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.
Dr. Timothy Chappell is a Lecturer in the School of Computer Science at Queensland University of Technology. His research focuses on developing efficient algorithms for large-scale data analysis, particularly in bioinformatics and remote sensing applications. He develops methods for clustering, similarity search, and metagenomic analysis that scale to massive datasets. Dr. Chappell's recent work includes the Crackling method for rapid CRISPR guide RNA design, metagenomic geolocation using read signatures, and parallel K-Tree clustering for extreme-scale datasets. He has applied these methods to diverse domains including urban flood mapping using satellite data, microbiome analysis, and biological sequence clustering. He teaches programming principles (CAB302) and systems programming (CAB403). Dr. Chappell maintains collaborations with government and industry partners, including developing change detection tools for Queensland's Department of Natural Resources.
Zsuzsanna Lipták is an Associate Professor in the Department of Computer Science at the University of Verona, Italy, where she has been a faculty member since November 2011. Her research is centered on string algorithms, combinatorics on words, and algorithmic bioinformatics, with a particular focus on the Burrows-Wheeler Transform (BWT) and its applications in data compression and biological sequence analysis. She is an active member of the Algorithmic Bioinformatics and Natural Computing Group and the Algorithms Group at the university. She leads research within the PRIN-funded project 'PINC – Pangenome Informatics: From Theory to Practice' and collaborates internationally with institutions in South Africa, Finland, Chile, and the USA. Her research interests include string indexing, suffix trees, suffix arrays, data compression, computational biology, and combinatorial properties of permutations and BWT. She has made significant contributions to the theory and application of BWT variants, matching statistics, and de Bruijn sequence construction. Her recent publications reflect a consistent focus on improving the efficiency and understanding of text indexing and compression methods, particularly in the context of genomic data. These works often involve both theoretical analysis and experimental validation, bridging the gap between pure theory and practical implementation. Lipták actively supervises PhD and master’s students, including Davide Cenzato, Sara Giuliani, Francesco Masillo, and Martina Lucà. She teaches advanced courses such as 'Fundamental Algorithms for Bioinformatics,' 'Computational Analysis of Genome-Scale Sequences,' and 'Advanced Data Structures for Textual Data.' She has also supervised numerous bachelor’s theses and student projects. She has secured research funding through national projects like PRIN and has collaborated on international initiatives, including a Marie Curie IEF fellowship during her postdoctoral work. She is deeply involved in the academic community, having served as PC chair for SPIRE 2024, PC co-chair for CPM 2023, and a member of the Steering Committee of SPIRE since 2024. She has served on the program committees of major conferences such as ESA, DLT, WABI, and IWOCA. She co-organizes the weekly 'Monday Meetings' seminar series for the Algorithms Group and has co-edited special issues and conference proceedings in journals like Theory of Computing Systems , Discrete Applied Mathematics , and European Journal of Combinatorics . She earned her Diplom in Mathematics from Freie Universität Berlin and her PhD in Computer Science from Bielefeld University, Germany, where her thesis addressed algorithmic problems in mass spectrometry. She has held research positions at ETH Zurich, Bielefeld University, and Salerno University, and has been a visiting researcher at the Rényi Institute (Hungary), University of the Witwatersrand, and SANBI (South Africa). She is the scientific coordinator for Erasmus+ exchanges with Bielefeld and Jena Universities.
Marc Sebban is a Professor in Computer Science at the Hubert Curien Laboratory (LabHC) and Deputy Director of this research unit. He leads the Inria project-team MALICE, focusing on machine learning, domain adaptation, and metric learning. Research Interests Metric learning with theoretical guarantees Domain adaptation via optimal transport Physics-informed neural networks Imbalanced data classification Tree-structured data similarity learning Recent Publications His 2025 work introduces provably accurate adaptive sampling for collocation points in PINNs and theoretically grounded quadrature methods using residual Hessians. 2024 publications explore physics-informed ML for laser-matter interaction, predictive modeling of body shape changes, and approximation error analysis in tanh neural networks. Earlier works address graph diffusion Wasserstein distances, metric learning for imbalanced data, and boosting algorithms with confidence oracles.
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.
