Ed Carr is an Associate Professor of Computer Science at the Department of Computer Science & Mathematics, Emory & Henry College. Previously, he held roles at North Carolina A&T State University as Undergraduate Program Director and Graduate Faculty. His research focuses on Cybersecurity (Intrusion Detection, Zero-Knowledge Proofs), Graph Theory (Hamiltonian Cycles, Graph Coloring), and Quantum Computing. He has advised numerous graduate and undergraduate students, contributing to cybersecurity, algorithms, and systems projects. Education: Ph.D./MS Computer Science (NC A&T), MS Applied Mathematics (Western Carolina), BS Mathematics (Wingate). Research interests span cybersecurity frameworks, graph theory applications, and quantum computing paradigms. His work has been supported by grants totaling $1.57M from NSF, DOD, DOE, and others. Notable projects include GenCyber Summer Camps (DOD/NSA) and quantum information science initiatives. Publications emphasize graph cycle existence and algorithmic developments. He teaches advanced courses like Operating Systems, Concurrent Programming, and Data Structures. Awards: None explicitly listed. Advising highlights include key permutation libraries, ZKP authentication systems, and network security tools. His professional experience spans academia and community colleges, with roles at Gardner-Webb University and Edgecombe Community College.
Fedor Fomin is a Professor in Algorithms at the Department of Informatics, University of Bergen, Norway, since 2002. His research focuses on fundamental problems in computer science and mathematics, particularly in algorithm design and graph theory. Research Interests: His work spans advanced algorithmic techniques such as Matroid algorithms Algorithmic graph minors Treewidth and its applications Exact and exponential time algorithms Pursuit-evasion games and graph searching Parameterized algorithms and kernelization Graph coloring Publications: He has authored over 150 peer-reviewed journal articles in venues like J. ACM, SIAM J. Computing, and Combinatorica, alongside 160 conference papers in top-tier events including FOCS, STOC, and AAAI. His research demonstrates expertise in bridging theoretical computer science and discrete mathematics. Scientific Awards: EATCS Fellow (2019) ERC Advanced Investigator Grant (2010) EATCS-IPEC Nerode Prize 2017 (with F. Grandoni and D. Kratsch) EATCS-IPEC Nerode Prize 2015 (with E. D. Demaine, M. T. Hajiaghayi, and D. M. Thilikos) Norway's Outstanding Young Investigator Award (2005) Grants: He has secured major grants from The Research Council of Norway (NFR), the Russian Ministry of Education and Science (mega-grant), and the European Research Council (ERC) as Principal Investigator.
Yoshio Okamoto is a Professor at the Department of Computer and Network Engineering, Graduate School of Informatics and Engineering, at The University of Electro-Communications in Tokyo, Japan. He has held this position since April 2017, after serving as an Associate Professor at the same institution from April 2012 to March 2017. Prior to his appointment at the University of Electro-Communications, he held academic positions at Tokyo Institute of Technology, Japan Advanced Institute of Science and Technology, and Toyohashi University of Technology. His educational background includes: Bachelor of Systems Science from The University of Tokyo (1999) Master of Systems Science from The University of Tokyo (2001) Doctor of Theoretical Science from ETH Zurich (2005) Professor Okamoto's research spans several interconnected areas in theoretical computer science and discrete mathematics. His primary interests include Discrete and Computational Geometry, Graph Algorithms, Combinatorial Optimization and Polyhedral Combinatorics, Discrete Mathematics and Combinatorics, and Game Theory. His work often explores the interplay between these fields, developing theoretical foundations with practical algorithmic implications. He has made significant contributions to understanding the structural properties of geometric and combinatorial objects, as well as designing efficient algorithms for related problems. His recent publications demonstrate a continued focus on fundamental problems in discrete mathematics and theoretical computer science, with increasing applications in quantum computing, fair division, and reconfiguration problems. His work often appears in top-tier journals such as ACM Transactions on Algorithms, Algorithmica, and Theoretical Computer Science, reflecting his standing in the theoretical computer science community. Professor Okamoto has received several prestigious awards recognizing his contributions to the field: IPSJ-CS Outstanding Achievement and Contribution Award (January 2024) Research Award from The Operations Research Society of Japan (September 2020) Best Review Paper