Mohit Singh is the Coca-Cola Foundation Professor at the H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology. He previously held positions at Microsoft Research (2011-2016) and as an Assistant Professor at McGill University (2010-2012). PhD in Algorithms, Combinatorics, and Optimization (ACO) at Carnegie Mellon University His research focuses on discrete optimization , approximation algorithms , and convex optimization , with applications to combinatorial optimization, submodular functions, and network design. He has contributed to topics like Sticky Brownian Rounding, integrality gaps, and online adaptive algorithms. His recent work includes theoretical advancements in matroid constraints, dimensionality reduction, and submodular maximization. He has published extensively in top conferences such as FOCS, SODA, ICML, and NeurIPS. He has been recognized with the Coca-Cola Foundation Professorship and served as Director of the Algorithms and Randomness Center at Georgia Tech (2019-2023). He has also held editorial roles and organized key academic workshops like the Bellairs Workshop on Approximation Algorithms (2011). His teaching includes advanced courses on approximation algorithms, combinatorial optimization, and linear inequalities. He has collaborated with institutions such as Microsoft Research and McGill University.
Calin Belta is a Professor in the College of Engineering at Boston University , with joint appointments in Mechanical Engineering, Systems Engineering, and Electrical and Computer Engineering. His research bridges control theory and formal methods, focusing on controller synthesis and automatic verification of hybrid systems with applications in robotics and systems biology . Education: Ph.D. in Control Theory, University of Pennsylvania His work emphasizes temporal logic specifications for ensuring safety and correctness in autonomous systems, particularly through control barrier functions (CBFs) , reinforcement learning , and model predictive control . Applications span from microrobotics to autonomous driving and biomolecular modeling . His recent articles (2025–2024) highlight advancements in safe control algorithms for autonomous vehicles, adaptive CBFs , temporal logic-guided learning , and microrobotics for cell manipulation. Common themes include formal verification , robustness , and human-in-the-loop safety . Scientific Awards: AFOSR Young Investigator Award (2008) NSF CAREER Award (2005) He also contributes to academia as a Senior Member of IEEE and Associate Editor for journals like SIAM Journal on Control and Optimization and IEEE Transactions on Automatic Control . His lab develops computational tools for safety-critical control in complex environments.
Marc Sachon is a Full Professor and Director of the Department of Operations, Information, and Technology at IESE Business School, University of Navarra . He serves as Academic Director for Advanced Management Programs (AMP) and specialized courses like Successful Change Management and Industry 4.0 . His leadership extends to the annual IESE Automotive Industry Conference (since 1986) and consulting engagements with global firms such as BMW Group , Phoenix Group , and Traton Group . PhD in Industrial Engineering and Engineering Management from Stanford University MBA from IESE Business School Master's in Aerospace Technology from University of Stuttgart His research focuses on operations strategy , particularly in the automotive industry , and the impact of Industry 4.0 on manufacturing and logistics. He explores how digital transformation reshapes value chains, emphasizing human-machine collaboration and supply chain resilience. His work spans academic journals like IEEE Transactions and business publications like IESE Insight , with case studies on companies such as Porsche and Netflix . Professor Sachon has received multiple teaching awards and maintains an active consulting practice across industries including airlines , pharmaceuticals , and logistics . He previously worked at Airbus and IBM , and currently advises a mobility startup board. His recent publications highlight trends in electric mobility , 3D printing , and supply chain sustainability .
