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Gaia NicosiaView profile

Professor

• Combinatorial Optimization
• Operations Research
• Scheduling
• +7 more

Professor Gaia Nicosia is a Full Professor of Operations Research at the Department of Civil, Computer Science and Aeronautical Technologies Engineering of Roma Tre University. She serves as the Deputy Director of the Department starting November 1, 2024, for the academic triennium 2024/2027. Previously, she held positions as Associate Professor (2012-2022) and Assistant Professor (2002-2011) at the same institution. Her educational background includes a Laurea degree in Mathematics from the University of Rome "Tor Vergata" (1995) and a Ph.D. in Operations Research from the University of Rome "La Sapienza" (1999). Part of her doctoral curriculum was completed at the Department of Systems and Industrial Engineering at the University of Arizona in Tucson. Professor Nicosia's research focuses on combinatorial optimization models for planning and scheduling problems, with applications in manufacturing and logistics. Her work encompasses the characterization of problems in terms of computational complexity, development of exact, heuristic, randomized, approximate and online combinatorial algorithms, and validation of proposed approaches through computational experiments. Her main research areas include: Scheduling Graph Optimization Knapsack Problems Applications in Production Systems and Services Multi-agent Systems Logistics and Transportation Her recent publications demonstrate a strong focus on multi-agent scheduling problems, fairness-utility tradeoffs, robust optimization, and applications in healthcare and logistics. She has developed sophisticated models for flow shop scheduling with inter-stage flexibility, organ donation process simulation, meal delivery scheduling, and Stackelberg games applied to knapsack problems. Professor Nicosia has received recognition through her editorial roles, serving as Associate Editor of the Central European Journal of Operations Research (Springer) since November 2024. She has also guest edited special issues of Discrete Applied Mathematics dedicated to CTW2020 and CTW2011 conferences. She has supervised approximately 50 Bachelor theses, 40 Master theses, and advised 4 Ph.D. students in Computer Science and Automation at Roma Tre University. Her teaching portfolio includes courses such as Decision Support Systems and Analytics, Decision-Making Methods Laboratory, Algorithms and Models for Optimization, and Operations Research II. Professor Nicosia has been actively involved in numerous research projects funded through competitive national and international calls, including PRIN2017 "AHeAD: efficient Algorithms for HArnessing networked Data" (2019-2022) and PRIN2012 "AMANDA: Algorithmics for MAssive and Networked DAta" (2014-2017). She has served on program committees for multiple international conferences including ODS 2025, ICORES 2025, and various Cologne-Twente Workshops.

• Computational Topology
• Geometry
• Linear Algebra
• +3 more

Claudia LANDI is a Full Professor at the University of Modena and Reggio Emilia, affiliated with the Department of Engineering Sciences and Methods. Her primary academic rank is Professor, focusing on research in computational topology, geometry, and their applications. She teaches courses such as Computational Topology, Geometry and Linear Algebra, and Additional Training Requirements for engineering programs. Her work integrates algebraic topology with data science, emphasizing topological data analysis (TDA) for biomedical and environmental applications. Research Interests: Dr. LANDI specializes in computational topology, with a focus on persistent homology, Reeb graphs, and discrete Morse theory. Her applied work spans proteomics, fibrosis treatment, and environmental safety assessments. She has contributed to developing algorithms for multi-parameter persistence and topological metrics, bridging theoretical mathematics with practical applications in imaging and machine learning. Teaching: She leads courses on computational topology for mathematics and engineering students, emphasizing practical skills in TDA libraries and visualization. Her geometry and linear algebra courses are foundational for management and mechatronic engineering programs, integrating theoretical rigor with problem-solving exercises. Education: Not explicitly detailed in provided texts. Labs/Teams: Engaged in interdisciplinary projects involving proteomic analysis and topological data science. Grants/Awards: None explicitly mentioned, though her extensive publication record suggests active research funding.

