Christoph Bostedt holds dual appointments as a Professor of Physical Chemistry at the Ecole Polytechnique Fédérale de Lausanne (EPFL) and as Head of the Laboratory for Synchrotron Radiation and Femtochemistry (LSF) at the Paul Scherrer Institut (PSI). He leads strategic operations for the LSF, managing five research groups and overseeing four beamlines at the Swiss Light Source and the Alvra Endstation at SwissFEL. His research focuses on ultrafast x-ray science, including single-shot imaging, non-linear x-ray spectroscopy, and femtosecond pump-probe techniques. He collaborates globally on initiatives like the Athos project, aiming to advance ultrafast x-ray technologies. Bostedt has over 150 publications and is a Fellow of the American Physical Society, recipient of the Röntgen Prize. Education: Ph.D. from the University of Hamburg with research at Lawrence Livermore and Berkeley National Laboratories. Prior roles include leadership at Argonne National Laboratory and SLAC National Accelerator Laboratory. Research Interests: Single-particle imaging and coherent diffraction X-ray free-electron laser applications Ultrafast dynamics in nanoparticles and molecular systems Non-linear x-ray spectroscopy Time-resolved x-ray pump-probe methods Awards: Fellow of the American Physical Society Röntgen Prize (University of Giessen) Labs & Projects: Spearheads the Athos beamline project at SwissFEL, developing the Maloja endstation for ultrafast x-ray studies. Oversees the Laboratory for Femtochemistry and collaborates on advanced imaging techniques for nanoscale science.
George Yin is a Professor in the Department of Mathematics at the University of Connecticut (since 2020). Previously, he held the position of Distinguished Professor at Wayne State University (2017–2020) and has been a faculty member there since 1988. He earned his Ph.D. in Applied Mathematics from Brown University in 1987, along with M.S. degrees in Applied Mathematics and Electrical Engineering, and a B.S. in Mathematics from the University of Delaware (1983). His research focuses on stochastic optimization, control theory, stochastic systems, and numerical methods, with applications to biology, finance, and engineering. He has held editorial roles at journals such as SIAM Journal on Control and Optimization and has received prestigious awards including SIAM Fellow (2015), IEEE Fellow (2002), and IFAC Fellow (2014–2017). Key funding includes continuous NSF support since 1989, grants from the Air Force Office of Scientific Research, and others. His work spans theoretical advancements in stochastic systems and practical applications in energy systems, control engineering, and data science. He has advised numerous students and maintains active collaborations internationally. Labs/Teams: Goldenson Center for Actuarial Research, Quantitative Learning Center. Grants: NSF, AFOSR, ARO, NSA, and multiple institutional grants.
Giancarlo Ferrari Trecate is an Adjunct Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) , affiliated with the School of Engineering and the SCI-STI-GFT department. He is also involved in teaching and research through the STI-SGM and EDRS-ENS programs. Research Interests : Automatic control, state estimation, system identification, machine learning, distributed control, hybrid systems, microgrids, biochemical networks, voltage and frequency stabilization in AC/DC microgrids. Publications Trends : His recent work focuses on integrating Neural ODEs and Hamiltonian structures for stable control systems, regret minimization in distributed control, and robust state estimation under uncertainty. Applications include autonomous mobility-on-demand , power grid optimization , and secure microgrid control against cyber-attacks. Scientific Awards : No specific awards mentioned in the provided data. Teaching & Advising : He supervises PhD students in mechanical engineering and teaches courses on Multivariable control and Networked control systems . His lab, DECODE , specializes in Dependable Control and Decision systems.
