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Richard ChangView profile

Associate Professor

• Computational Complexity Theory
• NP-optimization Problems
• Approximation Algorithms
• +7 more

Richard Chang is an Associate Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). He holds a Ph.D. from Cornell University (1991) and has been affiliated with UMBC since 1991, serving in various roles including tenure-track positions and joint appointments at the University of Maryland Institute for Advanced Computer Studies (1994–2004). His research focuses on computational complexity theory, particularly bounded query classes, NP-optimization problems, approximation algorithms, and relativized complexity. He has authored numerous papers on topics such as query complexity, Boolean hierarchies, and space-bounded computations. Chang's academic contributions include significant work on the theoretical foundations of computational complexity, including studies on the Boolean hierarchy, randomized reductions, and the structure of NP computations. He has secured multiple research grants, including from the National Science Foundation and the National Security Agency. His teaching spans a wide range of computer science courses, from introductory programming to advanced topics like compiler design and automata theory. He actively participates in academic service, serving on departmental, college, and university committees, as well as organizing conferences and reviewing for top journals. His work emphasizes bridging theoretical computer science with practical algorithmic challenges, contributing to both academic discourse and educational excellence.

• Data Structures
• Algorithms
• Theoretical Computer Science
• +3 more

Ian Munro is a University Professor at the Department of Computer Science, University of Waterloo. He is affiliated with the Data Structuring Group and can be reached at imunro@uwaterloo.ca. His research focuses on advancing the theoretical foundations of data structures and algorithms, with a particular emphasis on succinct data structures, computational geometry, and algorithm design. His work spans topics such as dynamic graph algorithms, efficient query processing, and space-efficient representations of trees and graphs. Recent contributions include studies on interval graphs, AVL tree optimizations, and Boolean formula evaluation. He is a prominent figure in the field of theoretical computer science. Dr. Munro’s research has been published in leading conferences and journals, consistently addressing challenges in algorithm efficiency, data structure optimization, and graph theory. His group, the Data Structuring Group, actively explores innovations in these areas.

Marcelo ArenasView profile

Professor

• Data Management
• Semantic Web
• Database Theory
• +3 more

Marcelo Arenas is a Professor at the Department of Computer Science and the Institute for Mathematical and Computational Engineering at the Pontifical Catholic University of Chile. He is a Fellow of the Association for Computing Machinery (ACM), former director of the Millennium Institute for Foundational Research on Data, and co-founder of the Center for Semantic Web Research. His Ph.D. in Computer Science was obtained from the University of Toronto in 2005. Research interests include data management , applications of logic in computer science , and Semantic Web technologies. He has published extensively on topics such as SPARQL query complexity , graph database systems , and incomplete database theory , with notable works like MillenniumDB and Foundations of Data Exchange . Scientific accolades: 2016 SWSA Ten-Year Award for "Semantics and Complexity of SPARQL" IBM Ph.D. Fellowship (2004) Nine Best Paper Awards across PODS, ISWC, ICDT, ESWC, WWW, and NeurIPS His work on approximate counting algorithms (e.g., FPRAS for #NFA) and explainable AI frameworks has influenced database theory, while serving on program committees for ICDT 2015, ISWC 2015, and PODS 2018 demonstrates leadership in the field. Current projects focus on probabilistic explanations for decision trees and temporal regular path queries in knowledge graphs.