Award (with colleagues) from Japan Society for Software and Technology (September 2014) Research Encourage Award from The Operations Research Society of Japan (September 2012) 8th EATCS/LA Presentation Award (February 2010) Editors' Choice 2003 from Discrete Applied Mathematics (September 2004) As an educator, Professor Okamoto has taught numerous courses at The University of Electro-Communications since 2012, including Discrete Mathematics, Graphs and Networks, Discrete Mathematical Engineering, and Foundations of Discrete Optimization. He has served as an editor for multiple prestigious journals including Graphs and Combinatorics (Managing Editor since 2020), Acta Informatica, Journal of Computational Geometry, and Journal of Graph Algorithms and Applications. His extensive service on program committees for major conferences in theoretical computer science demonstrates his active engagement with the research community. Professor Okamoto leads a research laboratory at The University of Electro-Communications, where his team explores fundamental questions in discrete mathematics and theoretical computer science. The lab maintains strong connections with researchers worldwide, as evidenced by his numerous international collaborations. His research has been supported through various channels, including Japan Society for the Promotion of Science grants, and he has served as a reviewer for international funding agencies including the Swiss National Science Foundation and The Netherlands Organization for Scientific Research.
Dr. Pradeesha Ashok is an Associate Professor and Controller of Examinations at IIIT Bangalore. She holds a Ph.D. from the Indian Institute of Science, Bangalore, and previously worked as a Postdoctoral Fellow at the Institute of Mathematical Sciences, Chennai. Her research focuses on Theoretical Computer Science, with specializations in Algorithms, Graph Theory, Combinatorics, and Parameterized Complexity. Her research interests include geometric problems such as polygon guarding, covering, and packing, as well as conflict-free coloring in graphs and hypergraphs. She has contributed to developing exact and parameterized algorithms for these problems. Dr. Ashok has also taught courses like Exact and Parameterized Algorithms, Graph Theory, and Design and Analysis of Algorithms. Her publications span conferences like IWOCA, CSR, and COCOON, as well as journals such as Discrete Applied Mathematics and SIAM Journal on Discrete Mathematics. Her work emphasizes algorithmic efficiency and combinatorial optimization in geometric and graph-theoretic contexts. Dr. Ashok has advised several students, including PhD candidates and MTech thesis students, focusing on topics like the chromatic art gallery problem and parameterized complexity of coloring problems. She is affiliated with the Department of Computer Science & Engineering at IIIT Bangalore and contributes to the institute's academic governance through her role as Controller of Examinations. Her research also extends to geometric separability, bichromatic covering problems, and experimental studies of the Steiner Tree problem. She collaborates on projects involving theoretical and applied aspects of computational geometry and algorithms.
Karthik C. S. is an Assistant Professor in the Department of Computer Science at Rutgers University, specializing in complexity theory, discrete geometry, and parameterized complexity. He is supported by the NSF CAREER Award Simons Foundation Junior Faculty Fellowship National Science Foundation grants . His research explores hardness of approximation, fine-grained complexity, and algorithm design in metric spaces.
Bernd Bickel is a Full Professor for Computational Design at ETH Zurich, embedded in the Design++ research center. He holds a Master's degree from ETH Zurich and a PhD from ETH Zurich under Markus Gross. Previously, he was at IST Austria (2015–2023), Disney Research, and TU Berlin as a visiting professor. His research focuses on computational design, digital fabrication, and simulation, with applications in robotics, computer vision, and material science. Key interests include physics-based simulation, geometry processing, and interdisciplinary engineering. Notable awards include the Academy of Motion Picture Technical Achievement Award (2019), SIGGRAPH's Significant New Researcher Award (2017), and the EUROGRAPHICS Best PhD Thesis (2012). He leads the Computational Design Lab at ETH Zurich and collaborates with institutions like Inria Nancy on projects such as MFX team collaborations. His work spans academic contributions (over 50 publications) and industrial applications, including the FlexMaps Pavilion (First Prize at IASS 2019) and computational tools for 3D printing. He actively mentors PhD students and oversees grants like ERC Starting Grant 'Materializable'.
