جست‌وجوی استاد و پژوهشگر

Marc Olanoمشاهده پروفایل

دانشیار

• Computer Graphics
• Real-Time Rendering
• Graphics Hardware
• +۴ مورد دیگر

Marc Olano is an Associate Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC), and serves as the Associate Dean of Academic Programs and Learning in the College of Engineering and Information Technology. He leads the Computer Science Game Development Track and co-directs the VANGOGH lab. His research focuses on interactive 3D computer graphics, programmable shading, graphics hardware, and surface appearance modeling, with contributions to foundational graphics technologies like procedural shading and normal mapping. Research Interests: Olano’s work spans real-time rendering, GPU algorithms, texture compression, and procedural shading. He has pioneered techniques such as LEAN mapping and variable bitrate texture compression, significantly impacting game development and real-time graphics. His research often explores the intersection of hardware capabilities and algorithmic innovation, with applications in medical visualization, visualization of scientific data, and haptic interaction. Key Contributions: Olano’s accomplishments include pioneering procedural shading on graphics hardware, developing homogeneous rendering techniques, and advancing normal mapping. His work on GPU-based curvature estimation and BT volumes for volume rendering exemplifies his focus on leveraging GPU parallelism for real-time visualization challenges. He has also contributed to standards in shading languages and GPU programming. Teaching & Mentorship: Olano teaches courses in computer graphics, game development, and advanced computer architecture. He mentors students in independent studies and has advised numerous MS theses exploring topics like GPU random number generation, volume rendering, and soft shadow algorithms. His students’ work often bridges theoretical research and practical GPU implementations. Labs & Projects: The VANGOGH lab under his co-direction focuses on advanced visualization and graphics research, including real-time rendering techniques, GPU algorithms, and interactive data visualization. His research collaborations span industry partners like Firaxis Games, contributing to titles such as Civilization V through texture compression innovations.

Aviad Rubinsteinمشاهده پروفایل

استادیار

• Algorithms
• Complexity Theory
• Game Theory
• +۳ مورد دیگر

Aviad Rubinstein is an Assistant Professor of Computer Science at Stanford University, specializing in theoretical computer science with a focus on algorithms, complexity, and game theory. He has taught courses such as Design and Analysis of Algorithms (CS161), Incentives in Computer Science (CS269i), and Topics in Intractability (CS354). His research interests include approximation algorithms, computational complexity, and fair division, with notable work on envy-free cake-cutting and prophet inequalities. He advises several PhD students including Joshua Brakensiek and Ruiquan Gao, and mentors postdocs like Soheil Behnezhad. His undergraduate mentoring includes students from Tsinghua and Berkeley. Rubinstein has received the Kalai Prize from the Game Theory Society and a FOCS Best Paper Award for his work on inapproximability of Nash equilibria. Rubinstein co-authored Algorithms for Toddlers with Mary Wootters, a book simplifying computational concepts for younger audiences. He also organizes workshops on topics like fine-grained complexity and early career mentoring in computer science. Beyond academia, he consults part-time for the blockchain startup Lava. His research frequently bridges theoretical foundations with practical implications, such as developing algorithms with real-world applications in auctions, mechanism design, and optimization under constraints.

Diptarka Chakrabortyمشاهده پروفایل

استادیار

• Theoretical Computer Science
• Algorithms on large data sets
• Sublinear algorithms
• +۵ مورد دیگر