Amaury Habrard is a Full Professor in Computer Science at Jean Monnet University (UJM) , Saint-Etienne, and a member of the Laboratoire Hubert Curien UMR CNRS 5516 . He leads the Data Intelligence Research Group and manages the international Machine Learning and Data Mining (MLMD) Master’s Program . PhD in 2004 at UJM Habilitation thesis in 2010 at Aix-Marseille University Assistant Professor at Aix-Marseille University (2005–2011) His research focuses on Machine Learning , with key interests in Metric and Similarity Learning , Domain Adaptation and Transfer Learning , Learning Theory , Fraud and Anomaly Detection , and Grammatical Inference . He co-authored a book on Metric Learning (Morgan & Claypool, 2015) and another on Domain Adaptation Theory (ISTE, 2019). His work explores theoretical foundations (e.g., PAC-Bayesian bounds, spectral learning) and practical applications (e.g., melody recognition, image classification, side-channel attacks). Recent publications highlight advancements in metric learning (robustness, structured data), domain adaptation (unsupervised methods, theoretical guarantees), and machine learning theory (concentration bounds, majority votes). Scientific Awards : ICTAI'11 Best Paper Award NeurIPS'21 Reviewer Award ICML'19 Top Reviewer Award NeurIPS'18 Reviewer Award IJCAI'18 Senior PC Award ICML'18 Top Reviewer Award Advising : Supervised PhD students including Julien Tissier (Binary Representation Learning), Jordan Frery (Ensemble Methods for Fraud Detection), Guillaume Metzler (Outlier Detection in Bank Fraud), Michael Perrot (Metric Learning), Mattias Gybels (Spectral Learning), Jean-Philippe Peyrache (Unsupervised Domain Adaptation), Emilie Morvant (PAC-Bayesian Domain Adaptation), Aurélien Bellet (Metric Learning with Generalization Guarantees), and Laurent Boyer (Probabilistic Edit Similarities). Labs/Teams : Leads the Data Intelligence Research Group and is part of the new Inria MALICE project-team on Physics-informed Machine Learning. Collaborated with the PASCAL2 Network of Excellence and European Marmota Project .
Koorosh Aslansefat is an Assistant Professor of Computer Science at the University of Hull, part of the Dependable Intelligent Systems Group (DEIS). He holds an M.Sc. in Control Engineering from Shahid Beheshti University (2014) and a PhD in Computer Science from the University of Hull (2021), focusing on the DREAM project for offshore wind farm maintenance optimization. His research spans AI safety, dependability evaluation, and safety-critical systems. **Education**: M.Sc. Control Engineering, Shahid Beheshti University (2014) PhD Computer Science, University of Hull (2021) **Research Interests**: Artificial Intelligence Safety & Explainability Markov Modelling & Probabilistic Systems Runtime Dependability Evaluation Offshore Wind Farm Maintenance Multi-Robot Systems Safety His work emphasizes real-time dependability analysis and safety assurance for complex systems, with notable contributions to SafeML and drone-assisted monitoring frameworks. **Awards**: IET Leslie H. Paddle Award (2020), Alan Turing Institute Postdoctoral Enrichment Award (2022), and grants from EU H2020 and EDF Energy. **Projects/Grants**: Leads initiatives like AKT TRUST-LLM (Safe LLMs for legal memory) and collaborates on offshore wind farm reliability (SESAME EU H2020). Active in grant-funded research with Innovate UK and the Engineering & Physical Sciences Research Council. **Teaching**: Courses include Real-Time Dependable Systems (MSc) and Safety-Critical Systems (BSc). Preparing a module on Responsible AI & Ethics.
Darren Narayan is a Professor of Mathematics in the School of Mathematics and Statistics at Rochester Institute of Technology (RIT), part of the College of Science. He holds a BS from the State University of New York at Binghamton and MS/PhD degrees from Lehigh University. His research focuses on Graph Theory , Combinatorics , Social Networks , and Functional Connectivity of the Brain , with applications to real-world systems like brain networks and transportation infrastructure. Narayan has authored numerous publications analyzing graph metrics for social and neural networks, including studies on helmet impact effects and network efficiency in subway systems. He leads NSF-funded research initiatives, such as the STEM Real World Applications of Mathematics Project , and has been honored by the Mathematical Association of America. He teaches courses like Graph Theory, Combinatorics, and Discrete Mathematics, emphasizing practical applications in STEM fields. His work integrates mathematical modeling to address challenges in neuroscience, sports safety, and network optimization, often collaborating with students on interdisciplinary projects. He also serves as Director of Undergraduate Research at RIT, fostering student engagement in advanced mathematical research.