Zheng Duan is a Senior Lecturer and Associate Senior Lecturer in the Department of Physical Geography and Ecosystem Science at Lund University. He holds roles as Principal Investigator in the BECC (Biodiversity and Ecosystem services in a Changing Climate) and MERGE (ModElling the Regional and Global Earth system) research groups. His research focuses on satellite remote sensing, hydrological modelling, glacier dynamics, and machine learning applications in environmental science. Duan leads several international projects, including BioClima (EU Horizon Europe) and initiatives on Tibetan Plateau lake bathymetry and Arctic hydrology. He has advised two students and published over 150 peer-reviewed articles. Notable contributions include advancing soil moisture downscaling, glacier-hydrological model integration, and drought monitoring using satellite data. His work aligns with UN SDGs 6 (Clean Water), 13 (Climate Action), and 15 (Life on Land). Education details are not explicitly provided in the text, but his research career spans institutions like the Swedish National Space Agency and Crafoord Foundation-funded projects. Collaborations include researchers from China, Sweden, and global networks through EGU General Assemblies and media engagements (e.g., interviews on lake color changes). Research interests are anchored in Earth observation technologies and their application to water cycle dynamics, ecosystem resilience, and climate change mitigation. Recent projects emphasize multi-source satellite data fusion, machine learning for hydrological prediction, and quantifying environmental variables like dissolved organic carbon in river systems. His articles collectively address themes such as drought detection, lake bathymetry estimation, and improving hydrological models through data integration. Key methodologies include triple collocation analysis, Bayesian optimization, and deep learning frameworks. Duan’s work bridges theoretical ecology with practical applications, supporting sustainable water management and climate policy. He leads 11 active/ongoing projects, including BioClima (2025–2028), which integrates Earth observations for biodiversity and climate monitoring. Grants include funding from the European Commission, Swedish National Space Agency, and Crafoord Foundation. Media participation highlights public communication of scientific findings, such as lake color changes linked to human activities. Lab affiliations include BECC and MERGE, where interdisciplinary teams tackle global environmental challenges. Future work emphasizes scaling up satellite-derived insights for policy-relevant solutions in water security and ecosystem conservation.
David Hobbs is a Professor in the Department of Physics at Lund University, specializing in astrometry and space missions. He is a key contributor to the European Space Agency's Gaia mission, focusing on astrometric data processing and fundamental physics applications. His research includes developing algorithms for the Astrometric Global Iterative Solution (AGIS) and analyzing Gaia's data to study galactic structure, exoplanets, and stellar dynamics. Hobbs has also proposed the GaiaNIR mission, aiming to expand astrometry into near-infrared wavelengths. Earlier work includes software development for XMM-Newton and Integral observatories, as well as spacecraft attitude control algorithms for Herschel and Planck missions. Research interests include: High-precision astrometry and mission design Galactic structure analysis using Gaia data Exoplanet detection via astrometric methods Near-infrared astrometry for obscured regions Data processing algorithms for large astronomical surveys Recent articles highlight advancements in Gaia DR3 data exploitation, including chemical cartography of the Milky Way and discovery of black holes via astrometric techniques. Future work focuses on GaiaNIR's development to enhance astrometric capabilities for 12 billion stars.