Diptarka Chakraborty is an Assistant Professor in the Department of Computer Science at the School of Computing, National University of Singapore (NUS). He obtained his PhD from the Indian Institute of Technology Kanpur in 2016 and held postdoctoral positions at Charles University, Prague, and the Weizmann Institute of Science, Israel. His educational background includes: PhD in Computer Science and Engineering, Indian Institute of Technology Kanpur, 2016 Dr. Chakraborty's research lies at the intersection of Theoretical Computer Science and Algorithms , specializing in sublinear algorithms , approximation algorithms , graph algorithms , and data structures for massive datasets. His work addresses computational hardness in edit distance, rank aggregation, and fault-tolerant graph structures, with recent emphasis on algorithmic fairness and streaming applications. Analysis of his 2020-2025 publications reveals dominant trends in fairness-constrained algorithms (e.g., fair rank aggregation) and scalable techniques for massive data (e.g., clustering permutations in streaming models), alongside foundational contributions to equivalence testing, model counting, and reachability under failures. His scientific contributions have been recognized with: Faculty Teaching Excellence Award 2024 Google South & Southeast Asia Research Award 2022 Best Paper Award at FOCS 2018 IBM Research India Outstanding Ph.D. Student Award 2015-16 Dr. Chakraborty actively mentors two PhD students and multiple research fellows while leading three major research projects: massive data handling under edit metrics, fair rank aggregation, and fault-tolerant graph structures. His Google Research Award supports investigations into computational challenges of fairness. He maintains an active research group with international collaborations and regular seminar participation across institutions including IITs, TIFR, and UC campuses.

• Clustering
• Edit Distance
• Approximation Algorithms
• +۹ مورد دیگر

Debarati Das is an Assistant Professor in Computer Science and Engineering , specializing in Clustering Algorithms , Edit Distance , and Approximation Algorithms . Her research focuses on theoretical computer science, particularly in algorithm design for data streams, permutation clustering, and sequence alignment. Grants: NSF CAREER Award (2024), NSF Student Travel Grant (2023). Her work includes breakthroughs in consensus clustering, achieving sub-2-approximation, and developing space-efficient algorithms for edit distance in distributed models. Recent projects explore dynamic shortest paths in planar graphs and pseudorandomness extraction. Her publications span journals like the Journal of the ACM and conferences such as SODA, STOC, and FOCS. Collaborations include researchers from institutions in the U.S. and Europe, with applications in computational biology and parallel computing.

• Computer Science
• Parallel Computing
• Dynamic Programming
• +۳ مورد دیگر

Jerry Lacmou Zeutouo is a Lecturer in the field of Networks and Data at Université de Picardie Jules Verne (UPJV), contributing to computer science research through advanced algorithm development and optimization. His work primarily focuses on parallel computing, dynamic programming, and computational biology. Academic Rank: Lecturer at UPJV Institution: Université de Picardie Jules Verne Research Interests: Dr. Zeutouo specializes in designing and optimizing parallel algorithms for dynamic programming and database security. His research spans: Coarse-Grained Multicomputer (CGM) architectures K-anonymity in distributed databases RNA folding and bioinformatics applications Longest Common Subsequence (LCS) constraints Publication Trends: His work emphasizes solving computational bottlenecks via parallelism, particularly in cloud environments, bioinformatics, and database security. Key themes include: Transformer-based models for cold start mitigation in FaaS Multicomputer algorithms for dynamic programming Four-splitting and Four-Russians techniques for optimization

