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Thomas EibertView profile

Professor

• High-Frequency Engineering
• Electromagnetic Modeling
• Antenna Design
• +3 more

Prof. Thomas Eibert holds the position of Full Professor of High-Frequency Engineering at the Technical University of Munich (TUM), part of the TUM School of Computation, Information and Technology. His academic journey includes a doctorate from the University of Wuppertal (1997), research at the University of Michigan (USA), and roles at Deutsche Telekom's Technology Center and the Fraunhofer Institute. Specializing in high-frequency electromagnetic processes, his research focuses on novel antenna technologies, electromagnetic modeling algorithms, and field transformation techniques. Key contributions include advancements in near-field imaging, inverse source solutions, and microwave sensor systems. Notable awards include the Best Measurement Paper Award at EuCAP (2020, 2019), the ESA Antenna Workshop's Best Innovative Paper (2013), and the VDE ITG Literature Prize (2008). His work bridges theoretical developments with practical applications in automotive antennas, radar systems, and environmental impact analysis of antenna performance. Current research emphasizes UAV-based measurement systems and low-frequency stabilization methods for electromagnetic simulations. Education: Doctorate in Electrical Engineering (University of Wuppertal, 1997) Professional Roles: Head of Fraunhofer Institute's Antennas and Scattering Department (2002–2005), Professor at University of Stuttgart (2005–2008) Research Groups: Active in the Department of Electrical Engineering, collaborating on projects involving radar imaging, antenna design, and electromagnetic field transformations. Publications span journals such as IEEE Transactions on Antennas and Propagation, focusing on topics like phase retrieval, decoupling networks, and frequency-selective surfaces. His lab develops practical measurement frameworks for large-scale antenna systems and environmental resilience testing.

Jonathan ScarlettView profile

Associate Professor

• Information Theory
• Machine Learning
• High-Dimensional Statistics
• +3 more

Jonathan Scarlett is an Associate Professor at the National University of Singapore (NUS), holding joint appointments in the Department of Computer Science, Department of Mathematics, and the Institute of Data Science. His research focuses on information theory, machine learning, and high-dimensional statistics, with contributions to topics such as group testing, Bayesian optimization, and coding theory. He has received notable awards including the NUS Presidential Young Professorship and MIT TR35 Innovators (Asia Pacific) recognition. Scarlett advises numerous PhD students, including Yang Sun, Zihan Li, and Arpan Losalka, whose theses address challenges in inverse problems, kernel-based optimization, and safety in Bayesian optimization. His work spans theoretical foundations and practical applications, with recent publications exploring optimal group testing algorithms, error exponents in DNA storage, and robust bandit optimization. He leads collaborative projects on distributed statistical estimation and adversarial machine learning, leveraging interdisciplinary methods from signal processing and information theory. His academic journey includes a postdoc at the LIONS laboratory (2014–2017) before his promotion to Associate Professor in 2024. Scarlett actively contributes to the academic community through editorial roles, conference organization, and over 100 peer-reviewed publications. His lab develops novel frameworks for handling uncertainty in data-driven systems, with applications to healthcare, communication networks, and autonomous systems.

Hongkai ZhaoView profile

Professor

• Computational and Applied Mathematics
• Numerical Analysis
• Scientific Computing
• +4 more

Hongkai Zhao is the Ruth F. DeVarney Distinguished Professor of Mathematics and Chair of the Department of Mathematics at Duke University's Trinity College of Arts & Sciences. He holds a Ph.D. in Mathematics from UCLA (1996) and has held prior positions including Chancellor's Professor at UC Irvine and Gábor Szegő Assistant Professor at Stanford University. His research focuses on computational and applied mathematics with applications in inverse problems, imaging, PDEs, and scientific computing. Key research areas include numerical methods for wave propagation, radiative transfer, and shape analysis. Zhao has led NSF-funded projects on PDE learning, radiative transfer, and analysis training. He has received prestigious awards including the Feng Kang Prize (2007) and SIAM Fellowship (2022). Teaching includes advanced courses on stochastic processes, numerical linear algebra, and mathematical analysis. His work bridges theory and practice, contributing to medical imaging algorithms, computational physics, and geometric modeling.

