Roger Pettersson is a Professor at the Department of Mathematics, Linnaeus University, with a focus on stochastic analysis, financial mathematics, and mathematical biology. He is affiliated with the Faculty of Technology and leads research in Stochastic Analysis and Stochastic Processes , Computational Mathematics for Predictive Digital Twins (PreDiTwin) , and Deterministic and Stochastic Modelling under the Linnaeus University Centre for Data Intensive Sciences and Applications (DISA). His work bridges theoretical and applied mathematics, emphasizing numerical methods and epidemic models. Research Interests : Stochastic differential equations (SDEs/SPDEs), Lévy processes, financial modeling, biological systems, numerical approximations, and epidemic dynamics. Publications : Over 20 peer-reviewed articles (2002–2025) in journals like Stochastics , Applied Mathematics and Computation , and Chaos, Solitons & Fractals , focusing on stochastic control in epidemics, Lévy noise applications, and numerical methods for SDEs. Supervised Theses : Mentored over 20 bachelor’s and master’s students in projects spanning queueing systems, SPSS data analysis, neural networks for hydropower forecasting, and epidemic modeling. Collaborations : Active in interdisciplinary research, including forestry, wood, and building technologies, and contributes to the International Center for Mathematical Modeling (ICMM) .
Agostino Cortesi is a Full Professor of Computer Science at Ca' Foscari University of Venice, where he has served since 2002. He holds significant administrative roles including Rector's Delegate for Research Quality Evaluation and Deputy Coordinator of the Scientific Committee of the Temporary Innovation Ecosystem Project Center. His academic home is the Department of Environmental Sciences, Computer Science and Statistics. Dr. Cortesi received his PhD in Applied Mathematics and Informatics from the University of Padova in 1992, followed by a post-doctoral position at Brown University. His academic career has included leadership positions as Dean of the Computer Science programme, Department Chair, and Vice-Rector of Ca' Foscari University for quality assessment and institutional affairs. His research focuses on programming languages theory, software engineering, and static analysis techniques with particular emphasis on security applications. His work spans abstract interpretation, information flow analysis, string analysis for program verification, and security applications in blockchain and IoT systems. He has published extensively with over 150 papers in high-level international journals and conference proceedings, with an h-index of 22 according to Scopus and 31 according to Google Scholar. His recent publications show a consistent focus on abstract interpretation techniques applied to string analysis, security verification for blockchain and IoT systems, and tools for static analysis. His work bridges theoretical foundations with practical applications, particularly in security-critical domains. Dr. Cortesi serves on the editorial boards of Computer Languages, Systems and Structures and Journal of Universal Computer Science, and has participated in numerous program committees for international conferences including SAS, VMCAI, CSF, CISIM, and ACM SAC. He teaches several advanced courses including Software Correctness, Security, and Reliability; Data Programming; Information Networks and Systems; and Software Engineering across both Computer Science and Business Administration programs. His research is supported by multiple funded projects from the European Union, Italian Ministry of Education, Veneto Region, and industry partners.
Karl-Ludwig Besser serves as Assistant Professor in the Division of Communication Systems within Linköping University's Department of Electrical Engineering since January 2025. Previously, he completed a postdoctoral fellowship at Princeton University's Department of Electrical and Computer Engineering (March 2023–December 2024), following his 2022 PhD from Technische Universität Braunschweig. His academic foundation includes: Dipl.-Ing. in Electrical Engineering, Technische Universität Dresden (2018) PhD in Electrical Engineering, Technische Universität Braunschweig (January 2022) Dr. Besser's research centers on ultra-reliable communication systems , physical layer security under resource constraints , and copula-based modeling of channel dependencies . His work bridges theoretical information theory with practical machine learning applications, particularly in reconfigurable intelligent surfaces (RIS) and integrated sensing-communication (ISAC) systems. Recent publications demonstrate expertise in secret-key budget management for resilient networks and UAV swarm coordination using 3D beamforming techniques. Analysis of his 15 most recent publications (2024–2025) reveals three dominant themes: (1) RIS optimization using physics-informed neural networks addressing mutual coupling challenges, (2) fundamental limits of physical layer security in dependent fading channels with secret-key budgets, and (3) machine learning solutions for UAV mobility management and mmWave resource allocation. His work consistently targets ultra-reliable low-latency communication (URLLC) requirements for next-generation networks. As part of Linköping University's Communication Systems division, Dr. Besser contributes to research initiatives including Wireless Communications for Distributed Intelligence and Integrated Sensing and Communications (ISAC) . While specific grant details aren't documented in source materials, his research aligns with European 6G initiatives and national security-focused wireless projects. The division actively supervises 25+ PhD students across topics including drone swarm communication, energy-efficient signal processing, and smart city sensor networks, where Dr. Besser likely participates in student mentorship.
