Romain Raveaux is an Associate Professor at the LIFAT Computer Science Laboratory, University of Tours, affiliated with Polytech Tours. His research focuses on Image Analysis, Machine Learning, Structural Pattern Recognition, Graph Matching, Graph Neural Networks, Discrete Optimization, Reinforcement Learning, and Transfer Learning . Email: romain.raveaux@gmail.com , romain.raveaux@laposte.net Address: 64 av. Jean Portalis, Tours, France, 37200 Phone: +33 (0)2 47 36 14 27 Research Interests Graph Matching and Neural Networks Discrete Optimization for Pattern Recognition Transfer Learning in Graph-Based Models Historical Document Analysis Scientific Trends His recent work bridges Graph Neural Networks with Mixed-Integer Programming , focusing on Image Semantic Segmentation and Graph Cycle Detection . Earlier studies emphasize Genetic Algorithms for graph classification and Graph Edit Distance optimization in pattern recognition.
Associate Professor Zhan Wu is a faculty member at the University of Sydney Business School, specializing in International Business. He holds a PhD from Nanyang Technological University in Singapore and has established himself as a leading researcher in international business, strategic management, and innovation in emerging markets and transition economies. His educational background includes BSc and MSc from Sichuan and PhD from Nanyang Technological University in Singapore. His academic journey has positioned him at the forefront of research examining how firms navigate complex institutional environments in developing economies. Professor Wu's research focuses on the intersection of international business, strategic management, and innovation in the context of emerging markets and transition economies. His work particularly examines the strategy of firms in and from emerging economies, OFDI and IFDI, competitive dynamics, green innovation and energy economics, entrepreneurship, and dynamic capabilities. His publications in top journals like Journal of Management Studies and Energy Economics demonstrate the theoretical and practical significance of his research, which has important implications for multinational corporations operating in developing economies and for understanding how emerging market firms can successfully expand globally. His extensive publication record shows a clear trajectory of research excellence across multiple domains of international business. A notable trend in his recent work is the growing emphasis on sustainability and green innovation within international business contexts, reflecting broader global concerns about environmental challenges. His 2024 and 2025 publications demonstrate continued research productivity with increasing attention to the intersection of artificial intelligence, new energy vehicles, and corporate sustainability strategies. Wayne Lonergan Outstanding Teaching Award (Early Career) valued at $10,000 Dean's Citation for Teaching Excellence USS Awards Research Excellence Award nomination (2024) Competitive research grant from China's NNSF (AUD80,000) Professor Wu has demonstrated significant leadership in academic service, serving as Associate Editor of Journal of Business Research and Senior Editor of Asia Pacific Journal of Management. He has secured research funding from China's National Natural Science Foundation and serves as an ARC assessor. In educational leadership, he has served as Learning and Teaching Associate, Undergraduate Program Coordinator for International Business, and Program Director for the Master of International Business, significantly contributing to curriculum development and student experience. As an academic ambassador for the Business School, Professor Wu has cultivated a strong global research network, including a visiting scholar position at Fudan University Management School in China. His research collaborations span multiple continents, reflecting the international nature of his scholarly interests in global business dynamics and the increasing interconnectedness of emerging and developed economies.
Matthias Becker is a Professor at the Institute for Practical Computer Science within the Faculty of Electrical Engineering and Computer Science at Leibniz University Hannover, where he has been a core member of the Human-Computer Interaction group since 2019. He serves as Internship Coordinator for Computer Science and Computer Engineering and holds key roles in the Computer Science Examination Board and Selection Committee, actively shaping academic governance and student development. His academic journey began with PhD studies at the University of Bremen (1996-2000) supported by a DFG grant, followed by a postdoctoral permanent position at Leibniz University Hannover (2000-2019), an Associated Assistant Professor role at École des Mines de Nantes (2000), and a Habilitation in Computer Science in 2013. This foundation enabled his transition to a full professorship in 2019. Becker's research spans Human-Computer Interaction, Simulation and Modeling, and Bio-inspired Computing, with applications in agriculture, renewable energy, and manufacturing. His work integrates distributed systems, optimization algorithms, and wireless sensor networks to solve complex real-world problems, such as greenhouse monitoring, wind farm logistics, and tire noise reduction. Recent publications reveal a strategic focus on practical validation of simulation models and cross-domain applications of nature-inspired algorithms. His 15 most recent publications (2018-2024) demonstrate consistent innovation in applying simulation techniques to offshore wind farm installation, agricultural pest management, and sports science. These works emphasize real-world validation, collaborative problem-solving, and the development of domain-specific optimization frameworks that bridge theoretical algorithms and industrial implementation. As Internship Coordinator, Becker facilitates critical industry-academia connections for students, while his examination board responsibilities ensure rigorous academic standards. His leadership in the Human-Computer Interaction group drives research on interactive systems for agriculture, energy, and health, with particular emphasis on user-centered design in complex operational environments like wind farm logistics and greenhouse automation.
