institute for Informatics in Saarbrücken, Germany. He previously served as a Research Scientist at INSAIT in Sofia, Bulgaria (2024-2025), and as a Postdoctoral Researcher at the Max Planck Institute for Informatics (2022-2024). His academic journey includes postdoctoral positions at the University of California, Berkeley (2020-2022) and Bar-Ilan University, Israel (2019-2020). Dr. Kociumaka received his PhD in Computer Science from the University of Warsaw in 2019, with a thesis titled Efficient Data Structures for Internal Queries in Texts under the supervision of Professor Wojciech Rytter. He also completed both his BSc and MSc in Computer Science at the University of Warsaw between 2009 and 2014. His research focuses on string algorithms , particularly sequence similarity measures , approximate pattern matching , lossless data compression , and text indexing data structures . He approaches string problems from multiple perspectives including fine-grained complexity, dynamic algorithms, streaming, sketching, sublinear algorithms, and quantum computing. His work bridges theoretical computer science with practical applications in text processing and bioinformatics. Analysis of his recent publications reveals a strong emphasis on edit distance algorithms, pattern matching techniques, and data compression methods. His work demonstrates increasing sophistication in handling complex string problems with optimal time and space complexity, with growing interest in quantum computing applications for string processing. Presburger Award (2025) Cor Baayen Award (2021) Witold Lipski Prize (2018) Dr. Kociumaka serves on program committees for major theoretical computer science conferences including STOC, FOCS, SODA, and ICALP. He is also on the editorial board of Information Processing Letters and served as a guest editor for a special issue of SIAM Journal on Computing related to STOC 2024. His professional activities demonstrate significant leadership in the theoretical computer science community, particularly in the subfield of string algorithms and combinatorial pattern matching.

• Data Science
• Artificial Intelligence
• Algorithm Design
• +۷ مورد دیگر

Bojie Shen is a Research Fellow in the Department of Data Science & AI at Monash University. His research lies at the intersection of artificial intelligence, data science, and algorithmic transportation systems, with a focus on efficient and eco-friendly route planning for electric vehicles, public transport, and multi-agent systems. His primary research interests include: Artificial Intelligence and Multi-Agent Path Finding Graph Algorithms and Route Planning Energy-Efficient Navigation for Electric Vehicles Public and Multi-Modal Transport Routing Combinatorial Search and Optimization Sustainable Mobility and Green Logistics The analysis of his recent publications reveals a consistent trajectory in developing scalable, exact, and energy-aware routing algorithms. His work frequently appears in high-impact venues such as IEEE Transactions on Intelligent Transportation Systems and AAAI conferences, demonstrating strong technical rigor and relevance to real-world transportation challenges. A significant portion of his research contributes to UN Sustainable Development Goals related to sustainable cities and climate action. Notable scientific contributions include: Taxonomy and survey of eco-friendly route planning algorithms Efficient public transport routing using transfer connection databases Advancements in multi-agent path finding beyond pairwise constraints Exact and dynamic shortest path computation for electric vehicles Dr. Shen actively collaborates with leading researchers including Muhammad Aamir Cheema, Daniel Harabor, and Peter Stuckey. While no formal advising or grant information is available in the text, his publication volume and co-authorship network suggest active involvement in funded research projects and potential mentorship roles within the department. He is affiliated with a vibrant research group in data science and AI at Monash University, contributing to advancements in intelligent transportation systems and combinatorial search algorithms.

• Algorithms
• Graph Theory
• Dynamic Graph Algorithms
• +۳ مورد دیگر

Gramoz Goranci is an Assistant Professor in the Faculty of Computer Science, specializing in dynamic graph algorithms, optimization, and theoretical computer science. His research focuses on developing efficient algorithms for dynamic graphs, including problems such as shortest paths, facility location, and spectral sparsification. Research Interests: His work spans dynamic algorithms for planar graphs, approximation algorithms, graph sparsification, and optimization techniques. Key areas include incremental and decremental algorithms for network flow, facility location, and clustering problems. He explores the intersection of electrical flows, oblivious routing, and spectral methods to design efficient solutions for modern computational challenges. Publications: Goranci's recent work emphasizes dynamic graph algorithms, with contributions to Euclidean facility location, Steiner tree problems, and spectral hypergraph sparsification. His 2025 papers advance sublinear-time dynamic algorithms for bi-chromatic matching and transitive reduction, while 2024 research includes near-optimal shortest paths in planar graphs and high-dimensional facility location. Professional Contributions: He co-organized the 2024 Austrian Computer Science Day and contributed to the Vienna Graduate School on Computational Optimization (2016-2020). His research is supported by grants focusing on dynamic algorithms and graph sparsification. Labs/Groups: He leads the Theory and Applications of Algorithms research group, focusing on algorithmic foundations with applications in network analysis and optimization.