Adam Teodor Polak serves as an Assistant Professor in the Department of Computing Sciences at Bocconi University, where his research centers on theoretical algorithms with dual emphases on fine-grained complexity and learning-augmented algorithms. His work investigates fundamental questions about computational hardness while developing prediction-enhanced algorithms that maintain worst-case guarantees. Polak earned his PhD from Jagiellonian University in 2019 under Paweł Idziak, including a research visit at MIT with Virginia Vassilevska Williams. He subsequently held postdoctoral positions at the Max Planck Institute for Informatics and EPFL before joining Bocconi. His research program addresses why computational problems resist efficient solutions and how imperfect predictions can robustly improve algorithmic performance. This manifests in two interconnected streams: establishing conditional lower bounds for problems like 3SUM and Orthogonal Vectors, and designing learning-augmented frameworks for dynamic graph problems, caching, and optimization that blend theoretical rigor with practical machine learning insights. Recent publications reveal accelerating momentum in algorithms with predictions, with over half of his 2023-2025 output appearing in top ML venues (ICML, NeurIPS, ICLR) alongside traditional theory conferences (STOC, SODA). This cross-pollination demonstrates how worst-case theoretical guarantees can coexist with data-driven performance gains across graph algorithms, scheduling, and combinatorial optimization. Scientific recognition includes: Best Paper Award at ESA 2024 for knapsack algorithm breakthroughs Bronze Medal at ACM ICPC World Finals (2011) 2nd Place in PACE 2018 Challenge for Steiner tree algorithms Polak actively shapes the field through program committee service (ESA, ICALP, SOSA) and community building, notably co-organizing the 2022 Workshop on Algorithms with Predictions (ALPS) and decade-long high-school algorithmics workshops. His industry collaborations with Teroplan and Google demonstrate real-world impact in route planning and distributed systems. Current teaching includes graduate Algorithms courses at Bocconi, while his experimental work on GPU-accelerated graph algorithms and medical computer vision continues to bridge theoretical insights with practical implementation challenges.
Nikolaos Sahinidis is the Gary C. Butler Family Chair and Professor in the H. Milton Stewart School of Industrial and Systems Engineering and the School of Chemical and Biomolecular Engineering at Georgia Institute of Technology. His research bridges computer science and operations research with applications across engineering and scientific domains, focusing on developing rigorous optimization methods for complex real-world problems. Dr. Sahinidis's research spans global optimization of mixed-integer nonlinear programs, informatics problems in chemistry and biology, process and energy systems engineering, and chemical product design. His work integrates theoretical algorithm development with practical applications in medical diagnosis, protein structure analysis, and environmentally benign chemical design. He has made significant contributions to inverse imaging problems in X-ray crystallography, biochemical network design, and black-box optimization. His recent publications demonstrate a clear trajectory toward integrating machine learning with traditional optimization approaches, particularly in derivative-free optimization, global optimization of nonconvex problems, and mixed-integer nonlinear programming. His work increasingly focuses on sustainable engineering applications, including rare earth element recovery, water network optimization, and perovskite solar cell design, reflecting a strong commitment to addressing contemporary engineering challenges. NSF CAREER award INFORMS Computing Society Prize Beale-Orchard-Hays Prize from the Mathematical Optimization Society Computing in Chemical Engineering Award Constantin Carathéodory Prize National Award and Gold Medal from the Hellenic Operational Research Society Member of the U.S. National Academy of Engineering Fellow of AIChE Fellow of INFORMS Dr. Sahinidis has secured substantial funding from the National Science Foundation, U.S. Environmental Protection Agency, and industry partners for his research. His group has developed several influential software tools including CMOS for protein structure alignment, GPU-BLAST for accelerated sequence alignment, R3 for protein side-chain conformation prediction, and SAS-Pro for protein structural alignment. The Sahinidis Optimization Group maintains active openings for graduate students and researchers nearly every year, fostering the next generation of optimization scientists. The Sahinidis Optimization Group at Georgia Tech is a leading research center in mathematical optimization and its applications. The group maintains strong collaborations with researchers across multiple disciplines and institutions, including the Hauptman-Woodward Medical Research Institute. Their work spans theoretical algorithm development to practical implementations in chemical engineering, bioinformatics, and materials science, with a consistent focus on developing rigorous, efficient methods for challenging optimization problems.
