Hakan Türeci is Professor of Electrical and Computer Engineering at Princeton University, with joint affiliation at the Princeton Materials Institute. A theoretical physicist by training, his research explores quantum optics, quantum information science, and superconducting circuits for quantum computing applications. Education: Ph.D. from Yale University (2003) M.S. in Physics from Bilkent University, Turkey (1996) B.S. in Physics from Bilkent University, Turkey (1994) Research Interests: Focuses on non-equilibrium quantum phenomena in optical and microwave platforms, quantum simulation, quantum error correction, and the development of near-term quantum devices for computation and machine learning. Publication Trends: Recent work emphasizes quantum system modeling, quantum measurement techniques, Josephson junction physics, and applications of reservoir computing in quantum information processing. Academic Leadership: Advises a large group of graduate students and researchers in quantum engineering projects, collaborating across physics and engineering disciplines to advance quantum technologies.
Alan Kaptanoglu is a Professor leading the Plasma Physics Group at New York University's Courant Institute. His research focuses on the intersection of applied mathematics, scientific machine learning, and nuclear fusion. He develops theoretical and computational tools for plasma modeling, control, and optimization in complex dynamical systems. Key areas include stellarator design optimization, machine learning-driven MHD simulations, and reactor-scale plasma confinement solutions. His work advances fusion energy research through innovations in magnetic field shaping, coil optimization, and data-driven modeling of plasma behavior. Research interests span plasma turbulence analysis, magnetohydrodynamics, and the application of physics-informed neural networks (PINNs). He explores how magnetic geometry influences plasma stability and turbulence using machine learning techniques. His team addresses challenges in fusion reactor design, such as minimizing Lorentz forces in electromagnetic coils and optimizing permanent magnet configurations for stellarators. Recent work emphasizes data-driven methods for discovering interpretable models in fluids and plasmas, including reduced-order modeling and sparse regression approaches. He collaborates on projects like the DIII-D tokamak and ITER, advancing fusion energy solutions through interdisciplinary computational and experimental efforts. His group's contributions bridge plasma physics with advanced mathematics and machine learning to tackle grand challenges in energy and astrophysical systems. Advising and grants are not explicitly detailed in the provided texts, but his leadership role suggests active mentorship in graduate research. The NYU Plasma Physics Group serves as a hub for cutting-edge research, hosting internships, summer schools, and collaborations with industry/academic partners.
Ashish Cherukuri is an Associate Professor at the University of Groningen's Faculty of Science and Engineering, affiliated with the Optimization and Decision Systems group. His research focuses on optimization-based control, game theory, and multi-agent systems applied to energy, transportation, and robotics. He holds a Ph.D. from UC San Diego and postdoctoral experience at ETH Zurich. Education: Ph.D., University of California, San Diego (2012–2017) M.Sc., ETH Zurich (2008–2010) B.Tech, Indian Institute of Technology Delhi (2004–2008) Research Interests: Data-driven optimization, distributed algorithms, networked cyber-physical systems, and uncertainty handling in energy and transportation systems. Recent work emphasizes stochastic optimization, game-theoretic routing, and risk-aware control. Awards: Robert E. Skelton Dissertation Award (2017) Outstanding Graduate Student Award (2016) Focht-Powell Fellowship (2012–2015) Grants & Service: Editor for the IEEE Control Systems Society, organizer of Energy-Open 2019, and member of professional societies (IEEE, INFORMS, SIAM). Active in conference organization and academic leadership roles. Labs/Teams: Part of the Jan C. Willems Center for Systems and Control and the Engineering and Technology Institute Groningen (ENTEG). Research integrates theoretical advancements with practical applications in energy networks and smart systems.
