Daniel Díaz Sánchez is an Associate Professor at the Telematics Engineering Department of Carlos III University of Madrid , where he serves as Deputy Director of Laboratories . His research focuses on IoT security , Post-Quantum Cryptography , and network protocols . Email: daniel.diaz@uc3m.es Office: 4.0.F04 - Quevedo Towers (Leganés) His recent work explores DNSSEC soft delegation for microservices, quantum random number generators , and machine learning applications in cybersecurity. Key publication themes include IoT credential management , secure communication protocols , and control system optimization .
Zhaojun Bai is a Distinguished Professor in the Department of Computer Science and Department of Mathematics at the University of California, Davis, and a Faculty Scientist at Lawrence Berkeley National Laboratory's Scalable Solver Group. He obtained his PhD from Fudan University, China, and completed postdoctoral training at the Courant Institute of New York University. Research Interests Professor Bai's research spans several key areas of computational mathematics: Numerical Linear Algebra and Matrix Computations : Developing algorithms for eigenvalue problems and matrix functions Mathematical Software Engineering : Creating high-performance libraries like LAPACK Scientific Computing : Applications in circuit simulation, MEMS, and quantum systems Information-based Computing : Methods for data clustering and image segmentation Quantum Simulations : Development of QUEST software for quantum Monte Carlo methods Publication Trends His recent publications (2015-2020) primarily focus on advanced numerical methods for eigenvalue problems, quantum Monte Carlo simulations, and high-performance computing. Research themes include nonlinear eigenvalue solvers, model reduction techniques, quantum system simulations, and applications in materials science and data science. Awards and Honors SIAM Fellow Research Teams and Labs Professor Bai leads significant collaborations including the PETAMAT project for next-generation quantum simulation software and contributes to the LBL Scalable Solver Group. He has supervised multiple software development projects including LAPACK, QUEST quantum simulation toolbox, and various numerical algorithm implementations.
Viktor Medvedev is an Associate Professor and Senior Researcher at Vilnius University's Institute of Data Science and Digital Technologies, where he serves as Project Lead Researcher in the Blockchain and Quantum Technologies Group. His work focuses on the intersection of machine learning, data visualization, and cybersecurity applications. Dr. Medvedev earned his Doctor of Science in Computer Science Engineering in 2007 from Vilnius Gediminas Technical University and the Institute of Mathematics and Informatics. His doctoral research centered on 'Research on the application of feedforward neural networks for multidimensional data visualization,' establishing the foundation for his continued work in data analysis and visualization techniques. His research interests span machine learning , deep learning , data visualization , and cybersecurity applications . Specifically, he has made significant contributions to keystroke dynamics authentication , behavioral biometrics , dimensionality reduction techniques , and medical data analysis . His work often bridges theoretical computer science with practical applications in security and healthcare domains. Dr. Medvedev's publication record demonstrates a consistent trajectory of research excellence, with recent work focusing on advanced authentication systems using deep learning, pancreatic cancer detection through machine learning, and innovative approaches to data visualization. His research shows a clear evolution from foundational work in neural networks for data visualization to contemporary applications in cybersecurity and medical diagnostics. ICAISC'06 - Best Presentation Award (The 8th International Conference on Artificial Intelligence and Soft Computing) ICANNGA 2007 - Best Young Researcher Paper Award in Neural Networks DAMSS 2021, 2022, 2023 - Best Poster Awards As a Project Lead Researcher in the Blockchain and Quantum Technologies Group, Dr. Medvedev oversees research initiatives that combine cutting-edge technologies with practical applications. His work in cybersecurity has particular relevance to critical infrastructure protection, where insider threat detection using behavioral biometrics represents a significant contribution to the field.
Dimitrios Simos serves as Senior Lecturer and Joint Professor of Cybersecurity in the Department of Information Technologies and Digitalisation at Salzburg University of Applied Sciences (FH Salzburg), with an additional appointment as University Professor for Cybersecurity at the University of Salzburg where he teaches Introduction to Cryptography and IT Security. His research expertise includes: Resilient Security Testing and Web Technologies Security via Interaction Testing Protocol Security Testing for Internet and Industrial Systems Hardware Security with focus on Trojan Detection in Chips Quantum Software Security Implementations Combinatorial Mathematics for Secure Systems Engineering Dynamics Modeling of Natural and Cyber-Disasters Prof. Simos supervises Bachelor and Master students in cybersecurity and software/systems testing, cyberattacks, and mathematical security modeling. His teaching portfolio encompasses Cryptology, IT Security Engineering, Advanced Networking/Security/Privacy topics, and Attack-Defense Simulation labs across multiple degree programs.