Husnu Yenigun is a Professor at Sabanci University's Computer Science and Engineering Program , Faculty of Engineering and Natural Sciences (Istanbul, Turkey). He earned his BSc, MSc, and PhD in Electrical and Electronics Engineering from Middle East Technical University (Ankara) in 1992, 1995, and 2000 respectively. His professional career includes roles at TUBITAK (1992-1997), Bell Laboratories (1997-1998), and as a consultant at Bell Labs (1999-2000). Research interests: Dr. Yenigun specializes in automata and concurrency theory, formal methods, software quality assurance, model checking, and complexity relief techniques for software verification. His work bridges theoretical automata analysis with practical testing frameworks. Key publication trends: His recent work spans automata synchronization (2018-2021), matrix optimization (2017-2018), and adaptive testing sequences (2016-2018), with applications in Wireless positioning systems Finite state machine verification Parallel computing Formal method implementations Professional activities: He serves on technical program committees for major conferences like MODELSWARD, QRS, and ICTSS (2016-2025). He chaired the programming committee for ICTSS 2013 and 2017, and co-chaired UYMS 2018. He also acted as guest editor for the International Journal on Software Tools for Technology Transfer (2016). Contact: yenigun@sabanciuniv.edu | Office: FENS 2054, Sabanci University
David Gianazza is a Researcher at the National School of Civil Aviation (ENAC) , focusing on air traffic management and trajectory prediction. His work combines machine learning and operational research to address challenges in aircraft conflict resolution, airspace configuration, and performance modeling. Specializes in neural networks and metaheuristic optimization Key research areas: air traffic complexity , 3D trajectory planning , and workload modeling His 15 most recent articles (2024-2012) demonstrate expertise in ADS-B data analysis, mass/thrust estimation, and hybrid deterministic-stochastic search algorithms. Collaborations include researchers like Nicolas Durand , Richard Alligier , and Jean-Marc Alliot across institutions in France, Netherlands, and USA. Publications highlight applications of ant colony optimization , A* algorithms , and gradient boosting machines to air traffic problems. His work bridges theoretical computer science and practical ATM solutions , with emphasis on safety and efficiency.
Artem Sokolov serves as an Honorary Professor in the Department of Computational Linguistics at Heidelberg University and as a Research Scientist at Google Berlin. His primary research focuses on machine translation and structured prediction within natural language processing. Previously, he held positions at Amazon, the Statistical NLP Group at Heidelberg University led by Prof. Stefan Riezler, LIMSI, and Orange Labs in France, contributing to advancements in statistical and neural machine translation systems. He earned his PhD in Computer Science and Artificial Intelligence from the IRTCITS research center in Kyiv. His doctoral thesis investigated randomized algorithms for locality-sensitive embeddings of the Levenstein edit distance, establishing foundational work for efficient string similarity search in computational linguistics and intrusion detection systems. Dr. Sokolov's research expertise spans machine translation, imitation learning, bandit algorithms, and weakly supervised learning. He has pioneered methods for learning from partial feedback in structured prediction tasks, particularly addressing exposure bias in sequence generation and multi-facet evaluation of translation systems. His work bridges theoretical machine learning with practical NLP applications, emphasizing robustness against noisy data and scalable optimization techniques for real-world deployment. Analysis of his recent publications reveals trends toward scalable influence functions for model interpretability, multi-attribute control in machine translation, and rigorous auditing of multilingual datasets. His research consistently intersects natural language processing, machine learning optimization, and data quality assessment, with increasing emphasis on ethical AI considerations and efficient learning from weak supervision signals. Scientific awards include: 1st place at ECML/PKDD Discovery Challenge 2010 (English quality task) 2nd place at ECML/PKDD Discovery Challenge 2010 (general task) 2nd place in Semi-Supervised Feature Learning Challenge at NIPS 2011 3rd place in Semi-Supervised Feature Learning Challenge at NIPS 2011 As co-Principal Investigator for the 2015-2017 grant "Weakly Supervised Learning of Cross-Lingual Systems", Dr. Sokolov developed techniques for learning cross-lingual rankings from weakly supervised data sources like patent citations and Wikipedia hyperlinks. He has mentored students through teaching advanced courses including Imitation Learning, Stochastic Learning, and Statistical Machine Translation at Heidelberg University, supervising seminar projects on structured prediction and optimization algorithms. Dr. Sokolov is an active member of the Statistical NLP Group at Heidelberg University and collaborates with research teams at Google Berlin. His current work focuses on advancing production-scale machine translation systems through scalable inverse reinforcement learning and robust training methodologies, building on his extensive background in both academic research and industrial applications.