Yevgeny Seldin is a Professor in the Department of Computer Science at the University of Copenhagen, specializing in Machine Learning Theory . He leads the Machine Learning Section and is a member of the DeLTA Lab . Education : PhD in Computer Science at The Hebrew University of Jerusalem under supervision of Prof. Naftali Tishby His research focuses on Machine Learning , particularly Online Learning and PAC-Bayesian Analysis , with applications to Bandit Algorithms , Reinforcement Learning , and Information Theory . Recent work includes optimal algorithms for delayed feedback, stochastic-adversarial trade-offs, and feedback graphs. Positions Available : PhD and Postdoc positions in Theoretical Machine Learning or energy sector applications Labs & Collaborations : Head of Machine Learning Section Member of DeLTA Lab
Professor Thanos Papadopoulos is a faculty member at the University of Kent , serving as Deputy Dean and Head of the Department of Analytics, Operations & Systems. He is affiliated with the Centre for Logistics and Sustainability Analytics (CeLSA) . PhD : Warwick Business School, University of Warwick MSc : Informatics, Athens University of Economics and Business Diploma : Computer Engineering and Informatics, Patras University His research focuses on Operations and Information Management , with emphasis on digital technologies in supply chains , resilience , and sustainability . Recent work explores AI, metaverse, and data-driven strategies. He has authored 150+ peer-reviewed publications and collaborates with journals like British Journal of Management as Associate Editor. Awards include Stanford's Top 2% Researchers and Clarivate Highly Cited distinctions. Professor Papadopoulos supervises students in supply chain management , big data , and sustainability . His projects address geopolitical disruptions and digital transformation in manufacturing and retail.
Xiaoze Pei is a Professor in the Department of Electronic & Electrical Engineering at the University of Bath, affiliated with the Institute for Advanced Automotive Propulsion Systems (IAAPS) and the Electronics Materials, Circuits & Systems Research Unit (EMaCS). His research focuses on superconductivity applications in electric systems, cryogenic power electronics, and DC network technologies for aerospace and renewable energy integration. Key projects include leading initiatives such as Towards Zero Emissions Electric Aircraft through Superconducting DC Distribution Network and HSTEA - Aerospace R&I , addressing challenges in electric propulsion, fault current limiters, and cryogenic power converters. His work contributes to UN Sustainable Development Goals related to clean energy and sustainable transport. Expertise: Superconducting fault current limiters (SFCL), DC circuit breakers, cryogenic power systems. Current roles: Principal Investigator (PI) on multiple UK and EU-funded projects. Collaborations: Extensive work with industry partners and academic institutions on electric aircraft, hydrogen control systems, and e-mobility technologies. Recent research emphasizes high-current cryogenic DC circuit breakers, superconducting air-core motors for aircraft, and topology optimization for power electronics. He actively supervises doctoral students in these areas and has published over 90 peer-reviewed articles. Labs/Teams: Leads research within EMaCS and collaborates with teams specializing in power electronics, cryogenics, and aerospace propulsion.
Francis Y. Yan is an Assistant Professor of Computer Science at the University of Illinois Urbana-Champaign (UIUC), holding an affiliate appointment in Electrical & Computer Engineering within the Grainger College of Engineering. He leads the Illinois Networked Systems and AI (NSAI) research group, focusing on building intelligent networked systems that are safe, robust, and performance-optimized through practical machine learning integration. Prior to joining UIUC in January 2025, he served as a Senior Researcher at Microsoft Research Redmond under Victor Bahl. His educational background includes: Ph.D. in Computer Science from Stanford University (2020), advised by Keith Winstein and Philip Levis B.S. in Computer Science (Yao Class) and B.A. in Economics from Tsinghua University (2015) Additional undergraduate studies at MIT Yan's research adopts a holistic approach to practical machine learning for networked systems, emphasizing judicious application rather than indiscriminate use. He builds real-world systems and research platforms to lay ML foundations, devises deployable algorithms using domain insights, and validates performance through extensive empirical evidence. His work consistently addresses operator concerns regarding ML deployment—focusing on safety, robustness, generalization, and efficiency—while strategically combining ML with classical networking and systems techniques. Analysis of his 15 most recent publications (2023-2025) reveals dominant themes in resource allocation for microservices (DeDe, Autothrottle), real-time video optimization (Mowgli, GRACE), and LLM-driven network algorithm design. His work bridges theoretical advances with industrial deployment, evidenced by platforms like Puffer (400,000+ users) and OpenNetLab that have become community standards for validating congestion control algorithms. His research has been recognized with top honors: USENIX NSDI Outstanding Paper Award (2024) for Autothrottle APNet Best Paper Award (2022) IRTF Applied Networking Research Prize (2021) USENIX NSDI Community Award (2020) USENIX ATC Best Paper Award (2018) for Pantheon Yan actively recruits master's and undergraduate researchers for his NSAI group, prioritizing self-motivated students for projects in networked systems and AI. His research is supported by industry collaborations (notably Microsoft) and manifests in deployable platforms like Puffer—which has enabled award-winning research at NSDI and SIGCOMM—and OpenNetLab for real-time communications. His work directly impacts production systems including Microsoft Teams and Bing. He founded and directs the Illinois Networked Systems and AI (NSAI) research group, which operates critical infrastructure including Puffer (a live TV service and research platform) and OpenNetLab. These platforms facilitate community-wide validation of novel algorithms, with Puffer alone supporting multiple best-paper awards at top conferences. Current workstreams span cloud resource management (Teal, Autothrottle, DeDe), low-latency video (Puffer, Tambur, Mowgli), and LLM-augmented systems (Nada, Designing Network Algorithms via LLMs).