Christoph T. Koch is a Professor of Physics at Humboldt-Universität zu Berlin, where he has held the W3 Chair since 2015. Previously, he held a similar position at Ulm University (2011–2015), supported by the Carl Zeiss Foundation. His research focuses on advanced electron microscopy techniques, including quantitative transmission electron microscopy (TEM), electron holography, and strain mapping. He leads the AG Strukturforschung/Elektronenmikroskopie group, advancing materials science through innovations in imaging and spectroscopy. Education: B.Sc./M.Sc. in Physics at Heidelberg University (1996–1998), followed by an exchange at Arizona State University (1997–1998). PhD in Physics from Arizona State University (2002, advisor: Prof. John C.H. Spence). Postdoctoral research at the Max Planck Institute for Metals Research, Stuttgart (2002–2011). Research interests include: Electron diffraction and phase retrieval Nanometer-scale strain and defect analysis Electron energy-loss spectroscopy (EELS) for plasmonics and bandgap mapping Development of FAIR data infrastructure for materials science Leadership: Managed the Department of Physics at Humboldt University (2020–2024). Collaborates widely, with key co-authors including P.A. van Aken, W. Sigle, and C. Felser. His work bridges experimental microscopy and computational modeling, addressing challenges in semiconductors, ceramics, and 2D materials. Notable contributions include pioneering methods for 3D reconstruction via electron ptychography, dynamic electron diffraction analysis, and strain mapping in advanced CMOS technologies. Current efforts emphasize real-time imaging and AI-driven data analysis in materials research.
Dr. Martin Zuidhof is a Professor in the Department of Agricultural, Food & Nutritional Science at the University of Alberta. His primary research focus is on Poultry Systems Modeling and Precision Feeding, particularly in optimizing broiler breeder management and energy partitioning. He holds a PhD in Animal Science from the University of Alberta and has pioneered transformative precision feeding systems that achieve unprecedented flock uniformity ( Education: PhD, Animal Science, University of Alberta Research Interests: Dr. Zuidhof’s work centers on advancing precision livestock systems through mathematical modeling (e.g., multiphasic growth models), optimizing pullet body weight for reproductive efficiency, and addressing societal concerns about animal welfare in poultry production. His innovations in precision feeding systems reduce nutrient waste while enabling precise metabolic studies. Publications: Over 50 peer-reviewed articles since 2004, spanning topics from energy partitioning to smart farming technologies. Recent work emphasizes low-cost sensor applications and Big Data integration in poultry systems. Teaching: Instructs courses like Applied Poultry Science (AFNS 571/AN SC 471) and Principles of Animal Agriculture , emphasizing experiential learning and critical scientific thinking. Grants/Advising: No explicit grants listed, but his research has received institutional support. No advisee names found in provided texts.
Stephen Lee is an Assistant Professor in the Department of Computer Science at the University of Pittsburgh, affiliated with Pitt Cyber. His research focuses on distributed systems, cyber-physical systems, and sustainability, emphasizing energy efficiency and cost optimization. Dr. Lee holds a PhD from the University of Massachusetts Amherst, a Master’s from Chennai Mathematical Institute, and a Bachelor’s from St. Stephen’s College, Delhi. He actively seeks students for his research group. Education: PhD, Computer Science, University of Massachusetts Amherst Master’s, Chennai Mathematical Institute Bachelor’s, St. Stephen’s College, Delhi Research Interests: Dr. Lee’s work integrates distributed systems, machine learning, and optimization to enhance sustainability. Key areas include IoT-enabled energy systems, emission-aware computing, and privacy-preserving frameworks. He leads projects like GreenWhisk (serverless emission reduction) and Sat2map (3D building modeling from satellite imagery). Recent Achievements: Best Paper Award in IEEE TPS 2024 DOE-funded Cyber Energy Center (2024) MCSI Seed Grant for Pitt building sustainability (2024) NSF Grant on sustainable distributed infrastructures (2023) Grants & Advising: Secured over $2M in grants, including NSF and DOE funding. Advises on energy-efficient systems and IoT security. Teaches CS 2510 (Operating Systems) and CS 1699 (Systems & Sustainability). Labs & Teams: Directs the Sustainable Systems Research Group, focusing on decarbonizing IT and optimizing renewable energy systems. Collaborates with industry partners on smart grid solutions and edge-cloud systems.