• Computer Vision
• Biometrics
• Deep Learning
• +12 more

Sambit Bakshi is an active academic researcher in the field of computer science, affiliated with the Department of Computer Science and Engineering at the National Institute of Technology Rourkela, India. His research spans computer vision, biometrics, deep learning, and intelligent systems, with a strong emphasis on real-world applications in surveillance, healthcare, agriculture, and IoT. His primary research interests include biometrics (especially ear and periocular recognition), person re-identification , UAV-based image analysis , multimedia forensics , and fog/edge computing . He has contributed significantly to the development of deep learning models for object segmentation, anomaly detection, and secure biometric systems. The recent publications demonstrate a consistent trend in applying deep neural networks , attention mechanisms , and lightweight architectures to solve complex problems in visual recognition and data analytics. His work often appears in high-impact IEEE and ACM journals, reflecting strong technical rigor and relevance. Scientific Contributions: Co-edited special issues in IEEE Access and Image and Vision Computing Contributed to benchmark challenges such as the Unconstrained Ear Recognition Challenge 2019 Developed novel methods in ear biometrics, periocular recognition, and UAV-based segmentation Active in editorial roles and collaborative research across international teams Advising and Collaborative Research: Frequent collaborator with researchers from NIT Rourkela, University of Beira Interior (Portugal), and other international institutions Co-authored over 120 publications, indicating active supervision and mentoring of students Involved in interdisciplinary projects spanning AI, security, and healthcare Labs and Research Groups: Though not explicitly stated, his work suggests involvement in a computer vision and biometrics lab at NIT Rourkela, focusing on AI-driven solutions for societal and industrial applications.

Matt ClayView profile

Professor

• Geometric Group Theory
• Automorphisms of Free Groups
• Mapping Class Groups
• +3 more

Matthew T. Clay is a Professor and Chair of the Department of Mathematical Sciences at the University of Arkansas, Fayetteville. He holds a PhD in Mathematics from the University of Utah (2006) and has held academic positions at Allegheny College and the University of Oklahoma before joining the University of Arkansas in 2012. His research focuses on Geometric Group Theory , particularly on automorphisms of free groups, mapping class groups, and group actions on trees. He has organized conferences such as the Redbud Topology Conference and co-edited the book *Office Hours with a Geometric Group Theorist* (Princeton University Press, 2017). His work bridges geometric, algebraic, and dynamical perspectives in group theory. Key honors include NSF grants, a Simons Foundation Collaboration Grant, and the University of Utah’s Outstanding Graduate Student Award. He has advised three doctoral students and contributed to software tools for geometric group theory, such as *sage-train-track* and *bsscl*. Clay’s outreach includes organizing the G3 conference and co-hosting the *Running* section of his webpage, reflecting his passion for ultrarunning and its intersection with problem-solving resilience in mathematics.

Ljiljana BrankovicView profile

Professor

• Cybersecurity and Privacy
• Algorithms
• Graph Theory
• +1 more

Dr. Ljiljana Brankovic is a Professor in Computational Science at the School of Science and Technology, University of New England (UNE). She holds a PhD in Computer Science from the University of Newcastle (1998) and a Graduate Electrical Engineer degree from the University of Belgrade (1987). Her research spans cybersecurity and privacy , algorithms , graph theory , and privacy-preserving machine learning , with significant funding from the Australian Research Council, German Research Foundation, London Mathematical Society, and Royal Society, UK. Education : PhD (Computer Science), University of Newcastle (1998); Graduate Electrical Engineer, University of Belgrade (1987). Her work on privacy integrates technological , ethical , legal , and psychological aspects . In algorithms, she focuses on parameterized approximation algorithms for NP-hard problems. Google Scholar articles (2025–2017) reflect her expertise in combinatorial algorithms , graph theory , privacy-preserving techniques , and machine learning applications in domains like healthcare , explosion prediction , and botanical identification . 2025 : Diverging Assessment: A Student Perspective; Advancements in Medicinal Plant Identification Using Deep Learning. 2024 : Diverging assessments: what, why, and experiences; Incremental and Zero-Shot Machine Learning for Vietnamese Medicinal Plant Classification. 2023–2017 : Domination chain complexity, ambient intelligence for health, upper domination facets, face-antimagic graphs. While no explicit awards are listed, her research funding and editorial roles (e.g., special issues on combinatorial algorithms and privacy) underscore her contributions. She has also co-authored works on statistical database security , mobile agent security , and network privacy , with over 150 publications in theoretical and applied domains.