Tomohiro IView profile

Associate Professor

• Algorithms on Strings
• String Pattern Matching
• Indexing
• +9 more

Tomohiro I is an Associate Professor in the Department of Artificial Intelligence at Kyushu Institute of Technology, Japan. He has been in this position since January 2019, following a research associate role at the same institution from 2015 to 2018. Prior to that, he held postdoctoral positions at Kyushu University and TU Dortmund, Germany. His academic foundation includes a Ph.D. in Science from Kyushu University, awarded in 2012. His research primarily centers on string algorithms , with a strong emphasis on compressed data structures , pattern matching , indexing , and algorithmic efficiency . Key interests include Lyndon factorization, Lempel-Ziv compression, palindrome matching, and reverse engineering of string data structures. He frequently collaborates with prominent researchers like Hideo Bannai and Shunsuke Inenaga, producing high-impact work in theoretical computer science. His recent publications demonstrate a consistent focus on improving algorithms for string processing in compressed formats. Work on Re-Pair , RLBWT , and SLP encoding highlights his expertise in space-efficient computation. The 2022 Best Paper Award at IWOCA for work on Lyndon subsequences underscores the quality and recognition of his contributions. His research bridges theoretical analysis with practical algorithm design. Best Paper Award, International Workshop on Combinatorial Algorithms (IWOCA) 2022 Tomohiro I advises graduate students in his laboratory, although he currently notes that the lab is not accepting new research students. His work involves significant algorithmic research, often supported by theoretical grants or institutional funding, leading to numerous publications in peer-reviewed journals and conferences. He has also presented his work in invited talks, such as at CompressedAI2025 and WCTA 2024, indicating active engagement with the research community. He leads a research laboratory at Kyushu Institute of Technology, focused on advanced string processing and compressed data structures. His team collaborates extensively on algorithm design and analysis, contributing to the broader field of combinatorial pattern matching.

• Algorithms
• Databases
• Programming Languages
• +3 more

Louis Jachiet is an Assistant Professor in Computer Science at Télécom Paris, affiliated with the Data, Intelligence and Graphs (DIG) team within the Information Processing and Communication Laboratory (LTCI) and the Computer Sciences and Networks (Infres) department. His research focuses on algorithms, databases, programming languages, and logic. His work spans query optimization distributed SPARQL evaluation graph algorithms formal language theory program synthesis data provenance with a strong emphasis on bridging theoretical and applied research. Analysis of his publications reveals expertise in database systems graph processing automata theory query enumeration SPARQL optimization probabilistic databases across both theoretical and practical applications.

Miao QiaoView profile

Lecturer

• Data Science
• Graph Analytics
• Brain Network Analytics
• +2 more

Miao Qiao is a faculty member at the School of Computer Science , University of Auckland , where she has worked since 2018. Her research bridges theoretical and applied aspects of data science and management , focusing on vector data management , graph search and analytics , and brain network analytics . Research Interests : Data Science, Graph Analytics, Brain Network Analytics Grants : Royal Society of New Zealand (NZD 300,000, 2018–2024): Subgraph Matching: Theory and Practice The Ministry of Business, Innovation and Employment (NZD 3,000,000, 2020–2024): Advanced Graph Analytics for Human Brain Connectivity Professional Services : Review Board Member, PVLDB 2021 Program Committee Member: PODS 2023, ADC 2018, IJCAI 2019–2021, CIKM 2019, AAAI 2020–2021 Invited Reviewer: SCG 2019, TODS 2015–2019, TKDE 2018–2021, KDD 2016, ICDM 2013–2014, WISE 2013, VLDBJ 2013–2021, NIPS 2021 Program Chair: ISWC Doctoral Consortium 2019, ADC 2021 Teaching : Database Systems (2017–2023) Algorithms for Massive Data (2018–2019) Systems Analysis and Modeling (2016–2018) Algorithms (2017–2018) Scientific Awards : PREMIA Best Student Paper Honourable Mention Award VLDB 2013 Travel Fellowship Shanghai Jiao Tong University 1st Class Scholarship ACM/ICPC 3rd Place, Singapore Computer World Scholarship Singapore Technology Engineering Scholarship ACM/ICPC 1st Place, Korea Silver medal, National Olympiad in Informatics, China