Rong Pan is a Professor at the School of Computing and Augmented Intelligence, Arizona State University (ASU). He holds a Ph.D. in Industrial Engineering from Pennsylvania State University (2002), an M.S. from Florida A&M University (1999), and a B.S. in Materials Science from Shanghai Jiao Tong University (1995). His research focuses on quality and reliability engineering, design of experiments, time series analysis, and statistical learning theory. Key projects involve NSF-funded research on reliability prediction, accelerated life testing, and degradation modeling. He serves as an Associate Editor for the Journal of Quality Technology and has authored over 80 publications. Courses taught include Reliability Engineering, Design of Experiments, and Statistics for Data Analysts. His academic service includes roles as a referee for IEEE Transactions and IIE journals. Research interests emphasize statistical methods for reliability improvement, with recent work on Bayesian inference models, optimal experimental design, and machine learning applications in industrial systems. Grants include collaborations with the NSF, Arizona Department of Transportation, and Science Foundation Arizona. His work bridges theoretical advancements and practical applications in manufacturing, energy systems, and semiconductor reliability. Education: Ph.D. (2002), M.S. (1999), B.S. (1995) Key Research Areas: Reliability Engineering, Bayesian Methods, Time Series, DOE Active Grants: NSF CMMI, SUNY IT Visiting Scholar Program Teaching: IEE 573 Reliability Engineering, DSE 501 Statistics Service: Journal of Quality Technology (Associate Editor), IEEE Transactions (Referee)
Jeff Shamma is the Department Head and Professor of Industrial and Enterprise Systems Engineering (ISE) at the University of Illinois at Urbana-Champaign, holding the Jerry S. Dobrovolny Chair. He is also courtesy Professor in Aerospace Engineering and Mechanical Science and Engineering. Formerly, he held the Julian T. Hightower Chair at Georgia Institute of Technology and faculty positions at KAUST. Dr. Shamma earned his PhD in Systems Science and Engineering from MIT (1988) and a BS in Mechanical Engineering from Georgia Tech (1983). He is a Fellow of IEEE and IFAC, recipient of the IFAC High Impact Paper Award, AACC Donald P. Eckman Award, and NSF Young Investigator Award. His research spans Decision and Control , Game Theory , and Multi-Agent Systems , focusing on human-machine networks, distributed autonomy, and adaptive robotic systems. Recent work examines crowd dynamics, risk-sensitive control, and feedback linearization for constrained optimization. Jeff has served as Editor-in-Chief of IEEE Transactions on Control of Network Systems (2020–2024) and held editorial roles in journals like Annual Reviews in Control and IEEE Transactions on Robotics . His 15 most recent publications (2024–2025) analyze learning dynamics, multi-agent optimization, and UAV-crawler systems, reflecting trends in autonomous systems, game-theoretic modeling, and industrial inspection technologies. Scientific distinctions include: Fellow of IEEE and IFAC IFAC High Impact Paper Award (2020) AACC Donald P. Eckman Award (1996) NSF Young Investigator Award (1992) Mohammed Dahleh Distinguished Lecture Award (2013) Dr. Shamma advises current PhD students Hassan Abdelraouf, Aya Hamed, and Nawaf Otaibi, with former advisees including Sarah Toonsi (2025) and Fat-hy Rajab (2025). His lab integrates theoretical research with applied projects like FalconScan, a UAV-crawler system for industrial inspection, and develops magnetic legs for curved surface UAV landing.
Roxana Geambasu is an Associate Professor at Columbia University's Department of Computer Science, with affiliations to the Software Systems Lab and the Cybersecurity Committee . She specializes in systems security, differential privacy, and resource management for modern computing environments. PhD: University of Washington (2011) Undergraduate: Polytechnic University of Bucharest, Romania Her research focuses on integrating differential privacy as a first-class computing resource in infrastructure systems, with key contributions in: Privacy Budget Management (PrivateKube, DPack, Turbo) Web & Mobile Privacy (Cookie Monster, Big Bird) Security Abstractions (POSIX, Vanish, XRay) Article trends show a strong emphasis on differential privacy (8/15), resource scheduling (5/15), and privacy-preserving advertising (3/15). Key subfields include budget optimization, browser APIs, and ML pipeline privacy. Scientific awards include: Alfred P. Sloan Fellowship NSF CAREER Award Google Ph.D. Fellowship in Cloud Computing Best Paper Awards at SOSP, EuroSys She advises M.S. and undergraduate students, teaching courses in Distributed Systems and Privacy Curriculum . Her lab develops tools like PixelDP and Sunlight for operationalizing privacy in real-world systems.
Parviz Moin holds the Franklin P. and Caroline M. Johnson Professorship in Stanford University's School of Engineering. As founding director of the Center for Turbulence Research (CTR)—a NASA-Stanford consortium established in 1987—he has pioneered computational methods for turbulence physics, including direct numerical simulation and Large Eddy Simulation (LES) techniques. CTR serves as an international hub for turbulence studies across engineering, mathematics, and physics disciplines. Moin's research encompasses computational physics of turbulent flows, with emphasis on boundary layer control, hypersonic aerodynamics, propulsion systems, and aircraft icing. His recent work advances high-fidelity simulations for aerospace applications, particularly developing wall models for LES that accurately capture separation phenomena under complex pressure gradients and Reynolds number effects. Recent publications demonstrate extensive applications of LES to aircraft design challenges, including transonic buffet prediction, high-lift configuration analysis, and icing aerodynamics. Investigations consistently address fundamental turbulence physics while developing practical computational tools for aerospace engineering, with particular focus on hypersonic boundary layers, flow separation mechanisms, and conjugate heat transfer in iced environments.
