جست‌وجوی استاد و پژوهشگر

Kanat Tangwongsanمشاهده پروفایل

پژوهشگر

• Algorithms
• Parallel Computing
• Streaming Algorithms
• +۶ مورد دیگر

Kanat Tangwongsan is a researcher and educator in computer science, with a PhD from Carnegie Mellon University and prior roles at IBM T.J. Watson Research Center. He has extensive experience in algorithm design, parallel computing, and self-adjusting computation, with publications in top-tier venues like DEBS, ICDE, SPAA, and TOCS. Education: PhD in Computer Science (Carnegie Mellon University), B.S. in Computer Science and Mathematics (Carnegie Mellon University) Research Interests: His work spans algorithms , parallel computing , streaming data structures , and self-adjusting programs , focusing on theoretical improvements and practical implementations for graph problems, convex hulls, and network design. Article Trends: His publications emphasize low-latency streaming , parallel SDD solvers , k-means clustering , and triangle enumeration , with applications in large-scale graph analysis, numerical systems, and cache-oblivious methods. Advising and Collaborations: He has collaborated with prominent researchers like Guy E. Blelloch, Martin Hirzel, and Anupam Gupta, contributing to open-source algorithm frameworks and educational materials.

Antoine Amarilliمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Data Management
• Probabilistic Computation
• +۷ مورد دیگر

Antoine Amarilli is an Associate Professor in Computer Science at Télécom Paris, part of Institut Polytechnique de Paris. Since September 2024, he has been on leave to work as an advanced researcher in the LINKS team at Inria Lille. His research spans theoretical computer science, focusing on query evaluation, probabilistic data, knowledge compilation, and computational logic. Member of DIG and LINKS research teams Part of LTCI (Information Processing and Communication Laboratory) Education: PhD in Computer Science (Télécom ParisTech, 2016) Habilitation (HDR) in Computer Science (Institut Polytechnique de Paris, 2023) Research Interests: He investigates efficient query evaluation on uncertain data, dynamic data maintenance, knowledge compilation techniques, and the interplay between circuits, automata, and logical languages. His work intersects database theory, graph theory, and computational complexity. Publications Trends: Recent articles address tractability of queries on probabilistic graphs, complexity classifications for MSO and regular path queries, and knowledge compilation frameworks for enumeration algorithms. Key subfields include probabilistic reasoning, graph constraints, and circuit-based optimizations. Awards: Best Paper at ICDT 2020 Best Paper at ICALP 2021 (Track B) Télécom Paris PhD Prize (2017) Beth Dissertation Award (2017) Teaching & Service: He taught competitive programming, algorithms, and data management at Télécom Paris and represented the institution on the MPRI master’s committee. He co-organized Highlights of Logic, Games, and Automata (2022) and served as a program committee member for ICDT, ICALP, and AAAI. Research Activism: Co-founded TCS4F (Theoretical Computer Scientists for Future) and No Free View? No Review! initiatives to address climate impact and open-access advocacy in academia.

• Theoretical Computer Science
• Database Theory
• Formal Logic
• +۵ مورد دیگر

Nicole Schweikardt is a Professor at the Institute of Computer Science within Humboldt University of Berlin . Her research focuses on Theoretical Computer Science , particularly in Database Theory , Formal Logic , and Algorithmic Meta-Theorems . Academic Rank: Professor Contact: schweikn@informatik.hu-berlin.de Research Interests : Nicole investigates logical characterizations of database query languages, algorithmic meta-theorems for sparse graphs, and efficient enumeration techniques. Her work bridges formal logic, computational complexity, and practical database systems. Scientific Awards : 2018 ACM PODS Alberto O. Mendelzon Test-of-Time Award Recent Article Trends : Nicole's recent publications emphasize schema matching , spanner evaluation , first-order logic extensions , and query enumeration . Her work spans theoretical foundations (e.g., counting quantifiers, Hanf normal forms) and practical applications (e.g., event stream analysis, document compression).