• Quantum Computing
• Machine Learning
• Algorithms
• +۴ مورد دیگر

Chunhao Wang is an Assistant Professor in the Department of Computer Science and Engineering at an unspecified university. His research focuses on quantum algorithms, optimal control of quantum systems, and their applications in computational chemistry and machine learning. He has active NSF-funded projects on quantum control and continuous-time open quantum systems. Active NSF Grants: FET Small (2023-2026), CAREER (2023-2028) His work spans quantum computing, algorithm design, and solving high-dimensional problems in optimization and statistical mechanics. Recent publications highlight quantum speedups for classical algorithms and efficient simulation of non-Markovian systems. Collaborations include researchers like Li, X. and Wu, X.

Wenting Zhengمشاهده پروفایل

استادیار

• System Security
• Applied Cryptography
• Secure Systems
• +۶ مورد دیگر

Wenting Zheng is an Assistant Professor in the Computer Science Department at Carnegie Mellon University (CMU), with a courtesy appointment in the Electrical and Computer Engineering Department. She is a core faculty member of CMU's CyLab and co-founder of Opaque Systems, a company focused on encrypted distributed analytics. Her research bridges system security and applied cryptography to enable secure computation in cloud environments and privacy-preserving machine learning. Education: Ph.D. in Computer Science from the University of California, Berkeley (RISE Lab, advised by Raluca Ada Popa and Ion Stoica) Bachelor's and Master of Engineering (M.Eng.) from the Massachusetts Institute of Technology (MIT), mentored by Barbara Liskov Her research interests center on building secure systems using practical cryptographic techniques. Key areas include encrypted AI, private information retrieval, secure collaborative computation among distrustful parties, and protocols for democratizing cryptography. She emphasizes combining systems and cryptography to enhance security guarantees and enable novel applications. Article Trends: Her publications span secure multi-party computation, privacy-preserving AI, cloud security, and distributed cryptographic protocols. Topics range from encrypted neural networks to efficient PIR and zero-knowledge proofs, reflecting her focus on practical systems for advanced cryptography. Scientific Awards: Distinguished Paper Award at USENIX Security 2021 Teaching & Advising: At CMU, she has taught courses like Distributed Systems (Fall 2021-2025), Secure Computer Systems (Spring 2023-2025), and Security for Software and Hardware Systems (Spring 2023). She collaborates with Ph.D. students (e.g., Edward Chen, Andrew Park) and has mentored Masters alumni now at Amazon, SpaceX, Meta, and UC Berkeley Ph.D. programs. Labs & Funding: Her work is supported by the National Science Foundation, AWS, Cisco, CMU CyLab, Google, and Samsung. She is affiliated with CMU's CyLab and UC Berkeley's RISE Lab during her Ph.D., and co-founded the DARE diversity program for undergraduate research opportunities.

Hoa Vuمشاهده پروفایل

دانشیار

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

Hoa Vu is an Associate Professor of Computer Science in the Department of Computer Science within the College of Sciences at San Diego State University. Her research spans theoretical computer science with a focus on algorithms for large-scale data processing and analysis. Dr. Vu completed her Ph.D. in Computer Science at UMass, Amherst under the supervision of Andrew McGregor. She subsequently spent a year as a postdoctoral researcher at Boston College working with Hsin-Hao Su. Her academic journey reflects a strong foundation in theoretical computer science with practical applications. Her primary research interests include sketching, streaming, and distributed algorithms, with significant contributions to graph algorithms, approximation algorithms, and parameterized algorithms. Dr. Vu also explores the intersection of machine learning and algorithms with machine learning predictions, developing novel approaches for handling massive datasets efficiently. Her work addresses fundamental challenges in data privacy, network analysis, and computational biology, with applications across multiple domains. Dr. Vu's publication record demonstrates consistent contributions to top-tier venues including SIAM Journal on Discrete Mathematics, Theoretical Computer Science, PODC, ICDT, and PODS. Her recent work shows an evolving research trajectory that increasingly incorporates machine learning predictions into traditional algorithmic frameworks, while maintaining strong theoretical foundations. NSF/TTIC Sub - Theme 3: AI-HARMONY: The National Institute on Strengthening AI for Health, Art, and Market Economy ($200.00) Algorithms and Machine Learning in Data Intensive Models ($200,000.00) New Aspects of Sketching Algorithms ($596,118.00) Dr. Vu has served on thesis committees for numerous graduate students across various research topics including numerical semigroups, streaming algorithms, 5G networks, and image classification. She is an active member of the theoretical computer science community, serving on program committees such as ISAAC 2025, and regularly presenting her research at major international conferences.

