Find Academic Supervisors

• Fine-Grained Complexity
• Theory of Computation
• Graph Theory and Algorithms
• +6 more

Amir Abboud is a Senior Scientist (equivalent to Associate Professor) in the Department of Computer Science and Applied Mathematics at the Weizmann Institute of Science, where his research centers on theoretical computer science with primary focus on fine-grained complexity. This field investigates exact computational complexity of fundamental problems to establish tight upper and lower bounds. His educational credentials include: Ph.D. from Stanford University M.Sc. from the Technion B.Sc. from the University of Haifa (via the "Etgar" program) Abboud's research spans graph theory and algorithms, dynamic data structures, pattern matching, sequence alignment, exact algorithms, parameterized complexity, distributed computing, and circuit complexity. His work bridges theoretical insights with practical algorithmic advancements, particularly in understanding computational limits within polynomial time. Analysis of his publications (2014-2022) reveals consistent focus on fine-grained complexity applications to graph algorithms and dynamic problems. Key contributions include breakthroughs in Gomory-Hu tree computation, hardness of approximation frameworks, and conditional lower bounds for dynamic data structures. His research demonstrates how conjectures about fundamental problems (e.g., SETH, 3SUM) propagate to diverse computational domains. Scientific awards: No specific prizes, fellowships, or medals are documented in the source material. Advising and grants: The provided text contains no information regarding graduate students supervised, postdoctoral mentoring, or research funding received. Labs and teams: No details about laboratory leadership, research groups, or collaborative teams are mentioned in the available documentation.

Jehoshua BruckView profile

Professor

• Information Theory
• Distributed Storage Systems
• DNA Data Storage
• +4 more

Jehoshua (Shuki) Bruck is the Gordon and Betty Moore Professor of Computation and Neural Systems & Electrical Engineering at the California Institute of Technology. He leads the Distributed Information Systems Group and serves as the founding director of Caltech's Information Science and Technology (IST) program. His work bridges information theory, storage systems, and computational biology. Bruck holds B.Sc. (Summa cum Laude) and M.Sc. degrees from the Technion-Israel Institute of Technology (1982-1985) and a Ph.D. in Electrical Engineering from Stanford University (1989). He has held academic positions at Caltech since 1994, advancing from Associate Professor to Moore Professor. Research Focus: His dual-track research explores Physical implementations of coding schemes for flash memories (rank modulation, rewriting codes) Biological computation mechanisms, particularly DNA sequence evolution via replication Secure distributed storage systems and RAID schemes Awards & Recognition: Recipient of the Feynman Teaching Prize (2008), Sloan Fellowship, NSF Young Investigator Award, and multiple IEEE best paper awards. Holds over 40 patents and authored 350+ publications. Industry Contributions: Co-founder of storage startups Rainfinity (acquired by EMC) and XtremIO, which pioneered flash storage solutions. Collaborates with NASA-JPL on non-volatile memory for space missions.

Patrik HaslumView profile

Researcher

• AI Planning
• Plan Quality Optimization
• Optimization for Decision Support
• +5 more

Roles and Affiliations : Patrik Haslum is a part-time Researcher in the AI group at the Research School of Computer Science (ANU College of Engineering and Computer Science), Australian National University. He is seconded to Data61's Decision Sciences program. Previously, he was at NICTA and Linköpings Universitet. Education : PhD in Computer Science from Linköpings Universitet (Sweden), with research on admissible heuristics for automated planning. Research Interests : Focuses on AI planning, particularly plan quality optimization and heuristic search. Specializes in optimal planning, domain analysis, and applying planning techniques to problems like genome edit distance, diagnosis, and robotics. His work bridges automated planning with Petri nets and discrete event systems. Advising & Students : Supervised four PhD students including Jing Cui (2018), Franc Ivankovic (2018), Fazlul Hasan Siddqui (2015), and Debdeep Banerjee (2015). Offers student projects in areas like story planning, optimal troubleshooting, and policy validation for MDPs. Labs & Contributions : Leads research in the AI group at ANU, contributing to tools like the INVAL plan validator and PDDL domain encodings. Active in international planning competitions and workshops.