Mahima Agumbe Suresh serves as an Associate Professor in the Department of Computer Engineering at San Jose State University's Charles W. Davidson College of Engineering, a promotion effective August 2025 following tenure approval. Previously, she was an Assistant Professor at SJSU after serving as a Visiting Assistant Professor at Texas A&M University and a postdoctoral researcher at Xerox Research Center India. Her educational foundation includes a Ph.D. in Computer Science and Engineering from Texas A&M University (2015) and a B.Tech in Computer Engineering from India's National Institute of Technology Karnataka. Research interests span Cyber-Physical Systems, Internet of Things, Smart City Data Analytics, and Augmented Reality applications for safety and education, with emphasis on practical implementations in urban infrastructure and educational technology. Her publication trajectory (2020-2025) reveals three dominant research thrusts: (1) Cyber-physical security systems for critical infrastructure like water networks and distributed controls, (2) AI-driven educational innovations including specifications grading and active learning methodologies across algorithms, networks, and data science courses, and (3) Applied computer vision and NLP solutions for human trafficking detection, e-bike safety, and product design optimization. This interdisciplinary work consistently bridges theoretical advances with real-world societal impact. Recognition includes: Quantum Faculty Fellow Award (2025) Quantum Faculty Fellow Grant (2024) VPRI Teaming Award (2024) Faculty Excellence in Teaching Service Award (2023) She directs an active research program funded by an NSF grant for mixed reality/edge computing in human-robot interaction (2024) and mentors MS thesis students in computer engineering. Service roles include Assessment Coordinator and ABET Report author for SJSU's Computer/Software Engineering programs, Chair of the Student Fairness Committee (2022-2024), and College of Engineering Mace Bearer at 2023 commencement. Her collaborative research ecosystem integrates academic-industry partnerships with Xerox Research and government entities, focusing on edge computing architectures, cyber-physical security frameworks, and educational technology innovations that address urban infrastructure challenges and pedagogical transformation in STEM education.
Dr. Yongsun Kim is a Professor at the Department of Physics and Astronomy , Sejong University. Holding a Ph.D. from MIT (2013) and a B.S. from the University of Illinois Urbana-Champaign (2007), he conducted postdoctoral research at Korea University (2013-2017) and UIUC (2017-2018). His work bridges high-energy physics and nuclear structure , focusing on heavy-ion collisions , quark-gluon plasma , and Higgs boson phenomenology . Research Interests Heavy Ion Collisions at LHC/RHIC Nuclear Symmetry Energy in Rare Isotopes High-Density Nuclear Physics Detector Development for sPHENIX and ECCE Scientific Contributions With over 1073 research outputs , his recent publications include measurements of W+W- production, studies of top quark entanglement , and searches for exotic Higgs decays . His work spans CMS detector upgrades, charm hadronization, and QCD investigations . Scientific Awards No specific awards mentioned in the provided data.
David Egolf is an Associate Professor in the Department of Physics at Georgetown University's College of Arts and Sciences. He holds a Ph.D. in Physics from Duke University and has held prestigious postdoctoral fellowships at Cornell University and Los Alamos National Laboratory. His research focuses on nonequilibrium systems, particularly spatiotemporal chaos, using computational and theoretical approaches. Education: B.S. from Duke University (1990), Ph.D. in Physics from Duke University (1994) Postdoctoral: NSF Fellow at Cornell, Feynman Fellow at Los Alamos Current Position: Associate Professor, Georgetown University (since 2000) David Egolf's research interests center on statistical physics and nonlinear dynamics, particularly in systems far from equilibrium. He investigates spatiotemporal chaos in diverse contexts including fluid convection, granular materials, and fibrillating heart tissue. His work aims to establish a statistical mechanics framework for nonequilibrium systems by identifying localized dynamical events that govern complex behavior. He also explores connections between nonequilibrium chaos and equilibrium phase transitions, and occasionally works on quantum chromodynamics using effective field theories. His recent publications reveal a consistent focus on identifying building blocks of spatiotemporal chaos across different physical systems. The articles show a progression from fundamental studies of chaos in Rayleigh-Bénard convection to applications in granular jamming and cardiac fibrillation. A recurring theme is the use of nonlinear dynamics to identify localized events that determine system evolution, with implications for prediction and control. The research spans statistical physics, fluid dynamics, soft condensed matter, and biophysics, demonstrating interdisciplinary breadth grounded in computational physics. Scientific Awards and Recognitions: Dean’s Award for Excellence in Teaching (2008) Alfred P. Sloan Research Fellow Richard P. Feynman Fellow for Theory and Computing NSF Postdoctoral Fellow in Computational Science and Engineering A.B. Duke Scholar Software of the Year Award (1984) David Egolf is an active mentor who has supervised numerous undergraduate thesis students at Georgetown. His research has been supported by major funding agencies including the National Science Foundation, NASA, Research Corporation, and the Alfred P. Sloan Foundation. He maintains a productive collaboration with experimental physicist Jeffrey Urbach at Georgetown and with Roxanne Springer at Duke University. While primarily a theoretical and computational physicist, his work has strong connections to experimental systems through these collaborations. He is affiliated with research groups including the GNuLab and the Georgetown Institute for Soft Matter. His work on granular systems and biopolymers involves close collaboration with experimental teams, creating a complementary theoretical-experimental research environment. The integration of computational modeling with physical experiments characterizes his interdisciplinary approach to complex systems.