Eli Ben-Michael is an Assistant Professor in the Department of Statistics & Data Science and the Heinz College of Information Systems and Public Policy at Carnegie Mellon University. He is also affiliated with the CMU-NIST AI Measurement Science & Engineering Cooperative Research Center (AIMSEC). Previously, he was a Post-Doctoral Fellow at Harvard University in the Institute for Quantitative Social Science and the Department of Statistics. Ben-Michael's research focuses on developing statistical and computational methods to solve practical issues in public policy and social science research. His work brings together ideas from statistics, optimization, and machine learning to create methods for credible and robust causal inference and data-driven decision making. His research spans multiple domains including healthcare policy, criminal justice reform, reproductive health, and education. His scholarly work shows a strong emphasis on causal inference methodology, with particular attention to synthetic control methods, balancing weights, policy learning, and sensitivity analysis. His recent publications demonstrate applications across diverse fields including healthcare, economics, social policy, and criminal justice. Ben-Michael completed his PhD in Statistics from UC Berkeley and earned his undergraduate degree in Computer Science and Statistics from Columbia University. His technical expertise includes developing open-source software, with contributions to the R packages 'augsynth' and 'multical' for synthetic control methods and multilevel calibration.
Krishnendu Chatterjee is a Professor at the Institute of Science and Technology Austria (IST Austria) , Department of Computer Science. His research spans formal verification, probabilistic systems, game theory, and evolutionary dynamics, with over 300 peer-reviewed publications in top venues such as DISC, AAAI, LICS, PNAS, Nature , and Journal of the ACM . His research focuses on developing theoretical foundations and practical algorithms for analyzing complex systems, including Markov decision processes, stochastic games, probabilistic programs, and evolutionary models. He has made significant contributions to topics such as reachability analysis, termination of probabilistic programs, synthesis of controllers, and evolutionary game dynamics. Chatterjee's work is highly interdisciplinary, bridging computer science, mathematics, and biology. He has collaborated extensively with leading researchers worldwide and has been involved in editorial roles and program committees for major conferences in formal methods and theoretical computer science.
Vijay Gupta is the Elmore Professor of Electrical and Computer Engineering and Associate Head of Graduate and Professional Programs at Purdue University's College of Engineering. His research focuses on distributed decision-making systems, combining data-driven and model-driven approaches for infrastructure networks like power grids, transportation systems, and water distribution networks. Key areas include compositional control, cyber-physical security, and incentive design in distributed estimation and control. Education: B.Tech from Indian Institute of Technology Delhi, M.S. and Ph.D. from California Institute of Technology, all in Electrical Engineering. Prior roles include faculty positions at Notre Dame and research roles at United Technologies Research Center. Research emphasizes resilient control strategies for large-scale systems, with recent work addressing secure estimation under adversarial attacks, model reduction techniques, and reinforcement learning frameworks for decentralized control. His publications span control theory, cyber-physical systems, and optimization algorithms. Grants and collaborations are not explicitly detailed here, but his work reflects significant engagement with foundational and applied research challenges in networked systems. No specific awards are listed in the provided texts.
Boshi Yang is an **Associate Professor** in the Department of Mathematical and Statistical Sciences at Clemson University. His research focuses on convexification, quadratic programming, mixed-integer programming, and conic programming applications. He holds a PhD from the University of Iowa (2015) and a BS from Zhejiang University (2010). Education: PhD, Applied Mathematical and Computational Sciences, University of Iowa, 2015 BS, Mathematics and Applied Mathematics, Zhejiang University, 2010 His research interests emphasize convexification techniques , quadratically constrained quadratic programming , and conic programming applications . Recent work explores data-driven optimization for energy systems and algorithmic improvements for combinatorial problems. His publications span journals like Mathematical Programming, Operations Research Letters, and IEEE Transactions on Power Systems. Key Awards: 2020 Faculty Teaching Award (Clemson School of Mathematical and Statistical Sciences) 2019 Outstanding Service to Graduate Students (Clemson School of Mathematical and Statistical Sciences) Teaching: Teaches courses including Linear Programming (MATH 4400/6400), Nonlinear Programming (MATH 8110), and Special Topics in Conic Programming (MATH 9880).