Ramon Ferrer-i-CanchoView profile

Associate Professor

• Quantitative Linguistics
• Computational Linguistics
• Complexity Science
• +3 more

Ramon Ferrer-i-Cancho is an Associate Professor in the Department of Computer Science at the Universitat Politècnica de Catalunya (UPC), affiliated with the Barcelona School of Informatics (FIB). He leads the Complexity & Quantitative Linguistics Lab and is part of the LQMC research group (Quantitative, Mathematical, and Computational Linguistics). His research focuses on quantitative linguistics, mathematical models of language structure, and computational methods to analyze linguistic universals like Zipf’s law and syntactic dependency distances. Education: PhD in Computer Science from UPC (2003), with a thesis on 'Language: universals, principles and origins.' Editorial Roles: Section editor of Encyclopedia of Language and Linguistics (3rd edition), editorial board member of Glottometrics, Journal of Quantitative Linguistics, and Lingua. Research Interests: Quantitative analysis of syntactic structures, language evolution, cognitive constraints in grammar, and cross-species communication patterns (e.g., chimpanzees, dolphins). He applies principles from complexity science, information theory, and optimization to linguistic problems. Notable Achievements: Recipient of the Joan Lluís Vives Prize (2003) for popular science communication, Barcelona City Research Prize (2003), and UPC’s Best PhD Thesis award (2003-2004). Authored influential papers on dependency distance minimization and Zipfian laws, with over 200 indexed publications. Teaching & Mentorship: Advised Master’s theses on dolphin communication, syntactic dependency distances, and data mining applications. Active in the Master in Innovation and Research in Informatics (MIRI), clarifying academic requirements like SIRI seminar credits. Outreach: Authored popular science books, including Lingüística quantitativa i lleis lingüístiques , and featured in media like National Geographic and Scientific American for bridging linguistics with biology and physics.

Virginia Vassilevska WilliamsView profile

Professor

• Theoretical Computer Science
• Algorithms
• Computational Complexity
• +2 more

Virginia Vassilevska Williams is Professor of Computer Science and Artificial Intelligence + Decision-making at MIT EECS. Her research focuses on theoretical computer science with emphasis on algorithms, computational complexity, and graph theory. She has made significant contributions to matrix multiplication complexity and fine-grained hardness results. Recent publications explore fundamental problems in graph algorithms including cycle detection, shortest paths, and clique enumeration. Her work demonstrates consistent advancement in understanding computational limits for graph problems and matrix operations. Key research themes include: Breaking barriers in matrix multiplication exponents Establishing hardness thresholds for approximation algorithms Developing efficient graph traversal methods for sparse structures Her 2024 publications continue this trajectory with refinements to the laser method for matrix multiplication and improved clique listing techniques. The research consistently pushes boundaries in algorithm optimality proofs and computational complexity theory.

Pål Grønås DrangeView profile

Associate Professor

• Parameterized Complexity
• Graph Algorithms
• Sparse Network Analysis
• +7 more

Pål Grønås Drange is an Associate Professor at the Department of Informatics, University of Bergen. His research focuses on parameterized complexity of graph algorithms, experimental algorithmics, and applications in machine learning and network interdiction. He leads projects on trustworthy AI and energy transition optimization. Dr. Drange holds a diploma in electronics, bachelor's in cognitive science, master's in logic for AI, and a PhD in algorithms. His work bridges computer science, mathematics, and engineering. Education: PhD in Algorithms (University of Bergen) Master's in Logic for AI Bachelor's in Cognitive Science Electronics Diploma His research interests include parameterized complexity, sparse network algorithms, and applied machine learning. Recent projects involve correlation clustering with overlapping data and autonomous drone inspection algorithms. He is affiliated with the Center for Data Science (CEDAS) and leads the Algorithmic Foundations for Trustworthy AI project funded by the Trond Mohn Foundation. Dr. Drange has received the 2024 outreach award for science communication. His lectures on AI ethics and societal impacts have reached diverse audiences, including policymakers and industry leaders. He is active in program committees for major conferences like ESA, COCOON, and SWAT. Key Achievements: PACE 2024: 1st in parameterized track, 2nd in heuristics track Lead software advisor for Equinor's energy transition projects Open-source contributions to algorithmic toolkits Teaching includes courses on algorithms, machine learning, and software engineering. He advises industry workshops and has conducted over 30 outreach lectures on AI ethics and technology governance in 2023–2025.