Sarah Schefflerمشاهده پروفایل

استادیار

• Cryptography
• Privacy Engineering
• Cybersecurity Policy
• +۳ مورد دیگر

Sarah Scheffler is an Assistant Professor at Carnegie Mellon University, jointly appointed between the School of Computer Science and the College of Engineering. She holds roles in the departments of Software and Societal Systems and Engineering and Public Policy, focusing on applied cryptography and its intersections with policy, law, and societal impact. Her research explores end-to-end encryption, privacy-preserving technologies, and their regulatory challenges, including content moderation, compelled decryption, and autonomous systems. Scheffler earned her Ph.D. in 2021 from Boston University under Mayank Varia, with postdoctoral roles at MIT and Princeton. She advises students in EPP and S3 programs and actively engages in interdisciplinary projects addressing privacy and cybersecurity. Her work emphasizes cryptographic protocols tailored to real-world use cases, such as zero-knowledge proofs, multi-party computation, and privacy engineering. Scheffler frequently contributes to policy discussions on encryption, data protection, and the societal implications of technology. She is a core member of CyLab, CMU's Security and Privacy Institute. Publications highlight advancements in secure computation, privacy-preserving algorithms, and legal-technical analyses. Current research includes privacy in age verification systems and scalable content moderation under encryption. She advocates for robust encryption standards despite regulatory pressures, as seen in her CNN commentary on Apple's UK data protection policies.

Samuel Jaquesمشاهده پروفایل

استادیار

• Cryptography
• Quantum Computing
• Quantum Cryptanalysis
• +۴ مورد دیگر

Samuel Jaques is an Assistant Professor in the Department of Combinatorics and Optimization at the University of Waterloo (since 2023). His research focuses on cryptography (particularly privacy/anonymity) and quantum computing, with notable work on quantum algorithms for cryptanalysis and the Landscapes of Quantum Computing blog. He completed a PhD at the University of Oxford and holds degrees from the University of Waterloo and University of Regina. Education: PhD, University of Oxford (supervised by Simon C. Benjamin and Christophe Petit) MMath, University of Waterloo (co-supervised by Alfred Menezes and Michele Mosca) B.Sc. in Math and Biology, University of Regina Research Interests: Jaques explores quantum computing's impact on cryptography, including post-quantum security, lattice-based algorithms, and quantum error correction. His work critiques quantum RAM assumptions and analyzes resource costs for breaking cryptographic systems like RSA and ECC. Recent projects include accumulator schemes for digital credentials and dynamic group signatures. Awards and Recognition: Best Young Researcher Paper (2019) for quantum claw-finding attacks on SIKE Advising & Grants: Jaques supervises graduate students and postdocs in cryptography/quantum computing. His lab focuses on real-world privacy solutions with a post-quantum lens. Recent advisees include Mojtaba Fadavi (postdoc) and Jérôme Guyot (visiting student). Labs/Teams: Active in the Institute for Quantum Computing (IQC) at Waterloo, collaborating on quantum algorithms and cryptographic security. Maintains open-source projects like the Q# quantum crypto library and surface code cost calculators.

• Theoretical Computer Science
• Combinatorics
• Discrete Mathematics
• +۵ مورد دیگر

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.

• Randomness and Computation
• Algorithms and Optimization
• Complexity Theory
• +۱۲ مورد دیگر

Avi Wigderson is the Herbert H. Maass Professor at the School of Mathematics, Institute for Advanced Study, Princeton, where he leads research in theoretical computer science and discrete mathematics. He organizes the CSDM (Computer Science and Discrete Mathematics) program at IAS, fostering interdisciplinary research and collaboration. Research Interests: His primary research areas include randomness and pseudorandomness, computational complexity, circuit and proof complexity, quantum computation, cryptography, and distributed computing. His work bridges computer science and pure mathematics, particularly through algebra, analysis, and combinatorics. He has pioneered foundational results in derandomization, lower bounds, and the interplay between computation and mathematical structures. The recent publications highlight his continued leadership in advancing algebraic and analytic techniques in complexity theory. Themes include operator scaling, invariant theory, expander graphs, arithmetic circuits, and the asymptotic analysis of tensors and matrix spaces. These works often unify computational problems with deep mathematical frameworks, demonstrating the power of interdisciplinary approaches. Scientific Awards: Edsger W. Dijkstra Prize in Distributed Computing (2023) for the paper 'Completeness Theorems for Non-cryptographic Fault-tolerant Distributed Computing' Advising and Grants: Wigderson has advised numerous PhD students, primarily at the Hebrew University and Princeton, and hosted over 50 postdoctoral researchers at IAS, including many who have become leading figures in theoretical computer science. While specific grants are not listed, his sustained productivity and influence suggest long-term funding from major sources such as the NSF, Simons Foundation, or IAS internal support. Labs and Teams: He leads the CSDM (Computer Science and Discrete Mathematics) group at the Institute for Advanced Study, organizing seminars, workshops, and collaborative research initiatives that bring together mathematicians and computer scientists from around the world.

