Dr. Markus Blumenstock is a Postdoctoral Teaching Associate at the Institute of Computer Science, Johannes Gutenberg University Mainz. His research focuses on approximation algorithms, graph theory, and combinatorial optimization with specific interests in arboricity, maximum flow algorithms, and Steiner trees. He has contributed to fast algorithms for pseudoarboricity and the approximation of connected subgraphs of high density. Blumenstock's academic work includes a PhD thesis on pseudoforest partitions and the development of efficient algorithms for complex graph problems. His teaching activities include courses like 'Berechenbarkeit, Unbeweisbarkeit und das Unendliche' (BUBU) and advanced algorithm complexity theory. He maintains transparency by sharing course materials under creative commons licenses. His research emphasizes theoretical computer science with practical applications in algorithm design and optimization. While no specific awards are listed, his prolific publication record demonstrates academic contributions to algorithmic research.
Tim Binz is a post-doctoral researcher at the Department of Mathematics, Technical University of Darmstadt. His research focuses on geophysical fluid dynamics, partial differential equations, and operator theory. He explores topics such as Navier-Stokes equations, curvature flows, and semigroup theory with applications to mathematical fluid mechanics. Binz has contributed to the analysis of geophysical flows, sea ice interactions, and numerical algorithms for mean curvature flows. His work bridges analytical methods with computational techniques, addressing challenges in climate modeling and geometric PDEs. Research interests include the rigorous analysis of quasi-linear evolution equations, maximum regularity, and operator matrices. He has published in journals like Mathematische Annalen and Interfaces and Free Boundaries , focusing on well-posedness of geophysical models and numerical algorithms. Binz has taught advanced courses on fluid mechanics and analysis, including a winter 2023/24 course on Navier-Stokes solutions. He collaborates with Prof. Matthias Hieber and has presented at conferences such as the International Workshop on Mathematical Fluid Mechanics and the DFG Research Unit on Geophysical Flows.
Heather Newman is an Assistant Professor in the Department of Computer Science at Vassar College. She earned her PhD from the interdisciplinary Algorithms, Combinatorics, and Optimization (ACO) program at Carnegie Mellon University through the Mathematics Department, advised by Ben Moseley. She received her A.B. in Mathematics with a minor in Computer Science from Princeton University in 2019 and an M.Sc. in Mathematical Sciences from Oxford in 2020. Her research lies at the intersection of discrete mathematics, theoretical computer science, and operations research, focusing on approximation and online algorithms for combinatorial optimization problems. She is particularly interested in clustering, scheduling, and developing new beyond-worst-case models for online algorithms with pessimistic lower bounds. The recent publications reflect a strong trend in algorithmic design for optimization problems, especially in clustering (e.g., correlation clustering, k-median), scheduling , and graph-based optimization . Her work emphasizes theoretical rigor with practical implications, often involving novel algorithmic frameworks and combinatorial techniques. She frequently publishes in top-tier theoretical venues such as ICALP, APPROX, SPAA, ICML, and SIGMETRICS. Scientific Awards No awards listed in the provided text. Advising and Grants There is no explicit mention of graduate students or PhD advisees in the provided information. Heather Newman does not list any specific grants or funded projects, but her research output suggests active engagement in theoretical computer science research. She was involved with Moon Duchin’s Metric Geometry and Gerrymandering Group, which applies mathematical methods to redistricting and social justice issues, indicating interdisciplinary outreach and potential collaborative funding. Labs and Research Teams While no formal lab is mentioned, Heather Newman has been affiliated with interdisciplinary research groups, notably the Metric Geometry and Gerrymandering Group led by Moon Duchin. This group brings together mathematicians and computer scientists to analyze gerrymandering through geometric and algorithmic tools. Her primary research collaborations appear to be with leading figures in theoretical computer science, including Ben Moseley, Kirk Pruhs, and Anupam Gupta, suggesting active participation in a broader research network focused on algorithm design and analysis.
Dr. Mikko Lauri is a Postdoctoral Researcher at the Department of Informatics, University of Hamburg, working in the Computer Vision Research Group. His research focuses on decision-making under uncertainty, active perception, and computer vision, with particular emphasis on multi-agent systems for cooperative tasks in robotics applications. His research interests include: Multi-agent decision-making under uncertainty Active perception and information gathering Computer vision for mobile robots Partially Observable Markov Decision Processes (POMDPs) Object pose estimation and visual object search Deep learning applications in robotics Lauri's recent work has focused on advancing theoretical frameworks for teams of agents to act cooperatively. His survey paper on POMDPs in robotics provides a comprehensive overview of decision-making under uncertainty in robotic systems. His research has practical applications in autonomous robots, including exploration, object pose estimation, and visual object search, with techniques that balance theoretical rigor with real-world implementation. Scientific contributions: Developed novel approaches for multi-agent active perception with prediction rewards Created methods for multi-sensor next-best-view planning using submodular optimization Advanced techniques for 6D object pose estimation using point clouds and deep learning Contributed to audio-visual signal processing for sound source separation Lauri collaborates extensively with researchers in the Computer Vision Research Group at the University of Hamburg and has worked with international collaborators from institutions including Aalto University (Finland) and the National University of Singapore. His work bridges theoretical foundations in decision-making under uncertainty with practical robotics applications.
Ostap Okhrin serves as a Professor of Econometrics and Statistics at Dresden University of Technology, holding the Chair of Econometrics and Statistics with a special emphasis on Transportation Systems. His academic career is marked by a strong focus on methodological advancements in econometrics and statistics, applied to complex real-world problems in transportation and finance. Professor Okhrin's research interests span econometrics, statistical theory, copula modeling, time series analysis, and financial risk management. He has significantly expanded into machine learning and reinforcement learning applications for autonomous systems, with deep expertise in traffic flow modeling, autonomous driving, maritime navigation, and financial volatility estimation. His work bridges theoretical statistics with practical engineering challenges, particularly in transportation systems and risk forecasting, addressing high-dimensional data and dynamic environments through innovative methodological frameworks. Analysis of Okhrin's recent publications (2024-2025) reveals a pronounced interdisciplinary trajectory integrating reinforcement learning with transportation engineering. Key themes include drone-based trajectory data collection for traffic monitoring, algorithms for autonomous ships on inland waterways, and Sim2Real transfer frameworks for autonomous driving. Concurrently, he advances financial econometrics through high-frequency risk forecasting models incorporating realized moments. This dual focus demonstrates his ability to transfer statistical innovations across domains while maintaining rigorous theoretical foundations in copula theory and time series analysis.
