Prof. Tobias Müller is a Professor at the Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence at the University of Groningen. His academic journey includes previous positions at Utrecht University, CWI (Centrum Wiskunde & Informatica), Tel Aviv University, and Eindhoven University of Technology, with a doctorate from the University of Oxford under Colin McDiarmid. His research focuses on combinatorics, probability theory, random graphs, percolation, discrete and stochastic geometry, and combinatorial game theory. He has contributed extensively to understanding complex networks, hyperbolic models, and geometric random structures. Research Interests: Random Graphs and Percolation Theory Discrete and Stochastic Geometry Hyperbolic Network Models Probabilistic Combinatorics Geometric Probability Graph Algorithms and Connectivity Notable Contributions: Analysis of Voronoi and Poisson-Voronoi percolation in hyperbolic planes. Studies on Mallows random permutations and their cycle structures. Research on component games and logical limit laws in graph theory. Investigations into the geometry and properties of random geometric graphs. Grants & Collaborations: Active in organizing workshops and conferences on random graphs and geometric networks, including the BIRS Workshop on Random Geometric Graphs and the STAR Workshops series. Labs/Teams: Member of the Bernoulli Institute’s research groups, focusing on stochastic studies, combinatorics, and algorithmic methods.
Ignasi Sau Valls is a Directeur de Recherche (DR2) at CNRS, affiliated with the LIRMM laboratory at Université de Montpellier, France. He is a member of the AlGCo team, focusing on algorithms for graphs and combinatorics. His academic background includes dual degrees in Mathematics and Telecommunications Engineering from UPC (Barcelona), a PhD in joint supervision between UPC and Projet Mascotte (Sophia Antipolis), and a postdoctoral position at the Technion (Israel). He has been with CNRS since 2010 and was promoted to his current role in October 2024. He also served as a Visiting Professor at UFC (Brazil) from 2016–2017. His research interests lie primarily in Graph Theory and Parameterized Complexity , with a focus on structural graph properties, kernelization, and algorithm design. He has made significant contributions to problems involving minor-closed graph classes, treewidth, and graph modification. His work bridges theoretical foundations with algorithmic applications, particularly in discrete optimization and network problems. The recent articles highlight a strong trend in parameterized algorithms, especially for graph modification, kernelization, and structural graph problems. Topics such as hitting minors, dynamic programming on tree decompositions, and edge contractions reflect a deep engagement with structural parameterizations and fixed-parameter tractability. His publications frequently appear in top-tier journals like SIAM Journal on Computing, Journal of Combinatorial Theory, and Algorithmica, as well as major conferences such as ICALP, SODA, and IPEC. Best paper award of Track C of ICALP'10 Best student paper award of WG'09 Ignasi Sau has been a principal investigator of the ANR JCJC project ELIT (ANR-20-CE48-0008-01), funded with 169k€ from 2021 to 2026. He serves as an editor for DMTCS and Information and Computation , and has held significant organizational roles, including PC member of numerous conferences (MFCS, WG, IPEC, COCOON) and as co-chair and main organizer of WG 2019, ICGT 2022, and JCALM 2023. He has delivered invited courses at international schools in France, Argentina, and Brazil. He is actively involved in the research community through editorial duties, conference organization, and collaborative research. His lab affiliation is the AlGCo team at LIRMM, a leading group in algorithmic graph theory and combinatorics.