Yixiang Fangمشاهده پروفایل

دانشیار

• Data Management
• Data Mining
• Artificial Intelligence
• +۸ مورد دیگر

Yixiang Fang is an Associate Professor at the School of Data Science, The Chinese University of Hong Kong, Shenzhen. Previously, he was a Research Associate at the School of Computer Science and Engineering, the University of New South Wales (UNSW) during 2018-2020, working with Prof. Xuemin Lin. He received his PhD from the Department of Computer Science at the University of Hong Kong (HKU) in 2017, advised by Prof. Reynold Cheng, and was a visiting scholar at Nanyang Technological University in 2016. Dr. Fang's research focuses on data management, data mining, and artificial intelligence over big data, particularly big graph data. His work centers on community search, densest subgraph discovery, path/connectivity computation, and LLM + Graph (including graph-based retrieval augmented generation). He has made significant contributions to the field, particularly in cohesive subgraph search and densest subgraph discovery, with his works on community search being well-recognized by leading IT companies like Huawei and ByteDance. His research has resulted in numerous publications in top-tier conferences and journals including SIGMOD, VLDB, ICDE, NeurIPS, WWW, AAAI, IJCAI, TODS, VLDBJ, and TKDE. Notably, his SIGMOD 2020 paper was selected as one of the Best Papers and was awarded the 2021 ACM SIGMOD Research Highlight Award. 2025 CUHK-Shenzhen Teaching Achievement Award (Excellent Prize) 2021 ACM SIGMOD Research Highlight Award Best Paper Award, International Conference on Spatial Data and Intelligence (SpatialDI) 2021 Presidential Young Fellow at CUHK-Shenzhen, 2021 One of the Best Papers, SIGMOD 2020 CCF-Ant Research Fund (2025) Dr. Fang has advised numerous students including PhD candidates, MPhil students, and undergraduate researchers. He has received research funding from Huawei Cloud, CCF-Huawei Populus euphratica Fund, and ByteDance. Currently, he serves as an Editorial Board Member for Information Processing & Management (IPM) and has held leadership roles in major conferences including serving as PC co-chair for LLM+Graph@VLDB2025 workshop. He is also the coach of the CUHK-Shenzhen Programming Contest Team.

Richard Pengمشاهده پروفایل

دانشیار

• Algorithms
• Theoretical Computer Science
• Graph Theory
• +۴ مورد دیگر

Richard Peng is an Associate Professor in the Computer Science Department at Carnegie Mellon University, part of the School of Computer Science. He specializes in designing efficient algorithms for fundamental computational problems, particularly in graph algorithms, dynamic algorithms, and linear algebraic computations. Prior to joining CMU in 2023, he earned his BMath from the University of Waterloo, a PhD from CMU under Gary L. Miller, and completed a postdoc at MIT's Applied Math department. His research focuses on advancing algorithmic efficiency, including work on sparse linear systems, graph convolutions, and flow optimization. He advises PhD students Hoai-An Nguyen, Alicia Stepin, and Junzhao Yang. His teaching includes courses such as 15495 and 15151, reflecting his engagement in both research and education. Peng’s articles span topics like approximate spanning tree counting, dynamic graph algorithms, and Laplacian solvers, emphasizing practical scalability and theoretical guarantees. His contributions bridge theoretical computer science with applied challenges in network analysis and numerical computation.