• Operations Research
• Logistics Optimization
• Scheduling Algorithms
• +5 more

Jeroen Belien is a Full Professor at the Faculty of Economics and Business (FEB) at KU Leuven. He leads the Operations Management Research Group and serves as Head of Subdivision 42 at the Brussels Campus. His roles include coordinating research activities and contributing to the KU Leuven Institute for Mobility (LIM). His research focuses on operations management, with emphasis on optimization models for logistics, waste collection, sports scheduling, and healthcare operations. He has pioneered educational tools like role-playing games for teaching linear programming and inventory management concepts. His work integrates simulation, mathematical programming, and real-world case studies. Key contributions include developing timetabling algorithms for international competitions, optimizing bio-waste collection in urban areas, and analyzing collaborative shipping strategies in the sharing economy. His publications span over 50 articles in journals like Operations Research for Health Care and INFORMS Transactions on Education. Belien actively engages in educational innovation through game-based learning and case competitions, emphasizing practical applications of operations research methods.

Miklos Z RaczView profile

Assistant Professor

• Probability
• Statistics
• Computer Science
• +7 more

Miklos Z Racz is an Assistant Professor of Statistics and Data Science and Computer Science at the Department of Statistics and Data Science, Northwestern University, within the Weinberg College of Arts & Sciences. He holds a PhD in Statistics from UC Berkeley (2015). His research focuses on the intersection of probability, statistics, computer science, and information theory, with an emphasis on combinatorial statistics, discrete probability, and applied probability. He studies statistical inference on large random discrete structures such as random graphs, including topics like inferring latent geometry in networks, community detection, and dynamics on networks. He also explores applications in social networks, voting systems, sequence reconstruction, and DNA data storage. His educational background includes a PhD in Statistics from UC Berkeley (2015), reflecting expertise in theoretical and applied statistical methods. His work often bridges theoretical foundations with practical challenges in data science and network analysis. Racz's research interests span a broad range of topics, including the analysis of random graphs, community detection, graph matching, and the development of algorithms for high-dimensional data. His contributions to DNA data storage and sequence reconstruction highlight his interdisciplinary approach to solving real-world problems through statistical and computational methods. His recent publications explore themes such as graph isomorphism analysis, efficient polymer synthesis, and reducing polarization in social networks. These works demonstrate a focus on advancing both foundational theory and applied technologies. He has not explicitly mentioned any scientific awards, but his prolific publication record reflects significant scholarly activity. While no specific grants or advising roles are detailed in the provided text, his research on DNA data storage and network dynamics suggests involvement in cutting-edge projects at the intersection of computer science and biotechnology. His work contributes to advancing methodologies for handling complex data structures and improving computational efficiency in network analysis.

Marc PinsonneaultView profile

Professor

• Stellar Structure & Evolution
• Asteroseismology
• Stellar Angular Momentum Evolution
• +4 more

Marc Pinsonneault is a Professor and University Distinguished Scholar in the Department of Astronomy at The Ohio State University . His research focuses on theoretical stellar structure and evolution, particularly utilizing asteroseismic data from missions like Kepler and spectroscopic surveys such as Apache Point Observatory Galactic Evolution Experiment (APOGEE) . He pioneered work on the solar neutrino problem and angular momentum evolution of stars. Education: B.S. in Physics, University of Texas at Austin (1983) Ph.D. in Astronomy, Yale University (1988) Research Interests: Stellar microphysics: diffusion, rotation, and energy transport Asteroseismic diagnostics of stellar interiors Galactic chemical evolution and stellar populations Helioseismology and magnetic activity cycles Key Contributions: Developed the standard solar model foundational to neutrino physics Leader in APOKASC collaboration combining APOGEE and Kepler data Advances in rotating stellar models and gyrochronology Honors: University Distinguished Scholar (2017) Labs/Teams: Apache Point Observatory Galactic Evolution Experiment (APOGEE) Kepler Asteroseismology Science Consortium (KASC) SDSS-IV/V collaborations

