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Thomas NeedhamView profile

Associate Professor

• Geometry
• Topology
• Optimal Transport
• +4 more

Thomas (Tom) Needham is an Associate Professor of Mathematics at Florida State University, where his work bridges pure geometry and topology with modern data-science challenges. Research Interests: Needham’s research centers on applying geometric and topological ideas—particularly optimal transport, Riemannian and symplectic geometry, and topological data analysis—to large and complex data sets. He develops theoretical frameworks, algorithms, and computational tools that reveal shape, structure, and relationships within high-dimensional data. Optimal transport theory and its generalizations (Gromov-Wasserstein, unbalanced transport) Metric and symplectic geometry in data contexts Topological summaries (persistence diagrams, Reeb/Merge trees) Statistical shape analysis and geometric statistics Funding & Support: His program is currently supported by the U.S. National Science Foundation through grants DMS–2324962 and CIF–2526630; prior support includes DMS–2107808. Laboratory & Collaboration: Needham collaborates extensively across mathematics, computer science, statistics, and application domains such as neuroscience and imaging. While no dedicated lab name is given, his projects involve multi-institutional teams and graduate students working at the intersection of geometry, topology, and data science.

Martin MorenoView profile

Assistant Professor

• Compiler Optimizations
• High-Performance Computing
• Quantum Computing
• +3 more

Martin Moreno is a tenure-track Assistant Professor in the Department of Computer Science and Engineering at The Ohio State University. His office is located in Dreese Laboratories at 2015 Neil Ave, Columbus, OH. His research focuses on the intersection of compilers, programming languages, high-performance computing, and quantum computing, with goals to enhance program execution speed, reliability, and portability across architectures. Research Interests: Dr. Moreno specializes in polyhedral compilation, GPU optimizations, quantum computing abstractions, distributed-memory algorithms, and energy-aware computing. His work bridges theoretical compiler design with practical performance improvements for modern hardware, including GPUs and quantum processors. Recent Research Trends: Analysis of his 15 most recent publications reveals strong emphasis on: 1) Automated code generation for distributed systems and tensor computations, 2) Quantum program optimization through affine transformations, 3) Energy-efficient GPU computing via polyhedral models, and 4) Novel compiler frameworks for task-based parallelism. His work consistently integrates formal methods (e.g., SMT solvers) with practical runtime systems. Student Advising: Actively seeks motivated students for research projects in compiler optimizations and quantum computing. Applicants to OSU are encouraged to list him as a potential advisor and mention PLSE/HPC interests in statements of purpose.

• Dynamical Systems
• Topology
• Algebraic Topology
• +5 more

Gioia Maria Vago is a Lecturer at the University of Burgundy , affiliated with the Institute of Mathematics of Burgundy (IMB) and the research team Geometry, Algebra, Dynamics and Topology . She holds a Habilitation à Diriger des Recherches (HDR) (2013) and a PhD in Mathematics (1998) under the supervision of Christian Bonatti. Research Focus : Topological, algebraic, combinatorial, and algorithmic aspects of discrete and continuous dynamical systems, including hyperbolic dynamics, Morse flows, cellular automata, and aperiodic tilings. Teaching : Diversified instruction in Mathematics, Computer Science, and Mathematics-Computer Science for audiences ranging from mathematicians to biologists, including courses on Discrete Mathematics, Graph Theory, Unix/Linux, programming, and statistical software (R, Maple, Statistica). Students : Co-directed PhD thesis of Abdelrazak Jmel (2013) and supervised post-doc Maria Alice Bertolim (2004-2005) under a French Ministry fellowship. Notable Collaborations : Work with Michel Boileau on three-dimensional Ogasa invariants, and with Alain Jacquemard on computational biology projects (allosteric regulation models). Software Achievements : Developed Maple programs for unstable manifold conjugacy analysis and VBA tools for biochemical data optimization. Scientific Awards : HDR in Mathematics (2013) PhD Thesis with Mention Très Honorable et Félicitations du Jury (1998) French Ministry Postdoctoral Fellowship for Maria Alice Bertolim (2004-2005) Research Trends : Her work bridges topological complexity (e.g., Ogasa invariant) with combinatorial and algorithmic methods, particularly in high-dimensional dynamics (2008 paper on diffeomorphism centralizers) and low-dimensional systems (1999-2001 papers on hyperbolic manifolds).

