Christine Di Martinelly is an Associate Professor in Operations Management at IÉSEG School of Management. She holds two PhDs in Economic and Management Sciences from Louvain School of Management and Applied Sciences from INSA Lyon. Her research focuses on operations management, healthcare systems, supply chain optimization, and resource allocation. Di Martinelly's extensive publication record addresses operational challenges in healthcare, including surgical scheduling, inventory management, and resource allocation. Her work employs mathematical modeling, optimization algorithms, and multicriteria decision analysis to improve efficiency in healthcare delivery systems. She has served as Academic Director at IÉSEG since 2014 and has professional experience as a consultant at Arthur Andersen earlier in her career.
Hasan Davulcu is a Professor in the School of Computing and Augmented Intelligence at Arizona State University (ASU). He holds a B.S. in Mathematics from Middle East Technical University (Turkey) and M.S./Ph.D. in Computer Science from Stony Brook University (NY). His research focuses on sociocultural modeling, AI, machine learning, and behavioral analytics for fraud detection. He leads the CIPS-AI Lab, developing data mining tools for semantic information extraction from social media and web data. Affiliations: Senior Global Futures Scientist (Global Futures Scientists and Scholars Program), Co-founder & CIO of ARTIS MAGI (AI-driven behavioral analysis startup). Education: Ph.D. Computer Science (Stony Brook, 2002), M.S. Computer Science (Stony Brook, 1995), B.S. Mathematics (METU, 1993). Research interests include: Sociocultural modeling and persuasive AI. Web/social media mining, information extraction, and database systems. Behavioral analytics for fraud detection and countering extremist influence. Key achievements: 2011 HSCB Focus Exceptional Scientific Achievement Award for work on sociocultural modeling in the DOD Minerva project. Principal Investigator on NSF and DoD grants, including behavioral analytics for financial fraud and social influence analysis of extremist groups. Grants & Projects: NSF PFI:BIC Grant (2014-2019): Behavioral analytics for fraud detection via visual analytics infrastructure. DoD Minerva (2015-2019): Measuring social influence of extremist groups. ONR Projects (2018-2021): Modeling polarization, adversarial framing, and disinformation tracking. Labs/Teams: Cognitive Information Processing Systems (CIPS-AI) Lab, which pioneers data mining techniques for unstructured social media data and semantic representation systems.
Grégoire Allaire is a Professor of Applied Mathematics at École Polytechnique, where he leads research in shape optimization , homogenization , and multi-scale modeling . His work bridges theoretical and applied domains, focusing on partial differential equations (PDEs), composite materials, and computational methods.
Robert K. Kaufmann is a Professor at Boston University's Department of Earth and Environment. His research focuses on three core areas: global climate change, world oil markets, and land-use changes. He explores the human impact on climate systems, the interplay of geological, engineering, and economic factors in oil markets, and socioeconomic drivers of land-use patterns through satellite and ground-based data analysis. Education includes a BS in Biology from Cornell University (1979), an MA in Economics from the University of New Hampshire (1984), and a PhD in Energy Management & Policy from the University of Pennsylvania (1988). He co-founded First Fuel Software (acquired by Uplight), which develops energy audit technologies for commercial buildings using mathematical and engineering methods to reduce costs and carbon emissions. Teaching responsibilities include courses such as EE 250: The Fate of Nations: Climate, Resources, & Institutions , EE 555: World Oil Markets , and EE 712: Regional Energy Models . Kaufmann is a co-author of the textbook Environmental Science , published by Trubooks, designed for undergraduate courses. The text integrates ecology, economics, and policy to address sustainability challenges, emphasizing interdisciplinary solutions and real-world applications.