Rafael Martí CunqueroView profile

Professor

• Operations Research
• Mathematical Programming
• Combinatorial Optimization
• +8 more

Rafael Martí Cunquero is a Full Professor ( Catedrático de Universidad ) in the Department of Statistics and Operations Research at the Faculty of Mathematics, University of Valencia, Spain. He leads the OPTIMATH Optimization and Mathematical Modelling research group and maintains an active research profile with a focus on optimization and heuristics. Research Interests: His primary fields of interest include Operations Research, Combinatorial Optimization, Metaheuristics (especially Scatter Search, GRASP, and Tabu Search), Global Optimization, Graph Drawing, and Network Design. His work bridges theoretical development and practical application in complex decision-making problems. The analysis of his recent publications reveals a consistent focus on developing and applying advanced metaheuristic methods—particularly hybridizations of GRASP, path relinking, scatter search, and variable neighborhood search—to challenging combinatorial optimization problems. Key application areas include graph drawing (e.g., edge crossing minimization), location problems (e.g., p-median, hub location), diversity and dispersion maximization, and routing. His work often emphasizes both algorithmic innovation and empirical validation. Dr. Martí has made substantial contributions to the field, co-authoring foundational texts on scatter search and editing comprehensive handbooks on heuristics. His research is supported by extensive collaborations, particularly with Manuel Laguna, Fred Glover, and Abraham Duarte.

Kevin WayneView profile

Lecturer

• Algorithms
• Data Structures
• Computer Science Education
• +4 more

Kevin Wayne serves as the Phillip Y. Goldman '86 University Lecturer in the Department of Computer Science at Princeton University's School of Engineering and Applied Science. His teaching responsibilities include foundational courses in data structures, algorithms, optimization, and introductory computer science, specifically COS 126, COS 226, and COS 423. Dr. Wayne's research interests center on algorithms, data structures, and computer science education. His work emphasizes practical applications across science, engineering, and commerce, with particular focus on making complex computational concepts accessible through interdisciplinary approaches. He has pioneered numerous educational methodologies that bridge theoretical computer science with real-world applications. His educational materials, particularly the Algorithms textbook series and associated MOOCs, have established new standards in computer science education globally. These resources have demonstrated exceptional impact during the pandemic era, with Algorithms Part I ranking among the top online courses worldwide. The textbook Algorithms, 4th Edition was named one of The 25 Best Programming Books of All-Time in 2020 The MOOC Algorithms, Part I was recognized as one of the Top 25 Most Popular Courses During the Pandemic Named the 2019 Outstanding Teacher in Computer Science at a Ph.D. Granting Institution in New Jersey Recipient of the 2014 ACM Distinguished Educator award Winner of Princeton University's 2013 Phi Beta Kappa Teaching Award Dr. Wayne has developed numerous innovative educational programs including the Summer Programming Experiences for first-year students and the Course Development Program. His 'Nifty Assignments' collection, designated by ACM SIGCSE, has been adopted by institutions worldwide and includes projects ranging from WordNet analysis to particle collision simulations. He maintains active development of educational software tools including the widely-used algs4 GitHub repository containing Java implementations of fundamental algorithms.