Philip N. KleinView profile

Professor

• Algorithms and Data Structures
• Graph Theory
• Computational Geometry
• +3 more

Philip N. Klein is a Professor at Brown University, specializing in Computer Science with a focus on Algorithms and Data Structures , Graph Theory , and Computational Geometry . His academic contributions span over two decades, marked by significant advancements in Approximation Algorithms for problems in planar graphs, redistricting, and network design. His research has been published extensively in top-tier venues such as Algorithmica , ACM Symposium on Theory of Computing (STOC) , and IEEE Symposium on Foundations of Computer Science (FOCS) . Key research areas include Planar Graphs , Network Flow , Vehicle Routing , Steiner Tree Problems , and Geometric Clustering . Collaborations with researchers like Claire Mathieu, Vincent Cohen-Addad, and Glencora Borradaile have led to breakthroughs in algorithmic efficiency and complexity analysis. Recent publications (2018-2023) highlight his work on Redistricting Algorithms using power diagrams, Correlation Clustering , and Two-edge-connected Augmentation . Earlier works (2000-2015) include foundational contributions to Steiner Forest , Multiway Cut , and Parallel Algorithms . Despite no explicit awards listed, his prolific publication record underscores substantial influence in theoretical computer science and algorithm design.

Bjoern AndresView profile

Professor

• Computer Vision
• Machine Learning
• Graph Algorithms
• +3 more

Prof. Dr. Bjoern Andres holds the position of Professor of Machine Learning for Computer Vision at Technische Universität Dresden . He is a Principal Investigator at the Center for Scalable Data Analytics and Artificial Intelligence and an Academic Fellow at the School of Embedded Composite Artificial Intelligence. His work bridges theoretical computer science, applied mathematics, and biomedical imaging through innovative graph algorithms. Education : PhD in Physics (Heidelberg University), Diploma in Physics (Heidelberg), Pre-Diploma in Physics and Computer Science (Technical University Dortmund) Research Interests : His research focuses on graph-based methods for computer vision and biomedical image analysis, including: Lifted multicut and correlation clustering algorithms Integer programming for anatomical network reconstruction 3D segmentation of neural tissue Optimal tracking of cellular lineages Partial ordering and quantum-inspired optimization Scientific Contributions : His recent publications emphasize: Advancing multicut polytope analysis (2023-2025) Quantum alternating operator ansatz for correlation clustering (2025) 4-approximation algorithms for Min Max correlation clustering Medical imaging applications for organoid segmentation Biomedical applications in vascular network reconstruction Development of scalable graph decomposition methods Awards & Recognitions : IEEE CVPR Outstanding Reviewer (2022) NIPS Best Reviewer (2017) MICCAI Best Paper (2015) DAGM Best Paper Runner-Up (2008) Heidelberg International Exchange Scholarship (2005) Studienstiftung Scholarship (2000-2007) Software Development : Maintains andres::graph , a C++ library for graph algorithms and multi-dimensional arrays, featuring: Efficient graph data structures with constant-time access Implementations of Prim's algorithm and max-flow methods Applications in medical imaging and computer vision

Michael MahoneyView profile

Professor

• Randomized Numerical Linear Algebra
• Machine Learning
• High Dimensional Statistics
• +3 more