Adrián Lozano-Durán is an Associate Professor of Aerospace at the California Institute of Technology (Caltech), affiliated with the Guggenheim Laboratory for Aeronautics (GALCIT). He holds a B.S., M.S., and Ph.D. from the Polytechnic University of Madrid (2010–2015) and joined Caltech as a Visiting Associate in 2024 before becoming a faculty member in the same year. His research focuses on fluid dynamics, turbulence, and machine learning applications in computational fluid dynamics (CFD), particularly for aerospace systems. He leads the Aerofluids, Learning & Discovery (ALD) Lab, collaborating with MIT’s AeroAstro department. Key research areas include causal inference in fluid systems, reduced-order modeling, and machine-learning-based closure models for large-eddy simulation (LES). His work addresses challenges in low-speed aerodynamics, supersonic, and hypersonic flows. Notable recent contributions include advancements in LES wall models and information-theoretic approaches to turbulence control. He frequently presents at international conferences and has co-authored high-impact papers in Nature Communications , Journal of Fluid Mechanics , and Physical Review Research . Education: B.S., Polytechnic University of Madrid (2010) M.S., Polytechnic University of Madrid (2012) Ph.D., Polytechnic University of Madrid (2015) Affiliations: GALCIT, Caltech AeroAstro, MIT (collaboration) Advising focuses on students like Álvaro Martínez-Sánchez and Tristan, whose work spans causality in turbulence and flow control. He actively engages in interdisciplinary research, bridging fluid mechanics with machine learning and information theory to advance aerospace engineering solutions.
Luca Cardelli is a Principal Researcher and Assistant Director at Microsoft Research Cambridge, UK, since 1997. He holds visiting professorships at Imperial College London (Department of Computing, 2004–2009) and the University of Trento (2005–2007). He earned his PhD in Computer Science from the University of Edinburgh in 1982. His research spans type theory , molecular programming , and principles of programming languages , with applications to systems biology and concurrency theory. Notable contributions include formal frameworks for modeling biochemical systems (e.g., the stochastic π-calculus) and designing DNA-based circuits. Key achievements include the AITO Dahl-Nygaard Senior Prize (2007) and multiple Most Influential Paper Awards at POPL and ETAPS. His work bridges computer science and biology, advancing both theoretical foundations and practical molecular computing.
Uzi Vishkin is a Professor at the University of Maryland's Institute for Advanced Computer Studies (UMIACS) and Department of Electrical and Computer Engineering, with additional affiliation in the Department of Computer Science. His work focuses on parallel computing, including the PRAM-On-Chip vision to bridge parallel algorithms and hardware. He holds a D.Sc. from Technion (1981), M.Sc. and B.Sc. in Mathematics from Hebrew University (1975/1974). Research interests span parallel algorithms, PRAM (Parallel Random Access Machine) architecture, machine learning applications, and pattern matching. His PRAM-On-Chip project aims to create a coherent computing stack for many-core processors. Vishkin has contributed to theoretical foundations and practical implementations, including the XMT architecture and compiler. He has been recognized with ACM Fellow (1996), Highly Cited Researcher (2003), and National Academy of Inventors Fellow (2024). Awards highlight his pioneering work in parallel algorithms and computing systems. Teaching spans courses like Parallel Algorithms and Mathematical Foundations for Computer Engineering. He emphasizes parallel algorithmic thinking in education and has developed teaching materials for high school and university levels. Key projects include the XMT architecture, simulations, and tools for parallel programming. His work integrates algorithmic theory with hardware design to address programming challenges in many-core systems.
Prof. Oliver Seitz leads the Bioorganic Synthesis research group at the Department of Chemistry, Faculty of Mathematics and Natural Sciences, Humboldt University of Berlin. His lab focuses on cutting-edge chemical biology approaches for protein/nucleic acid interrogation, with recent work advancing DNA/RNA-programmed assemblies for cellular imaging and therapeutic applications. Research spans chemical protein synthesis, glycoprotein/phosphoprotein engineering, and nucleic acid-templated reactions. Key innovations include Forced Intercalation (FIT) probes for wash-free RNA imaging, loss-of-affinity principles for catalytic efficiency, and peptide-PNA conjugates for targeted cellular delivery. The group actively develops tools for live-cell protein labeling and biomolecular spatial screening. Recent publications (2021-2024) emphasize fluorescence-based detection systems, catalytic templated reactions, and therapeutic peptide synthesis. Trends show increasing sophistication in multi-dye probes, glycan engineering, and RNA-triggered pro-drug activation. Scientific awards include: Max Bergmann Award (2019) Prof. Seitz actively advises doctoral students, with recent graduates Marvin Björn Stutz (2023, magna cum laude ), Dino Gluhacevic von Krüchten (2023, summa cum laude ), and Sophie Schöllkopf (2023, magna cum laude ). Current PhD candidates include Ekaterina Kazakova (glycoprotein synthesis), Alina Herfort (phosphoproteins), and Lina-Marie Beck (peptide-nucleic acid conjugates), with postdocs like Dr. Mandana Oloub (viscosity sensors). The Bioorganic Synthesis lab operates within Berlin's vibrant chemical research ecosystem, utilizing specialized techniques for chemical protein synthesis and nucleic acid detection. Recent team growth reflects ongoing projects in RNA imaging, catalytic templated reactions, and therapeutic conjugate development, supported by open positions for new researchers.