Michael PetersView profile

Professor

• Micro Theory
• Competing Mechanisms
• Directed Search
• +3 more

Michael Peters is a Professor at the Vancouver School of Economics, University of British Columbia, where he has established himself as a leading researcher in microeconomic theory, particularly in the areas of competing mechanisms and directed search. His work bridges theoretical foundations with practical applications in market design and strategic interactions. Dr. Peters obtained his Ph.D. from Queens University in Kingston, Ontario, Canada, forming the foundation for his distinguished academic career focused on economic theory and mechanism design. His research interests center on micro theory with specialized expertise in the theory of competing mechanisms and directed search. This work explores how strategic agents interact in complex market environments, how information flows affect outcomes, and how market institutions can be designed to achieve desirable economic results. His theoretical contributions have significantly advanced our understanding of strategic behavior in markets with multiple competing mechanisms, where traditional game-theoretic approaches face significant challenges. Analysis of his recent publications reveals a consistent focus on mechanism design problems, particularly those involving competing mechanisms where multiple principals interact with common agents. His work often combines deep theoretical insights with empirical applications, spanning areas including auction theory, contract theory, matching markets, and labor economics. The trend shows increasing sophistication in modeling strategic interactions with incomplete information and the development of new equilibrium concepts applicable to complex market structures. Econometric Society Fellow Associate Editor of Journal of Economic Theory Associate Editor of Theoretical Economics Member of the Board of Directors of Econjobmarket While specific details about his advising and grant activities are not provided in the available information, his position as a full Professor at a major research university suggests significant involvement in graduate student supervision and research funding. His editorial roles at top journals indicate recognition as a leading scholar in economic theory. His research program appears to focus on fundamental questions in economic theory that have implications for market design across various economic contexts. Though specific laboratory or research team information isn't detailed in the available text, Professor Peters' extensive collaborative work with researchers such as Li Hao, Kun Li, Pai Xu, and others suggests an active research group focused on advancing theoretical frameworks in microeconomics. His work on directed search and competing mechanisms likely involves both theoretical development and empirical validation through collaborations with other economists.

Mohammad T. HajiaghayiView profile

Professor

• Algorithms and Theory
• Algorithmic Game Theory
• Network Design
• +4 more

Dr. Mohammad T. Hajiaghayi is the Jack and Rita G. Minker Professor of Computer Science at the University of Maryland, with a joint appointment in the Institute for Advanced Computer Studies (UMIACS). He holds additional affiliations as a Research Affiliate at MIT CSAIL and a Permanent Member of DIMACS at Rutgers. His expertise spans algorithmic game theory, network design, approximation algorithms, and distributed computing. He has authored over 200 papers and secured significant grants, including NSF CAREER and ONR awards. Notable accolades include ACM, IEEE, and EATCS Fellowships, along with the Washington Academy's Distinguished Career Award (2023). He advises multiple PhD students and contributes to interdisciplinary initiatives in machine learning and big data. Education: PhD from MIT (2005), M.Sc. from University of Waterloo (2001), B.Sc. from Sharif University (2000). His research emphasizes practical applications of theoretical algorithms, bridging gaps between foundational computer science and real-world systems. He is actively involved in academic leadership, including coaching UMD's ACM-ICPC team and editorial roles in top journals.