• string algorithms
• data structures
• graph algorithms
• +۵ مورد دیگر

Shay Golan is a Senior Lecturer in the Computer Science department at Ariel University, where he teaches core algorithms and data structures courses. Previously, he held postdoctoral positions at Reichman University, Haifa University (hosted by Shay Mozes and Oren Weimann), and was a Fulbright Postdoctoral Fellow at UC Berkeley's EECS department (hosted by Jelani Nelson). He earned his Ph.D. in Computer Science from Bar-Ilan University under the supervision of Ely Porat and Tsvi Kopelowitz. Dr. Golan's primary research focuses on string algorithms with emphasis on streaming, small space, and dynamic settings. His work spans multiple subfields including data structures, graph algorithms, and discrete algorithms. He has made significant contributions to pattern matching, edit distance problems, and string compression techniques, with applications extending to bioinformatics for DNA sequence analysis. His recent publication trajectory (2022-2026) reveals a strong focus on string algorithmic challenges across multiple dimensions: theoretical complexity analysis (LZ77 compression, string covers), specialized string problems (2D string matching, hairpin structures), graph algorithm applications (planar graph distance oracles), and bioinformatics implementations (DNA minimizers). His work consistently appears in top theoretical computer science venues including STOC, SODA, and CPM, demonstrating both theoretical depth and practical relevance. Fulbright Postdoctoral Fellow at UC Berkeley As an educator, Dr. Golan has consistently taught foundational computer science courses including Algorithms 1, Algorithms 2, Data Structures, and Computability since 2016. His research is supported through collaborations with leading institutions including Reichman University, Haifa University, and UC Berkeley. He maintains active research partnerships with prominent computer scientists including Itai Boneh, Ely Porat, and Tsvi Kopelowitz. Dr. Golan participates in the broader theoretical computer science community through conference presentations and workshop participation, with recorded talks available for several of his publications. His work bridges theoretical computer science with practical applications, particularly in computational biology and genomic sequence analysis.

Marko Đukanovićمشاهده پروفایل

استادیار

• Combinatorial Optimization
• Graph Theory
• Bioinformatics
• +۴ مورد دیگر

Marko Đukanović serves as an Assistant Professor at the Department of Computer and Information Sciences within the Faculty of Natural Sciences and Mathematics at the University of Banja Luka. His academic career spans combinatorial optimization, graph theory, and bioinformatics applications, with significant contributions to algorithm design for complex computational problems. His research focuses on combinatorial optimization , particularly in graph domination problems and longest common subsequence variants . His work bridges theoretical computer science with practical applications in bioinformatics, social network analysis, and computational biology. Recent publications demonstrate expertise in developing advanced algorithms including biased random-key genetic algorithms, variable neighborhood search, and neural network-guided optimization techniques. Key publication trends show his specialization in solving constrained optimization problems on graphs, with growing emphasis on integrating machine learning components into traditional combinatorial algorithms. His 2024 publications in IEEE Transactions on Evolutionary Computation and Applied Mathematics and Computation represent significant contributions to the field. Best Paper Award at EvoStar Conference 2024 for 'A Neural Network Based Guidance for a BRKGA: An Application to the Longest Common Square Subsequence Problem' Dr. Đukanović actively participates in international academic collaboration, including recent panel discussions on knowledge transfer between Bosnia and Herzegovina and global expert communities. His work demonstrates strong international collaboration, particularly with researchers from TU Wien and institutions across Europe.

Milana Grbicمشاهده پروفایل

استادیار

• Chemistry
• Physics
• Computer Science
• +۳ مورد دیگر

Dr. Milana Grbić is an Assistant Professor at the Faculty of Natural Sciences and Mathematics, University of Banja Luka. She is actively involved in academic and research activities across multiple disciplines, including Chemistry, Physics, Computer Science, and Education. Her roles include organizing preparatory classes for entrance exams, coordinating international projects like the Erasmus+ program, and contributing to events such as the Science Festival and Republic Physics Competition. Research interests span interdisciplinary fields, with a focus on bioinformatics (protein complex analysis, PPI networks), graph theory (Roman domination problems, Kneser graphs), and computational biology (sequence alignment, algorithm design). She also engages in environmental and public health studies, such as the impact of weather on disease spread and clustering of pandemic data. Dr. Grbić collaborates on projects like the ChemTeach Erasmus+ initiative for teacher training and the SMART ERA project for rural tourism innovation. She actively participates in university governance, including agreements with institutions like the Institute of Forensic Medicine and the Children’s Education Association “Mio Mondo” to enhance educational outreach. Her contributions include organizing workshops, conferences, and student engagement programs, emphasizing science communication and community resilience. Recent research trends highlight her expertise in algorithm development for biological networks, theoretical graph problems, and applied studies in pandemic analysis and environmental policy. Her work bridges academia and practical applications, fostering collaboration between universities, industries, and governmental bodies.