Dr. Yanchao Liu is an Associate Professor at Wayne State University's College of Engineering, Department of Industrial and Systems Engineering. He has received research funding from the National Science Foundation and the State of Michigan, including the NSF Career Award. His academic career spans prior industry roles as a Data Scientist and Manager of Advanced Analytics at Sears Holdings Corporation (2016-2017) and Director of Brand Marketing Analytics at Catalina Marketing Corporation (2017). He teaches courses in data science, IoT, and stochastic processes. B.S. Industrial Engineering, Huazhong University of Science and Technology (2006) M.S. Industrial Engineering, University of Arkansas (2008) Ph.D. Industrial and Systems Engineering, University of Wisconsin-Madison (2014) Dr. Liu's research focuses on mathematical modeling for transportation systems, industrial AI, and data analytics. His work addresses drone traffic management, battery-constrained delivery routing, and optimization algorithms for urban mobility. He has developed novel methods for UAV safety diagnostics, random forest implementations, and fairness-aware path planning in urban air mobility. His publications span journals like Journal of Guidance, Control and Dynamics , Transportation Research Part C , and IEEE Transactions on Intelligent Transportation Systems , with conference contributions at IISE and FAIM. His research combines theoretical advancements with practical applications in smart cities and logistics. NSF Career Award (2020) Faculty Research Excellence Award (2021) IEEE PES Best Conference Paper (2015) IEEE Transactions on Smart Grid Best Reviewer (2015) Hubei Province Distinguished Bachelor’s Thesis Award (2006) Dr. Liu advises PhD students like Zhenyu Zhou and J. Chen. He has contributed to energy market modeling (with M.C. Ferris) and published extensively on drone operations, machine learning algorithms, and stochastic processes. His work includes U.S. patent pending applications for UAV safety systems.
Rayadurgam Srikant is the Fredric G. and Elizabeth H. Nearing Endowed Professor of Electrical and Computer Engineering at the University of Illinois Urbana-Champaign, affiliated with the Coordinated Science Lab. He co-directs the C3.ai Digital Transformation Institute, focusing on AI-driven solutions for global challenges. His research spans machine learning, communication networks, stochastic systems, and game theory. Srikant has authored influential textbooks including Communication Networks: An Optimization, Control and Stochastic Networks Perspective . He holds IEEE Fellow status and has received prestigious awards like the ACM SIGMETRICS Achievement Award (2021) and IEEE Koji Kobayashi Award (2019). Over 20 of his advisees hold faculty positions globally. Education: PhD (1991), MS (1988) in Electrical Engineering from UIUC; B.Tech (1985) from IIT Madras. He has taught advanced courses on optimization, stochastic systems, and game theory. His work bridges theory and practice, with contributions to congestion control, cloud computing, and reinforcement learning. Current projects include AI applications for pandemic response and digital transformation initiatives. Research highlights include foundational work on Lyapunov drift methods for network stability and distributed algorithms. He serves as Area Editor for Mathematics of Operations Research and has led editorial roles for IEEE/ACM Transactions on Networking. His lab collaborates with industry leaders like Microsoft and C3.ai, leveraging supercomputing resources for societal impact.