Krishnaiyan Thulasiraman is a Professor and Hitachi Chair Emeritus at the School of Computer Science, University of Oklahoma. He holds adjunct and emeritus positions at the University of Waterloo (Electrical and Computer Engineering) and Concordia University (Electrical and Computer Engineering). His career spans over five decades, including roles as Chair of Electrical and Computer Engineering at Concordia (1981–1994) and faculty appointments at IIT Madras (1965–1981). Education: PhD (1968), M.S. (1965), and B.E. (1963) in Electrical Engineering from institutions in India, including the Indian Institute of Technology Madras (IITM). His research focuses on graph theory, combinatorial optimization, algorithms, and their applications to fault diagnosis, network science, and VLSI systems. He pioneered work in network science and engineering, with contributions to logical topology design, survivable networks, and graph-based circuit analysis. Research interests span graph theory applications in circuits, systems, and computing; fault-tolerant network design; and network science fundamentals. His work bridges theoretical foundations with practical applications in WDM optical networks, power systems, and distributed computing systems. Key awards include the IEEE CAS Society Technical Achievement Award (2006), AAAS Fellowship (2007), and recognition as a 2017 Pioneer of Circuits and Systems. He has authored/co-authored over 100 papers, three books, and edited a major handbook. Professional roles include Deputy Editor-in-Chief of IEEE Transactions on Circuits and Systems, and leadership in IEEE technical committees. Collaborations span academia and industry, with partnerships at institutions like Arizona State University, Tsinghua University, and the Tokyo Institute of Technology. His research teams have addressed challenges in network reliability, algorithmic graph theory, and distributed systems.
Zvi Galil is a distinguished academic and former Dean of Computing at Georgia Institute of Technology (2010-2019). He holds the title of Storey Chair and serves as Executive Advisor for Online Programs. His academic journey includes leadership roles at Columbia University (Fu Foundation School of Engineering Dean, 1995-2007) and Tel Aviv University (President, 2007-2009). He earned degrees in Applied Mathematics from Tel Aviv University and a PhD in Computer Science from Cornell University. Galil’s research focuses on algorithms, complexity theory, cryptography, and stringology. He has authored over 200 papers and edited 5 books, with contributions to graph algorithms, parallel computing, and data structures. He is a Fellow of the ACM and American Academy of Arts and Sciences, and a member of the National Academy of Engineering. His work has influenced fields like online education through initiatives like OMSCS (Georgia Tech’s Online Master of Science in Computer Science). Educations: BS and MS (summa cum laude), Applied Mathematics, Tel Aviv University PhD, Computer Science, Cornell University His research trends span dynamic graph algorithms, real-time string processing, and scalable graph isomorphism techniques. He has also contributed to foundational areas like suffix trees and text indexing. His awards include the Columbia Great Teacher Award (2009) for pedagogical excellence. Awards: ACM Fellow Member, American Academy of Arts and Sciences Member, National Academy of Engineering Columbia Society of Graduates Great Teacher Award Galil advises on online education programs and collaborates with the Algorithms and Randomness Center (ARC) at Georgia Tech. He has held editorial roles at major journals and advised Oxford University Press on computer science publications.
Raghavendra Sridharamurthy is an Assistant Professor at the Center for Visual Information Technology (CVIT), International Institute of Information Technology (IIIT) Hyderabad. He holds a PhD and M.Sc. (Engg) in Computer Science from the Indian Institute of Science (IISc), Bengaluru, and a BE in Information Technology from National Institute of Technology Karnataka (NITK) Surathkal. His research focuses on scientific visualization, computational topology, and topological data analysis, with applications in data interpretation and visualization. Education: PhD and M.Sc. (Engg) in Computer Science, Indian Institute of Science (IISc), Bengaluru BE in Information Technology, National Institute of Technology Karnataka (NITK), Surathkal Previous Affiliations: Postdoctoral Researcher, Scientific Computing and Imaging Institute (SCI), University of Utah Research Fellow, Department of Computer Science and Automation (CSA), IISc Bengaluru Member of the Visualization and Graphics Lab (VGL) at IISc Research Interests: Development of topological tools for scientific data analysis Comparative analysis of merge trees and topological structures Applications of computational topology in visualization Professional Activities: Delivered over 20 talks on topological data analysis and visualization at institutions like IIT Bombay, IISER Pune, and international conferences (IEEE Vis, TopoInVis) Presented work on merge tree comparison using edit distances and locality-sensitive hashing Labs/Teams: Previously affiliated with the Visualization and Graphics Lab (VGL) at IISc, collaborating on visualization and graphics research.
Laurent G. Deluc is an Associate Professor in the Department of Horticulture at Oregon State University, affiliated with the College of Agricultural & Life Sciences. He leads the Deluc Lab, focusing on functional genomics and plant physiology in specialty and cereal crops, particularly grapevine and Brachypodium distachyon. His research emphasizes understanding plant responses to developmental programs and environmental stresses, with goals to improve crop genetics through traditional and accelerated breeding. He holds a PhD in Plant Science from the University of Bordeaux (2004) and has held academic positions since 2009, including roles at the University of Nevada, Reno as a postdoctoral researcher (2005–2009). Professional affiliations include membership in the steering committee of the International Grape Genome Program (IGGP) and roles as an associate editor for Frontiers in Plant Sciences and BMC Plant Biology . His service contributions span university committees, the Oregon Wine Research Institute, and the Center for Genome Research and Biocomputing. Research interests include plant hormone signaling, long-distance stress communication, and plant-pathogen interactions, supported by advanced techniques like Next-Generation Sequencing and metabolomics. His lab accepts graduate students in Horticulture and collaborates internationally on grape genomics projects. Key research themes involve transcriptomic responses to water deficit, hormone regulation in berry ripening, and genetic engineering for crop improvement. Recent publications highlight work on auxin dynamics in fruit development, abiotic stress responses in Vitis species, and biotech applications in viticulture.