Professor Peter Chin is a Professor of Engineering at Dartmouth College and Director of the Learning, Intelligence + Signal Processing (LISP) Lab. He holds affiliations with the Thayer School of Engineering and serves as Associate Editor of IEEE Transactions on Computational Social Systems. His research bridges signal processing, machine learning, game theory, and differential geometry, with applications in cybersecurity, healthcare, and network analysis. Education: Bachelor of Science in Electrical Engineering, Computer Science, and Mathematics from Duke University (1993) Doctor of Philosophy in Mathematics from MIT (1998) Research Interests: Chin’s work focuses on fundamental questions at the intersection of machine learning, game theory, and signal processing. His lab explores topics like adversarial defense mechanisms, topological machine learning, and computational neuroscience. Recent projects include cybersecurity resilience modeling, medical imaging enhancements via GANs, and multi-agent reinforcement learning frameworks. Publications Trends: His most recent articles address cutting-edge challenges in cybersecurity (e.g., autonomous defense systems), medical AI (e.g., Alzheimer’s classification), and adversarial robustness. A notable 2025 focus is on quantitative resilience modeling for cyber defense, reflecting growing demand for AI-driven security solutions. Awards: Faculty Scholar Award, Duke University George Sherred III Award, Duke University Julia Dale Memorial Award, Duke University Grants & Leadership: Recipient of DARPA cybersecurity research grants Co-chair for SPIE/DSS Cyber Sensing Conference (2013–2020) Developed novel compressive sensing microscope for biological imaging LISP Lab: This interdisciplinary lab pioneers projects like nFlip (multiplayer security game models) and topological machine learning frameworks, emphasizing practical applications of theoretical advancements.
Soledad Villar is an Assistant Professor in the Department of Applied Mathematics and Statistics and a member of the Mathematical Institute for Data Science at Johns Hopkins University. She also contributes to the Data Science and AI Institute . Her research focuses on computational methods for extracting information from data, emphasizing optimization for data science, machine learning, equivariant representation learning, and graph neural networks. Dr. Villar holds a PhD in Mathematics from the University of Texas at Austin and has been a research fellow at New York University and the Simons Institute at UC Berkeley. Her work bridges theoretical foundations with practical applications in fields like scientific computing and political analysis. Awards include the National Science Foundation CAREER Award (2024). Her research has addressed topics such as gerrymandering detection, fluid dynamics modeling, and graph representation learning. She collaborates on interdisciplinary projects and organizes academic events like the One World MINDS Seminar and the Cibercoloquio Latinoamericano de Matemáticas . Her research interests span computational methods, equivariant machine learning frameworks, and graph neural networks, with applications in physics, engineering, and data-driven decision-making. She actively engages in advancing machine learning techniques for scientific and engineering challenges.
Pavel Kireyev is an Assistant Professor in the Department of Management at the London School of Economics and Political Science. His research explores quantitative marketing strategies in digital marketplaces using modern analytics methods. Kireyev holds a Doctorate in Business Administration from Harvard Business School, an MA in Statistics from Yale University, and a BSc in Business Mathematics and Statistics from LSE. His research focuses on: Coordination of pricing and advertising across platforms Leveraging crowd intelligence and data analytics Market design in multi-sided digital platforms Innovative marketing technologies Kireyev's publications examine driver misconduct in delivery systems, ideation contest design, multichannel pricing strategies, and digital advertising attribution. His work bridges quantitative methods with practical business applications.