Anant Sahai is the Qualcomm Chair Professor in the Department of Electrical Engineering and Computer Sciences (EECS) at the University of California, Berkeley. He holds affiliations with the Berkeley Artificial Intelligence Research Lab (BAIR), Berkeley Laboratory for Information and System Sciences (BLISS), and the Berkeley Wireless Research Center (BWRC). His academic journey includes a BS from UC Berkeley (1994), and MS (1996) and PhD (2001) degrees from MIT. He previously worked at Enuvis, Inc., focusing on adaptive software radio techniques for low-SNR GPS environments. Research interests span machine learning, wireless communication, information theory, signal processing, and decentralized control, with a focus on intersections between these fields. Key areas include spectrum sharing, ultra-reliable low-latency wireless protocols, and the foundations of overparameterized machine learning. Recent work explores in-context learning in modern AI models. He has received awards such as the IEEE ComSoc Leonard G. Abraham Prize (2012) and teaching/mentorship accolades at Berkeley. He advises UC Berkeley’s Eta Kappa Nu chapter and coordinates machine learning efforts for NSF’s SpectrumX. Current teaching includes CS 182/282A on deep neural networks. His lab focuses on theoretical and applied challenges in communication systems, AI, and control theory. Awards: IEEE ComSoc Leonard G. Abraham Prize (2012), Teaching Excellence Awards (2015–2017) Grants: NSF Center for Spectrum Innovation (SpectrumX), multiple collaborative projects in wireless and AI Labs/Teams: BLISS, BAIR, BWRC
Suyi Li is an Associate Professor in the Department of Mechanical Engineering at Virginia Tech's College of Engineering, where he leads the Dynamic and Architected Robot and structurE (DARE) Lab. Previously, he served as an Assistant Professor at Clemson University from 2016-2022 after completing postdoctoral research at the University of Michigan. Ph.D. in Mechanical Engineering, University of Michigan, Ann Arbor (2014) M.Sc. in Mechanical Engineering, Pennsylvania State University (2008) B.S. Summa Cum Laude in Mechanical Engineering, University of Michigan, Ann Arbor (2006) Dr. Li's research focuses on pioneering new paradigms of intelligent robots and functional structures by exploiting the interplay between geometry, mechanics, actuation, and computation. His work spans origami-inspired morphing structures, physically computing materials that perform machine learning tasks without traditional electronics, and soft/reconfigurable robots that can move like animals or grow like plants. His innovative approach combines mechanical engineering principles with computational thinking to create systems with 'mechano-intelligence'. Analysis of Dr. Li's recent publications reveals a strong trajectory toward embodied intelligence and mechanical computing, where physical structures themselves perform computational tasks. His work increasingly integrates origami/kirigami principles with advanced materials to create systems that can sense, process information, and actuate without conventional electronics. The research shows progression from fundamental mechanics of adaptive structures to sophisticated applications in robotics and computing. Dean's Awards of Excellence – Faculty Fellow, Virginia Tech (2024) C.D. Mote Jr Early Career Award, ASME Design Engineering Division (2022) Gary Anderson Early Achievement Award, ASME Aerospace Division (2021) Junior Researcher of the Year Award, College of Engineering, Clemson University (2020) CECAS Dean's Faculty Fellow, Clemson University (2018) CAREER Award, National Science Foundation (2018) ASME Freudenstein Young Investigator Award Dr. Li has secured nearly two million dollars in research funding, including the prestigious NSF CAREER award and an NSF EFRI project to build mechano-bio hybrid reservoir computers. He advises multiple Ph.D. and Master's students in the DARE Lab, with recent successes including Vishrut Deshpande's Ph.D. defense. His research has generated close to 80 journal and conference papers, demonstrating significant impact in the fields of adaptive structures and materials systems. Dr. Li also serves on editorial boards for several prominent journals including Journal of Intelligent Material Systems and Structures and Philosophical Transactions of the Royal Society A. The DARE Lab at Virginia Tech comprises a multidisciplinary team of researchers working on origami-inspired meta-structures, physically computing materials, and soft robotics. Current projects include developing electronics-free crawling robots with mechanical central pattern generators, creating kirigami-based wearable medical devices, and engineering metamaterials with programmable mechanical properties. The lab actively collaborates with institutions across the country and has received recognition for its innovative approaches to combining mechanical design with computational capabilities.
