Matúš Benko is a Researcher affiliated with the Institute of Computational Mathematics at Johannes Kepler University (JKU) in Linz, Austria. He holds a PhD in Mathematics from JKU (2016) and has been a Postdoc at JKU since 2017, followed by a position at the University of Vienna’s Applied Mathematics and Optimization group since 2019. His research focuses on optimization and variational analysis, with contributions to nonsmooth optimization, constraint systems, and numerical methods. Education: Bachelor’s in Mathematics (2008–2011), Comenius University, Bratislava Master’s in Mathematics (2011–2013), Comenius University, Bratislava PhD in Mathematics (2013–2017), Institute of Computational Mathematics, JKU Research interests include variational analysis, optimization theory, and numerical methods for constrained optimization problems. His work emphasizes stability analysis, stationarity conditions, and algorithmic approaches like SQP methods. Recent publications address metric subregularity, tilt-stable minimizers, and generalized calculus in nonsmooth settings. No scientific awards are explicitly mentioned. He teaches Numerical Optimization and contributes to the NuMa Team at JKU. No advising or grant details are provided.
Jianjun Miao is a Professor recognized for his significant contributions to economic theory, evidenced by his 2025 election as Fellow of the Society for Advancement of Economic Theory. His academic work bridges theoretical economics with practical policy applications across multiple domains. His research spans economic theory, asset pricing, monetary and fiscal policy, rational inattention, and asset bubbles. Miao's work demonstrates particular expertise in modeling decision-making under uncertainty, financial market dynamics, and macroeconomic policy interactions. His research integrates advanced mathematical techniques with real-world economic phenomena, focusing on how information frictions and ambiguity affect market outcomes and policy effectiveness. Analysis of his 2022-2025 publications reveals three dominant research trajectories: (1) asset bubbles and their macroeconomic implications, (2) rational inattention frameworks in discrete choice and asset pricing, and (3) fiscal-monetary policy coordination in constrained environments. His work consistently addresses methodological challenges in modeling uncertainty while providing policy-relevant insights for financial stability and economic growth. Scientific Awards: Fellow of the Society for Advancement of Economic Theory (2025) Professor Miao maintains an active research program with substantial scholarly output. His work appears in leading economics journals and influences both academic discourse and policy discussions regarding financial stability, economic growth, and optimal policy design under uncertainty. He contributes significantly to academic knowledge through theoretical model development and empirical applications.
Thomas O. de Jong is a Researcher at the Eindhoven University of Technology , affiliated with the Control Systems Group in the Department of Electrical Engineering . He is supervised by Dr. Mircea Lazar and focuses on data-driven methods for nonlinear system control. Education: BSc in Mechanical Engineering (2021) MSc in Systems and Control (2023) His research explores nonlinear systems , predictive control , and microgrid energy management . His recent work emphasizes robust stability and distributed control frameworks. Key publication trends include: 2024 : Kernelized offset-free control, Koopman-based stability guarantees. 2023 : Output-feedback control stability, energy-based microgrid applications.
Adam Wierman is the Carl F. Braun Professor of Computing and Mathematical Sciences at the California Institute of Technology (Caltech). He holds academic roles including Professor (2012–2024), Braun Professor (2024–present), and served as Executive Officer (2015–2020), Director of Information Science and Technology (2016–2025), and Associate Director (2015–2016). His research focuses on designing sustainable and resilient networked systems through advances in machine learning, optimization, control, and economics. Key applications include data centers, electricity grids, and transportation systems. Education: B.S., M.S., and Ph.D. in Computer Science from Carnegie Mellon University (2001–2007). His work bridges theoretical foundations and practical deployment, emphasizing provably efficient algorithms and market mechanisms. Recognitions include IEEE Fellow, ACM Distinguished Member, and the Northrop Grumman Prize for Excellence in Teaching. Research Interests: Sustainable Computing, Online Algorithms, Optimization, Control Systems, Network Economics, and Applied Probability. His lab develops tools for robust voltage control, carbon-aware scheduling, and scalable reinforcement learning for multi-agent systems. Recent projects include SustainGym, a reinforcement learning benchmark for sustainability tasks. Teaching: Courses include Networks: Structure & Economics, Projects in Networking, and Computer Science Education in K-14. He actively mentors graduate students and postdocs in areas like learning-augmented control and smart grid systems. Affiliations: Member of Caltech’s RSRG (Resilient Sociotechnical Systems Research Group), DOLCIT (Dynamical Learning for Optimization and Control), CSIS (Center for Social and Information Sciences), and CMI (Computation and Mathematical Sciences Institute).
Luis Ammann is a Researcher at the University of Duisburg-Essen, Faculty of Mathematics, working in the research group 'Optimal Control of Partial Differential Equations' led by Prof. Dr. Irwin Yousept. He is based in Room WSC-W-4.19 at Thea-Leymann-Straße 9, D-45127 Essen, and can be contacted at luis.ammann@uni-due.de. His primary research interests include Analysis and Optimization of Wave Phenomena, Algorithms for Nonlinear Problems, Numerical Analysis of PDEs, Full Waveform Inversion, and Sequential Quadratic Programming. His work focuses on mathematical and numerical methods for inverse problems in wave propagation, particularly using PDE-constrained optimization techniques for acoustic imaging applications. Ammann's recent publications (2023-2024) demonstrate expertise in hyperbolic PDE-constrained optimization, with emphasis on full waveform inversion and sequential quadratic programming methods. His research bridges theoretical analysis with computational implementation for seismic imaging problems, showing strong trends in second-order optimization techniques and numerical analysis of wave equations. He has taught multiple courses at the University of Duisburg-Essen since Winter Term 2020/2021, including Numerical Mathematics Practical, Acoustic and Electromagnetic Wave Phenomena, Optimization Practical, and Optimal Control of Partial Differential Equations across various semesters through Summer Term 2024. Luis Ammann is an active member of the 'Optimal Control of Partial Differential Equations' research group, collaborating with Prof. Dr. Irwin Yousept and colleagues on projects involving wave phenomena, inverse problems, and numerical optimization techniques for partial differential equations.