Xiaokang QiuView profile

Associate Professor

• Programming Languages
• Software Engineering
• Program Verification
• +2 more

Xiaokang Qiu serves as Associate Professor in Purdue University's Elmore Family School of Electrical and Computer Engineering, specializing in Programming Languages and Software Engineering with core expertise in program verification, program synthesis, and automated deduction. His research establishes critical bridges between enumerative and deductive synthesis methodologies, developing novel frameworks for verified program generation. Key contributions include string transformation synthesis with concurrency guarantees, bit-vector manipulation optimization via syntax-guided enumeration, and network design automation through comparative learning techniques. This work consistently advances formal verification foundations while addressing practical software engineering challenges. Publication trends from 2017-2025 reveal escalating complexity in synthesis targets—from basic data-structure manipulations to concurrent string operations and network configurations. His approach increasingly integrates machine learning elements with formal methods, demonstrating how query-based learning can drive near-optimal system design while maintaining provable correctness guarantees across diverse computational domains.

• Database Systems
• Query Processing
• Data Stream Management
• +5 more

Mirek Riedewald is a Professor in the Department of Computer Science at Northeastern University's College of Engineering. With a prolific publication record spanning from 1999 to 2025, he has established himself as a leading researcher in database systems, particularly in query processing, join algorithms, and exploratory search. His work bridges theoretical database foundations with practical applications in data visualization and data lake management. Dr. Riedewald's research focuses on innovative approaches to database query processing, with recent work emphasizing ranked enumeration algorithms, relational diagram visualization, and data lake disambiguation. His publications in top-tier venues like SIGMOD, VLDB, and ICDE demonstrate his significant contributions to the field. He has developed novel techniques for efficient computation of quantiles over joins, any-k algorithms for ranked enumeration, and relationship-based semantic table union search through his SANTOS system. His recent publications reveal a growing interest in the intersection of database systems with fairness considerations, as seen in his work on fixing top-k rankings that lack individual fairness. He also explores the visualization of complex database queries through relational diagrams, making query understanding more accessible. His research has evolved from traditional database topics like data cubes and stream processing to more contemporary challenges in large-scale data management and analysis. Dr. Riedewald has maintained a strong collaborative network, with frequent co-authors including Wolfgang Gatterbauer (31 papers), Johannes Gehrke (23 papers), and Nikolaos Tziavelis (15 papers). His work demonstrates both theoretical depth and practical relevance, addressing real-world challenges in data management systems. His research group has made significant contributions to exploratory search in databases through the ExploreDB workshop series, which he co-organized for multiple years. He has also contributed to important reference works, including chapters in the Encyclopedia of Database Systems on topics like event and pattern detection over streams and database techniques for scientific simulations.

• Machine Learning
• Algorithm Engineering
• Combinatorial Optimization
• +3 more

Dr. Deepak Ajwani is an Assistant Professor in the School of Computer Science at University College Dublin . His research focuses on leveraging machine learning techniques for solving combinatorial optimization problems, with expertise in algorithm design, algorithm engineering, and graph algorithms. He holds a PhD from the University of Saarland, Germany, and has held postdoctoral positions at Aarhus University (Denmark) and University College Cork (Ireland). He also worked at Nokia Bell Labs (2012–2018) on learning systems for unstructured content analysis. Dr. Ajwani has received over 50 peer-reviewed publications in top-tier conferences and journals. He serves on the editorial board of the Machine Learning journal and is a frequent senior program committee member for conferences like WWW, IJCAI, AAAI, and ALENEX. He is a funded investigator at the SFI Centre for Research Training in Machine Learning (ML-Labs) . His teaching excellence has been recognized through multiple nominations for the UCD Teaching and Learning Award . His students have won the Franz Geiselbrechtinger Medal (2023, 2024) for best final-year projects in theoretical computer science. Key grants include the Irish Research Council and IBM Research Grant (2010–2012) for graph partitioning techniques in exascale computing. His research explores integrating machine learning with optimization algorithms, graph neural networks, and algorithm engineering for real-world applications.