Fabio PellaciniView profile

Professor

• Computer Graphics
• Computer Science
• 3D Design
• +3 more

Full Professor of Computer Science at the University of Modena and Reggio Emilia. Previously held faculty positions at Sapienza Università di Roma (Associate and Full Professor), Dartmouth College (Assistant and Associate Professor), and Cornell University (Visiting Assistant Professor). Also worked in the R&D division of Pixar Animation Studios. Dr. Pellacini received an MS and Ph.D. in Computer Science from Cornell University and a Laurea degree in Physics from the University of Parma. His research focuses on using Computer Graphics methods to solve design problems, with emphasis on the design, creative and entertainment industries. His group combines algorithms and efficient systems to allow professional designers as well as novices to create 3D scenes with significantly less effort. They investigate algorithms based on numerical methods and machine learning to enable intuitive and interactive editing of complex environments. Key research areas include appearance design (patterns, materials, and lights), appearance fabrication, evaluation and visualization of designers' workflows, and cloud-based collaborative design. His recent publications demonstrate a strong focus on procedural modeling, appearance design, and collaborative 3D content creation. His work bridges the gap between theoretical computer graphics research and practical applications for designers and artists, with significant contributions to node-based editing systems, boolean operations on surfaces, and differentiable rendering techniques for appearance design. NSF CAREER Award Alfred P. Sloan Fellowship Nominated as Junior Faculty Fellow for Sapienza's Scuola Superiore di Studi Avanzi Dr. Pellacini has supervised numerous students and has been actively involved in research grants related to computer graphics and design systems. He teaches Computer Graphics, Introduction to Scientific Python, and Programming courses at the University of Modena and Reggio Emilia, continuing a long teaching career that spans multiple institutions including Sapienza University of Rome and Dartmouth College. He leads a research group focused on developing innovative tools for 3D design and content creation, with applications spanning from entertainment to industrial design. His work on collaborative design systems, appearance editing interfaces, and procedural modeling techniques continues to shape how designers interact with 3D content creation tools.

• Theoretical Computer Science
• Computational Complexity
• Data Structures
• +3 more

Prof. Michal Koucký is a faculty member at the Institute of Informatics, Faculty of Mathematics and Physics, Charles University in Prague. His research focuses on theoretical computer science, particularly computational complexity, data structures, algorithms, and combinatorics. He leads significant research projects including EPAC (supported by GA CR) and LBCAD (funded by ERC). His educational background includes a Ph.D. from Rutgers University (2003) and a Master's degree from Charles University (1998). Prof. Koucký's research spans multiple areas of theoretical computer science with emphasis on computational complexity , data structures , algorithms , and combinatorics . His work explores fundamental questions about computational efficiency, lower bounds, and algorithmic techniques. He has made significant contributions to edit distance algorithms, string processing, and circuit complexity. His research often bridges theoretical foundations with practical algorithmic applications, particularly in areas like dynamic data structures and approximation algorithms. An analysis of his recent publications (2019-2024) reveals a strong focus on string algorithms, particularly edit distance problems. His work develops novel approximation techniques, sketching methods, and sublinear-time algorithms for pattern matching. There's also significant work on data structure lower bounds and connections to popular complexity conjectures. His research demonstrates a consistent pattern of tackling fundamental problems with innovative approaches that push theoretical boundaries while maintaining practical relevance. Antonin Svoboda award for best dissertation in 2014 (awarded to Jan Bulánek) Best paper award at SAGT 2016 for 'The Big Match in Small Space' Best paper award at FOCS 2018 for 'Approximating Edit Distance Within Constant Factor in Truly Sub-Quadratic Time' He has successfully advised numerous graduate students including Pavel Dvořák, Karel Král, Debarati Das, and Jan Bulánek who have gone on to positions at prestigious institutions like Bristol, Google (Zurich), and Penn State. His research is supported by significant grants including the EPAC project (GA CR) and LBCAD project (ERC), demonstrating the impact and relevance of his work in theoretical computer science. Prof. Koucký is an active member of the theoretical computer science community, regularly presenting at major conferences including STOC, FOCS, ICALP, and SODA. His work often involves collaborations with researchers from institutions worldwide, contributing to a vibrant research environment at Charles University's Institute of Informatics.

• enumerative combinatorics
• extremal combinatorics
• number theory
• +3 more

Martin Klazar is an Associate Professor at the Department of Applied Mathematics, Faculty of Mathematics and Physics, Charles University. He has been affiliated with the university since 1995, initially as an assistant professor before becoming an associate professor in 2004. Since 2000, he has also worked at the Institute for Theoretical Computer Science (ITI). His education includes a Ph.D. (1995) under Jiří Nesetril at Charles University, following undergraduate studies at the same institution (1984–1989). Klazar's research spans multiple areas of discrete mathematics, with primary interests in enumerative and extremal combinatorics, number theory, power series, generating functions, and elementary mathematical analysis. His work frequently bridges combinatorial methods with analytic techniques, particularly in asymptotic enumeration and extremal problems. His publications demonstrate a strong focus on combinatorial structures (permutations, set partitions, matchings), graph theory algorithms, and combinatorial number theory. Recent trends include applications of combinatorial duality, growth rate classification of discrete structures, and interdisciplinary topics linking physics-inspired models (e.g., Potts model) with graph invariants. Honors include the Alexander von Humboldt Stiftung fellowship (1997/98) and a prize from the Rector of Charles University for co-editing the book Topics in Discrete Mathematics . He has supervised Ph.D. students including Vít Jelínek and Jaroslav Hančl. Klazar contributes to the ITI research group, focusing on theoretical computer science and combinatorial mathematics. His current teaching includes courses in mathematical analysis, combinatorial counting, and number theory.