• Algorithms
• Bioinformatics
• Computational Complexity
• +۴ مورد دیگر

Maria Jose Blesa Aguilera is a Professor at the Department of Computer Science, Polytechnic University of Catalonia, affiliated with the ALBCOM research group (Algorithms, Bioinformatics, Complexity, and Formal Methods). She has 198 academic activities recorded, including publications, conference papers, and teaching innovation projects. Research focus spans algorithms, bioinformatics, network science, and educational technology. Active in competitive R+D+I projects like Modelos y Técnicas para el Procesamiento de Información a Gran Escala . Collaborates on network alignment algorithms with Christian Blum and others, contributing to AntNetAlign software. Key conferences: European Conference on Operational Research, ANTS, CSEDU. Recent publications address data reduction in color quantization, network alignment via ant colony optimization, influence propagation in social networks, and educational well-being metrics. Her work intersects theoretical computer science with practical applications in multimedia systems, swarm intelligence, and educational innovation. She holds an ORCID (0000-0001-8246-9926) and participates in open-access initiatives. Contact: mjblesa@cs.upc.edu .

David Becerraمشاهده پروفایل

مدرس ارشد

• Computational Biology
• Bioinformatics
• Algorithms
• +۵ مورد دیگر

David Becerra is a Senior Lecturer in the School of Computer Science at McGill University, where he joined in 2020. His expertise spans computational biology, algorithms, and programming competitions, with a focus on interdisciplinary applications in life sciences. Dr. Becerra earned his PhD in Computer Science with emphasis in Bioinformatics from McGill University in 2017 under Professor Jérôme Waldispühl. He completed a postdoctoral fellowship at the Terrence Donnelly Centre for Cellular & Biomolecular Research (2018-2020) supervised by Professor Philip Kim. His undergraduate and master's degrees are from Universidad Nacional de Colombia, where he worked with Professor Luis Niño. His research integrates machine learning, bio-inspired computing, and optimization to solve molecular biology problems at sequential and structural levels. Key areas include protein structure prediction, molecular docking, sequence alignment, and algorithm development for computational biology. He applies these techniques to model complex life science phenomena through computational simulation and data mining. Analysis of his 15 most recent publications reveals a strong trajectory in computational structural biology, with increasing use of deep learning (particularly graph neural networks) for protein design and folding. His work consistently bridges theoretical algorithms with practical biological applications, emphasizing multi-objective optimization approaches. Qualified for ICPC World Finals (2020, 2021) Dr. Becerra actively mentors students through undergraduate research projects (COMP396/400/401), course instruction (COMP204/251), and programming competition teams. He fosters an inclusive environment where students develop algorithmic confidence and interpersonal skills through collaborative problem-solving. He leads McGill's competitive programming initiative, which has created a thriving community preparing students for international competitions like the ICPC. This program emphasizes both technical excellence and team cohesion, resulting in consecutive world final qualifications.

• Combinatorial Optimization
• Metaheuristics
• Evolutionary Computation
• +۸ مورد دیگر

Günther Raidl is an Associate Professor at the Vienna University of Technology's Faculty of Informatics, specializing in the Algorithms and Complexity department. His research focuses on combinatorial optimization, metaheuristics, evolutionary computation, and operations research, with applications in logistics, bioinformatics, and scheduling. He leads projects on algorithmic problems, including vehicle routing, scheduling, and network design. His academic career includes extensive contributions to heuristic optimization techniques, mathematical programming, and the development of efficient algorithms. He has collaborated with institutions like Honda Research Institute Europe and Alfie's GmbH on projects involving e-mobility optimization, battery station planning, and dynamic vehicle routing. Raidl's work emphasizes practical applications of theoretical research, bridging algorithm development with real-world challenges in logistics and transportation. His recent publications address problems like longest common subsequence algorithms, Roman domination in graphs, and scheduling with interactive human availability learning. Key projects include studies on cooperative optimization for service point distribution, electric dial-a-ride systems, and battery swapping station location planning. He teaches courses on heuristic optimization, algorithms, and mathematical programming, contributing to both education and industry-relevant research.