Christoph Berkholzمشاهده پروفایل

استاد

• Theoretical Computer Science
• Algorithm Design
• Computational Complexity
• +۳ مورد دیگر

Prof. Christoph Berkholz is a University Professor and head of the Algorithms Group at the Institute for Theoretical Computer Science within the Department of Computer Science and Automation at Humboldt-Universität zu Berlin since August 2022. He holds a PhD from RWTH Aachen University (2014) and completed postdoctoral research in Stockholm, Berlin, and Berkeley. Prior to his current role, he served as a Junior Professor of Logic and Complexity at Humboldt-Universität, leading the DFG-funded Emmy Noether Junior Research Group on Representation Complexity of Enumeration and Counting Algorithms. His research focuses on theoretical computer science, particularly algorithmic methods and their fundamental limits. Core questions driving his work include conditions for efficient algorithms, structural differences between 'light' and 'heavy' input instances, and the capabilities/limitations of algorithmic strategies. Research applications span query optimization in databases, SAT solving, and constraint solving. He has published ~30 papers and actively participates in program committees for AI, theoretical computer science, and database systems conferences. Prof. Berkholz's group investigates methods ranging from classical decision problems (e.g., propositional logic satisfiability) to dynamic algorithms supporting efficient updates. Key application areas include probabilistic databases and SAT/constraint-solving data structures. No specific awards are listed, though his Emmy Noether Research Group indicates significant grant recognition.

Angelika Stegerمشاهده پروفایل

استاد

• Probabilistic Methods
• Randomized Algorithms
• Graph Theory
• +۳ مورد دیگر

Prof. Angelika Steger is a Full Professor in the Department of Computer Science at ETH Zurich, leading research in theoretical computer science since 2003. She holds a Master's in Applied Mathematics from Stony Brook University (1985) and a PhD from the University of Bonn (1990). Her career includes roles at Kiel, Duisburg, and TU München before joining ETH. She is a Leopoldina member (2007), ICM speaker (2014), and Collegium Helveticum Fellow (2009+). Research focuses on probabilistic methods, randomized algorithms, graph theory, and combinatorial optimization. She has contributed to understanding discrete structures, neural networks, and algorithmic resilience. Awards include recognition in both computer science and mathematics circles. Her work bridges theoretical foundations with applications in AI, neuroscience, and distributed systems.

LI Yuchenمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Data Mining
• Graph Mining
• +۶ مورد دیگر

Yuchen Li is an Associate Professor at the School of Computing and Information System (SCIS) at Singapore Management University (SMU), where he also holds the Lee Kong Chian Fellowship for Research Excellence. He earned his Ph.D. in Computer Science from the National University of Singapore (NUS) in 2017. His research focuses on social analytics, high-performance graph mining, and fintech applications leveraging large language models (LLMs). He has led significant projects funded by MoE, including 'Next-Gen Competitive Intelligence' and 'CONQUEROR' for concurrent graph query processing. His academic contributions span influential publications in top-tier conferences like SIGMOD, KDD, and ICML, with notable works on graph algorithms, fraud detection, and knowledge graph robustness. He advises Ph.D. students and has supervised over a dozen researchers, including current students Xiao Hanhua, Wang Sha, and Ye Chang. His awards include the Lee Kong Chian Fellowship and the Best Demo Paper Award at CIKM 2021. Li’s research bridges theoretical foundations and practical applications, addressing challenges in graph processing, social media analysis, and fintech. His team develops systems like 'Dupin' for fraud detection and 'ThunderRW' for in-memory graph processing, showcasing expertise in GPU acceleration and scalable algorithms.

James Ian Munroمشاهده پروفایل

استاد

• Algorithms
• Data Structures
• Theoretical Computer Science
• +۱ مورد دیگر

James Ian Munro is a Professor of Computer Science at the University of Waterloo. His research focuses on algorithms, data structures, and theoretical computer science. He has been recognized with prestigious awards including the ACM Fellow (2008) and Fellowship in the Royal Society of Canada. Education: Earned degrees from the University of British Columbia, University of Toronto, and University of New Brunswick. His doctoral advisor was Allan Borodin. Research Interests: Specializes in succinct data structures, graph algorithms, search trees, and efficient algorithm design. His work emphasizes space-efficient and optimal data representation. Awards: ACM Fellow (2008) for contributions to algorithms and data structures; Royal Society of Canada Fellow. Advising: Supervised numerous doctoral students including Erik Demaine and Venkatesh Raman. His academic lineage spans foundational contributions to theoretical computer science.