Yang Kaidi is an Assistant Professor in the Department of Civil and Environmental Engineering at the National University of Singapore (NUS), affiliated with the Institute of Operations Research and Analytics (IORA) within NUS’s Smart Nation Research Cluster. Their research focuses on intelligent transportation systems, traffic control, shared mobility, and machine learning applications in mobility. Key interests include connected and automated vehicles, privacy-preserving data sharing, and reinforcement learning for traffic optimization. Research highlights include developing parameter privacy-preserving strategies for mixed-autonomy platoons, enhancing safety in autonomous driving via transformer-based trajectory prediction, and optimizing traffic signal timing using connected vehicle data. Their work bridges theoretical control systems with practical urban mobility challenges, addressing issues like ridesourcing-public transit integration, modular transit service operations, and weaving section management in mixed traffic environments. Recent publications emphasize real-time control frameworks, cooperative safety mechanisms, and data-driven solutions for urban and highway systems. Yang’s interdisciplinary approach integrates robotics, optimization, and cybersecurity to advance smart transportation infrastructure. Their contributions are particularly notable in privacy-preserving techniques for traffic state estimation and federated learning applications. While no specific awards or grants are listed, their research aligns with Singapore’s Smart Nation initiatives through IORA’s strategic focus areas. Yang’s work has implications for future traffic management systems, autonomous vehicle coordination, and sustainable urban mobility solutions.
Charles Fine is the Chrysler Leaders for Global Operations Professor of Management at MIT Sloan School of Management and concurrently serves as CEO, President, and Dean of the Asia School of Business (ASB) in Kuala Lumpur since 2015. He holds an AB in Mathematics and Management Science from Duke University, MS in Operations Research, and PhD in Business Administration from Stanford University. His research focuses on supply chain strategy, value chain roadmapping, and operations management in fast-clockspeed industries such as automotive and aerospace. He has pioneered frameworks for strategic innovation, entrepreneurial operations, and urban mobility systems. Key contributions include the concept of 'clockspeed' in industry dynamics and co-authoring Clockspeed (1998) and Faster, Smarter, Greener (2017). Fine co-directs MIT Sloan’s Driving Strategic Innovation executive program with IMD, Switzerland. He previously served on the board of Greenfuel Technologies, a biotech startup he co-founded. His work has been published in top journals like Management Science , Production and Operations Management , and Interfaces . Recent research highlights include analyzing unintended consequences of automated vehicles and exploring supply chain strategies for market expansion through O2S (Online-to-Store) models. He advises global corporations on supply chain resilience, value chain design, and innovation scaling.