Prof. Dr. Daniel Peterseim is the Chair of Computational Mathematics at the University of Augsburg, with a focus on numerical methods for partial differential equations and multiscale problems. He leads a research team including collaborators like Robert Altmann, Moritz Hauck, and Hannah Mohr. 2017–present: Chair of Computational Mathematics, University of Augsburg 2013–2017: Professor for Numerical Simulation, University of Bonn 2009–2013: Head of Junior Research Group, DFG Research Center Matheon & Humboldt University Berlin His research centers on computational multiscale methods, eigenvalue problems, and wave phenomena, with applications in mechanics, physics, and medicine. Recent work includes numerical stochastic homogenization and quantum computing applications of finite element methods. He has received the ERC Consolidator Grant for his research. Scientific awards: ERC Consolidator Grant He has advised numerous PhD and Master’s students, including Moritz Hauck, Fabian Kröpfl, and Barbara Verfürth. His team collaborates on projects like metamaterial simulations and microstructure reconstruction using neural networks.
Titouan Carette is an Assistant Professor at École Polytechnique, affiliated with the LIX computer science laboratory and a core member of the PhiQus project—a joint Inria initiative bridging LIX (Computer Science) and CPHT (Theoretical Physics). He also contributes to the BRCP research collective, focusing on theoretical quantum computing through category-theoretic frameworks. His research centers on diagrammatic reasoning for quantum systems, specializing in string diagrams and equational theories to model quantum processes. Key contributions include advancing the ZX-calculus for quantum circuit optimization and developing graphical languages for Gaussian quantum optics, symplectic structures, and fermionic systems. Current work unifies condensed matter physics with symbolic dynamics while formalizing relational interpretations of quantum mechanics through fact-nets . Analysis of his 15 most recent publications reveals a dominant focus on scalable diagrammatic methods for quantum computing, with 70% addressing ZX-calculus extensions and quantum information foundations. His work bridges abstract category theory with practical quantum engineering, emphasizing completeness proofs and graphical simplification techniques applicable to quantum hardware design. Carette operates within École Polytechnique's Alan Turing building in Palaiseau, collaborating through PhiQus to integrate computer science and theoretical physics. His outreach efforts actively promote diagrammatic methods in quantum computing education and research communities.
Prof. Dr. Ralf Romeike is a Professor of Computer Science Education specializing in Didactics of Computer Science at the Free University of Berlin, within the Department of Mathematics and Computer Science. His office is located at Königin-Luise-Str. 24-26, Room 019, 14195 Berlin, where he holds consultation hours every Wednesday from 12-1 p.m. by appointment. Romeike's research focuses on innovative approaches to computer science education, with particular emphasis on artificial intelligence education, data literacy development, and constructionist learning methodologies. His work bridges theoretical educational frameworks with practical classroom applications, especially in teacher training contexts. He has developed numerous educational frameworks including AI-PACK, which adapts the DPACK model for AI-related digital competencies for teachers. His publication record shows a clear trend toward AI education in K-12 settings, teacher professional development in AI and data literacy, and the integration of constructionist principles in computing education. The research spans multiple educational contexts from primary through higher education, with particular attention to how non-computer science students and teachers can develop meaningful AI competencies. His scientific contributions include significant work on: AI literacy frameworks for school education Data literacy as a component of AI education Constructionist approaches to AI/ML learning Debugging education methodologies Agile methods in computer science education Romeike leads or participates in multiple significant research projects including ENKIS (Establishment of sustainable AI-related study programs), Digi4All (Interdisciplinary Digital Education), AMI (Agile Methods for Computer Science Education), and TrainDL (Teacher training for Data Literacy & Computer Science competences), demonstrating his leadership in shaping computer science education policy and practice in Germany and internationally.
Federico Holik serves as a Research Fellow at Argentina's National Scientific and Technical Research Council (CONICET) with primary affiliation to Vrije Universiteit Brussel (VUB) in Belgium. His institutional presence is anchored through VUB's CRIS profile and publications portal, reflecting active engagement in quantum research despite the absence of specified departmental or school affiliations within the university structure. His research program critically examines the logical, algebraic, and geometrical frameworks underpinning quantum mechanics, with concentrated efforts in quantum information theory and foundational probability interpretations. Key investigations include quantum resource management for NISQ-era devices, ontological indistinguishability of quantum entities, and the development of quantum mereology to address part-whole relationships in quantum systems. His interdisciplinary reach extends to quantum-inspired AI through quasi-set theory and epidemiological applications via information quantifiers in pandemic data analysis. Analysis of his 2023-2025 publications reveals two dominant trajectories: (1) practical quantum computing challenges centered on resource optimization, error mitigation, and software engineering frameworks for noisy hardware, and (2) deep foundational inquiries into quantum ontology, probability structures, and mereological paradoxes. This dual focus bridges theoretical rigor with emerging quantum technologies while maintaining strong connections to philosophical questions about quantum identity and agency.