Thomas Depian is a PreDoc Researcher affiliated with the Faculty of Informatics at Technische Universität Wien, specializing in the Department of Algorithms and Complexity. His research focuses on computational geometry, graph algorithms, parameterized complexity, and data visualization, with particular emphasis on boundary labeling, dynamic map labeling, and linear layout optimization. Current projects: Engineering Linear Ordering Algorithms for Optimizing Data Visualizations (2020–2025) Past projects: HumAlgo (2018–2023) His work spans theoretical algorithm design, complexity analysis, and practical applications in geographic information systems and data visualization. Publications include contributions to top-tier conferences such as ISAAC, GD, GIScience, and WALCOM. Areas of investigation include constraint satisfaction, geometric optimization, and parameterized algorithm frameworks. Depian completed his Diploma Thesis at TU Wien in 2023, focusing on grouping and ordering constraints in boundary labeling. His academic output demonstrates expertise in solving complex computational problems with interdisciplinary applications.
Alexander Dobler is a PreDoc Researcher at the Algorithms and Complexity Department of Technische Universität Wien. His research focuses on algorithmic visualization, graph drawing, and combinatorial optimization. He contributes to optimizing layout algorithms for diagrams, treemaps, and storylines, with a particular emphasis on minimizing crossings and corners in geometric representations. Key projects include 'Engineering Linear Ordering Algorithms' (2020–2025) and participation in the PACE competition with solver 'Touiouidth'. His work bridges theoretical computer science with practical visualization challenges, addressing both discrete mathematics and computational geometry. Education: BSc, Dipl.-Ing. (engineering degree) Labs/Teams: Part of the Algorithms and Complexity group at TU Wien Grants: Involved in HumAlgo (2018–2023) and REVEAL-AI (2020–2024) His recent publications (2023–2025) address topics like cluster vertex splitting complexity, hoop diagrams for set visualization, and optimizing linear diagrams. He has supervised Marcel Holzmüller’s 2025 diploma thesis on expanding planar storyplan problems.
Jie He is a PreDoc Researcher at the Department of Cyber-Physical Systems, Technische Universität Wien. His research spans computational social choice, algorithmic game theory, and formal methods in robotics and IoT systems. He works on multidisciplinary problems involving complexity analysis, fair division, and preference modeling. Current projects: EdgeAI (2022–2025), TAIGER (2023–2027), ADEX (2020–2024) Key collaborations: Research with R. Grosu, E. Bartocci, D. Nickovic Research interests focus on computational aspects of collective decision-making , including fair division, matching problems, and preference modeling. He works on both theoretical foundations (e.g., parameterized complexity) and practical applications (e.g., robotic-IoT systems). His publication history reveals deep expertise in computational complexity of social choice problems, with recent work on 3D stable roommates , fair division in graph-structured settings , and preference modeling through Euclidean and Manhattan geometries. As an advisor, he supervised diploma theses on: Optimization strategies for 5G transceivers Dynamic object detection in multi-agent systems His work appears in top conferences like ACM/IEEE DAC, ICSE, and various computational social choice venues.
Ping Yang is a University Distinguished Professor and holder of the David Bullock Harris Chair in Geosciences at Texas A&M University (TAMU). He currently serves as the Senior Associate Dean for Research (2022–2024) in the College of Arts & Sciences, overseeing 32 institutes/centers and 18 departments, including Atmospheric Sciences, Oceanography, and Physics & Astronomy. Previously, he held roles as Department Head of Atmospheric Sciences (2012–2018), Associate Dean for Research in the College of Geosciences (2019–2022), and Adjunct Professor in Oceanography and Physics & Astronomy since 2009. Prof. Yang earned his Ph.D. in Atmospheric Sciences from the University of Utah. His research focuses on light scattering by nonspherical particles (e.g., ice crystals, dust aerosols), radiative transfer modeling for Earth-atmosphere systems, and remote sensing applications using satellite instruments like MODIS, CERES, and CALIPSO. His work bridges atmospheric science, oceanography, and astrophysics, with significant contributions to climate modeling and particle property databases. Yang has been internationally recognized for his achievements, including four prestigious fellowships (AGU, OPTICA, AMS, APS), the 2024 IRC Gold Medal, and the 2020 Humboldt Research Award. He has also received TAMU’s highest faculty honor, the University Distinguished Professor title, and multiple NASA awards for his contributions to cloud and aerosol research. His research group develops computational tools like the TAMU-VRTM vector radiative transfer model and databases (TAMUice series, TAMUdust2020) widely used globally. They address challenges in radiative transfer submodels for climate systems and explore 3D radiation effects, ocean surface albedo, and particle orientation impacts on scattering signals.