Dan McCammon is a Professor in the Department of Physics at the University of Wisconsin-Madison, affiliated with the College of Letters & Science. His research focuses on X-ray astronomy, including studies of the diffuse X-ray background, interstellar and intergalactic media, and the development of advanced X-ray instrumentation. He is a key contributor to the XRISM (X-ray Imaging and Spectroscopy Mission) satellite, leading efforts in high-resolution X-ray spectroscopy and mission operations. McCammon's work emphasizes understanding cosmic plasma dynamics, galaxy cluster physics, and supernova remnant evolution through cutting-edge observational techniques and detector technology. His research interests span multiple subfields, including the thermodynamic properties of galactic clusters, charge-exchange processes in astrophysical plasmas, and the design of cryogenic microcalorimeters for space-based observatories. He has pioneered advancements in transition-edge sensors (TES) and superconducting detectors, enhancing the precision of X-ray spectral measurements. McCammon has contributed to numerous sounding rocket missions, such as Micro-X, and has been instrumental in the development of the Line Emission Mapper (LEM) probe concept, aimed at mapping the soft X-ray sky with unprecedented resolution. His work on the Hitomi (ASTRO-H) satellite demonstrated breakthroughs in resolving the thermal and dynamic properties of cosmic plasmas, such as the Perseus galaxy cluster and the Crab Nebula. His publications highlight a focus on high-resolution X-ray spectroscopy of cosmic sources, including galaxy clusters, active galactic nuclei, and supernova remnants. He has explored topics like non-thermal pressure contributions in cluster cores, ionized plasma diagnostics, and the role of charge-exchange emissions in interpreting diffuse X-ray backgrounds. McCammon's instrumentation innovations have enabled breakthroughs in measuring spectral features with sub-eV resolution, advancing our understanding of astrophysical processes. Despite the absence of explicitly listed awards or grants in the provided text, his leadership in major space missions and pioneering detector technologies underscores his contributions to the field. His research team collaborates on international projects, such as XRISM and LEM, reflecting a commitment to advancing observational astrophysics through interdisciplinary collaboration.
Minah Oh is a Professor and Chair of the Department of Mathematics & Statistics at James Madison University (JMU), where she has served since 2010. Her research focuses on numerical analysis, scientific computing, finite element methods, and optimal control, with a particular emphasis on axisymmetric problems and multigrid techniques. She holds a Ph.D. in Mathematics/Numerical Analysis from the University of Florida (2010) and degrees from Yonsei University (B.S., 2005). Her work bridges theoretical mathematics and computational applications, addressing challenges in PDE discretization, optimal control problems, and geometric numerical methods. Recent publications explore finite element approaches for state-constrained control problems and the analysis of axisymmetric domains using de Rham complexes and Fourier-based methods. No scientific awards are explicitly listed in the provided materials. Her advising and grants sections remain unspecified in the text. Dr. Oh maintains an academic website at educ.jmu.edu/~ohmx for further details.
Sandra Paterlini is a Full Professor in the Department of Economics and Management at the University of Trento, Italy. She holds academic roles including Co-Chair of the ERCIM Working Group on Optimization Heuristics and Vice-Chair of the IEEE Task Force on Portfolio Optimization. Her career includes visiting positions at institutions such as the University of Minnesota and Ludwig-Maximilians-Universität München. She earned a PhD in Computational Methods for Financial and Economic Decisions from the University of Bergamo, an MSc in Financial Mathematics from the University of Warwick, and a Laurea in Economics from the University of Modena and Reggio E. Her research focuses on quantitative finance, risk management, portfolio optimization, and network analysis, with applications to ESG, systemic risk, and financial stability. Key research contributions include methodologies for sparse graphical modeling, systemic risk analysis, and ESG scoring frameworks. She has received multiple awards for research excellence and serves on editorial boards of journals like Computational Statistics & Data Analysis and Frontiers in Applied Mathematics and Statistics . Her work bridges academia and policy, with contributions to the European Central Bank’s Financial Stability Directorate and involvement in global conferences on computational finance and econometrics.
Dr. Joyoung Lee is an Associate Professor in the Department of Civil and Environmental Engineering at New Jersey Institute of Technology (NJIT). He previously served as Laboratory Manager at the Federal Highway Administration's Saxton Transportation Operations Laboratory. His research focuses on Connected Vehicle (CV) systems, including applications in traffic management, signal control optimization, and autonomous vehicle infrastructure integration. Dr. Lee holds a Ph.D. (2010) and M.S. (2007) in Transportation Engineering from the University of Virginia, and a B.S. (2000) in Transportation Engineering from Hanyang University. His work emphasizes CV-based solutions for real-time traffic systems, cooperative vehicle-infrastructure systems (CVIS), and autonomous vehicle integration. Notable achievements include the 2019 IEEE CAVS Best Paper Award and multiple best paper recognitions from PTV User Group Meetings. His research also addresses traffic safety through innovations like the Virtual Guide Dog system for visually impaired pedestrians and advanced traffic monitoring frameworks using LiDAR and computer vision. Education: Ph.D., Transportation Engineering, University of Virginia (2010) M.S., Transportation Engineering, University of Virginia (2007) B.S., Transportation Engineering, Hanyang University (2000) Dr. Lee's research interests span smart city infrastructure, edge computing for traffic systems, and sustainable transportation solutions. He has pioneered algorithms for cooperative intersection management, automated platooning systems, and federated learning-based traffic optimization. His work bridges theoretical models with real-world implementation through partnerships with FHWA and industry stakeholders. Key contributions include development of the Cumulative Travel-Time Responsive (CTR) traffic signal control system, smart arrival notification systems for paratransit services, and advanced microsimulation calibration techniques. His lab focuses on translating CV data into actionable strategies for safer, more efficient transportation networks. Awards: IEEE CAVS Best Paper Award (2019) ASCE Grand Challenge Innovation Contest Honorable Mention (2017) PTV VISSIM Best Paper Awards (2012, 2008) Excellence in Research Award (University of Virginia, 2011) Ongoing projects include semi-decentralized graph neural networks for traffic forecasting and low-cost LiDAR-based traffic monitoring systems. His work addresses critical challenges in autonomous vehicle integration, incident management, and infrastructure resilience through interdisciplinary collaborations.