Dr. Jianqiang Cheng is an Associate Professor in the Department of Systems and Industrial Engineering at the University of Arizona, College of Engineering. He is also a member of the Graduate Faculty and affiliated with the Applied Mathematics and Statistics Graduate Interdisciplinary Programs. His research is centered on optimization under uncertainty with applications in energy systems and logistics. Research Interests: His primary research areas include stochastic programming, robust optimization, distributionally robust optimization, semidefinite programming, and chance-constrained optimization. He applies these methodologies to challenges in power systems, renewable energy integration, microgrid design, and resilient supply chains. The recent publications (2020–2022) reflect a strong trend toward data-driven and computationally efficient methods in optimization. Key themes include distributionally robust optimization under moment and Wasserstein ambiguity, chance-constrained AC optimal power flow, and resilient supply chain modeling under disruptions such as the COVID-19 pandemic. His work frequently appears in top journals like INFORMS Journal on Computing , IEEE Transactions on Power Systems , and European Journal of Operational Research . Scientific Awards: Best Short Paper Award, INFORMS Workshop on Data Science (Fall 2022) NSF CAREER Award, National Science Foundation (Spring 2022) Science Foundation Arizona's 2017 Bisgrove Scholar (Spring 2017) Dr. Cheng has secured significant research funding, including the NSF CAREER Award, supporting his work in data-driven optimization. He collaborates extensively with researchers in energy systems and operations research, including K. Pan, M. Cheramin, A. M. Fathabad, and A. Lisser. While specific advisees are not listed, his role as a member of the Graduate Faculty indicates active supervision of graduate students in systems engineering, applied mathematics, and statistics. His research contributes to the development of advanced optimization models for real-world systems affected by uncertainty, particularly in energy and logistics. Though no specific lab is mentioned, his work implies involvement in computational optimization and energy systems modeling research groups within the College of Engineering.
Prof. Rob Goverde is a Professor of Railway Traffic Management & Operations at the Department of Transport & Planning, TU Delft, and Director of the Digital Rail Traffic Lab. He holds a PhD in Railway Operations (2005) from TU Delft and an MSc in Mathematics from Utrecht University (1993). His research focuses on resilient railway timetabling, disruption management, energy-efficient train control, and railway safety. He teaches MSc courses like 'Railway Operations and Control' and coordinates the MSc Annotation Railway Systems and a collaborative BSc program with Beijing Jiaotong University. Research interests include railway operations optimization using operations research, max-plus algebra, and data analytics. Projects funded by Shift2Rail, NSFC, and NWO address topics like high-speed rail network capacity and resilient railway systems. He leads the Railway Systems theme at TU Delft Transport Institute and serves as Editor-in-Chief of the Journal of Rail Transport Planning & Management. His work bridges mathematical methodologies with railway domain knowledge to enhance system performance and sustainability.
Linda Turunen is an Assistant Professor in Statistics, Mathematics, and Data Science at Aalto University's School of Business, Department of Information and Service Management. She holds adjunct professorships (title of docent) in Mathematics at the University of Helsinki (since 2019) and in Applied Mathematics at the University of Vaasa (since 2016). She earned her Doctor of Science (DSc) in Mathematics from Aalto University School of Science in 2014. Her research focuses on sustainability communication, circular business models, and luxury brand strategies, particularly exploring second-hand markets and consumer behavior in fashion industries. Her recent work analyzes systemic challenges in textile industries, pandemic impacts on luxury sectors, and innovative sustainability frameworks like the 'Shades of Green' instrument. She actively supervises theses across bachelor/master/PhD levels, emphasizing data-driven approaches to sustainable consumption patterns.
Ameya Jagtap is an Assistant Professor (Tenure-Track) in the Department of Aerospace Engineering at Worcester Polytechnic Institute (WPI), USA. Prior to this, he served as an Assistant Professor of Applied Mathematics (Research) at Brown University from 2021 to 2024. He holds a Ph.D. and M.E. in Aerospace Engineering from the Indian Institute of Science (IISc), and completed postdoctoral research at TIFR-CAM (India) and Brown University's Division of Applied Mathematics. His research bridges mechanical/aerospace engineering, applied mathematics, and computation, focusing on scientific machine learning algorithms that integrate data and physics. Key areas include physics-driven deep learning, uncertainty quantification, multi-scale simulations, and novel neural network architectures like quantum and graph networks. He serves on editorial boards for Neural Networks , Neurocomputing , and others. His work emphasizes interpretable neural operators for PDE solutions, domain decomposition methods, and adaptive activation functions to enhance PINN convergence. Notable contributions include XPINNs (extended physics-informed neural networks) and causal sweeping frameworks for PDEs. His research has been widely cited, particularly for PINN applications in supersonic flows and high-dimensional PDEs. Jagtap has delivered invited talks at institutions like Los Alamos National Laboratory, Tsinghua University, and the Alan Turing Institute. He is also recognized as a Top 2% World Scientist by Stanford University.