Dr Anna Hickman is a Lecturer in the Department of Ocean and Earth Science at the University of Southampton, UK. Her research focuses on marine phytoplankton, emphasizing their role in biogeochemical cycles, ecological dynamics, and photo-acclimation mechanisms. She bridges biological, physical, and chemical oceanography with marine optics, employing both observational and modeling approaches to study light-phytoplankton interactions. PhD Oceanography, University of Southampton (2007) MSc Oceanography (w. Distinction), University of Southampton BSc Geology and Geophysics, Durham University Her research spans phytoplankton biogeography , light availability controls , and satellite ocean color applications . Recent publications highlight interdisciplinary themes such as: Chromatic acclimation in oligotrophic oceans Subsurface chlorophyll maximum dynamics Backscattering proxies for carbon biomass Wind-mixing impacts on bloom evolution Functional trait modeling of plankton communities Climate change signatures in marine productivity Scientific Awards : Dean's Prize for Citizenship and All Round Contribution (2014) As Programme Lead for MSc Ocean and Earth Science by Research, she supervises PhD students including James Alexander Bowen Williams, Hans Hilder, and Zhibo Shao. External roles include consultancy with QinetiQ and membership on the International Ocean Colour Coordinating Working Group.
Chun-Hung Liu is an Associate Professor at Texas A&M University, affiliated with the College of Arts & Sciences. His research focuses on structural graph theory, combinatorics, and discrete mathematics, with a particular emphasis on graph minors, coloring problems, treewidth, and algorithmic aspects of graph theory. He has contributed significantly to understanding extremal graph properties, metric dimensions, and well-quasi-ordering in graph structures. His work often intersects theoretical computer science, exploring algorithmic solutions to graph partitioning, coloring, and decomposition challenges. Recent trends in his publications highlight advancements in conflict-free coloring, asymptotic dimensions of graph families, and structural characterizations of graphs excluding specific minors or subgraphs. Liu’s research also delves into phase transitions in degeneracy and the interplay between graph parameters like treewidth and layered treewidth. His studies on quasi-tree partitions and product structures further illustrate his expertise in graph decomposition techniques. While no awards are explicitly listed, his prolific publishing record indicates sustained contributions to the field. His advising and grant activities, though not detailed in the provided texts, likely align with his research focus on foundational and applied graph theory problems. Collaborations and lab affiliations remain unspecified in the available information.
Dr. Blair D. Sullivan is a Professor in the School of Computing at the University of Utah. She holds a Ph.D. in Mathematics from Princeton University and a B.S. in Computer Science and Applied Mathematics from Georgia Tech. Prior to Utah, she was an Associate Professor at NC State University (2013–2019) and a Research Scientist at Oak Ridge National Laboratory (2008–2013). Research Interests: Parameterized algorithms, structural graph theory, applied discrete mathematics, random graphs, and combinatorial scientific computing. Current work focuses on integrating structural graph theory into scalable network analysis tools, with applications in robotics, fairness in algorithms, and biomedical networks. Awards & Honors: Fulbright U.S. Scholar to France (AY2025–26) Collegium de Lyon Fellow (2025–2026) Moore Investigator in Data-Driven Discovery (2014–2024) Inaugural Pathway of Progress Alumnae Honoree (Georgia Tech) Key Projects: Developed software tools like CONCUSS , spacegraphcats , and BEAVr for graph analytics and network analysis. Explores edge augmentation for fairness in social networks and robotic motion planning using parameterized algorithms. Grants: NSF-funded projects on fairness in network information flow ($1.5M). NIH grant for network-based drug repositioning algorithms. Labs & Collaborations: Active in the Laboratoire de l'Informatique du Parallélisme (LIP) at ENS de Lyon during her 2025–2026 sabbatical. Collaborates with SIAM and ICERM on workshops and conferences.
