Marko Tanasković (born December 6, 1986) is a researcher at Singidunum University with a PhD in Information Technology and Electrical Engineering from ETH Zurich (2015). His academic background includes master's (ETH Zurich, 2011) and bachelor's (University of Belgrade, 2009) studies in Electrical Engineering. Doctoral studies: Information Technology and Electrical Engineering, ETH Zurich (2011-2015) Master studies: Information Technology and Electrical Engineering, ETH Zurich (2009-2011) Basic studies: Electrical Engineering, University of Belgrade (2005-2009) Tanasković's research focuses on control systems , predictive modeling , and optimization algorithms for mechanical and electrical systems. His work addresses adaptive model predictive control (MPC), sensorless motor positioning, and data-driven approaches for nonlinear systems. Recent publications (2018-2024) demonstrate expertise in: Embedded control systems (rotor polarity detection) Drone forensics and autonomous navigation Industrial automation (LabVIEW applications) Biomedical sensor development ('Smart Anklet') Machine learning optimization (firefly algorithm)
Yu Li is a Lecturer at the University of Picardie Jules Verne (UPJV) and a member of research unit UR 4290 “Optimization and Cryptography, AI – OCIA.” His office is located in room 302, reachable by internal telephone extension 5900. Research Interests Dr. Li’s research spans several inter-related domains: Optimization & Control Theory – developing dynamic optimization algorithms for industrial processes such as continuous casting in steel manufacturing. Cryptography & Security – investigating secure and dependable models for cloud and distributed systems. Artificial Intelligence & Robotics – integrating AI perception and decision-making into cloud-connected robotic platforms, including exoskeletons for rehabilitation and autonomous ground vehicles. Cloud & Fog Computing – designing middleware and domain-specific languages that seamlessly connect robotic devices with cloud and edge resources. Publication Trends Over the past decade, Dr. Li’s publication record reveals a clear evolution from foundational work in software architecture and component-based systems (2010-2016) toward cutting-edge applications in cloud/fog-enabled robotics and AI-driven cyber-physical systems (2017-2023). His studies increasingly emphasize real-world deployment, simulation-driven resource estimation, and human-centric interaction in rehabilitation robotics. Scientific Awards & Recognition No specific awards are listed in the provided materials. Advising & Funding No explicit information about supervised students or funded grants is available in the text supplied. Laboratories & Teams He carries out his research within the UR 4290 research unit “Optimization and Cryptography, AI – OCIA” at UPJV, focusing on collaborative projects that bridge mathematics, computer science, and robotics engineering.
Bas Testerink is a Researcher at the Faculty of Science , Utrecht University , focusing on Responsible AI . He works in the AI & Data Science subtheme with research interests in Human-centered Artificial Intelligence and Applied Data Science . Email: b.j.g.testerink@uu.nl Office: Minnaert Building, Leuvenlaan 4, Room 304, Utrecht His research explores Norm Enforcement in distributed systems, Multi-Agent System design, and Runtime Verification mechanisms. Key projects include AI-assisted message processing for police and Norm-based traffic control systems . Recent publications highlight his work on Argumentation-based Inquiry (2022) and Collaborative Monitoring (2016). He also investigates Security Threats in collaborative verification and Organizational Replication through inheritance models. He contributes to: Autonomous Vehicles regulation Crime Analysis through AI Peer-to-Peer Argumentation frameworks His work appears in venues like PRIMA , ECAI , and AAMAS .
Tim Mitchell is an Assistant Professor in the Department of Computer Science at Queens College / CUNY and the CUNY Graduate Center. His research focuses on designing fast and reliable algorithms for robust control and stability analysis of dynamical systems, with applications in nonsmooth constrained optimization and benchmarking of numerical algorithms. Queens College / CUNY, Assistant Professor, Computer Science CUNY Graduate Center, Faculty Affiliation, Data Science His work spans numerical linear algebra, optimization, and scientific computing, addressing both small-scale and large-dimensional problems. He develops open-source software packages like GRANSO (non-smooth optimization), ROSTAPACK (stability measures), and betaRMP (benchmarking visualization). The 15 most recent articles reflect his expertise in nonsmooth optimization, stability measures for dynamical systems, and computational methods in control theory, with recurring themes of algorithm design, benchmarking, and applications in machine learning and numerical analysis. Keywords include numerical radius, pseudospectral abscissa, hybrid expansion-contraction algorithms, and Kreiss constants. He actively collaborates with institutions such as the Max Planck Institute for Dynamics of Complex Technical Systems and the Courant Institute of Mathematical Sciences.