• Matching Theory
• Game Theory
• Mechanism Design
• +2 more

Dr. Ayse Yazici is an Associate Professor and Director of the Non-Single Honours in Economics Programme at Durham University Business School. Her research specializes in microeconomic theory with a focus on matching problems, market design, and mechanism design. Yazici investigates stability and strategic behavior in various matching markets including one-to-one, many-to-many, and decentralized matching environments. Her work extends theoretical insights to practical applications in hospital-intern markets, college admissions, labor markets, and resource allocation problems. Her recent publications analyze stability and incentive compatibility in senior-level decentralized matching markets, probabilistic stable rules in two-sided matching, and extensions of the Rural Hospital Theorem to many-to-many matching contexts. Earlier work established strategy-proof and fair allocation mechanisms for indivisible goods. At Durham, Yazici leads the Non-Single Honours Programme in Economics and Finance and teaches core microeconomics and game theory courses. She currently supervises doctoral research in matching theory and mechanism design.

Peihan MiaoView profile

Assistant Professor

• Cryptography
• Security
• Theoretical Computer Science
• +3 more

Peihan Miao is an Assistant Professor in the Department of Computer Science at Brown University, affiliated with the Theory Group. She holds a PhD from UC Berkeley (2019) under Sanjam Garg and a BS from Shanghai Jiao Tong University. Prior to Brown, she was at the University of Illinois Chicago (2020–2022) and Visa Research (2019–2020). Her research focuses on cryptography and security, particularly secure multi-party computation (MPC), with applications in genomics and privacy-preserving machine learning. She has received NSF, Meta, Google, and Amazon awards. Her work bridges theoretical foundations and practical implementations, addressing challenges in private set intersection (PSI), updatable encryption, and federated learning. Teaching includes courses on cryptography and secure computation at Brown and UIC. She mentors PhD students and postdocs, leading collaborative projects in privacy-enhancing technologies. Her lab explores MPC protocols, privacy-preserving techniques, and interdisciplinary applications in bioinformatics. Key awards include the NSF CAREER Award and Google/Amazon Research Scholarships. Grants support projects like privateQTL for genomic data analysis and secure PSI protocols. Her service includes program committees for CRYPTO, TCC, and ASIACRYPT.

Patrick BennettView profile

Associate Professor

• Combinatorics
• Graph Theory
• Probabilistic Combinatorics
• +1 more

Dr. Patrick Bennett is an Associate Professor in the Department of Mathematics at Western Michigan University. He holds a Ph.D. in Algorithms, Combinatorics and Optimization from Carnegie Mellon University and was a Postdoctoral Research Fellow at the University of Toronto prior to joining WMU in 2015. His research focuses on probabilistic and extremal combinatorics, specializing in randomized combinatorial algorithms for proving existence theorems about mathematical objects with specific properties. Dr. Bennett's research interests center on graph theory and combinatorial optimization, particularly using algorithmic approaches to solve fundamental problems in discrete mathematics. His work often employs probabilistic methods to achieve rigorous analysis of complex combinatorial structures. His publications predominantly explore combinatorial problems through advanced probabilistic methods and algorithmic frameworks, with consistent focus on Ramsey theory, graph coloring, random graph processes, and combinatorial game theory. The work demonstrates sophisticated applications of probability to extremal combinatorics problems. Awards: Simons Foundation Collaboration Grant (2016-2026) National Science Foundation Conference Grant (2017-2019) Faculty Research and Creative Activities Award (2018-2019) He has supervised PhD candidate Ryan Cushman (graduated 2021) and founded the annual Lake Michigan Workshop in combinatorics. His research is funded by multiple grants from the Simons Foundation and National Science Foundation.