Adam Teodor PolakView profile

Assistant Professor

• Algorithms
• Fine-Grained Complexity
• Learning-Augmented Algorithms
• +3 more

Adam Teodor Polak serves as an Assistant Professor in the Department of Computing Sciences at Bocconi University, where his research centers on theoretical algorithms with dual emphases on fine-grained complexity and learning-augmented algorithms. His work investigates fundamental questions about computational hardness while developing prediction-enhanced algorithms that maintain worst-case guarantees. Polak earned his PhD from Jagiellonian University in 2019 under Paweł Idziak, including a research visit at MIT with Virginia Vassilevska Williams. He subsequently held postdoctoral positions at the Max Planck Institute for Informatics and EPFL before joining Bocconi. His research program addresses why computational problems resist efficient solutions and how imperfect predictions can robustly improve algorithmic performance. This manifests in two interconnected streams: establishing conditional lower bounds for problems like 3SUM and Orthogonal Vectors, and designing learning-augmented frameworks for dynamic graph problems, caching, and optimization that blend theoretical rigor with practical machine learning insights. Recent publications reveal accelerating momentum in algorithms with predictions, with over half of his 2023-2025 output appearing in top ML venues (ICML, NeurIPS, ICLR) alongside traditional theory conferences (STOC, SODA). This cross-pollination demonstrates how worst-case theoretical guarantees can coexist with data-driven performance gains across graph algorithms, scheduling, and combinatorial optimization. Scientific recognition includes: Best Paper Award at ESA 2024 for knapsack algorithm breakthroughs Bronze Medal at ACM ICPC World Finals (2011) 2nd Place in PACE 2018 Challenge for Steiner tree algorithms Polak actively shapes the field through program committee service (ESA, ICALP, SOSA) and community building, notably co-organizing the 2022 Workshop on Algorithms with Predictions (ALPS) and decade-long high-school algorithmics workshops. His industry collaborations with Teroplan and Google demonstrate real-world impact in route planning and distributed systems. Current teaching includes graduate Algorithms courses at Bocconi, while his experimental work on GPU-accelerated graph algorithms and medical computer vision continues to bridge theoretical insights with practical implementation challenges.

Daniel LokshtanovView profile

Professor

• Theoretical Computer Science
• Discrete Mathematics
• Algorithmic Graph Theory
• +7 more

Daniel Lokshtanov is a Professor and Vice Chair at the Department of Computer Science at the University of California, Santa Barbara (UCSB) , with a visiting professor affiliation at the University of Bergen . He is renowned for his work in Theoretical Computer Science and Discrete Mathematics , particularly focusing on Algorithmic Graph Theory and Parameterized Complexity . Key roles: Professor (UCSB, since 2020), Vice Chair (UCSB), Visiting Professor (University of Bergen) Research focus: Kernelization, Graph Minors, Exact Algorithms, Treewidth, Subexponential Algorithms Research Trends : His recent publications (2022-2020) demonstrate expertise in applying Parameterized Complexity to Graph Algorithms , including work on Unit Disk Graphs , Graph Reconfiguration , and Subexponential Time Algorithms . Topics span Graph Contraction , Obstacle Removal , and Kemeny Rank Aggregation . Scientific Awards : Outstanding Young Researcher Meltzer Award Best ESA Paper Award (2015) Advising : While not actively seeking new PhD students, he supervises MS students at UCSB and has mentored numerous advisees through Parameterized Algorithms research. He co-organizes the Inter-Collegiate Programming Contest at UCSB and the Norwegian Informatics Olympiad for high school students.