Farah ChancharyView profile

Lecturer

• Computer Science
• Cloud Computing
• Data Structures
• +7 more

Farah Chanchary is a Lecturer and Lab/Course Coordinator at the School of Computer Science, Carleton University. She specializes in areas such as data structures, cloud computing, and educational technologies. Her work spans algorithm design, privacy in advertising, and challenges in e-learning, particularly in Saudi Arabia. She coordinates laboratory and course activities, supporting academic infrastructure. Research Interests: - Data Structures and Algorithms - Cloud Computing and Migration - Privacy-Preserving Models in Advertising - E-Learning Readiness and Challenges - Machine Learning Applications (Neural Networks, Genetic Algorithms) - Geometric Query Processing Publications Trends: Her recent work focuses on efficient data structures for window queries, privacy concerns in online advertising, and cloud computing challenges. Earlier research includes studies on e-learning adoption in Saudi Arabia and neural network applications in rainfall forecasting. No awards or grants are listed. She has no documented advisees. She manages laboratory facilities and course coordination in the School of Computer Science.

Marc RothView profile

Lecturer

• Theoretical Computer Science
• Algorithmics
• Computational Complexity Theory
• +5 more

Dr. Marc Roth is a Lecturer in Theoretical Computer Science at Queen Mary University of London (QMUL), affiliated with the School of Electronic Engineering and Computer Science. He is also an Associate Member of the Department of Computer Science at the University of Oxford. Previously, he served as a Senior Research Associate at Oxford (until 2023) and held a Junior Research Fellowship at Merton College, Oxford. He earned his PhD in Computer Science from Saarland University under Prof. Holger Dell. His research focuses on computational counting problems, particularly in the context of graph theory and algorithmic complexity. Key interests include parameterized and fine-grained complexity, exact and approximate counting algorithms, and applications in network analysis. He has contributed to foundational work on subgraph counting, constraint satisfaction, and the theoretical limits of computational problems. Dr. Roth’s work bridges theoretical computer science and practical algorithm design, addressing challenges in analyzing large networks and understanding complexity hierarchies. He actively collaborates with institutions like the Centre for Fundamental Computer Science at QMUL and has supervised a PhD studentship exploring motif counting in higher-order networks.

Wentao LiView profile

Lecturer

• Data Management
• Data Mining
• Graph Data Processing
• +6 more

Dr. Wentao Li is a Lecturer at the University of Leicester's School of Computing and Mathematical Sciences since 2024. Previously, he held postdoctoral roles at the Hong Kong University of Science and Technology (2023–2024) and the University of Technology Sydney (2020–2022). He earned his Ph.D. from UTS in 2021, focusing on graph data processing and mining. His research emphasizes data management, graph processing, and AI-driven databases. He has authored over 20 CORE A*-level papers, a monograph, and a patent yielding significant business revenue. He serves on program committees for ICDE and AAAI, reviewing for ACM TODS and IEEE TKDE. Li has received the 2021 Baidu Scholar Rising Star award and SIGMOD Travel Award (2019). He advises Ph.D. students in graph data, vector databases, and AI4DB. Teaching roles include modules on big data analytics and machine learning at Leicester and UTS.

Rodrigo I. SilveiraView profile

Associate Professor

• Computational Geometry
• Combinatorial Geometry
• Geographic Information Science (GIS)
• +7 more