Michael Mahoney is Professor of Statistics at the University of California, Berkeley, with additional affiliations at the International Computer Science Institute (ICSI) where he is Vice President and Director of the Big Data Group, the Lawrence Berkeley National Laboratory (LBNL) where he leads the Machine Learning and Analytics Group, and the EECS department's RISELab. He is also an Amazon Scholar. Education: While specific degrees are not listed in the provided text, his extensive record of teaching, research leadership, and publications indicates doctoral-level training in Statistics and Computer Science. Research Interests: Mahoney's work centers on the applied mathematics of data , spanning algorithmic and statistical foundations of big data, randomized numerical linear algebra (RandNLA), high-dimensional statistics, machine learning, and scientific machine learning. He develops theory, scalable implementations, and real-world applications in internet analytics, social networks, genetics, astronomy, and climate science. Recent software contributions include the RandBLAS and RandLAPACK libraries (standardizing RandNLA routines), Landscaper for visualizing deep-learning loss landscapes, and packages such as FreeAlg , DetKit , Imate , and LeaderBot . Awards & Honors: NeurIPS 2020 Best Paper Award (co-authored work on column subset selection) Director, NSF TRIPODS UC Berkeley FODA Institute Key contributor to the BALLISTIC project for next-generation BLAS/LAPACK Grants & Leadership: Principal Investigator, NSF TRIPODS FODA Institute (Foundations of Data Analysis) Group Lead, Machine Learning and Analytics, LBNL Vice President & Director, Big Data Group, ICSI Advising & Mentoring: Mahoney has an extensive network of current and former PhD students, postdocs, and visiting researchers, including placements at MIT, Stanford, Waterloo, Stevens, Tsinghua, and Georgia Tech. Current advisees include Shengaho Yang, Zhichao Wang, Hyunsuk Kim, and Pu Ren, among many others. Labs & Teams: He directs research efforts across UC Berkeley Statistics, ICSI’s Big Data Group, LBNL’s Machine Learning and Analytics Group, and the RISELab (formerly AMPLab), fostering cross-disciplinary collaboration between statistics, computer science, and domain sciences.

Sotiris NikoletseasView profile

Professor

• Wireless Sensor Networks
• Internet of Things (IoT)
• Artificial Intelligence of Things (AIoT)
• +6 more

Sotiris Nikoletseas is a Full Professor and Founding Director of the Internet of Things Laboratory (IoT-Lab) at the Computer Engineering and Informatics Department of the University of Patras, Greece. He also serves as a Senior Researcher of the Algorithms Group at the Computer Technology Institute and Press "Diophantus" (CTI), Greece. His academic career includes Visiting Professor positions at the Universities of Geneva, Ottawa and Southern California (USC). Professor Nikoletseas's research spans several cutting-edge domains in computer science, with primary focus on algorithmic aspects of wireless sensor networks and the Internet of Things (IoT). His work extends to Artificial Intelligence of Things (AIoT), wireless energy transfer protocols, probabilistic algorithms and random graphs, and innovative applications of psychoanalysis driven computing to social networks and interactive media. His interdisciplinary approach bridges theoretical computer science with practical applications in networked systems. His publication record is exceptional, with over 300 publications in international journals and refereed conferences, three influential books, and 30 invited chapters in publications by major publishers. His most recent book, 'Wireless Power Transfer Algorithms, Technologies and Applications in Ad Hoc Communication Networks' (Springer, 2016), represents a significant contribution to the field. Best Paper award at the 18th International Conference on Distributed Computing and Networking (ICDCN), Hyderabad, India, 2017 Best Paper Award at the 6th IEEE International Conference on Distributed Computing in Sensor Networks (DCOSS), Santa Barbara, USA, 2010 Inclusion in the list of highly cited computer scientists based on Google Scholar h-index As an academic leader, Professor Nikoletseas has supervised over 50 diploma theses and numerous graduate students, including 7 PhD candidates who have completed their degrees under his guidance. His research has been supported by substantial external funding, including multiple European Union projects where he served as site leader or coordinator. His work with industry includes direct contracts with organizations like Pfizer through their Center for Digital Innovation. The IoT-Lab he founded at the University of Patras serves as a hub for research in algorithms and systems for the Internet of Things, fostering collaboration between academia and industry. His current research directions include AI-enhanced IoT applications, wireless power transfer systems, and innovative approaches to cybersecurity and environmental monitoring through advanced sensor networks.

• Machine Learning
• Human-Computer Interaction
• Network Science
• +3 more

Hasti Nariman Zadeh is a Researcher at the Department of Computer Science , Aalto University . Her work spans interdisciplinary domains, combining computational methods with applications in social sciences and agronomy. Research Interests Network science and community detection algorithms Crowdsourcing methodologies with bias mitigation Machine learning for high-dimensional data representation Data-driven approaches in agricultural informatics and phenology modeling Analysis of social media dynamics and cross-domain engagement patterns Publications Her research output demonstrates expertise in network science, crowdsourcing, and computational modeling. Key themes include: modularity optimization for graph partitioning, social behavior analysis during global crises, and uncertainty quantification in crop simulations. Collaborative Networks She has collaborated extensively with international research groups across agronomy, computational social sciences, and machine learning domains, as evidenced by co-authorships on interdisciplinary publications.