Prof. Felix Krahmer is a TUM Tenure Track Assistant Professor of Optimization and Data Analysis at the Department of Mathematics, Technische Universität München (TUM), part of the School of Computation, Information and Technology. Previously, he held a Junior Professorship (W1) for Mathematical Data Analysis at the University of Göttingen (2012–2015). His research focuses on mathematical foundations of data science, including compressed sensing, signal quantization, uncertainty quantification, and optimization algorithms. He has led an Emmy Noether research group and contributed to the ZeMat interdisciplinary project. Education : PhD in Mathematics (2009), New York University, advisors: Percy Deift & Sinan Güntürk MSc in Mathematics (2007), New York University BSc in Mathematics (2004), Jacobs University Bremen Research Interests : Krahmer’s work bridges theoretical mathematics and applied data science. Key areas include compressed sensing algorithms, high-dimensional signal reconstruction, quantization theory for analog-to-digital conversion, and optimization methods for inverse problems. He also explores applications in imaging (e.g., MRI reconstruction) and stochastic processes. Key Contributions : His research on phase retrieval, sigma-delta quantization, and compressed sensing recovery has advanced signal processing techniques. Recent efforts focus on uncertainty quantification for high-dimensional inverse problems and the mathematical analysis of consensus-based optimization. Grants & Awards : Emmy Noether Research Group Grant (DFG) Funding from BMBF (ZeMat project) Teaching & Outreach : Krahmer has taught advanced courses on compressed sensing, functional analysis, and random matrix theory. He co-organized workshops on probabilistic techniques and clinical risk prediction, emphasizing interdisciplinary collaboration. Labs/Teams : Member of the TUM Data Science Research Group, focusing on optimization, imaging, and uncertainty quantification. Active in the Munich Center for Quantum Science and Technology (MCQST).
Alfons Oude Lansink is a Professor and Chairholder in Business Economics at Wageningen University, Netherlands. He holds adjunct professorships at Universitas Padjadjaran (Indonesia) and the University of Florida (USA), and serves on the Dutch Ministry of Agriculture's CDM committee and Rabobank's scientific advisory board. His academic career spans roles as director of Wageningen School of Social Sciences (WASS) and Secretary-General of the European Association of Agricultural Economists. Education: MSc and PhD in Agricultural Economics from Wageningen University Leadership: Head of Business Economics group since 2003 His research focuses on dynamic technical and economic efficiency , sustainable performance of food supply chains , and economics of plant health . Recent work examines climate adaptation strategies, circular economy applications, and cross-border agri-food innovation dynamics using advanced econometric models. Key projects include MINDSTEP (modeling farm decisions), Closing the Loop (insect-based agriculture), and Food Pro-tec-ts (transboundary food technologies). Publications address topics like: Technical efficiency in dairy and arable farming Economic impacts of climate change on agriculture Corporate social responsibility in food manufacturing Policy evaluation for biogas and organic farming He serves as: Secretary/Treasurer of agricultural economics journal foundation Advisor to Universitas Padjadjaran (Indonesia) on policy and PhD supervision Editorial board member of Agronomy Journal and European Review of Agricultural Economics