Cécile Mailler is a Reader in Probability at the University of Bath, where she is a member of the probability group Prob-L@B. She has held significant research positions including an EPSRC postdoctoral fellowship (2018-2021) titled "Random trees: analysis and applications" and previously worked as a postdoc at Prob-L@B (2013-2016) as part of Peter Mörters' EPSRC project "Emergence of Condensation in Stochastic Networks". She earned her PhD under the supervision of Brigitte Chauvin and Danièle Gardy at the Laboratoire de Mathématiques de Versailles. Her research focuses on probability theory with emphasis on branching processes, random trees, reinforcement mechanisms, Pólya urns, stochastic approximation, random networks, and statistical physics. She has made significant contributions to understanding preferential attachment models, zero-range processes, and random Boolean trees. Her work bridges theoretical probability with applications in statistical physics and combinatorics. Analysis of her recent publications shows a strong focus on random tree structures, branching processes, and reinforcement learning algorithms, with applications spanning from network theory to statistical mechanics. Her research demonstrates sophisticated mathematical techniques applied to complex stochastic systems, particularly those with reinforcement mechanisms and memory effects. Associate Editor of the Applied Probability Trust (since October 2020) Associate Editor of Stochastic Processes and Their Applications (since March 2022) Author of a general introduction to Pólya urns for the LMS Newsletter (November 2020) Co-organizer of the "Random Walks: Applications and Interactions" conference at CIRM (January 2026) She actively supervises PhD students working on topics including the multi-city ants process, Pólya urns with growing initial composition, large deviations for the Monkey walk, and competing growth processes. She has secured research funding through EPSRC fellowships and has been involved in multiple collaborative projects with prominent researchers in probability theory. Mailler regularly teaches mini-courses on advanced probability topics at international summer schools and workshops, demonstrating her commitment to knowledge dissemination in the field.
Krishna Jagannathan is a full-time Professor in the Department of Electrical Engineering at the Indian Institute of Technology Madras (IIT Madras), India. He specializes in stochastic modeling, communication networks, information theory, and queuing theory. He obtained his B.Tech from IIT Madras in 2004, followed by S.M. and Ph.D. degrees from MIT in 2006 and 2010, respectively. After post-doctoral positions at Caltech and MIT, he joined IIT Madras in 2011. Education: B.Tech in Electrical Engineering, IIT Madras (2004) S.M. in Electrical Engineering and Computer Science, MIT (2006) Ph.D. in Electrical Engineering and Computer Science, MIT (2010) Research Interests: His research focuses on stochastic modeling and analysis of communication networks , information theory , and queuing theory . He has made significant contributions to understanding network performance, resource allocation, and risk-aware decision-making in complex systems. He leads the Networks and Stochastic Systems lab at IIT Madras, mentoring a large cohort of Ph.D. and M.S. students working on cutting-edge problems in networking, optimization, and stochastic systems. Scientific Awards: Best Paper Award at WiOpt 2013, Tsukuba, Japan Young Faculty Recognition Award for Excellence in Teaching and Research, IIT Madras (2014) Teaching & Mentorship: He has taught a wide range of courses including Probability Foundations , Stochastic Modeling and Queuing Theory , Convex Optimization , and Signals & Systems , consistently receiving high teaching evaluations. He has supervised over 15 Ph.D. and M.S. students to completion and continues to guide several active researchers.
Ting He is a Professor in the Department of Computer Science and Engineering, specializing in interdisciplinary research at the intersection of network sciences, energy systems, and cybersecurity. Their work addresses critical challenges in network tomography, software-defined networking, and cyber-physical systems, with a strong emphasis on advancing edge computing and decentralized learning paradigms. NSF-funded research on Distributed Edge Intelligence (2024–2025) Collaborative projects on Overlay Networks and Adversarial Reconnaissance in SDN Recent publications analyze network topology inference, energy-efficient decentralized learning, and secure cloud file systems. Their research aligns with UN SDGs through contributions to sustainable energy systems and secure IT infrastructure. Key collaborations with Silvestri, La Porta, and Chaudhuri Active in Smart Grid resilience and cascading failure mitigation
Leo Goldmakher is an Associate Professor of Mathematics at Williams College, where he teaches courses in cryptography, topology, Fourier analysis, measure theory, and analytic number theory. He holds a B.A. in Mathematics from Princeton University (2004) and a Ph.D. in Mathematics from the University of Michigan (2009). Research interests Teaching areas Publications His research focuses on number theory, including topics such as Gauss sums, character sums, multiplicative functions, and analytic methods. He has contributed to refinements of classical theorems like Lagrange’s four-square theorem and Artin’s primitive root conjecture. His recent publications explore bounds on character sums, spectral properties of random graphs, and algebraic structures in number theory. The work demonstrates a strong emphasis on analytic and algebraic techniques. Leo has been affiliated with Williams College’s Department of Mathematics and Statistics, which received the 2014 Exemplary Department Award from the American Mathematical Society. He has taught advanced courses such as Cryptography, Topology, and Analytic Number Theory, though these were not offered in the 2025/26 academic year.