Yann Strozeckiمشاهده پروفایل

دانشیار

• Enumeration Complexity
• Algorithmic Game Theory
• Simple Stochastic Games
• +۷ مورد دیگر

Yann Strozecki is an Associate Professor (Maître de Conférences HDR) at the University of Versailles Saint-Quentin, where he is based in the DAVID Laboratory and leads the ALMOST research team focused on algorithms and stochastic models. He is currently on a part-time assignment at LIGM, Gustave Eiffel University, and has previously held positions at LIP6 (RO team), Paris-Sud University (ALGO team), and completed a postdoctoral fellowship at the University of Toronto's Theory Group. He earned his PhD from Paris Diderot (Paris 7) under Arnaud Durand. His research lies at the intersection of theoretical computer science and discrete mathematics, with core interests in: Enumeration complexity, especially delay and space constraints Algorithmic game theory, particularly simple stochastic games (SSGs) Graph and matroid algorithms Cheminformatics and molecular structure generation Sparse polynomials and algebraic complexity Analysis of his recent publications reveals a strong trend in developing efficient enumeration algorithms with provable delay and space bounds, advancing the theoretical foundations of output-sensitive computation. He also contributes to practical algorithms for Cloud RAN scheduling and cheminformatics, often combining theoretical rigor with real-world applications. His work on geometric amortization and strategy improvement in SSGs demonstrates innovation in algorithm design. Notable scientific contributions include: Generic strategy improvement methods for SSGs Polynomial-delay enumeration via closure operations Efficient deterministic scheduling for low-latency networks Tools for molecular cage generation in chemistry Yann Strozecki actively supervises PhD and master’s students, including Noé Demange, Maël Guiraud, and Xavier Badin de Montjoye. He co-organizes the ALMOST team seminar and has advised numerous interns in algorithmics and game theory. His research has been supported through collaborations with Nokia Bell Labs (CIFRE thesis) and interdisciplinary projects in cheminformatics and networking.

Surya Mathialaganمشاهده پروفایل

پژوهشگر ارشد

• Theoretical Computer Science
• Cryptography
• Algorithms
• +۳ مورد دیگر

Surya Mathialagan is a Research Fellow at the University of California, Berkeley 's Simons Institute . He will join NTT Research (CIS Lab) as a postdoctoral researcher in the fall. His research spans algorithms , theoretical cryptography , and computational complexity , with applications in blockchain verification and secure computation. Education: PhD in Theoretical Computer Science from Massachusetts Institute of Technology , advised by Vinod Vaikuntanathan and Virginia Vassilevska Williams . Research Interests focus on indistinguishability obfuscation, zero-knowledge proofs, and cryptographic protocols. His work addresses challenges in succinct verifiable computation , LWE-based security , and graph algorithm optimization . Recent publications at FOCS 2025 and Crypto 2025 explore obfuscation efficiency and adaptive proof systems. Scientific Awards include the Best Young Researcher Award at TCC 2023. His collaborations with experts like Abhishek Jain and Omer Paneth highlight interdisciplinary contributions.

Inge Li Gørtzمشاهده پروفایل

استاد

• Algorithms
• Data Structures
• Design and Analysis of Algorithms
• +۷ مورد دیگر