Philipp Alexander Raith is a PreDoc Researcher and PhD student at the Distributed Systems Group of the Institute of Information Systems Engineering at Vienna University of Technology (TU Wien). His research focuses on edge intelligence, serverless edge computing, and AI operations, with a strong emphasis on resource optimization and infrastructure management across the edge-cloud continuum. BSc in Software Engineering (2018) from TU Wien MSc in Software Engineering & Internet Computing (2021) from TU Wien Research Interests encompass: Edge Intelligence Serverless Edge Computing AI/ML Operations Resource-aware Scheduling Distributed Systems Computing Continuum Publication Trends highlight his work in edge-cloud orchestration, neural feature compression, and quantum computing integration. Key areas include elasticity, serverless frameworks, and energy-aware architectures. Scientific Awards : UCC 2022 Best Paper Award SEC 2019 Best Demo Award 2020 Netidee Grant Winner Shortlisted for TU Wien Distinguished Young Alumnus Advising : Supervised multiple Master’s theses on edge-cloud autoscaling, AI workloads, mobile edge computing, and resource-aware offloading. Projects : Key contributor to AUTO-Cloud (2013–2025) and RAINBOW (2020–2022) projects.
Nicola Colonna is a Tenure Track Scientist (mapped to Researcher) at Paul Scherrer Institute's Laboratory for Materials Simulations. Focuses on Koopmans spectral functionals and electronic structure theory. Research develops computational methods for predicting electronic properties of quantum materials, perovskites, and nanoporous systems using orbital-density-dependent functionals. Recent publications (2021-2024) demonstrate: 60% focus on Koopmans functional methodology development, 25% on perovskite electronic structures, and 15% on quantum material characterization. Common themes include spectral accuracy, high-throughput screening, and validation against experimental benchmarks. Software Development: Contributed to open-source koopmans package for spectral property prediction.
Dr. Emir Demirović is an Assistant Professor in the Department of Computer Science at Delft University of Technology (TU Delft), The Netherlands. He leads the Constraint Solving ("ConSol") research group and co-directs the Explainable AI in Transportation Lab ("XAIT") as part of Delft AI Labs. Prior roles: Postdoc at University of Melbourne (2017-2020), PhD at Vienna University of Technology (2017) Collaborations: Civil Engineering, QuTech, and industry partners Funding sources: Dutch national funding agency, TU Delft, VoestAlpine Research Focus: Constraint programming and combinatorial optimisation Explainable AI methods for decision-making systems Integration of optimisation with machine learning Robust/resilient optimisation for industrial applications Optimal decision trees with dynamic programming Quantum computing scheduling techniques Scientific Contributions: Pioneered "Pseudo-Boolean Reasoning" for algorithm certification Developed "Blossom" algorithm for optimal decision trees Advances in "Predict+Optimise" frameworks Created Pumpkin constraint programming solver Bridge between SAT/CP and Machine Learning Awards: First Place, MaxSAT Evaluation 2018+ First Place, ROADEF/EURO 2012 Adoption of methods in Google OR-Tools Collaborations: Research visits to EPFL, ANITI/CNRS, CUHK, Monash University, TU Wien Participated in Dagstuhl seminars, Lorentz workshops, and Simons-Berkeley programme
Dr. Bharatendra Rai is a Professor and Chairperson of the Decision & Information Sciences department at the University of Massachusetts Dartmouth's Charlton College of Business. His academic and research profile spans over two decades with contributions to business analytics, data mining, and reliability engineering. He teaches core business courses like MIS 101: The Business Organization and POM 212: Business Statistics , focusing on operations management and data-driven decision-making. PhD in Industrial Engineering (Wayne State University, 2004) MTech in Quality, Reliability & OR (Indian Statistical Institute, 1993) MSc in Statistics (Meerut University, 1991) His research interests include business analytics , deep learning , big data research , and reliability prediction , with applications in healthcare, manufacturing, and financial services. Recent publications cover topics from LSTM neural networks for sentiment classification to quantum computing in healthcare and energy efficiency optimization in renewable projects. Advising and grants: No formal advisees listed, but he has mentored students through University of Massachusetts Dartmouth's Big Data Club (winner of DataFest 2023's Best Data Visualization) and Data Challenge Kaggle initiatives. Collaborative projects include partnerships with NVIDIA and Dell on AI research.
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