Elina Rönnberg is a Professor and Deputy Head of Department at the Department of Mathematics, Linköping University, where she leads research in discrete optimisation and intelligent decision-making. Her work bridges theoretical method development and real-world applications in sectors such as healthcare, aviation, mining, and transportation. She is actively involved in the Wallenberg AI, Autonomous Systems and Software Program (WASP) and has collaborated with industry leaders like Saab and Scania. Her research focuses on advanced optimisation techniques including Dantzig-Wolfe decomposition, Lagrangian relaxation, column generation, branch-and-price, and logic-based Benders decomposition. She also explores hybrid methods combining mathematical programming with constraint programming and machine learning. Applications span nurse rostering, electric vehicle routing, aircraft arrival scheduling, and underground mine planning. Recent publications highlight a strong trend toward integrating AI and machine learning—particularly graph neural networks—with classical optimisation frameworks to accelerate solution methods. Her work emphasizes practical impact, robustness, and scalability in solving complex scheduling and resource allocation problems. Nurse Rostering with Strategic Planning of Skills for Sick-Leave Robustness (2024) Pricing for the EVRPTW with Piecewise Linear Charging (2024) Speeding Up Logic-Based Benders Decomposition with Graph Neural Networks (2024) Elina supervises several PhD students and has co-supervised doctoral research at international institutions including Makarere University (Uganda) and the University of Exeter (UK). She has contributed to applied projects through student theses in collaboration with Scania and Saab, focusing on electric vehicle routing and search-and-rescue optimisation. She previously served as a Specialist in Optimisation at Saab Aeronautics (2014–2020) and co-founded Schemagi, a scheduling tool aimed at improving quality in healthcare. She teaches courses such as Introduction to Optimization (TAOP07) and Project - Applied Mathematics (TATA62). Her research group, 'Mathematics and algorithms for intelligent decision-making,' operates within the Division of Applied Mathematics (TIMA) at the Department of Mathematics. The team develops decision support tools that enhance efficiency and sustainability in complex systems, particularly under the growing demands of electrification and digitalisation in transport and logistics.
Professor David Taubman is a distinguished academic serving as Professor and Deputy Head of School (Research) at the School of Electrical Engineering and Telecommunications (EE&T) at the University of New South Wales (UNSW) in Sydney, Australia. He is also co-director of Kakadu Software Pty. Ltd. and its affiliates Kakadu R&D and Kakadu GPU. With a career spanning over three decades, Professor Taubman has made significant contributions to the field of image and video compression, most notably as the author of the EBCOT coding algorithm adopted in the JPEG2000 international standard. Professor Taubman earned his B.Sc. in Mathematics and Computer Science (1986) and B.E. (Medal) in Electrical Engineering (1988) from the University of Sydney, followed by an M.Sc. (1992) and Ph.D. (1994) in Electrical Engineering from the University of California at Berkeley. His professional journey includes engineering work at the Electricity Commission of N.S.W. (1988-1990), research positions at Hewlett-Packard Laboratories in Palo Alto (1994-1998), and an academic career at UNSW where he progressed from Senior Lecturer (1998-2003) to Associate Professor (2004-2009) and finally to Professor (2009-present). He has held various leadership roles including Head of the EE&T Telecommunications Research Group (2003-2014), Head of the EE&T Signal Processing Research Group (2014-present), Director of Research for the School of EE&T (2011-2016), and Deputy Head of School (Research) since 2017. Professor Taubman's research interests center on image and video compression, with particular expertise in JPEG2000 standards and implementations. His work spans signal processing, wavelet transforms, scalable video coding, motion modeling, and multimedia systems. He has pioneered numerous compression algorithms and frameworks, including the EBCOT coding algorithm that became central to the JPEG2000 standard. His recent research focuses on efficient motion modeling with cuboidal partitioning, learned lifting-based transform structures, and high-throughput implementations of JPEG2000 for video applications. His work bridges theoretical foundations with practical implementations, as evidenced by the commercially successful Kakadu Software tools that have garnered around 500 commercial licensees. Analysis of Professor Taubman's recent publications reveals a consistent focus on advancing compression technologies with particular emphasis on