Inge Li Gørtz is a Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), where she leads research in algorithms and data structures. She is affiliated with the Algorithms, Logic and Graphs section and has been a principal investigator on externally funded research projects. She also serves as chairman of the Danish Society for Computer Science since 2009. Research Interests: Her work centers on the design and analysis of algorithms, with a focus on approximation algorithms, pattern matching, graph algorithms, and compressed data representations. She investigates efficient query processing, string indexing, and algorithmic challenges in highly repetitive data, including applications in bioinformatics and data streams. The recent publications highlight a strong trend in theoretical and practical algorithm design, particularly in string indexing, compressed automata, and dynamic data structures. These works span venues like SOFSEM and STACS, emphasizing innovations in sublinear query time, sliding window models, and memory-efficient representations. Postdoc stipend from Carlsberg Foundation (2006–2008) for 'Approximation of Transportation on Demand Problems', kr 865,183 Advising and Grants: She actively supervises multiple PhD students on projects such as biomedical image segmentation using graph cuts, hierarchical compression of DNA data, and dynamic graph algorithms. She has served as main or co-supervisor in several funded PhD projects. She has been principal investigator on a Carlsberg Foundation grant and has participated in multiple Danish Council for Independent Research projects. Labs and Teams: She is a core member of the Algorithms, Logic and Graphs (ALG) section at DTU, contributing to a vibrant research environment focused on theoretical computer science and algorithmic problem solving.

Manolis Zampetakisمشاهده پروفایل

استادیار

• Theoretical Machine Learning
• Statistics
• Optimization
• +۴ مورد دیگر

Manolis Zampetakis is an Assistant Professor of Computer Science at Yale University. Previously, he was a postdoctoral researcher in the EECS Department at UC Berkeley working with Michael Jordan. He earned his Ph.D. and M.S. from the EECS Department at MIT, where he was advised by Constantinos Daskalakis. His undergraduate education was completed at the National Technical University of Athens (NTUA). His research interests span Theoretical Machine Learning, Statistics, Optimization, Computational Complexity, Game Theory, Mechanism Design, and Sublinear Algorithms. His work bridges theoretical computer science with practical applications in machine learning and economics. His research consistently appears in top-tier conferences such as STOC, FOCS, COLT, NeurIPS, and EC, with a strong focus on the theoretical foundations of machine learning and game theory. Zampetakis has received numerous prestigious awards including the ACM SIGEcom Doctoral Dissertation Award and the Google Ph.D. Fellowship. His publications from 2023-2025 demonstrate a consistent output of high-quality research across multiple domains, with a notable emphasis on bridging statistical theory with algorithmic approaches. His work shows increasing integration of robust statistics with mechanism design and game-theoretic approaches. ACM SIGEcom Doctoral Dissertation Award Google Ph.D. Fellowship Special Issue at SAGT 2015 Special Issue at WINE 2013 Second Prize in IMC 2012 Top 5 in Panhellenic Physics Competition 2008 Zampetakis actively advises multiple graduate students including Anay Mehrotra, Jane Lee, Vikram Kher, Katerina Mamali, Shuchen Li, and Nikolaos Koumpis. He has served on program committees and organized workshops including the Workshop on Algorithms for Learning and Economics (WALE 2019, WALE 2022). His research has been supported by grants from Google and likely other major funding agencies given his publication record in top venues. His research group focuses on theoretical aspects of machine learning with connections to statistics and game theory. He collaborates extensively with researchers across institutions including MIT, UC Berkeley, and Yale colleagues.

Paul Valiantمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۵ مورد دیگر

Paul Valiant is an Associate Professor of Computer Science at Purdue University, joining in Fall 2021. His research focuses on sublinear algorithms for big data analysis, statistical inference, and the 'unseen' portions of probability distributions. He explores machine learning methodologies, fluid dynamics structures, and cognitive neuroscience, investigating parallels between human brain functions and artificial intelligence. Education: Paul Valiant earned his PhD in Computer Science from MIT in 2008, focusing on testing symmetric properties of distributions. His academic background includes studies leading to his undergraduate degree, though specific details are not provided here. Research Interests: His work spans algorithms and computational complexity, sublinear algorithms for statistical inference, machine learning optimization, fluid dynamics analysis of vorticity and energy flow, and cognitive neuroscience exploring the cerebellum's role. He emphasizes algorithmic efficiency, instance optimality, and theoretical bounds in these domains. Key Contributions: Paul Valiant has developed instance-optimal algorithms, improved entropy estimation methods, and contributed to understanding neural network architectures. His fluid dynamics research employs computational simulations and mathematical analysis to uncover vorticity patterns. Awards: 2008 Best Student Paper Award (Theory of Cryptography Conference) 2019 Test of Time Award (for 'Incrementally Verifiable Computation') 2005 Best Student Paper Award (co-winner, FOCS) NeurIPS 2020 Oral Presentation ACM Best of Computing Notable Article (2014) Advising and Grants: While specific student names are not listed, his research has been supported by institutional grants and collaborations. His work bridges theoretical computer science with applied domains like neuroscience and fluid dynamics, reflecting interdisciplinary impact.