Xiaoyi JiangView profile

Professor

• Image Analysis
• Pattern Recognition
• Machine Learning
• +3 more

Xiaoyi Jiang is a Professor at the Institute of Computer Science at the University of Münster, leading the Jiang Lab focused on Pattern Recognition and Image Analysis. He holds roles in the CiM-IMPRS Graduate Programme Management Board and participates in projects like the Multiscale Imaging Centre. His research emphasizes biomedical image analysis, machine learning, and medical applications such as tumor segmentation, vessel network extraction, and automated tracking systems for small organisms. Key contributions include FIMTrack software for locomotion analysis, the Voreen visualization framework, and complex-valued neural network architectures. He has authored over 200 papers and holds patents in imaging and segmentation technologies. His work bridges computer science with medical and biological applications, aiming to solve challenges in automated analysis and interpretation of biomedical data.

Nathan WilliamsView profile

Associate Professor

• Algebraic Combinatorics
• Reflection Groups
• Braid Groups
• +3 more

Nathan Williams is an Associate Professor in the Department of Mathematical Sciences at the University of Texas at Dallas (UTD), tenured since 2024. He holds a Ph.D. from the University of Minnesota (2013) and completed postdoctoral research at LaCIM (Université du Québec à Montréal) and the University of California, Santa Barbara. His research focuses on algebraic combinatorics, particularly in reflection groups, braid groups, and Catalan combinatorics. Key contributions include work on noncrossing Catalan structures, dynamical algebraic combinatorics, and bijections in plane partitions. He has received UTD’s Outstanding Teaching Award (2020) and completed the ACUE Effective College Instruction program (2021). Williams has advised numerous students, including Ph.D. and honors thesis candidates, and serves on UTD’s website/computer committee and K-12 outreach initiatives. His recent grants include Simons Foundation and NSF proposals targeting combinatorial tools and braid varieties. Education: Ph.D. in Mathematics (2013, UMN), B.A. in Mathematics (2008, Carleton College) Research Interests: Coxeter groups, noncrossing partitions, combinatorial representation theory Teaching contributions include 33 courses at UTD, with a focus on discrete mathematics and graduate combinatorics. He organizes workshops like BIRS’s Dynamical Algebraic Combinatorics and co-edits the Annals of Combinatorics .

• Machine Learning
• Chemoinformatics
• Graph Theory
• +3 more

Benoit Gauzère is affiliated with the DIEM Department at the University of Salerno, Italy. His research focuses on kernel methods applied to graph structures, particularly in chemoinformatics for predicting molecular properties. He defended his thesis in 2015, titled 'Kernels on graphs,' which addressed scalable similarity measures for molecular graphs. His work integrates graph theory, machine learning, and chemoinformatics to enhance predictive models. He has participated in numerous academic events, including the 15th IAPR International Workshop on Document Analysis Systems (DAS 2022) and conferences like CICLing 2019. He contributed to projects such as CircularSeas and NewsEye, and presented at seminars on topics like multiple kernel learning and graph edit distance computation. Benoit has been involved in organizing and participating in workshops, doctoral conferences, and science festivals, including events at the University of La Rochelle and the SATT AST. His contributions span academic outreach, innovation awareness, and interdisciplinary collaborations in computer science and applied mathematics.

Alexandr AndoniView profile

Associate Professor

• Algorithmic Foundations of Massive Data
• Sublinear Algorithms
• High-Dimensional Computational Geometry
• +4 more

Alexandr Andoni is an Associate Professor in the Department of Computer Science at Columbia University and a member of the Data Science Institute. His research focuses on the algorithmic foundations of massive data, including sublinear algorithms, high-dimensional computational geometry, and machine learning. He holds a PhD from MIT (2009) under Piotr Indyk, with postdoctoral work at Princeton University and visits at NYU and the Institute for Advanced Study (IAS). He previously worked at Microsoft Research Silicon Valley and the Simons Institute for the Theory of Computing at UC Berkeley. His research interests span sublinear algorithms (streaming, property testing), metric embeddings, and Locality-Sensitive Hashing (LSH). He maintains an active LSH page and the FALCONN software package. He teaches graduate courses on algorithms for massive data and advanced algorithms. Andoni advises a cohort of brilliant researchers, including current students Negev Shekel Nosatzki and Shunhua Jiang, and has mentored former students and postdocs like Ilya Razenshteyn and Peilin Zhong. His work bridges theoretical computer science with practical applications in data analysis and machine learning.