Po ChenView profile

Associate Professor

• Seismology
• Computational Geophysics
• 3D Seismic Tomography
• +3 more

Po Chen is an Associate Professor in the Department of Geology and Geophysics at the University of Wyoming , affiliated with the College of Engineering and Physical Sciences and the School of Energy Resources . His research bridges computational methods with seismology for advanced Earth structure and earthquake source modeling. Education : PhD in Seismology (University of Southern California, 2005), BS in Geophysics and Economics (Beijing University, 2000). Po Chen's research focuses on Full-3D Waveform Tomography (F3DT) , leveraging high-performance computing to solve nonlinear structural inversion problems. He develops and applies numerical methods for seismic wave propagation, fault-plane ambiguity resolution, and ground-motion estimation. Recent work integrates machine learning for real-time earthquake detection (e.g., RED-PAN, RockNet) and landslide parameter inversion. His publications highlight advancements in seismic source inversion, GPU-accelerated algorithms, ambient-noise tomography, and critical zone imaging. Key collaborations include tools like UCVM (3D velocity model querying) and pSIN (seismic interferometry). Teaching emphasizes active learning and the synergy between research and education, with graduate advisees contributing to projects in seismic imaging and hazard analysis.

Olha MatsyiView profile

Assistant Professor

• Operations Research
• Mathematical Optimization
• Computer Science
• +3 more

Olha Matsyi serves as an Assistant Professor (post-doc) within the Division of Applications of Contemporary Mathematical Analysis at Lodz University of Technology, with contact details including email olha.matsyi@p.lodz.pl and phone (+48) 42 631-36-17. Her research focuses on Operations Research and Mathematical Optimization, specializing in combinatorial algorithms for location theory, knapsack problems, community detection, and VLSI routing. She employs metaheuristic and bio-inspired methods to solve NP-hard optimization challenges, bridging theoretical mathematics with engineering applications in healthcare logistics and crisis management. Publications from 2020-2025 demonstrate increasing emphasis on real-world implementations like mobile medical service optimization and decision support systems, alongside theoretical advances in continuous coverage and constrained classification. Collaborative work with researchers such as Oksana Pichugina highlights her interdisciplinary approach to algorithm design. No scientific awards were documented. Information regarding student mentorship, research grants, or specific laboratory teams was not provided in the source material.

John EngbersView profile

Associate Professor

• Graph Theory
• Combinatorics
• Discrete Mathematics
• +3 more

Dr. John Engbers is an Associate Professor and Undergraduate Chair in the Department of Mathematical and Statistical Sciences at Marquette University. His research focuses on Graph Theory and Combinatorics , with particular emphasis on extremal problems related to graph colorings, independent sets, and combinatorial structures. He teaches courses such as Calculus I and Foundations of Mathematics. Research Highlights Explores coloring conjectures (e.g., Tomescu’s conjecture) and extremal graph properties Analyzes maximal matchings and independent sets in structured graphs Investigates combinatorial identities and multipermutation structures Recent Work Recent publications (2023–2017) emphasize extremal graph theory and combinatorial enumeration, with applications to tree structures, H-colorings, and matrix inversion problems. His work bridges theoretical results with algorithmic and pedagogical insights. Academic Leadership Serves as Undergraduate Chair, overseeing program development and curriculum Active contributor to departmental teaching and research initiatives

Julian StanderView profile

Associate Professor

• Bayesian Statistics
• Data Science
• Public Health Analytics
• +3 more

Dr Julian Stander is an Associate Professor in Mathematics and Statistics at the University of Plymouth, affiliated with the School of Engineering, Computing and Mathematics within the Faculty of Science and Engineering. His research focuses on Bayesian statistical methods, statistical disclosure control, sports analytics, and public health applications. He has supervised six PhD students and numerous Italian project students. Dr Stander is a Senior Fellow of the Higher Education Academy and a notable educator, recognized as the runner-up for the SSTAR Postgraduate Teacher/Supervisor of the Year Award 2017/18. His teaching spans statistical data modeling, R programming, and data science, with a particular emphasis on Bayesian inference using Stan software. He actively contributes to the development of R packages like plotrix and geofacet , advancing tools for data visualization and spatial analysis. Recent research trends highlight his work on UK political representation, pandemic policy evaluation, historical demographic studies, and ecological modeling. He frequently applies statistical methodologies to diverse fields such as criminal justice, healthcare, and sports, emphasizing interdisciplinary collaboration. Awards reflect his dual excellence in research and pedagogical innovation.