Giulia Giordano is a Full Professor in the Department of Industrial Engineering at the University of Trento, Italy, where she leads the Dynamical Networks and Systems Biology research group. She also holds a dual appointment as Visiting Professor and Delft Technology Fellow at the Delft Center for Systems and Control, Delft University of Technology, The Netherlands. Her career includes previous positions as Assistant Professor at Delft University of Technology (2017-2019), Postdoctoral Research Fellow at Lund University, Sweden (2016-2017), and Research Fellow at the University of Udine, Italy (2016). Giulia earned her Ph.D. in Industrial and Information Engineering: Automation (Excellent) from the University of Udine with a thesis titled "Structural Analysis and Control of Dynamical Networks." She completed her M.Sc. and B.Sc. in Electrical Engineering (both Summa cum laude) at the same institution. She also undertook research visits at Caltech (2012) as a SURF Fellow and at the University of Stuttgart (2015) as a DAAD Research Scholar. Her primary research focuses on the analysis and control of dynamical networks with applications in systems biology, mathematical ecology, and mathematical epidemiology. She develops mathematical frameworks that bridge control theory, network theory, and dynamical systems to address complex problems in biological systems. Her recent work spans epidemic modeling, opinion dynamics, biochemical networks, and neurological disorders, with a particular emphasis on structural analysis of networked systems. She employs both theoretical and computational approaches to understand system behavior under uncertainty. Giulia's publications reveal a strong interdisciplinary focus, spanning from theoretical control systems to practical applications in epidemiology and biology. Her recent work shows increasing emphasis on epidemic modeling (particularly related to mpox and SARS-CoV-2), network synchronization, and the application of control theory to biological phenomena like fibromyalgia pathogenesis and opinion formation. Many of her papers appear in top-tier control journals including Automatica and IEEE Transactions on Automatic Control. 2024: Outstanding Service as Associate Editor of IEEE Control Systems Letters 2021: SIAM Activity Group on Control and Systems Theory Prize 2020: Outstanding Reviewer, Annals of Internal Medicine 2017: NAHS Best Paper Prize and EECI PhD Award 2016: Outstanding TAC Reviewer, IEEE Transactions on Automatic Control Giulia actively mentors students and postdoctoral researchers, currently supervising five postdoctoral researchers and two Ph.D. students at the University of Trento. She has advised numerous M.Sc. and B.Sc. students on topics ranging from bio-inspired modeling to optimal control of epidemic systems. Her research is supported by competitive grants including the ERC Starting Grant INSPIRE (Integrated Structural and Probabilistic Approaches for Biological and Epidemiological Systems). She serves as Associate Editor for IEEE Control Systems Letters and Automatica, and is a Senior Member of IEEE and the Control Systems Society. Giulia leads the Dynamical Networks and Systems Biology research group at the University of Trento, which maintains strong international collaborations across Europe and North America. The group's work combines theoretical advances in control theory with practical applications to pressing problems in public health and biological systems, demonstrating the power of mathematical approaches to understanding complex phenomena in the life sciences.
Christian Enz is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL), where he serves as Director of the Institute of Microengineering and Head of the Integrated Circuits Laboratory. With M.S. and Ph.D. degrees in electrical engineering from EPFL (1984 and 1989), he has established himself as a leading researcher in low-power analog circuit design and semiconductor device modeling. His research interests focus on very low-power analog and RF IC design , semiconductor device modeling , and increasingly on cryogenic electronics for quantum computing applications . Professor Enz is particularly known for his work on FDSOI MOSFET behavior at cryogenic temperatures, developing comprehensive models that address challenges in subthreshold swing saturation, threshold voltage shifts, and self-heating effects. As a Life Fellow of IEEE with 282 publications and over 7,400 citations, Professor Enz has made significant contributions to the field. His recent work demonstrates how the $G_{m}/I_{D}$ design methodology remains effective in advanced technology nodes and can be extended to cryogenic temperature operation. His research bridges fundamental semiconductor physics with practical circuit design considerations for quantum computing interfaces. Life Fellow, IEEE Director of the Institute of Microengineering, EPFL Head of the Integrated Circuits Laboratory 282 publications with 7,400+ citations Specialist in cryogenic CMOS for quantum computing Professor Enz's work on cryogenic electronics addresses critical challenges for quantum computing scalability. By developing accurate models for transistor behavior at temperatures as low as 3.3K, his research enables the design of specialized control electronics that can operate inside dilution refrigerators, potentially solving major wiring constraints that currently limit quantum computer scaling. His laboratory continues to advance the understanding of semiconductor device physics at cryogenic temperatures while developing practical circuit design methodologies for this emerging application domain.