Luke Mathieson is a Senior Lecturer and Deputy Head of School (Teaching and Learning) in the School of Computer Science at the University of Technology Sydney. His academic career spans theoretical computer science with a focus on computational complexity and its applications. Dr. Mathieson's educational background includes a PhD in Theoretical Computer Science from Durham University, a Masters and Postgraduate Diploma in Higher Education from Macquarie University, and dual Bachelor's degrees in Computer Science (Honors) and Science (Chemistry) from the University of Newcastle Australia. His research interests are centered on parameterized complexity and its applications, extending to various areas of complexity theory, algorithmics, quantum computing, graph theory, and related mathematics. A major theme of his research is the complexity of graph editing problems, a topic in which he specializes. His recent work bridges theoretical complexity with practical applications in AI education, network science, and quantum computing. Dr. Mathieson has taught an extensive range of computer science subjects, particularly focusing on the theory of computation, computational complexity, and algorithmics. At UTS, he teaches or has taught subjects including Data Structures and Algorithms, Applications Programming, Computing Science Studio, Theory of Computing Science, Programming, and Advanced Algorithms. He serves as the Course Director for the Bachelor of Science in Information Technology suite of degree programs and the Course Coordinator for the IT Core. Senior Lecturer, University of Technology Sydney, School of Computer Science (2022-present) Lecturer, University of Technology Sydney, School of Computer Science (2021-2022) Scholarly Teaching Fellow, University of Technology Sydney, School of Computer Science (2017-2021) Research Associate, University of Newcastle Australia, Centre for Information Based Medicine (2014-2017) Adjunct Lecturer, Macquarie University, Department of Computer Science (2014) Postdoctoral Fellow, Macquarie University, Department of Computer Science (2011-2013) Research Associate, University of Newcastle Australia, School of Electrical Engineering and Computer Science (2010-2011) His research demonstrates consistent productivity across theoretical computer science with notable contributions to parameterized complexity and network controllability. Recent publications show an expanding scope incorporating quantum computing applications and educational technology innovations. The QB-suite: a framework for quantum algorithm design and benchmarking (2024-2027) National Industry PhD Program: Improving biosecurity through livestock history recording (2024-2028) Random Number Generation and Analytics for Client Understanding (2018-2019) He maintains active research collaborations across multiple institutions and is affiliated with the Faculty Centre for Quantum Software and Information (QSI) at UTS, reflecting his growing involvement in quantum computing research.
Keivan Navaie is a Professor of Intelligent Networks at Lancaster University’s School of Computing and Communications. He serves as a member of the Independent Scientific Advisory Committee at the Alan Turing Institute, overseeing the £100 million BridgeAI programme, and previously as Principal AI Technology Advisor to the UK Information Commissioner’s Office (ICO). He is recognized with Fellowships from the Institution of Engineering and Technology (IET), Chartered Engineer status in the UK, Senior Fellowship of the Higher Education Academy (HEA), and the IEEE Young Investigator Award. Research Focus: Wireless communications, mathematics, artificial intelligence, 6G networks, blockchain technology, edge computing, cognitive radio networks, and non-orthogonal multiple access (NOMA). Supervision: Actively supervises PhD students in areas like wireless communications and mathematical modeling. Projects: Involved in distributed learning, blockchain integration, 6G research, and spectrum sharing systems. Awards: IEEE Young Investigator Award, Fellow of IET, Chartered Engineer, Senior Fellow of HEA.