Ram Mohapatra is a Professor in the Department of Mathematics at the University of Central Florida (UCF), part of the College of Sciences. His research interests span mathematical analysis, operator theory, variational inequalities, approximation theory, cybersecurity, and fluid dynamics. He has published extensively on topics including inverse scattering problems, generalized inverses, and optimal control theory. His work often intersects with applications in engineering and data science. Recent research focuses on operator theory applications, tensor decompositions, and mathematical modeling of physical systems. He has contributed to advancements in frame theory, numerical radius studies, and cybersecurity methodologies. His academic activities include teaching undergraduate and graduate courses in mathematics, such as MAC 1105C and MAC 2311C, and maintaining active collaborations in interdisciplinary research areas. Professional contributions include over 150 peer-reviewed articles and editorial roles in mathematics journals. While no explicit awards are listed in the provided texts, his prolific publication record underscores his scholarly impact in applied and theoretical mathematics.
Marco Aldinucci is a Full Professor and Head of the Parallel Computing group at the University of Torino's Computer Science Department. He leads the HPC Key Technologies and Tools (HPC-KTT) national lab under CINI, involving 38 Italian universities. His expertise spans parallel programming models, HPC systems, federated learning, and energy-efficient computing. Aldinucci has secured over €10M in EU research funding, contributed to frameworks like Fastflow and Streamflow, and pioneered initiatives like the HPC4AI lab and the CINI HPC-KTT lab. His research focuses on advancing exascale computing, cloud-HPC integration, and AI-driven medical solutions. Notable projects include the Gaia AVU-GSR solver for exascale systems and the DeepHealth Toolkit for medical AI. He has held governance roles in EuroHPC and chairs the Observatory on Trends and Applications of Supercomputing in Italy. Aldinucci’s publications (150+) address parallel algorithms, distributed learning, and sustainable HPC infrastructure. His work has been recognized with awards from HPC Advisory Council, NVIDIA, IBM, and Autodesk. Current initiatives include the Software & Integration lab at the Italian National HPC Centre (ICSC) and leadership in the OpenScience working group at Torino. His advising includes Iacopo Colonelli, whose thesis won CINI’s 2023 best award. He actively engages in EU projects, workflow systems, and standards for hybrid computing environments. Aldinucci’s labs and collaborations drive innovations in HPC portability, energy efficiency, and AI scalability.
Professor Ian Loram is a leading academic in neuromuscular control and human movement science at Manchester Metropolitan University's Institute for Biomedical Research into Human Movement and Health (IRM). He holds roles as Academic Director of IRM and Academic Lead of the Biomechanics and Motor Control Research Group. His research focuses on sensorimotor control, postural stability, and applications of deep learning in medical imaging. Key contributions include studies on neuromuscular disorders, balance control mechanisms, and automated analysis of muscle function using ultrasound and neural networks. Education: PhD from University of Birmingham (2003) Awards: Leverhulme Early Career Fellowship (2004-2005) Grants: EPSRC Grants EP/F068514/1, EP/F069022/1, and EP/F06974X/1 ("Intermittent Control of Man and Machine") His research interests span neuromuscular control, biomechanics, postural dynamics, and clinical applications of imaging technologies . Recent work emphasizes automated analysis of muscle structure via ultrasound and deep learning, with clinical relevance to conditions like spinal muscular atrophy and cervical dystonia. Publications highlight interdisciplinary approaches, integrating control theory, neurophysiology, and computational methods to understand human movement. His lab develops tools for objective assessment of trunk control in children with cerebral palsy and explores the role of intermittent control in motor learning and balance. Labs/Teams: Biomechanics and Motor Control Research Group Healthcare Science Research Institute
Kevin Tomsovic is the Chancellor's Professor in the Min H. Kao Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville, and Director of the CURENT Engineering Research Center. His expertise spans smart grids, power systems optimization, and cyber-physical resilience. He holds a PhD from the University of Washington (1987) and prior roles at Washington State University. Research focuses on renewable integration, microgrid scheduling, and grid security, with notable contributions to IEEE standards. Awards include IEEE Fellow status. Education: PhD/M.S. Electrical Engineering, University of Washington (1987/1984); B.S. Electrical Engineering, Michigan Tech (1982). Research interests include: - Intelligent systems for power distribution design - Electricity market analysis and stability control - Cybersecurity for grid infrastructure - Resilience quantification in energy systems Publications highlight advancements in grid emulation platforms, adversarial ML defenses, and microgrid optimization. His work is supported by NSF/DOE grants (e.g., NSF EEC-1041877). CURENT testbed facilities enable real-time grid control validation. Awards: IEEE Fellow (20XX). Grants & Collaborations: Lead CURENT NSF ERC; industry partnerships include power utilities and tech firms. Active in developing standards for grid resilience metrics. Labs/Teams: CURENT Engineering Research Center, UT Power Systems Lab.