Professor C Macrae holds the Chair in Psychology at the University of Aberdeen , affiliated with the School of Psychology . His research focuses on social cognition , particularly the interplay between self-perception , stereotyping , and attentional mechanisms . Key research themes include: Self-prioritization effects and their modulation by mindfulness Stereotype-based learning and cognitive flexibility Attentional capture by self-relevant stimuli Neural correlates of self-other discrimination Recent publications (2023-2025) emphasize prediction errors , mindfulness interventions , and cross-cultural self-ownership studies. His work spans experimental psychology , neuroimaging , and social cognitive theory , with over 199 publications. Teaching responsibilities include: Level 3: Social Psychology Level 4: The Social Mind He serves as School Research Committee and REF Steering Committee member, leading the Social Cognition Theme at the School of Psychology.
Sreeram Kannan is an Affiliate Associate Professor in the Department of Electrical & Computer Engineering at the University of Washington, Seattle. His research spans multiple interdisciplinary domains including information theory, blockchain systems, machine learning, and computational biology. Dr. Kannan received his Ph.D. in Electrical and Computer Engineering and M.S. in Mathematics from the University of Illinois Urbana Champaign. He was a postdoctoral scholar at the University of California, Berkeley and a visiting postdoc at Stanford University between 2012-2014. His research interests focus on the theoretical foundations of information processing with applications to blockchain systems, machine learning, computational biology, and wireless networking. He works on both mathematical theory and engineering system development, with particular emphasis on how information theory principles can solve practical problems in these domains. His publication record shows a consistent trajectory of high-impact research in top venues including NIPS, ICML, ISIT, and specialized conferences in bioinformatics. His work demonstrates a unique bridge between theoretical information theory and practical applications, particularly in blockchain algorithms and RNA sequence analysis. Early Faculty Career Award from NSF for project on Information theoretic methods for RNA Analytics NIH R01 Award for Optimal Algorithms for RNA Sequence Assembly (with Lior Pachter and David Tse) Dr. Kannan leads the UW Blockchain Lab and the Information Theory Lab, where he mentors students and researchers working on cutting-edge problems at the intersection of information theory and practical systems. His lab develops both theoretical frameworks and practical tools like the Shannon RNA-Seq assembler, which applies information-theoretic principles to genomic sequence assembly problems.
Kari Tammi serves as Professor and Dean of Aalto University's School of Engineering since 2015, concurrently holding the position of Chief Engineer Counselor at Finland's Administrative Supreme Court. His career spans industrial research leadership at VTT Technical Research Centre (2000-2015), postdoctoral work at North Carolina State University (2007-2008), and foundational research at CERN (1997-2000). His academic credentials include: MSc, Helsinki University of Technology, 1999 LicSc, Helsinki University of Technology, 2003 DSc, Helsinki University of Technology, 2007 Teacher’s Pedagogical Qualification, Häme University of Applied Sciences, 2017 Research Focus: Tammi pioneers in Mechatronics , Autonomous/Electric Vehicle Systems , and Energy Efficiency Optimization , with specialized expertise in Dynamics , Control Systems , and Digital Twin Applications . His work bridges theoretical innovation with industrial deployment across maritime, automotive, and manufacturing sectors. Publication Trends: Recent output (2024-2025) demonstrates concentrated advancement in industrial digital twins for crane operations, winter-condition autonomous perception, and marine energy systems. Key patterns include GPU-free real-time processing, semantic-enhanced metaverse architectures, and snow-robust sensor fusion techniques. Professional Leadership: As former VTT Team Leader and current Engineering Dean, Tammi directs cross-disciplinary research initiatives connecting academic theory with industrial practice, particularly in sustainable transportation and smart manufacturing ecosystems.