WANG Qinghai is an Associate Professor (Educator Track) at the National University of Singapore (NUS), specializing in Non-Hermitian PT-symmetric quantum mechanics, quantum field theory, and mathematical physics. His research explores the stability of non-Hermitian systems through periodic driving, time-dependent PT-symmetric frameworks, and applications of 2×2 matrices in quantum dynamics. Recent publications focus on advanced topics in quantum mechanics, thermodynamics, and cosmological instantons, reflecting his interdisciplinary expertise. While no formal student lists or scientific awards are documented in the provided texts, his work bridges theoretical physics and applied mathematics.
Dr. Edouard Boujo is a Scientist and Lecturer at the Swiss Federal Institute of Technology Lausanne (EPFL) , affiliated with the School of Engineering (STI) and working in the Institute of Mechanical Engineering (IGM) and Laboratory of Fluid Mechanics and Instabilities (LFMI) . He also teaches in the SGM-ENS department of the School of Engineering. Scientist at EPFL STI IGM LFMI Lecturer at EPFL STI-SGM SGM-ENS His research focuses on Fluid Dynamics with expertise in Flow Stability , Flow Control , Aeroacoustics , Thermoacoustics , Fluid-Structure Interaction , and Coating Flow Dynamics . He employs advanced mathematical modeling and computational methods to study complex fluid behaviors. Recent publications highlight his work on stochastic modeling of fluid instabilities, adjoint-based optimization of flow systems, and nonlinear dynamics of coating flows. His 15 most recent papers cover topics ranging from symmetry-breaking bifurcations to spin coating optimization and noise-induced transitions in fluid systems. Dr. Boujo actively collaborates with institutions across Europe and New Zealand, mentoring PhD student Atharva Lagwankar . He has received research funding from the Swiss National Science Foundation for two PhD theses and contributes to major fluid dynamics conferences like the European Fluid Dynamics Conference and APS Division of Fluid Dynamics meetings. His laboratory work at LFMI involves experimental and computational studies of fluid instabilities, with applications in aerospace, mechanical engineering, and industrial coating processes. He develops adjoint-based control methods for optimizing flow systems and reducing drag in various fluid configurations.
Dr. Sam Schreyer is a Professor of Economics at the Department of Economics, Finance & Accounting, Fort Hays State University. He holds a Ph.D. in Economics from Claremont Graduate University (2009), an M.A. in Economics (2004), and a B.M. in Music (2001), both from Wichita State University. His research focuses on applied macroeconomics, developing economies, financial crises, and inflation dynamics. Education: Ph.D. in Economics, Claremont Graduate University, 2009 M.A. in Economics, Wichita State University, 2004 B.M. in Music, Wichita State University, 2001 Research Interests: Dr. Schreyer examines macroeconomic policies in emerging markets, the impact of financial crises, and inflation dynamics. His work often integrates econometric models to analyze sudden stops, currency crises, and university contributions to local economies. Recent projects include annual economic impact reports for Fort Hays State University, emphasizing institutional roles in regional development. Collaborations & Grants: He frequently collaborates with Emily Breit and Tom Johansen on institutional impact studies and Docking Institute-funded projects. His research also explores educational policy through online vs. in-person learning outcomes, addressing retention strategies and selection bias. Labs/Teams: Affiliated with the Department of Economics, Finance & Accounting and the Docking Institute of Public Affairs. Office: McCartney Hall 203D.
Shen Wei is the KoGuan Distinguished Professor of Law at the Shanghai Jiao Tong University Law School, with a concurrent role as Visiting Professor (2025). His academic career spans legal practice and academia, focusing on international investment law, corporate governance, financial regulation, and international commercial arbitration. Concurrently, his research extends into computational and mathematical domains, including machine learning, deep neural networks, and approximation theory. He teaches international investment law, international financial regulation, company law, and international economic law. His interdisciplinary work bridges legal scholarship with advanced mathematical modeling and algorithmic analysis. Recent research emphasizes neural network architecture, optimization techniques, and approximation theory applied to complex systems. Notable contributions include studies on deep network expressivity, gradient methods, and wavelet-based image restoration. Awards and grants are not explicitly mentioned, but his work reflects significant contributions to both legal and computational fields.