Prof. Dr. Erik van Nimwegen is a Professor at the Biozentrum of the University of Basel, where he leads a research group focused on computational and systems biology. His laboratory is housed in Room 08.012 at the Biozentrum, with contact information including phone number +41 61 207 15 76 and email erik.vannimwegen@unibas.ch. His administrative support is provided by Rita Ruppen and Jonila Vladi. Van Nimwegen's research centers on understanding the function and evolution of genome-wide regulatory networks. His group investigates how cells control gene expression using both theoretical and experimental methods, with particular interest in deciphering the genome's regulatory code, analyzing gene expression dynamics from single cells to genome-wide regulatory programs, and uncovering quantitative laws of genome evolution. His interdisciplinary approach combines computational predictions with experimental data to model regulatory networks across organisms from E. coli to humans. His work has significant implications for understanding cellular behavior, developing evolutionary theory based on measurable quantities, and potential applications ranging from taming pathogens to engineering human tissue. Analysis of his recent publications reveals a consistent focus on gene regulatory networks, with increasing emphasis on single-cell analysis, computational modeling of biological systems, and the evolutionary aspects of gene regulation. His work spans bacterial systems (particularly E. coli) to higher eukaryotes, with recent publications showing expansion into visualization techniques for single-cell data, cancer research related to RNA processing, and even virology with work on COVID-19 origins. A notable trend is the integration of multiple data types (multiomics) and the development of sophisticated computational tools for analyzing complex biological systems. Van Nimwegen's group has developed numerous software tools and web services for regulatory and comparative genomics, including ISMARA (Integrated System for Motif Activity Response Analysis), CRUNCH (for ChIP-seq data analysis), SwissRegulon database, Phylogibbs, and others. These resources reflect his commitment to creating practical tools that advance the field of computational biology. His laboratory operates as an interdisciplinary team with researchers from diverse backgrounds including theoretical physics, computer science, and molecular biology. The group actively recruits postdocs and engineers, as evidenced by a 2018 posting seeking researchers for computational analysis of bioimages. His work has established important theoretical frameworks for understanding gene regulatory networks and their evolution, with significant contributions to the field of systems biology.
Daniel C. Fredrickson is a Professor of Chemistry at the University of Wisconsin–Madison, where he leads the Fredrickson Group in the Department of Chemistry. His research program integrates solid-state synthesis, advanced crystallography, and quantum-mechanical theory to uncover the chemical principles governing the complex structures of intermetallic compounds and to exploit these principles for the design of energy-relevant materials. Education B.S. 2000, University of Washington Ph.D. 2005, Cornell University Postdoctoral Associate 2005–2008, Stockholm University Research Focus The Fredrickson group explores the intersection of solid-state chemistry, crystallography and chemical bonding theory . Core themes include: • Chemical Pressure Analysis : quantifying steric and electronic influences on intermetallic structures. • Structure–Property Engineering : using structural diversity to tune superconductivity, thermoelectricity and catalysis. • Quasicrystals & Complexity : deciphering long-period superstructures and incommensurate order. • Data-Driven Discovery : machine-learning approaches to accelerate materials discovery. Methodologically, the group couples density-functional theory (DFT) and extended Hückel calculations with high-temperature synthesis, single-crystal X-ray diffraction and custom open-source software (DFT Chemical Pressure, eHtuner, DFT-raMO). Selected Scientific Impact Between 2012 and 2023 the group published more than fifteen high-profile articles that collectively advance (i) theoretical frameworks for chemical pressure, (ii) predictive rules for electron counting in transition-metal intermetallics, and (iii) machine-learning models for navigating structural complexity. Studies range from fundamental analyses of NaCd 2 and YbCd 5.7 to applied investigations of Nb 3 Ge superconductors. Awards & Recognition No specific external awards are listed in the provided text; emphasis is placed on sustained publication record and leadership in the solid-state chemistry community. Students & Mentoring Recent Ph.D. graduates under his supervision include: Kyana Sanders (2023) Jonathan Van Buskirk (2023) Amber Lim (2023) Undergraduate alumnus Joe Kraus continued to MIT for doctoral studies. Laboratories & Facilities The group operates four integrated labs: Glove Box Lab: three inert-atmosphere glove boxes with arc-melter, pellet press and optical microscope. Furnace Lab: ten high-temperature furnaces (900–1800 °C) for annealing and flux synthesis. X-ray Crystallography Lab: dedicated single-crystal diffractometer for rapid structure solution. Library and Theory Lab: high-performance computing cluster (dual Xeon workstations + 60 parallel cores) for DFT and Hückel calculations.
Dr. Eng. Jarosław Rudy is an Assistant Professor at the Wrocław University of Science and Technology, affiliated with the Department of Automatic Control, Mechatronics and Control Systems under the Faculty of Computer Science and Telecommunications. He completed his Master of Science in Engineering in 2011 and earned his Ph.D. in technical sciences in 2016, focusing on computational models and programming languages for software evolution and adaptation. His research spans operations research, task scheduling, discrete optimization, quantum computing, machine learning, metaheuristic algorithms, computational complexity, and vehicle routing . He has authored over 45 scientific publications and teaches more than 30 courses, including Computer Automation Systems and Trusted Artificial Intelligence Systems , with a focus on laboratory and lecture-based education. Rudy leads the Discrete Systems Laboratory, supervises over 30 graduate theses, and contributes to advanced optimization techniques and data structures curricula. His work integrates computational theory with practical applications in parallel computing and multi-criteria optimization.