Carlo D'Eramo is a Professor of Reinforcement Learning and Computational Decision-Making at the University of Würzburg. He leads the LiteRL group at hessian.AI until 2025 and is affiliated with the Intelligent Autonomous Systems group at TU Darmstadt's Computer Science Department, as well as the Hessian Centre for Artificial Intelligence. Ph.D. : Information Technology, Politecnico di Milano (2019) Double MSc : Computer Engineering, Politecnico di Milano (2015) and University of Illinois at Chicago (2015) BSc : Computer Engineering, Politecnico di Milano (2011) His research focuses on lightweight reinforcement learning methods for adaptive autonomous agents, spanning multi-task/curriculum RL, multi-agent RL, deep RL, uncertainty quantification, residual learning, and planning. He developed MushroomRL, a widely adopted RL library, and investigates how agents can acquire real-world expert skills efficiently. The 15 most recent publications highlight trends in deep reinforcement learning architectures, adversarial and multi-agent systems, domain randomization, and curriculum design. Key subfields include optimal transport applications, entropy maximization, neural network distillation, and bounded rationality frameworks for robust learning. He has contributed to top venues like ICML, NeurIPS, AAAI, ICLR, JMLR, and IEEE Transactions on Pattern Analysis and Machine Intelligence, with a focus on advancing scalable and adaptive RL methodologies.
Prof. Raimon Jané Campos is a leading figure in biomedical signal processing at the Universitat Politècnica de Catalunya (UPC) and Universitat de Barcelona (UB). As co-director of UPC's Biomedical Signal and System Group (CREB) and coordinator of the Biomedical Engineering PhD Programme, he bridges engineering and clinical applications. His work focuses on respiratory and sleep disorder diagnostics, with significant contributions to COPD and sleep apnea monitoring through wearable devices and machine learning. PhD in Biomedical Engineering (UPC, 1989) Visiting researcher at Université de Nice-Sophia Antipolis Vice-president of Spanish Society of Biomedical Engineering Research spans respiratory mechanics , sleep-disordered breathing , acoustic biomarkers , bioimpedance , and machine learning in biomedical contexts . His 2025 work on microcalorimetric pathogen classification and 2024 spiking neural networks for apnea detection demonstrate cutting-edge integration of computational methods with physiological monitoring. Articles from 2017-2024 reveal consistent focus on non-invasive diagnostics , cardiorespiratory synchronization , and smartphone-based health solutions . Awarded the Barcelona City Technology Research Award (2005) and serving on the International Advisory Board for Physiological Measurement since 2010, his career combines academic leadership with real-world clinical translation through IBEC's technology transfer initiatives.
Toshiaki Adachi serves as Professor in the Department of Computer Science at Nagoya University's Graduate School of Engineering, with research centered on differential geometry and dynamical systems on complex manifolds. His work bridges theoretical mathematics with geometric structures relevant to computer science applications. Education: Doctor of Science, Nagoya University (1987) Master of Science, Nagoya University (1981) Research Focus: Adachi specializes in trajectories, magnetic flows, and circular motions within complex geometries including projective and hyperbolic spaces. His investigations span characteristic magnetic focal values on Kaehler manifolds, extrinsic circular trajectories on real hypersurfaces, and structural properties of Kaehler graphs. This research reveals fundamental connections between geometric configurations and dynamical behaviors in high-dimensional spaces. Publication Trends: Recent works (2022-2024) demonstrate consistent advancement in geometric trajectory analysis, with emphasis on type (A) real hypersurfaces in complex space forms. His publications increasingly integrate tensor/Cartesian product structures in graph theory with classical differential geometry, showing evolving methodological sophistication while maintaining core focus on magnetic flow dynamics. Awards: No specific scientific awards are documented in the provided materials. Professional Activities: Adachi actively contributes to academic discourse through international conference presentations across Europe and Asia, and serves in leadership capacity within the Mathematical Society of Japan. His editorial work on World Scientific publications since 2011 has significantly shaped contemporary differential geometry literature. Research Infrastructure: While no dedicated laboratory is specified, Adachi leads the Center for Research and Development in Higher Engineering-Education, directing institutional initiatives in engineering pedagogy and curriculum development.