• Pattern Recognition
• Applied Machine Learning
• Document Analysis
• +7 more

Fischer Andreas is a Professor at HES-SO (University of Applied Sciences and Arts Western Switzerland) , specifically affiliated with the School of Engineering and Architecture of Fribourg (HEIA-FR) . His research spans Pattern Recognition , Applied Machine Learning , Handwriting Recognition , and Graph-based Methods . He has secured significant grants from TAINA Technology , Swisscom , and the Hasler Foundation for projects including automatic handwriting recognition for tax forms , Swiss German translation , and graph-based keyword spotting in Vietnamese steles . Education : BSc in Computer Science from HEIA-FR. Research Highlights : Graph Neural Networks for Büchi automata classification Annotation-free alignment in historical documents Graph edit distance optimization for keyword spotting Hybrid deep learning for Vietnamese stele analysis Graph-based tumor budding analysis in digital pathology Collaborative Impact : Fischer's work bridges historical document analysis and medical imaging , demonstrated through frameworks like DIVA-DAF and tools like GammaFocus for histopathology. His 2024 conferences suggest ongoing exploration of LLM integration in document processing and OCR-free models for information extraction.

• Algorithms
• Data Structures
• String Algorithms
• +3 more

Panagiotis Charalampopoulos is a Lecturer (Assistant Professor) in the Department of Informatics at King's College London, part of the School of Computing and Mathematical Sciences. He holds a PhD in Computer Science from King's College London, an MSc in Advanced Computing from the same institution, and a BA in Mathematics from the University of Cambridge. Previously, he was a Lecturer at Birkbeck, University of London and a postdoctoral fellow at Reichman University and the University of Haifa. His research focuses on algorithms and data structures, particularly string algorithms (e.g., pattern matching, indexing) and planar graph algorithms (e.g., shortest path computations). Applications of his work include bioinformatics, text mining, and medical imagery analysis. He teaches modules such as 'Software and Programming I' and 'Problems in Mathematics.' His research has produced impactful contributions in both theoretical and applied domains, addressing challenges in efficient algorithm design and practical implementations. His work often bridges foundational computer science principles with real-world applications. Dr. Charalampopoulos' publications span topics like resilient pattern mining, efficient string processing, and planar graph optimization, reflecting his dual focus on algorithmic efficiency and practical utility. His academic journey includes research internships at the University of Warsaw and IDC Herzliya, highlighting his international collaboration and expertise in computational theory.

Tomasz KociumakaView profile

Professor

• String Algorithms
• Pattern Matching
• Edit Distance
• +5 more

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.

Isabel ValeraView profile

Professor

• Machine Learning
• Fairness in AI
• Causal Inference
• +7 more

Isabel Valera is a full Professor in the Department of Computer Science at Saarland University, Saarbrücken, Germany. She also holds an adjunct faculty position at the Max Planck Institute for Software Systems (MPI-SWS) and is a fellow of the European Laboratory for Learning and Intelligent Systems (ELLIS), contributing to the Robust Machine Learning Program and the Saarbrücken AI & Machine Learning (Sam) Unit. Research Interests: Her work lies at the intersection of machine learning, fairness, and causality. She focuses on developing methods for algorithmic recourse , fair decision-making , causal modeling , and robust learning . She is particularly interested in designing models that provide actionable explanations and ensure equitable outcomes in AI systems. Her research leverages probabilistic modeling, variational inference, and deep generative architectures. Publication Trends: Her recent publications (2021–2023) show a strong emphasis on algorithmic recourse , fairness under uncertainty , causal representation learning , and multimodal and graph-based generative models . She frequently publishes in top-tier venues such as NeurIPS, ICML, ICLR, AAAI, and FAccT, often in collaboration with leading researchers like Bernhard Schölkopf and Zoubin Ghahramani. Scientific Awards and Fellowships: Humboldt Post-Doctoral Fellowship Minerva Fast Track Fellowship (Max Planck Society) ELLIS Fellow Advising and Grants: She has mentored several early-career researchers who are now active contributors in machine learning, including Adrián Javaloy and Amir-Hossein Karimi. While specific grant details are not listed, her leadership roles and fellowships indicate sustained funding support from major research organizations. She has served as a co-editor for major conferences such as AISTATS and ECML PKDD, demonstrating her active role in the academic community. Labs and Research Groups: She leads or has led research groups at both Saarland University and the Max Planck Institutes (MPI for Intelligent Systems and MPI for Software Systems), focusing on foundational and applied aspects of machine learning with societal impact.