Peisen Yaoمشاهده پروفایل

پژوهشگر

• Program Analysis and Verification
• Program Synthesis and Optimizations
• Logic and Automated Reasoning
• +۲ مورد دیگر

Peisen Yao is an academic researcher affiliated with Zhejiang University in China. His work spans programming languages, software engineering, and cybersecurity, with a focus on developing techniques to enhance software security, usability, and performance. He actively contributes to premier conferences as a committee member (e.g., PLDI, ICSE, ASE, ISSTA, OOPSLA, SAS). Research Focus: Yao's research integrates program analysis, verification, synthesis, and optimization. Key areas include: Advanced static analysis for security vulnerabilities (e.g., buffer overflows, divide-by-zero errors) Efficient symbolic abstraction and SMT solver testing Context-sensitive program analysis using formal language theory Automated invariant generation and code synthesis Publication Trends: His 15 most recent articles (2021–2025) emphasize scalable program analysis techniques, security validation, and practical tools for industry applications. Common themes include static analysis optimizations, formal verification, and novel testing methodologies for complex systems.

Ilias Zadikمشاهده پروفایل

استادیار

• High-Dimensional Statistics
• Machine Learning Theory
• Computational-Statistical Trade-offs
• +۵ مورد دیگر

Ilias Zadik is an Assistant Professor at Yale University's Department of Statistics and Data Science. His research focuses on computational-statistical trade-offs in modern machine learning, high-dimensional statistics, and probability theory. He has held postdoctoral positions at MIT (2021-2023) and NYU (2019-2021), and earned his PhD from MIT (2019), advised by David Gamarnik. He teaches courses like Stochastic Processes and has contributed to numerous conferences and workshops. His awards include MIT's Best Student Paper Honorable Mention (2017) and scholarships from Trinity College and the Onassis Foundation. Education: PhD in Operations Research, MIT (2019) MASt in Mathematics (Part III), University of Cambridge (2014) BA in Mathematics, University of Athens (2013) Internship at Microsoft Research New England (2017) Research Interests: Computational-statistical trade-offs, phase transitions in inference (e.g., All-or-Nothing phenomena), cryptographic methods in statistics, privacy-cost analysis, and algorithmic lower bounds. Awards: MIT Operations Research Best Student Paper Honorable Mention (2017) Trinity College Senior Scholarship (2014) Onassis Foundation Scholarship (2013-2014) SEEMOUS Gold Medal (2011), IMC First Prize (2011) Teaching & Service: Instructor for Yale's S&DS 351 (Stochastic Processes) and advanced courses on computational-statistical trade-offs. Co-organized the MaD+ seminar during the pandemic. Served on program committees for COLT, NeurIPS, and FOCS. Labs/Teams: Active in MIT's NSF/Simons Collaboration on Theoretical Foundations of Deep Learning and NYU's Math and Data group.

• Economics
• Computer Science
• Algorithmic Game Theory
• +۳ مورد دیگر

Professor Rahul Savani is a faculty member at the University of Liverpool in the Department of Computer Science . His work bridges the domains of computer science, economics, and finance, with a focus on equilibrium computation for game-theoretic models and algorithmic trading. Academic Rank: Professor Fields of Interest: Algorithmic Game Theory, Automated Trading, Reinforcement Learning, Agent-Based Modelling His research explores the intersection of algorithms, computational complexity, and strategic interaction, with applications in financial markets and multi-agent systems. Recent publications highlight advancements in game-theoretic solvers, natural language-to-game modeling, and materials science through machine learning. Scientific achievements include the EPSRC Postdoctoral Fellowship in Theoretical Computer Science (2008) . He also contributes to editorial responsibilities as an Associate Editor of the International Journal of Game Theory since 2017.