Xin Liمشاهده پروفایل

دانشیار

• Theory of Computation
• Randomness Extractors
• Pseudorandomness
• +۶ مورد دیگر

Xin Li is an Associate Professor in the Department of Computer Science at the Whiting School of Engineering, Johns Hopkins University. He is a member of the theory group and conducts research in the theory of computation, with a focus on randomness in computation, complexity theory, coding theory, and cryptography. He has advised several Ph.D. students and is actively involved in organizing seminars and serving on program committees of major theoretical computer science conferences. Ph.D., University of Texas at Austin, 2011 B.S. and M.S., Tsinghua University, Beijing, China His research interests lie broadly in theoretical computer science, particularly in pseudorandomness , randomness extractors , and explicit combinatorial constructions . He has made significant contributions to the construction of two-source extractors and non-malleable codes. His earlier work includes research in quantum computing and human-computer interaction. The most recent publications show a strong trend in coding theory (especially insertion-deletion codes), extractors (two-source, affine, non-malleable), and streaming algorithms. His work combines deep combinatorial insights with applications in cryptography and complexity theory. Scientific Awards and Honors: Simons Postdoctoral Fellowship NSF CAREER Award CCF-1845349 Invited to Theory of Computing Special Issue (RANDOM 2018) Invited to SICOMP Special Issue (FOCS 2013) SICOMP Special Issue (FOCS 2011) Advising and Grants: Xin Li has advised multiple Ph.D. students including Kuan Cheng, Zhengzhong Jin, Yu Zheng, Songtao Mao, and Yan Zhong. He has served as a postdoctoral mentor and research assistant supervisor. He is supported by several grants including NSF Award CCF-1617713, NSF CAREER Award CCF-1845349, and the Johns Hopkins Catalyst Award. He co-organizes the theory seminar at CS@JHU and has served on the program committees of STOC, RANDOM, and other top conferences. Labs and Teams: Xin Li is a member of the theory group at the Johns Hopkins Department of Computer Science, which focuses on foundational aspects of computation, algorithms, and complexity.

Aleksandar Karteljمشاهده پروفایل

دانشیار

• Optimization
• Data mining
• Computational biology
• +۴ مورد دیگر

Aleksandar Kartelj is an Associate professor at the Department for Computer Science, Faculty of Mathematics, University of Belgrade. He has been working at the Faculty of Mathematics since 2008, starting as a Teaching assistant, then becoming Assistant professor (2015-2023), and currently Associate professor (since October 2023). He also serves as a Guest professor at the Faculty of Natural Science and Mathematics, University of Banjaluka since 2018. Education: B.Sc. in Computer Science from Faculty of Mathematics, University of Belgrade (2005-2008, GPA 9.94/10.00) M.Sc. in Computer Science from Faculty of Mathematics, University of Belgrade (2008-2010, GPA 9.92/10.00) PhD in Computer Science from Faculty of Mathematics, University of Belgrade (2010-2014, GPA 10.00/10.00) M.Sc. in Quantitative Finance from Faculty of Economics, University of Belgrade (2010-present) Aleksandar Kartelj's research primarily focuses on optimization and data mining. He is a member of the "Modelling and optimization group" at the Faculty of Mathematics. His work involves designing exact and non-exact optimization techniques such as integer (linear) programming models, branch & bound, beam search, variable neighborhood search, and genetic algorithms. These techniques are applied to solve NP-hard problems in computational biology, transportation, logistics, social networks, and for improving machine learning algorithms. His recent publications (2021-2025) span diverse areas including symbolic regression, constrained longest common subsequence problems, graph protection algorithms, Roman domination in graphs, and applications in computational biology and epidemiology. His work frequently appears in high-impact journals such as Journal of Big Data, Expert Systems with Applications, and Knowledge-Based Systems, with many publications classified as M21a (Q1). Scientific Awards: Best paper award at the 2023 Second Serbian International Conference on Applied Artificial Intelligence (SICAAI) for "Integrating Top-level Constraints into a Symbolic Regression Search Algorithm" Aleksandar Kartelj has been involved in several research projects, including being a Research member on the project "Mathematical Models and Optimization Methods for Large-Scale Systems" (2011-2020) and a Research leader on the project "Combinatorial optimization for cancer progression inference and comparison" (2019-present). He has also developed numerous software applications for various organizations including the UN FAO, International Monetary Fund, and several private companies. He has been actively involved in teaching, serving as a Computer science teacher at Computer Gymnasium and Mathematical Grammar School in Belgrade, in addition to his university teaching responsibilities.