Chethan Kamath is an Assistant Professor in the Department of Computer Science and Engineering at IIT Bombay, where he is a member of the Theory Group and Trust Lab. His primary research focus is on cryptography, particularly its foundations, with broader interests extending to theoretical computer science. His educational journey includes: PhD from IST Austria (2014-2020) under Krzysztof Pietrzak, with thesis titled "On the Average-Case Hardness of Total Search Problems" Master's in CS from IISc Bangalore (2010-2013) under Sanjit Chatterjee, with thesis titled "Constructing Provably Secure Identity-Based Signature Schemes" Bachelor's in CS from University of Kerala (2005-2009) at TKM College of Engineering, Kollam Dr. Kamath's research interests span the theoretical foundations of cryptography, with particular focus on secure computation, complexity theory, and cryptographic hardness assumptions. His work often bridges theoretical computer science with practical cryptographic applications, exploring the boundaries of what can be efficiently computed while maintaining security guarantees. His research frequently addresses fundamental questions about the relationship between cryptographic primitives and complexity classes, especially the PPAD and TFNP complexity classes. His recent publications demonstrate a consistent focus on foundational aspects of cryptography, with particular emphasis on secure computation (garbled circuits, Yao's protocol), proofs systems (proofs of work, proofs of exponentiation), and complexity-theoretic aspects of cryptographic primitives. A notable trend is his exploration of the connections between complexity classes like PPAD and cryptographic assumptions, as well as his work on verifiable delay functions and their underlying number-theoretic assumptions. His research often employs tools from algorithmic graph theory (treewidth, separators) to analyze cryptographic protocols. His notable scientific achievement includes: Azrieli Fellowship during his post-doc at Tel Aviv University Dr. Kamath actively mentors students and researchers, currently advising several PhD and MS students at IIT Bombay, often in collaboration with Sruthi Sekar. His service to the academic community includes extensive program committee memberships for major conferences including Crypto, Eurocrypt, and TCC, demonstrating his standing in the cryptographic research community. He has co-organized educational events like the "Introduction to Cryptography" school as part of the ACM India Summer School 2025 and the "Theoretical Foundations of Cryptography" school as part of the ACM India Summer School 2024. He leads research activities within the Trust Lab at IIT Bombay, which focuses on theoretical and applied aspects of cryptography and security. The lab actively recruits MS/PhD students and post-docs, with ongoing research in foundational cryptography and its applications to secure computation, verifiable delay functions, and complexity-theoretic aspects of cryptographic security.
Hari Sundaram is a Professor in the Computer Science Department at the University of Illinois at Urbana-Champaign with affiliate appointments in the Charles H. Sandage Department of Advertising, the Institute for Communication Research, and the Center for Social & Behavioral Science. His academic journey includes positions as Associate Professor at the University of Illinois (2014-2021) and Arizona State University (2002-2014), where he also served as Associate Director of the Arts, Media and Engineering program (2012-2009). Dr. Sundaram's educational background includes a Ph.D. in Electrical Engineering from Columbia University (2002), an M.S. in Electrical Engineering from Stony Brook University (1995), and a B.Tech in Electrical Engineering from the Indian Institute of Technology, Delhi (1993). His research, conducted through the Crowd Dynamics Lab, focuses on designing computational systems that empower individuals to make better decisions. His work spans Applied Machine Learning (particularly recommender systems), Network Science (studying how platform rules induce strategic behavior), Human-Computer Interaction (developing systems to elicit truthful preferences), and Mechanism Design (creating rules to incentivize pro-social behavior). His research has significant implications for understanding fairness and discrimination in online markets. Dr. Sundaram's work has been recognized with numerous awards including multiple Best Paper Awards from ACM CSCW (2023), Best Article Award from the Journal of Interactive Advertising (2020), ACM Distinguished Member (2019), IEEE Senior Member (2019), and several IBM Faculty Awards. He has also been consistently recognized for teaching excellence, receiving the "Teacher Ranked as Excellent" award multiple times. As leader of the Crowd Dynamics Lab, Dr. Sundaram oversees research that bridges computer science with social sciences, focusing on how computational systems can enhance human decision-making while addressing fairness concerns. His work has practical applications in online marketplaces, social media platforms, and educational technologies.
Francisco Facchinei is a Professor at Sapienza University of Rome, affiliated with the Department of Computer, Automatic and Management Engineering Antonio Ruberti within the College of Engineering. His research spans nonlinear and non-differentiable optimization, complementarity problems, variational inequalities, and game theory applications in telecommunications. His work focuses on developing algorithms for nonconvex optimization with ghost penalties, asynchronous distributed methods, and applications in healthcare and communications systems. Key Contributions: Foundational work in variational inequality theory, generalized Nash equilibrium problems, and optimization over dynamic networks. Recent Trends: Emphasis on asynchronous parallel algorithms, stochastic optimization, and non-invasive medical diagnostics via machine learning. He has held academic positions at Sapienza University since 1990, progressing from Ricercatore to Professore Ordinario. No scientific awards are explicitly mentioned in the provided texts.