Jiri KosinkaView profile

Associate Professor

• Computer Graphics
• Computational Geometry
• Medical Visualization
• +5 more

Jiri Kosinka is an Associate Professor at the University of Groningen , affiliated with both the Faculty of Science and Engineering and the Faculty of Medical Sciences/UMCG . His work bridges Scientific Visualization and Computer Graphics with Robotics and Image-Guided Surgery . Research spans computational geometry, medical visualization, and fluid dynamics Key contributions in subdivision surfaces, distance transforms, and point cloud processing Recent publications focus on 3D surgical planning , turbulent flow simulations , and medical image analysis . His work integrates deep learning techniques for geometry processing and virtual reality applications in medical education. Notable collaborations include interdisciplinary projects with UMCG and Siemens . Awards and grants are not explicitly listed in the provided data.

Soumen ChakrabartiView profile

Professor

• Graph Neural Networks
• Knowledge Graphs
• Natural Language Processing
• +3 more

Soumen Chakrabarti is a Professor in the Department of Computer Science and Engineering at Indian Institute of Technology Bombay. His work bridges graph theory, natural language processing, and knowledge representation, focusing on scalable solutions for complex information retrieval problems. He has held visiting positions at Carnegie-Mellon University and Google. Education: B.Tech from IIT Kharagpur, PhD from UC Berkeley Key research areas: Graph Neural Networks, Knowledge Graphs, Code-Switched Text Analysis, Temporal Reasoning His recent publications emphasize neural graph matching , code-switched NLP , and multi-modal QA systems , often integrating symbolic reasoning with deep learning. Awards include the WWW 1999 Best Paper Award and ECML/PKDD 2008 Best Student Paper . He leads projects like CRUSH4SQL for text-to-SQL parsing and CSAW for annotated web search. His lab collaborates with IBM and Google on graph representation learning and temporal knowledge graph completion.

Anthony PEREZView profile

Lecturer

• Parameterized Complexity
• Graph Algorithms
• Community Detection
• +3 more

Anthony PEREZ is a Lecturer in Computer Science at the University of Orleans, affiliated with LIFO (Laboratory of Fundamental Computer Science of Orleans) and part of the Graphs, Algorithms, and Computational Models team. He previously held a full-time ATER position at the same university from 2011 to 2012 and completed his PhD at LIRMM (Montpellier II University) under the supervision of Stéphane Bessy and Christophe Paul. Research Focus: Parameterized complexity, graph modification problems, community detection in directed graphs, and sparse graph embeddings Software Development: Created SINr (sparse interpretable node representations), Directed Louvain (modularity optimization for directed networks), and GeST (graph embedding-based image segmentation) His research explores polynomial kernelization for graph editing problems, particularly in triangulated graph subclasses, and challenges the common practice of ignoring edge direction in community detection algorithms. He has co-supervised multiple PhD students including Joanne Dumont and Maël Dumas, and collaborates extensively on graph-theoretical problems with applications to social network analysis.

• Automata theory
• Formal language theory
• Combinatorics on words
• +2 more

Giuseppa Castiglione is a Researcher (INFO-01/A) in the Department of Mathematics and Computer Science at the University of Palermo's School of Basic and Applied Sciences. She maintains regular office hours on Tuesdays and Thursdays from 2:00 PM to 3:00 PM in Room 209, second floor, and teaches Theoretical Computer Science for both Computer Science and Mathematics degree programs. Her primary research interests span several interconnected areas within theoretical computer science and discrete mathematics. These include automata theory, formal language theory, combinatorics on words, enumerative combinatorics, and discrete two-dimensional structures. Her work demonstrates a consistent focus on the mathematical foundations of computer science, particularly in the analysis of string algorithms, word combinatorics, and discrete geometric structures. Analysis of her publication record from 2017-2025 reveals a strong concentration on combinatorial aspects of words and automata, with recent work expanding into isometric words, Fibonacci cubes, and Wheeler automata. Her research shows a progression from foundational work on polyominoes and automata minimization toward more complex combinatorial structures and their applications in string processing. Castiglione has participated in numerous significant research projects including the Prin 2010-11 project on mathematical aspects of automata and formal languages, the PO FESR 2007/2013 Neverlost project, PON Research and Competitiveness 2007/2013 SMART HEALTH initiative, and GNCS 2016 on combinatorial methods for handling large data sets. These projects demonstrate her ability to secure competitive research funding across multiple domains. She collaborates with researchers including Marinella Sciortino, Gabriele Fici, Chiara Epifanio, Sabrina Mantaci, and Antonio Restivio, forming a productive research network focused on theoretical computer science and combinatorics. Her extensive publication record in high-quality venues demonstrates her sustained contribution to the field over more than two decades.