• Combinatorial Optimization
• Machine Learning
• Operations Research
• +۳ مورد دیگر

Marc Huber is a PreDoc Researcher (academic rank: Researcher) at the Institute of Logic and Computation within the Faculty of Informatics at TU Wien. Currently on leave, he conducts research at the intersection of combinatorial optimization and machine learning, focusing on algorithmic innovations for complex scheduling and sequence problems. His core research interests include: Combinatorial Optimization Machine Learning for Optimization Beam Search and Metaheuristics Staff and Flow Shop Scheduling Sequence Problems (Longest/Shortest Common Subsequence) Algorithm-ML Integration Huber's publication trajectory (2021-2024) reveals a consistent methodological evolution: progressively integrating reinforcement learning and value function approximation into beam search frameworks. This approach has yielded significant advances in staff rostering, multi-agent path finding, and sequence alignment, demonstrating how learned heuristics outperform traditional optimization techniques in solution quality and computational efficiency. Scientific Awards: No awards, fellowships, or medals were documented in available sources. As a thesis supervisor, Huber has guided four master's students at TU Wien through research on graph neural networks for clique problems, reinforcement learning for flow shop scheduling, e-mobility fleet scheduling, and staff resortering algorithms. His advising emphasizes practical implementation of learning-based optimization techniques. He operates within the Algorithms and Complexity research group (E192-01), collaborating closely with Prof. Raidl on algorithm engineering projects funded through TU Wien's research infrastructure. Current work focuses on extending learning beam search to multi-objective combinatorial problems.

Rolf Fagerbergمشاهده پروفایل

استاد

• Algorithms
• Data Structures
• Computational Biology
• +۷ مورد دیگر

Rolf Fagerberg is a Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark (SDU). His research centers on algorithms, data structures, and their applications in computational biology and cheminformatics. His research interests include: Algorithms and data structures, particularly dynamic and geometric data structures Graph theory with emphasis on subgraphs, Yao graphs, and hypergraphs Algorithmic cheminformatics and modeling of chemical reaction networks Computational biology, including metabolic pathway analysis Theoretical computer science and discrete mathematics Recent publications highlight a strong trend toward interdisciplinary research combining computer science with chemistry and biology, particularly in modeling chemical reaction networks using hypergraphs and mixed-integer linear programming. His work also includes algorithmic solutions for palindromic subsequence problems and efficient extraction of reaction rules from large databases, reflecting a blend of theoretical and applied algorithm design. Rolf Fagerberg has served as senior coordinator on multiple research projects, including 'Algorithmic Cheminformatics' and 'Fundamental Data Structures', funded by the Danish Ministry of Higher Education and Research. He has also contributed to peer review and editorial work for major conferences such as the ACM Symposium on Parallelism in Algorithms and Architectures and the International Symposium on Experimental Algorithms. He has supervised PhD students and is actively involved in academic service, including membership in assessment committees at Aarhus University and IT University of Copenhagen. His research has received media attention, including coverage of a 'mathematical breakthrough' and applications in understanding intestinal systems in obesity.