• Formal Methods
• Model Checking
• Satisfiability (SAT) Solving
• +4 more

Tommi Junttila serves as a Senior University Lecturer in the Department of Computer Science at Aalto University, Finland, where he conducts cutting-edge research at the intersection of formal methods and computational logic. His academic profile demonstrates sustained contributions to theoretical computer science with practical applications in system verification and blockchain technology. His research program centers on advancing formal verification techniques, with core expertise in: Symmetry reduction algorithms for state space explosion SAT/SMT solving with specialized parity and XOR reasoning Bounded model checking of timed and asynchronous systems Blockchain protocol verification (notably DeFi lending pools) Canonical labeling tools for graph automorphism detection Recent work shows increasing focus on decentralized finance applications while maintaining foundational contributions to solver technology. Analysis of his publication trajectory (2011-2022) reveals consistent innovation in SAT solving methodologies, with symmetry reduction and parity reasoning forming persistent research threads. His 2022 work on DeFi lending pools represents a strategic expansion into blockchain verification, leveraging established formal methods expertise for emerging financial technologies. Tool development (bliss, PySMT) demonstrates commitment to practical research impact. No scientific awards were documented in the source materials. Similarly, no information regarding student supervision, grant funding, or laboratory affiliations was present in the provided text. His independent tool development (including bliss for graph canonical labeling and PySMT for SMT solver interfaces) indicates significant technical leadership within the formal methods community.

Sylwia CichaczView profile

Assistant Professor

• Graph Theory
• Combinatorics
• Zero-Sum Problems
• +3 more

Dr. Sylwia Cichacz is an Assistant Professor in the Department of Discrete Mathematics within the Faculty of Applied Mathematics at AGH University of Science and Technology in Kraków, Poland. Her primary research focuses on Graph Theory , with expertise in combinatorial structures, algebraic methods, and labeling techniques. Research Interests: Her work spans zero-sum problems in Abelian groups, magic and antimagic graph labelings, irregular graph decompositions, and applications of group theory to combinatorial optimization. Key themes include distance magic labeling, acyclic coloring, fault-tolerant graph structures, and the interplay between algebraic invariants and graph properties. Publications: Recent articles (2022–2025) explore graph labeling innovations (e.g., cordial, antimagic, and distance magic techniques), zero-sum partitions of Abelian groups, and combinatorial designs. Her research consistently integrates algebraic frameworks with discrete optimization, demonstrating applications in spectral graph theory and extremal combinatorics. Grants & Affiliations: She is supported by the NCN grant 2011/01/D/ST1/04104. No student advisees, team structures, or additional awards are documented in the provided materials.

Guan YuView profile

Associate Professor

• Statistical Machine Learning
• High-Dimensional Statistics
• Multi-Modality Data Integration
• +3 more

Guan Yu is an Associate Professor in the Department of Biostatistics at the University of Pittsburgh's Graduate School of Public Health. His methodological research focuses on statistical machine learning, high-dimensional statistics, and non-parametric methods, with applications to biomedical problems such as Alzheimer’s disease, cancer, autoimmune diseases, and environmental health. He received his PhD in Statistics from the University of North Carolina at Chapel Hill (2016), an MS in Statistics from Nankai University (2011), and a BS in Mathematics from Nankai University (2008). His teaching includes courses like Biostatistics Seminar (BIOST2025) and Introduction to Statistical Theory 2 (BIOST2044) . Research interests include: Multi-modality data integration High-dimensional inference Transfer learning Uncertainty quantification for predictions Penalized regression techniques Robust classification methods His recent publications address challenges in block-missing data, sparse modeling, and cost-constrained regression. Awards include the 2020 ICSA Outstanding Young Researcher Award and multiple ASA recognitions for student papers and teaching excellence (2015-2017). He actively collaborates on interdisciplinary projects and mentors graduate students in biostatistical research.

Anusch TarazView profile

Professor

• Discrete Mathematics
• Graph Theory
• Combinatorics
• +5 more

Anusch Taraz is a Professor at the Institute of Mathematics , Hamburg University of Technology. His research focuses on Discrete Mathematics , particularly in Graph Theory , Hypergraphs , and Combinatorics . He holds the Chair of Discrete Mathematics and has contributed extensively to topics like Ramsey Theory , Random Graphs , and Algorithmic Complexity . Education : International Baccalaureate (United World College of the Atlantic, UK), MSc in Mathematics (University of Bonn), PhD in Theoretical Computer Science (Humboldt-University of Berlin, supervisor: Hans Jürgen Prömel) His research explores Size-Ramsey Numbers , Graph Embeddings , and Resilience in random graphs. Recent publications emphasize Random Graphs , Hypergraph Theory , and Extremal Graph Problems . No specific scientific awards are highlighted in the provided texts. Email: taraz@tuhh.de .