scalability, efficiency, and adaptability. His work spans traditional image compression (JPEG2000 extensions), video coding (cuboid-based partitioning for UHD/360-degree video), and emerging applications (nanopore sequencing data compression). A notable trend is the integration of machine learning techniques with traditional compression frameworks, as seen in his work on learned lifting-based transform structures. His research maintains strong connections to real-world applications across diverse domains including medical imaging, astronomical data processing, and genomic sequencing. IEEE Fellow Engineers Australia Fellow (by invitation) Professor Taubman has served as Associate Editor for the IEEE Transactions on Image Processing for two four-year appointments (2003-2005 and 2010-2013). He has been actively involved in numerous research grants focused on image and video compression technologies, particularly those related to the JPEG2000 standard and its extensions. His work has received significant industry support, reflected in his consultancy with various U.S., Japanese, and Australian corporations. He has also contributed to international standards development as a member of Standards Australia Technical Committee MS-065 (mirroring ISO TC42 on Digital Photography) and as a constitutional member of Standards Australia Technical Committee IT-029 (Coded Representation of Picture, Audio and Multimedia/Hypermedia Information). Professor Taubman co-directs Kakadu Software Pty. Ltd. and its research affiliates Kakadu R&D and Kakadu GPU, which have developed the commercially successful Kakadu Software tools for JPEG2000. His research group at UNSW focuses on advanced image and video compression techniques, with particular expertise in wavelet-based methods, scalable coding, and motion modeling. The group maintains strong industry connections and has contributed significantly to the development and standardization of image compression technologies worldwide.
Walid G. Aref is a Professor of Computer Science at Purdue University since Fall 1999. His research focuses on database systems, spatial and spatio-temporal data management, query processing, and indexing. He has led projects funded by NSF, NIH, and industry partners. Aref is a Fellow of the IEEE and has received awards including the NSF CAREER Award (2001) and VLDB’s Ten-Year Best Paper Award (2016). He serves as Editor-in-Chief of ACM Transactions on Spatial Algorithms and Systems and has authored numerous influential papers. Education: BSc/MSc from Alexandria University (Egypt), PhD from University of Maryland (1993). Research Interests: Extending database functionality for emerging applications like spatial, graph, and sensor databases. Notable contributions include the Chameleon project, AQWA system for big spatial data, and privacy-preserving Casper framework. Selected Projects: NSF-funded work on multi-predicate spatial queries, in-memory graph relational systems, and adaptive spatio-textual processing. Systems like Tornado (spatio-textual streams) and LocationSpark (distributed spatial data) exemplify his contributions. Awards: Multiple best paper awards, IEEE Fellow, and leadership roles in ACM SIGSPATIAL.
Xihong Lin is a Professor of Statistics at Harvard University and a Professor of Biostatistics at the Harvard T.H. Chan School of Public Health. She is a distinguished academic, holding membership in both the National Academy of Sciences and the National Academy of Medicine. Her research focuses on scalable statistical inference for big data, statistical machine learning, causal inference, and integrative data analysis, with applications in genomics, public health, and precision medicine. Lin’s work addresses challenges in analyzing large-scale genomic and multi-ancestry data, including methods for rare variant association testing, ancestry-adjusted sample analysis, and scalable computing frameworks. Her contributions span biobank studies (e.g., UK Biobank, TOPMed) and clinical applications in lung cancer, cardiovascular health, and smoking cessation. Her scientific awards reflect her leadership in statistical genetics and public health. Key research trends include leveraging single-cell sequencing for functional genomics, developing ensemble machine learning methods for health subtyping, and enhancing polygenic risk prediction across diverse populations. Lin’s methodologies prioritize interpretability and scalability, enabling impactful analyses of complex observational and genomic datasets. Awards: Member, National Academy of Sciences; Member, National Academy of Medicine Her grants and advising efforts focus on interdisciplinary collaborations, bridging statistics, AI, and domain sciences. Lin leads initiatives to improve genomic data management and ethical use of federated data (e.g., FADI framework). She is affiliated with labs advancing statistical genetics and cloud-based workflows (e.g., STAAR workflow).