James Riely is a Professor in the School of Computing at DePaul University , United States. His research focuses on Programming Languages , Concurrency , and Formal Methods , particularly in modeling Relaxed Memory and Security in distributed systems. He has contributed to leading conferences like POPL , SPLASH , and VMCAI , addressing topics such as Concurrent Programming , Event Structures , and Security Automata . Education : PhD in Computer Science from University of North Carolina at Chapel Hill (1999). Research Trends : His work spans Relaxed Memory Models , Software Verification , and Security Protocols , often integrating Formal Verification and Concurrency Theory . Key contributions include modeling Speculative Execution Attacks and designing Security Automata for enforceable policies. Conference Involvement : Active in program committees for POPL , VMCAI , and PPoPP , with session chair roles in tracks like Separation Logic and SLE .
Benjamin C. Pierce serves as the Henry Salvatori Professor of Computer and Information Science in the School of Engineering and Applied Science at the University of Pennsylvania. He is a Fellow of the ACM with extensive editorial experience, having served as co-Editor in Chief of the Journal of Functional Programming and Managing Editor for Logical Methods in Computer Science. His research spans programming languages, type systems, language-based security, formal verification, differential privacy, and synchronization technologies. Pierce is renowned for developing foundational frameworks in bidirectional transformations (lenses), property-based testing, and differential privacy verification. His work bridges theoretical computer science with practical implementation, particularly evident in his development of the Unison file synchronizer and the Clowdr virtual conference platform. The trends in his recent publications reveal a sustained focus on verification techniques for privacy-preserving systems, particularly differential privacy, alongside continued innovation in bidirectional data transformations and property-based testing methodologies. His work increasingly integrates formal verification with practical implementation concerns. Fellow of the ACM Pierce has mentored numerous researchers through PLMW (Programming Languages Mentoring Workshop) events and has served on numerous program committees for major conferences including POPL, PLDI, and ICFP. His editorial work has significantly shaped the programming languages research community through his leadership roles in key journals. He leads development of the Unison file synchronizer, a cross-platform tool used worldwide, and co-developed the Clowdr virtual conference platform that gained prominence during the pandemic. His work demonstrates a consistent pattern of creating practical tools grounded in deep theoretical foundations.
Ippei Obayashi is a Professor at Okayama University's Center for artificial intelligence and mathematical data science, with a visiting professorship at Tohoku University's Advanced Institute for Materials Research (AIMR). His academic career spans prestigious institutions including RIKEN, Tohoku University, and Kyoto University, where he earned his Doctor of Science degree. Okayama University, Center for AI and Mathematical Data Science, Professor (2021-present) RIKEN, Center for Advanced Intelligence Project, Researcher (2018-2021) Tohoku University, Institute for Advanced Materials Science, Associate Professor (2018) Tohoku University, Advanced Institute for Materials Science, Assistant Professor (2015-2018) Kyoto University, Research Fellow (2010-2015) Educational Background: Kyoto University, Graduate School of Science, Department of Mathematics and Mathematical Analysis (2006-2010, Doctoral) Kyoto University, Graduate School of Science, Department of Mathematics and Mathematical Analysis (2004-2006, Master's) Kyoto University, Faculty of Science (2000-2004, Bachelor's) Professor Obayashi's research focuses on topological data analysis (TDA) , particularly persistent homology and its applications, alongside dynamical systems theory. His work bridges pure mathematics with practical applications in materials science, where he has developed innovative methods to analyze complex material structures. He has made significant contributions to understanding magnetic materials, amorphous structures, and crystal formation through topological approaches, often integrating machine learning techniques with traditional mathematical analysis. His research has practical implications for energy materials, battery technology, and materials characterization. His publication record demonstrates a strong trend toward applying topological methods to solve real-world materials science problems, with increasing integration of machine learning techniques. Recent work shows sophisticated applications of persistent homology to analyze neutron scattering data, magnetic properties, and structural characteristics of materials, reflecting his ability to translate abstract mathematical concepts into practical analytical tools. Scientific Awards: JCS-JAPAN Excellent Paper Award, Ceramic Society of Japan (2020) 11th Sakuramai Research Encouragement Award, RIKEN (2020) Japan Society for Industrial and Applied Mathematics Best Author and Best Paper Awards (2017) 6th Fujiwara Hiroshi Mathematical Sciences Encouragement Award (2017) AIMR International Symposium Best Poster Award (2017) Professor Obayashi actively mentors graduate students through Okayama University's Graduate Student Program and leads research initiatives including the Japan Society for Industrial and Applied Mathematics Topological Data Analysis Research Group, which he chairs. His research is supported by multiple competitive grants, including Japan Society for the Promotion of Science (JSPS) funding for projects on mathematical data science and topological structure analysis. He collaborates extensively with researchers across disciplines, particularly in materials science and engineering. He is affiliated with the Center for artificial intelligence and mathematical data science (Angels) and the Cyber-Physical Engineering Informatics Research Division (Cypher) at Okayama University, where he leads efforts to develop and apply topological data analysis methods to complex scientific problems. His laboratory focuses on creating practical software tools like HomCloud for persistent homology analysis, bridging the gap between theoretical mathematics and applied scientific research.