• Soft Condensed Matter
• Atomic and Molecular Clusters
• Percolation
• +4 more

Dr Mark Miller is an Associate Professor in the Department of Chemistry at Durham University. He serves as Chair of the Board of Examiners in Chemistry (2022-2025) and previously held the Presidential role at the Senior Common Room of St Cuthbert's Society (2015-2024). Education : Ph.D. in Natural Sciences (Cambridge University); Postdoctoral work at University of Washington and AMOLF Amsterdam Research Interests focus on computational methods for molecular and colloidal systems, including: Coarse-grained modeling (Martini force field optimization) Percolation theory in active matter and nanoparticle suspensions Energy landscapes of finite clusters and self-assembly Simulation methodology development Biomolecular condensate modeling Recent Publications (2021-2025) examine percolation thresholds, membrane partitioning predictions, and self-assembly control. Collaborations include industry and regulatory partners for high-throughput compound screening.

Jiří FialaView profile

Associate Professor

• Graph algorithms
• Computational complexity
• Restricted graph classes
• +4 more

Jiří Fiala is an Associate Professor at the Department of Applied Mathematics (KAM), Faculty of Mathematics and Physics, Charles University in Prague, Czech Republic. His academic career spans over two decades with significant contributions to graph theory and computational complexity. He has held positions at prestigious institutions including University of Kiel (Germany), University of Bergen (Norway), and University of Oregon (USA) as a Fulbright fellow. Dr. Fiala completed his Master's degree (Mgr.) at Charles University in 1997 followed by his PhD in 2000. His academic journey progressed from research and teaching assistant (2000-2008) to his current position as Associate Professor since 2008. His research focuses on graph algorithms and their computational complexity , with particular emphasis on restricted graph classes including bounded width structures, topological restrictions, and geometric intersection graphs. He has made significant contributions to algebraic graph theory , especially in homomorphisms and covering projections, as well as graph coloring and distance labeling problems. His work on Hamiltonian properties of graphs represents another important research direction. Analysis of his recent publications (2021-2025) reveals a continued focus on computational complexity of graph covering problems across various graph structures including regular trees, multigraphs with semi-edges, and disconnected multigraphs. His work frequently intersects with computational geometry, particularly in graph drawing problems related to outerplanar graphs and forest structures. The consistent publication record across top venues demonstrates sustained research productivity in theoretical computer science and discrete mathematics. Scientific recognition includes being awarded a Fulbright fellowship for research at the University of Oregon (2012-2013). His habilitation thesis Structure and Complexity of Locally Constrained Graph Homomorphisms (2007) represents a significant scholarly achievement. Dr. Fiala maintains active research collaborations, particularly with Jan Kratochvíl (45 joint publications), Daniël Paulusma (19), and Jan Bok (12), among others. His teaching responsibilities include advanced courses such as Introduction to Parameterized Algorithms for the winter term 2025-2026. His work is well-documented through comprehensive publication records available via DBLP, ResearcherID, ORCID, and Scopus.

Petr KolmanView profile

Associate Professor

• Theoretical Computer Science
• Combinatorial Optimization
• Graph Theory
• +7 more

Assoc. Prof. Petr Kolman is an academic at the Department of Applied Mathematics , Charles University , where he serves in advisory bodies of the faculty management. His research focuses on theoretical computer science , combinatorial optimization , and graph algorithms , particularly in approximation techniques and network flow problems . He has taught courses such as Linear Algebra 1/2 and Mathematical Programming and Polyhedral Combinatorics . Recent publications highlight his work on spanning tree congestion (2025), length-bounded cuts (2020), and treewidth-based extended formulations (2020). His research spans algorithms , flow theory , and graph modification problems . He maintains contact through kolman@kam.mff.cuni.cz and office hours in Prague Malostranské nám. 2/25, 2nd floor, room S225 .