Daniel Horsley is an Associate Professor and ARC Future Fellow at the School of Mathematical Sciences, Monash University. His research focuses on combinatorial designs and edge decomposition of graphs, with notable contributions to extremal graph theory, Zarankiewicz problems, and graph decomposition theorems. Current Role: ARC Future Fellow, Associate Professor Affiliation: School of Mathematical Sciences, Monash University Active Projects: 'The Zarankiewicz problem through linear hypergraphs and designs' (2022–2025), 'Edge decomposition of dense graphs' (2017–2022), and more Research interests span combinatorial designs, graph decomposition, and extremal combinatorics. His work emphasizes theoretical advancements in design theory, with applications in discrete mathematics and optimization. Recent articles address semi-inducibility, Zarankiewicz numbers, and embedding partial designs, reflecting his expertise in structural and extremal combinatorics. Key awards include the ARC Future Fellowship. His research outputs include over 57 publications in journals like Journal of Graph Theory , SIAM Journal on Discrete Mathematics , and European Journal of Combinatorics . Grant projects include collaborations with ARC, University of Queensland, and University of Melbourne, focusing on Steiner systems, compressed sensing, and combinatorial structure analysis. Advising PhD students and mentoring researchers in discrete mathematics and combinatorial design theory.
Christopher G. Brinton is the Elmore Associate Professor of Electrical and Computer Engineering at Purdue University, where he leads the ION research lab. He is affiliated with the Department of Electrical and Computer Engineering in the College of Engineering at Purdue University's West Lafayette campus. Dr. Brinton received his PhD from Princeton University, where he was previously the Associate Director of the EDGE Lab and a Lecturer of Electrical Engineering. His research focuses on the intersection of networking, communications, and machine learning, with particular emphasis on Fog computing systems, the Internet of Things (IoT), NextG Wireless, and social learning networks. His research integrates foundational techniques including convex and non-convex optimization, machine learning, and signal processing to address challenges in networked intelligent systems. The ION lab under his leadership develops both theoretical frameworks and practical implementations for next-generation networking solutions, with strong industry collaborations including Qualcomm, Nokia, Intel, Cisco, Dell, and Ericsson. Recent publications reveal a strong trend toward federated learning, decentralized algorithms, and edge intelligence, with significant contributions to model partitioning, communication-efficient learning, and robust network architectures. His work increasingly bridges traditional communication theory with modern machine learning techniques to solve emerging challenges in distributed networked systems. NSF CAREER Award ONR Young Investigator Program (YIP) Award DARPA Young Faculty Award (YFA) AFOSR Young Investigator Program (YIP) Award Intel Rising Star Faculty Award (RSA) Dr. Brinton teaches several courses including ECE 647: Performance Modeling of Computer Communication Networks, ECE 301: Signals and Systems, and ECE 547: Introduction to Computer Communication Networks. He has co-authored the book 'The Power of Networks: Six Principles That Connect Our Lives' and taught three Massive Open Online Courses (MOOCs) with over 400,000 cumulative students. While not currently actively recruiting students, he remains open to connecting with highly motivated individuals. Dr. Brinton leads the ION (Intelligent Optimization and Networking) research lab, which focuses on creating theoretical foundations and practical implementations for next-generation networked systems. The lab has recently published significant work on 6G taxonomy in collaboration with major industry partners and continues to push boundaries in distributed learning and network optimization.