Dr. Huseyin Topaloglu is the Howard and Eleanor Morgan Professor at the School of Operations Research and Information Engineering at Cornell University and Cornell Tech . He holds a B.S. in Industrial Engineering from Bogazici University (1997), an M.A. in Operations Research from Princeton University (1999), and a Ph.D. in Operations Research from Princeton University (2001). Education: B.S. Industrial Engineering (1997) – Bogazici University M.A. Operations Research (1999) – Princeton University Ph.D. Operations Research (2001) – Princeton University His research focuses on revenue management , pricing analytics , assortment optimization , and stochastic dynamic programming . Recent work includes advancements in multinomial logit models , network revenue management , and dynamic inventory allocation . He has published extensively in leading journals such as Operations Research , Management Science , and M&SOM , often collaborating with researchers like Y. Bai , P. Rusmevichientong , and O. El Housni . His 15 most recent publications (2024–2003) demonstrate expertise in revenue management using multinomial logit models , dynamic programming , and stochastic optimization . Key subfields include assortment planning , network revenue , pricing under uncertainty , and approximation algorithms for complex systems like ambulance redeployment and airline capacity control . He has also authored books like Revenue Management and Pricing Analytics (2019) and Fundamentals of Linear Optimization (2021).
M. Austin Creasy serves as an Associate Professor at Purdue Polytechnic Institute, Purdue University, and is an active member of the American Society for Engineering Education (ASEE) and the American Society of Mechanical Engineers (ASME). His expertise spans vibration and acoustic modeling and control, adaptive control systems, and engineering technology education innovation. His academic credentials include: Ph.D. in Mechanical Engineering, Virginia Tech, 2011 M.S. in Mechanical Engineering, Virginia Tech, 2006 B.S. in Mechanical Engineering, Virginia Tech, 2002 Dr. Creasy's research integrates mechanical engineering principles with biological systems and pedagogical innovation. He has developed deterministic models for biomolecular networks and droplet interface bilayers while pioneering adaptive control techniques for noise absorption in payload fairings and acoustic cavities. His educational research focuses on flipped classroom methodologies, graphical user interfaces for assignment feedback, and z-score assessment systems in mechanics and capstone courses, significantly advancing engineering technology pedagogy. Analysis of his 2006-2022 publications reveals an evolution from foundational vibration control research toward educational technology innovation. Recent work emphasizes data-driven assessment tools and industry-academia partnerships, while maintaining contributions to mechanical systems dynamics and biomolecular modeling. His scientific recognition includes: Purdue Polytechnic Research Award for 2024 (awarded January 2025) Purdue Polytechnic Research Award for 2023 (awarded January 2024) Dr. Creasy's collaborative projects like 'The Seamless Pathway' demonstrate active engagement with industry and community partners for workforce development. While specific graduate student advising details aren't provided, his extensive educational publications indicate significant mentorship contributions. Laboratory facilities and dedicated research teams aren't specified in available materials.
Ryan W. Matzke is an NSF Mathematical Sciences Postdoctoral Research Fellow at Vanderbilt University's Department of Mathematics, sponsored by Professor Edward B. Saff. His research focuses on Potential Theory, Discrepancy Theory, and Discrete Geometry, with applications in Harmonic Analysis, Frame Theory, and Additive Combinatorics. He has held postdoctoral positions at Technische Universität Graz and completed his Ph.D. at the University of Minnesota-Twin Cities under Professor Dmitriy Bilyk. His research bridges theoretical mathematics with computational methods, including studies on energy optimization, spherical configurations, and deterministic point distributions. Current projects analyze multivariate kernels, Riesz potentials, and geometric discrepancy principles. His work has been published in journals like the Proceedings of the American Mathematical Society, Mathematika, and Probability Theory and Related Fields. In 2025, he will return to the job market for postdoctoral or tenure-track positions. He has received the American Institute of Mathematics' 2025 Alexanderson Award for collaborative research on spherical energy measures. Education Ph.D. in Mathematics, University of Minnesota-Twin Cities (2021) Grants NSF Mathematical Sciences Postdoctoral Research Fellowship (current)
Thomas Robert is an Associate Professor at Télécom Paris, actively contributing to the AUTONOMOUS CRITICAL EMBEDDED SYSTEMS (ACES) research team under the INFORMATION PROCESSING AND COMMUNICATION LABORATORY (LTCI) . His research spans real-time systems, distributed computing, fault tolerance, and security policy modeling. Research Trends: Recent publications focus on software supply chain security (2025), intrusion detection mechanisms (2024), and mixed-criticality scheduling frameworks like RUN (2015). Earlier works include AADL-based fault tolerance (2010) and real-time error detection (2008). Teaching: Offers courses in programming, operating systems, fault tolerance, and embedded systems at Télécom ParisTech (engineering cycle) and Master 2 programs (SAR, SETI, COMASIC). Collaborations: Works with Laurent Pautet, Jean Leneutre, and colleagues on real-time scheduling and security projects. Tools: Develops PRISM models for system reliability analysis and contributes to fault tolerance practical works.