Dominik Kempa is an Assistant Professor in the Department of Computer Science at Stony Brook University, NY. He previously held postdoctoral positions at Johns Hopkins University (2021), UC Berkeley (2020), and the University of Warwick (2019). His PhD (2015) and MS (Computer Science) are from the University of Helsinki, while his BS (Mathematics) and MS (Computer Science) are from Jagiellonian University, Kraków. Research focuses on string algorithms, data compression, compressed data structures, bioinformatics, and parallel/external-memory algorithms. Notable contributions include compressed indexing techniques for large-scale genomic data and theoretical advancements in LZ77 parsing and suffix array construction. He leads projects like the NSF-funded CAREER initiative on scalable compressed sequence indexing. Key awards include the NSF CAREER Award (2024), Junior Researcher Award, and Outstanding Doctoral Dissertation Award. His work bridges theoretical foundations and practical implementations, with over 30 publications in top venues like STOC, FOCS, and SODA. Education: PhD Computer Science (2015, University of Helsinki), MS Computer Science & BS Mathematics (Jagiellonian University) Selected Awards: NSF CAREER, Junior Researcher Award Professional Activities: Program committee member for STACS, SODA, DCC, and multiple conferences since 2020 Research highlights include resolving the Burrows-Wheeler Transform conjecture (2020) and developing sublinear-time LZ77 factorization (2024).
Dr. Walter Lucia is an Associate Professor at the Concordia Institute for Information Systems Engineering, Concordia University. His research focuses on secure and resilient control of cyber-physical systems and model predictive control strategies for autonomous vehicles. He supervises MASc and PhD students in programs such as Information Systems Security, Electrical and Computer Engineering, and Information and Systems Engineering. His research interests encompass cybersecurity in control systems, including strategies against false data injection and setpoint attacks. He develops resilient control architectures and applies model predictive control to autonomous systems like self-driving cars and mobile robots. Recent work emphasizes data-driven safety mechanisms, encrypted control systems, and collision-free platooning of mobile robots. No scientific awards or grants are explicitly mentioned in the provided text. Dr. Lucia's advising roles include overseeing multiple graduate programs, though specific student names are not listed. His publications reflect a strong focus on control theory applications, cyber-physical system security, and optimization techniques. Labs or collaborative teams are not explicitly detailed in the text, but his research themes suggest involvement in interdisciplinary projects at the intersection of engineering and cybersecurity.
Nathaniel Alan Brunsell is a Professor and Director of the Environmental Studies Program at the University of Kansas. His research focuses on biometeorology, remote sensing of surface energy balance, and climate change impacts. He leads the Environmental Studies Program, overseeing interdisciplinary environmental research and education. Key research areas include land-atmosphere interactions, turbulence measurements using scintillometry and eddy covariance, and the ecological consequences of regional climate change. He collaborates on NASA missions like ECOSTRESS to advance evapotranspiration measurement capabilities. His grants include leadership in the Konza Cluster for the AmeriFlux Network and contributions to studies on urban heat islands and carbon cycle science. Brunsell teaches courses such as Land-Atmosphere Interactions, Atmospheric Turbulence, and Microclimatology, integrating theoretical and applied aspects of environmental science. His work bridges field observations, remote sensing, and numerical modeling to address challenges in climate science, sustainable agriculture, and environmental policy.
Igor Molybog is an Assistant Professor at the University of Hawai'i at Manoa, holding joint appointments in the Departments of Electrical and Computer Engineering and Information and Computer Sciences. His research focuses on advancing artificial intelligence, particularly through large language models (LLMs), multimodal modeling, and core machine learning optimization. He leads the HawAII research group, exploring applications like LLM alignment, efficient inference systems, and scaling properties of foundation models. Education: Ph.D. in Engineering from UC Berkeley (2022), specializing in optimization algorithms for complex systems. Previously worked at Meta AI on LLaMa model development. Research Interests: Efficient LLM development and evaluation frameworks Multimodal AI integration (video/audio + text) Scalable optimization for large models Computational efficiency in training/ inference Recent Work: Presented REAL alignment method (2024), developed long-context scaling techniques (2023), contributed to Llama 2 chat models (2023). Collaborates with organizations like Epoch AI on scaling challenges. Teaching: Offers courses in AI, machine learning, and optimization across ECE and ICS departments. Labs/Teams: Leads HawAII Initiative fostering AI collaboration at UH Manoa, organizes paper reading seminars, and hosts technical talks with industry experts.