Karan Singh serves as an Assistant Professor of Operations Research at Carnegie Mellon University's Tepper School of Business, where he develops theoretically rigorous algorithms for machine learning systems with emphasis on reinforcement learning and control theory. His work synthesizes techniques from online learning, optimization, and statistics to address complex interactive learning challenges. His academic journey includes: PhD in Computer Science from Princeton University under Elad Hazan Postdoctoral research at Microsoft Research (Redmond) Bachelor's degree in Computer Science from Indian Institute of Technology (IIT) Kanpur Singh's research program centers on three interconnected pillars: Algorithmic Reductions : Creating efficient methods to solve complex learning problems (e.g., reinforcement learning) using solvers for simpler tasks, yielding breakthroughs in online boosting and RL with concave rewards Nonstochastic Control : Establishing an algorithmic foundation for control theory through provably efficient instance-optimal algorithms that extend online learning to stateful systems Privacy-Preserving Online Learning : Investigating fundamental limits of regret minimization under differential privacy constraints while maintaining performance His approach consistently bridges theoretical computer science and practical control applications. Analysis of his 15 most recent publications reveals a clear evolution toward integrating algorithmic reductions with nonstochastic control frameworks. Recent work (2023-2025) demonstrates increasing focus on sample efficiency in agnostic boosting, privacy-aware optimization without smoothness assumptions, and competitive ratio analysis in online control. A unifying thread is the development of regret-optimal algorithms for linear dynamical systems under adversarial disturbances. His contributions have earned significant recognition: Best Paper Award at OptRL workshop (NeurIPS 2019) Spotlight Prize from New York Academy of Sciences' ML Symposium (2018) Multiple oral presentations at NeurIPS/ICML (acceptance rate Though specific student advisees aren't listed, Singh's extensive publication record with junior co-authors indicates active mentorship. His research has secured substantial support including a US patent (11,138,513 B2) for dynamic learning systems and collaborations through CMU's Machine Learning and Optimization group. Current projects involve interdisciplinary work on differentiable control libraries (Deluca) and medical applications like mechanical ventilation control. Singh leads research within CMU's Machine Learning and Optimization ecosystem, collaborating across computer science and engineering departments. His team develops foundational tools like the Deluca differentiable control library while pursuing real-world applications in healthcare systems, demonstrating strong cross-disciplinary integration.
Prof. Dr. Wolfgang Steimle is a Professor at the Institute of Mathematics within the Faculty of Mathematics, Natural Sciences and Technology at the University of Augsburg, Germany. He serves as the Erasmus representative for the Institute and is a core member of the Differential Geometry research team, collaborating with Professors Bernhard Hanke and Peter Quast. His office is located in space 3020 (L1) with contact email wolfgang.steimle@math.uni-augsburg.de. Steimle completed his academic training at the University of Münster, earning a diploma (Master's equivalent) in 2007 with thesis "Whitehead-Torsion und Faserungen" and a PhD in 2010 under Tom Farrell and Wolfgang Lück with dissertation "Obstructions to Stably Fibering Manifolds". His research centers on Differential Geometry and Algebraic Topology , with primary focus on manifold classification , automorphisms of manifolds , Algebraic K- and L-theory , and positive scalar curvature . He bridges abstract homotopy theory with geometric applications, particularly through cobordism categories, Waldhausen K-theory, and the assembly map. His work connects higher category theory with classical manifold problems, yielding insights into metric spaces and curvature constraints. Analysis of his recent publications reveals a dominant trend in applying stable infinity-categories to geometric topology, with significant contributions to Hermitian K-theory and the topology of positive scalar curvature metrics. His research consistently integrates algebraic techniques with differential geometric structures, advancing understanding of manifold automorphisms and classification. As an educator, Steimle has taught extensively across all levels, including Bachelor courses in Linear Algebra and Topology, Master lectures in Algebraic Topology and K-Theory, and specialized seminars on Lie Groups, Reflection Groups, and Cobordism Categories. He has supervised doctoral researchers including Georg Frenck, Helge Frerichs, Andreas Huber, and Lukas Schönlinner within the Differential Geometry group.