Alessandra Cavarra is a University Lecturer in Software Engineering at the University of Oxford, holding roles as Director of Graduate Studies for Professional Programmes and Supernumerary Fellow at Kellogg College. She earned her MSc and PhD in Computer Science from the University of Catania (Italy), with research periods at U.S. and German institutions. Her research focuses on formal methods, UML behavioral diagrams, model-based testing, and integrating formal/semi-formal languages. She teaches postgraduate courses in Object-Oriented Design and Software Testing. Research interests include formal semantics for UML diagrams, tool development for symbolic execution and test case generation, and abstract state machines (ASMs). Her work bridges software engineering theory and practice, emphasizing rigorous methods for system validation. Publications span model-based testing, UML formalization, and concurrency analysis. Key areas include data-flow approaches for multi-agent systems, workflow testing, and formal verification of software models. Recent work addresses challenges in concurrent workflows and abstract state machine analysis. No scientific awards were explicitly mentioned. She has advised at least one student, Aadya Shukla, and contributes to academic service roles. No lab or team affiliations are detailed in the provided information.
Stefan Biffl is an Associate Professor of Software Engineering at Technische Universität Wien's Institute of Information Systems Engineering. He holds MS and PhD degrees in Computer Science from Vienna University of Technology and an MS in Social and Economic Sciences from the University of Vienna. His research focuses on software-intensive systems engineering, particularly in process improvement, quality assurance, and cyber-physical production systems. He has led over €4M in research projects, including the 7-year Christian Doppler Laboratory for Software Engineering Integration. Biffl has authored/co-authored over 250 publications and serves on program committees for top conferences like ICSE and ESEC/FSE. His current roles include co-leading TU Wien's Center for AI and Machine Learning (CAIML) Business group and supervising the Doctoral College on Trustworthy Autonomous Cyber-Physical Systems. Key research areas span systems architecture, empirical software engineering, and digital humanism principles in automation. He has advised major automotive companies like Volkswagen and Neuman Aluminium through the Comet Center for Digital Production. Biffl's involvement in academic organizations includes steering committees for ECSA and SEAA, and他曾担任多个期刊的客座编辑。他的著作包括《基于价值的软件工程》等 Springer 出版的书籍。研究项目注重工业自动化与知识工程的交叉应用，特别是在集体智能系统和 Industry 4.0 领域。 近期项目包括探索 AI 在商业绩效提升中的应用（2025 年）、数字化工单的安全风险分析（与 Secure Business Austria 合作）以及基于数据的汽车生产优化。他的工作强调将人本主义原则融入数字化转型，确保技术进步与社会价值的结合。
Caleb Kemere is an Associate Professor in the Departments of Electrical and Computer Engineering and Bioengineering at Rice University. His work bridges neuroscience, engineering, and computer science, focusing on neuroengineering, neural decoding, and real-time brain-computer interfaces. He holds a B.S. in Electrical Engineering (with Honors) and a B.A. in Economics from the University of Maryland, College Park, and a Ph.D. in Electrical Engineering from Stanford University. Before joining Rice in 2011, he was a postdoctoral fellow at the Keck Center for Integrative Neurosciences at UCSF. His research explores hippocampal function in spatial navigation and memory, signal processing for neural interfaces, and developing technologies like miniature microscopes and low-power sensors. He has pioneered frameworks such as Spyglass for reproducible neuroscience research and RealtimeDecoder for online neural decoding. Kemere has received prestigious awards including the NSF CAREER Award (2013), HFSP Young Investigator Award (2014), and BRAIN: EAGER Award (2015). His grants include a five-year NSF grant to study Deep Brain Stimulation (DBS) and a neural engineering IGERT grant. His lab, the Realtime Neural Engineering Lab (RNEL), develops tools for understanding and modulating neural activity. Ongoing work addresses closed-loop brain stimulation, environmental uncertainty modeling in foraging behavior, and sleep-based memory consolidation.