Igor Kortchemski is a CNRS researcher at the Department of Mathematics and Applications (DMA) at École Normale Supérieure, Paris, and a lecturer in the Department of Applied Mathematics at École Polytechnique. His primary research focuses on the continuous limits of random discrete models, particularly examining how discrete combinatorial structures converge to continuous objects under appropriate scaling. His educational background includes a PhD in Mathematics (2012) under Jean-François Le Gall at École Normale Supérieure and a Habilitation à diriger des recherches (HDR) in Mathematics (2016). Kortchemski's research spans several interconnected areas: Random trees and Galton-Watson processes with heavy-tailed distributions Random planar maps and their geometric properties Growth-fragmentation processes and their connections to Lévy processes Scaling limits of combinatorial structures and their continuous counterparts His publication record shows a consistent focus on the geometric properties of random discrete structures, with recent work (2023-2025) exploring uniform attachment processes with freezing, critical tree phenomena, and the mesoscopic geometry of sparse random maps. His research often involves sophisticated probabilistic analysis combined with combinatorial insights. Scientific recognition includes: prix de thèse solennel Perrissin-Pirasset / Schneider de la chancellerie des Universités de Paris (2012) Kortchemski actively contributes to academic service: Examiner for the minor math exam at École Polytechnique (FUF) since 2023 Member of the mathematics jury for ENS International Selection (2023) Member of the jury for the external mathematics competitive examination (Agrégation) since 2021 Member of the jury for the Arts and Economic and Social Sciences Bank (B/L) mathematics exams (2015-2018) He mentors the next generation of researchers as co-director of Antoine Aurillard's and Vanessa Dan's theses (both since 2023), and previously directed Etienne Bellin's (2020-2023) and Paul Thevenin's (2017-2020) theses.
Prof. Michael Krivelevich holds the Baumritter Chair in Combinatorics at the School of Mathematical Sciences, Tel Aviv University. His research focuses on probabilistic methods in combinatorics, random graphs, and positional games. He has authored influential books such as Positional Games and contributed to foundational work in random graph theory. Currently teaching Introduction to Combinatorics and Graph Theory (Spring 2025), he has extensive experience in courses like Graph Theory and Hypergraph Coloring. His work bridges theoretical computer science, coding theory, and combinatorics, with over 150 publications. Recent research explores game-theoretic thresholds, random graph evolution, and equitable coloring algorithms. Education: Ph.D. in Mathematics, Tel Aviv University (not explicitly stated, inferred from career trajectory). Research Interests Krivelevich's work emphasizes random structures , extremal graph theory , and probabilistic combinatorics . He investigates phase transitions in random graphs, positional game strategies, and algorithmic challenges in graph coloring. His contributions include proving sharp thresholds for Hamilton cycle games and analyzing WalkSAT performance on smoothed k-CNF formulas. Collaborations span theoretical computer science and discrete mathematics. Publications Recent articles address Hamiltonicity in Maker-Breaker games, equitable coloring of random graphs, and smoothed analysis of satisfiability processes. His work often combines rigorous proofs with algorithmic insights. Teaching & Mentorship Guides students through advanced combinatorial topics and has taught foundational courses since 2002. No explicit student listings available in provided texts.
Wenjing Rao is an Associate Professor at the Department of Electrical and Computer Engineering, College of Engineering, University of Illinois at Chicago (UIC). Her research focuses on VLSI test, fault-tolerance, reliability, and hardware security in emerging nanoelectronic systems. Education: Ph.D. in Computer Science from University of California, San Diego (2008) B.S. in Computer Science from Peking University (2001) Her work explores novel computation paradigms through physical unclonable functions (PUFs), defect-tolerant logic implementation, and scalable fault tolerance in many-processor arrays. Publications emphasize hardware security, reconfiguration strategies, and reliability challenges in nanoscale architectures. Notable honors include the 2017 Harold A. Simon Award for Excellence in Teaching and the 2012 NSF CAREER Award. She has contributed to key journals like IEEE Transactions on Computer-Aided Design and conferences including DATE, ASPDAC, and NANOARCH.