Tatiana Starikovskaya is an Assistant Professor in the Talgo team at the Computer Science Department of École normale supérieure (ENS Paris), France. Her work focuses on the design and analysis of algorithms for string processing, space-efficient computation, and complexity theory, with applications in bioinformatics and data security. Education : PhD in Mathematics (2013) and MSc in Mathematics (2009) from Lomonosov Moscow State University; MSc in Data Science (2009) from Moscow Institute of Physics and Technology and Yandex School of Data Analysis. Her research explores the intersection of algorithm theory and practical applications, particularly in streaming models , probabilistic text indexing , and clustering of sequence data . Key themes include handling wildcards , k-mismatch problems , and edit distance under space constraints. Current projects include PARSe (ANR-20-CE48-0001) , aiming to develop ultra-efficient algorithms for noisy string data, and AlgoriDAM (ANR-19-CE48-0016) , focusing on algorithmic theory for modern data models. She co-supervises PhD students Gabriel Bathie and T. El Ghazi, with collaborations spanning institutions like University of Wrocław and IRISA. She has served on program committees for major conferences (STACS, ESA, ICALP, CPM) and organized the CPM Summer School (2023) and New Horizons of Stringology (2024) workshop at Cirm. Teaching roles include courses at ENS Paris (Algorithmique, Programming for Non-CS Students) and Higher School of Economics (Moscow).
Michael J. Moore is a Professor of Biology at Oberlin College in the College of Arts and Sciences. His research focuses on plant systematics, with particular interests in flowering plant evolution, molecular phylogenetics, and phylogeography. Dr. Moore's laboratory investigates evolutionary relationships among plants by generating and analyzing DNA sequence data, with special emphasis on the desert Southwest flora and the diverse plant order Caryophyllales. Dr. Moore's research interests include: Plant Systematics and Evolutionary Biology Molecular Phylogenetics and Phylogeography Gypsum Endemism in the Chihuahuan Desert Evolution of Breeding Systems in Hawaiian Plants Flowering Plant Phylogenomics Adaptation to Extreme Environments His lab conducts research that spans fieldwork, laboratory work, and computational analysis. Students in the Moore Lab learn various techniques including DNA isolation, sequence editing and analysis, and cloning. During summers, the lab often conducts expeditions to the desert Southwest to collect plants for research. Dr. Moore's work has significantly contributed to understanding angiosperm phylogeny, particularly through plastid genome sequencing. Dr. Moore has received funding for his research, including being part of a team that secured a $500,000 grant from the National Science Foundation to purchase and install a high-performance computing cluster at Oberlin College. His research has been featured in media outlets such as the "In Defense of Plants" podcast. Dr. Moore teaches several courses including Organismal Biology (BIOL 100), Plant Systematics (BIOL 323/324), Biogeography (BIOL 423), and a First-Year Seminar on biodiversity conservation. He emphasizes hands-on learning and research opportunities for undergraduate students.
Beth Wrenn-Estes is a Lecturer at the School of Information , San José State University, since 2006. She previously worked in school and public libraries, including as Manager of Technical Services at Denver Public Schools (2000-2004) and youth services librarian at Lone Tree Library (Douglas County, CO). Academic Certificate of Advanced Study, School Library Media, University of Denver (2001) MLS (Library Science), University of Denver (2000) BA (English Literature & Secondary Education), University of Colorado (1974) Her research focuses on Early Childhood Literacy and Learning in library and non-library environments, alongside Information Seeking Behaviors of Youth and serving Disconnected Youth through literature and programming. She emphasizes Intellectual Freedom and Censorship in YA services and Storytelling in educational contexts. Beth's publications include collaborative works on electronic portfolios as capstone projects (2013, 4 parts) and editions of Young Adult Literature and Multimedia: A Quick Guide (2014, 2011). These works highlight her contributions to LIS education, curriculum design, and digital assessment tools. Scientific and professional recognition includes the iSchool Outstanding Teacher Award (2012), Most Distinguished Faculty Service Award (2014), and Faculty Outstanding Lecturer Award (2018). She actively serves in leadership roles in California Library Association (Treasurer, Finance Committee) and BAYA / BAYNET (Treasurer).