Marco Martino Rosso is a Research Fellow at the Department of Structural, Building and Geotechnical Engineering (DISEG) at the Polytechnic University of Turin, where he also serves as an external lecturer and teaching assistant in both DISEG and the Department of Mathematical Sciences (DISMA). He is affiliated with the Doctoral School (SCDOTT) and completed his PhD under the supervision of Professor Giuseppe Carlo Marano. His academic work bridges civil engineering with advanced computational methods, focusing on structural health monitoring, optimization, and machine learning applications. His research interests center on Structural Health Monitoring , Machine Learning in Civil Engineering , Earthquake Engineering , Structural Optimization , Operational Modal Analysis , and AI-driven diagnostics for infrastructure. He applies deep learning, neural networks, and hybrid modeling techniques to problems such as damage detection, post-earthquake assessment, tunnel and bridge monitoring, and dynamic analysis of timber and concrete structures. His recent publications, spanning from 2023 to 2025, demonstrate a strong trend toward integrating artificial intelligence with structural engineering, particularly in automating modal analysis, optimizing structural forms, and enhancing seismic resilience. These works appear in journals like Mechanical Systems and Signal Processing , Computers & Structures , and Bulletin of Earthquake Engineering , as well as in proceedings of international conferences such as IOMAC and EWSHM. Marco Rosso has not received any explicitly mentioned scientific awards in the provided text. However, his extensive publication record and active role in research projects indicate strong recognition in his field. He has contributed to teaching as a course collaborator in subjects including Dynamic Identification of Structures , Statistics , Construction Techniques , and Safety Assessment and Retrofitting of Structures . He has also been involved in the ARTISTE 2025 Summer School, indicating engagement in advanced training programs. While no formal lab or team name is specified, his frequent collaborations with researchers such as Angelo Aloisio, Giuseppe Carlo Marano, and Jonathan Melchiorre suggest he is part of a vibrant research group focused on intelligent structural systems and data-driven engineering at Politecnico di Torino.
Dr. Helen Cai is a Senior Lecturer in International Business and Circular Economy at Middlesex University Business School, where she serves as Programme Leader for the BA International Business Administration and BA Business Management (Top-Up). She also leads the Doctor of Business Administration (DBA) programme delivered in China through a partnership with United Business Institutions (UBI). Additionally, she holds a Full Visiting Professorship at Jiaxing University, China. Her academic leadership extends to editorial roles as Senior Editor of Cogent Business and Management and member of the editorial board of the Journal of World Business . She is Vice President of Marketing for the International Case Study Research Association (ICRA). Dr. Cai’s research focuses on international business, sustainability, green innovation, and corporate environmental governance. She employs advanced methodologies such as fuzzy-set qualitative comparative analysis (fsQCA), econometrics, and scientometric analysis. Her work addresses critical issues including green supply chains, carbon emissions, foreign direct investment, and institutional influences on innovation. The 15 most recent publications highlight a strong trend toward environmental sustainability, circular economy, and data-driven policy analysis. Her recent work spans blockchain in green supply chains, AI in rehabilitation, carbon governance, land use, and migration policy, demonstrating interdisciplinary breadth and policy relevance. Bronze award, The First National Collegiate Olympiad Mathematical Mirror Competition (2022) Honourable Mention, Interdisciplinary Contest in Modelling (2022) First Prize, Asia and Pacific Mathematical Contest in Modelling (2021) Third Prize, THE International Case Competition (2021) Dr. Cai has extensive supervisory experience, currently guiding multiple PhD candidates and having chaired over 30 DBA viva panels. She has supervised over 60 BA, 150 MSc, and 75 MBA dissertations. She has participated in 72 PhD/DBA committees in roles including external examiner, internal examiner, and panel chair. Her grants and funding are not explicitly mentioned, but her research output and editorial roles suggest sustained scholarly engagement. She leads research teams focused on circular economy and international business, and her future work is likely to expand into AI-driven sustainability analytics, cross-border green innovation, and climate governance. Her prior industry experience as a Senior Economist at China’s Central Bank enriches her applied research perspective.