Rodrigo I. Silveira is an Associate Professor in the Department of Mathematics at Universitat Politècnica de Catalunya (UPC), where he conducts research in computational and combinatorial geometry. He is a member of the UPC research group on Discrete, Combinatorial and Computational Geometry and has a strong academic background, having earned his PhD at Utrecht University under Marc van Kreveld and completed prior studies at the Universidad de Buenos Aires. His research focuses on Computational Geometry , particularly problems arising from Geographic Information Science (GIS) , such as map construction, trajectory analysis, and visibility. He also works on Graph Drawing and algorithms for geometric structures. His work combines theoretical algorithm design with practical applications in spatial data. His recent publications reflect a consistent trend in geometric algorithms, including shortest paths in complex environments (e.g., portalgons), Voronoi diagrams with color constraints, robot motion coordination, and map inference from GPS data. These works appear in top-tier journals and conferences such as Algorithmica , Computational Geometry: Theory and Applications , SoCG, WADS, and LATIN. He has received scientific recognition, including a Best Paper Award at AGILE 2009 and another at SpatialGems 2021. He has supervised several PhD students to completion, including Guilermo Esteban (2024) and Pilar Cano (2020), and continues to advise current students. Rodrigo has been deeply involved in the academic community, organizing major events such as the European Workshop on Computational Geometry (EuroCG 2023), Graph Drawing 2018, and the Intensive Research Program on Discrete Geometry (2018). He has served on the program committees of SoCG, WADS, LATIN, and CCCG, and has chaired tracks for EGC and CG:YRF. His work is supported by collaborations with leading researchers such as Kevin Buchin, Maarten Löffler, Prosenjit Bose, and Vera Sacristán, and he contributes to both theoretical advances and practical implementations in geometric computing.

• Quantum Computing
• Theoretical Computer Science
• Combinatorics
• +5 more

Mario Szegedy is a Professor in the Department of Mathematics at Rutgers, The State University of New Jersey, and a member of the university’s Graduate Faculty. His research integrates quantum computing , theoretical computer science , combinatorics , and algorithms . A dominant theme is elucidating the power and limits of quantum algorithms relative to classical counterparts. Representative directions include: Quantum Monte Carlo methods and speed-ups Locally testable codes and quantum error correction Combinatorial optimization and approximation algorithms Graph-theoretic processes and Markov chains Communication complexity and decision-making under constraints Across more than two decades, Szegedy’s publications reveal a steady trajectory toward deeper quantum–classical separations, practical quantum algorithm design, and structural insights into hard combinatorial problems. His 2024–2025 work pushes further into non-linear quantum Monte Carlo and exotic quantum codes, while earlier contributions laid foundational results on query complexity and randomized algorithms. Although the provided text does not enumerate awards or funded grants, the breadth and longevity of publication in top venues implicitly signal sustained research funding and recognition within the theoretical computer-science community. Laboratory or center affiliations are not specified in the text, but his presence in the Mathematics Department and Graduate Faculty suggests active involvement in mentoring PhD students and postdocs in discrete mathematics and quantum information.

• Data Management and Analysis
• Machine Learning
• Knowledge Representation
• +5 more

Assoc Prof John Wang is an Associate Professor at the School of Information and Communication Technology , Griffith University. His work spans Data Management and Analysis (graph, text, spatial-temporal data), Machine Learning , Knowledge Representation , and Algorithm Design . He has published extensively in top venues like ACM Transactions on Database Systems , IEEE Transactions on Knowledge and Data Engineering , and conferences including SIGMOD , VLDB , and ICDE . Appointments : Member of the Institute for Integrated and Intelligent Systems (2003–2024). Education : PhD in Computer Science (Griffith University, 2003). His research has led to real-world implementations in Graph Data Processing , including techniques for shortest distance queries and temporal reachability. He actively contributes to program committees of conferences like VLDB and ICDE , and his funded projects include "Developing Soil Knowledge..." (ACIAR, 2024–2029) and "Harnessing Social Media..." (QLD Inspector-General, 2020). Recent publications focus on Graph Indexing , Causality Detection , and Spatial-temporal Querying . Examples include optimizing road network queries (2023), 2-hop labeling for shortest paths (2021), and geospatial skyline algorithms (2020). Co-authored works appear in venues such as IEEE Transactions on Knowledge and Data Engineering and Neurocomputing . Consultancy : Delivered commercial research on pandemic-related data analytics (2020). Supervision : Mentored 15+ PhD/Master’s students, including projects on deep learning, data leakage prevention, and XML querying. Teaching : Directed programs like Graduate Certificate in Information Technology and Diploma of Information Technology .