جست‌وجوی استاد و پژوهشگر

Heng Guoمشاهده پروفایل

دانشیار

• Algorithms
• Computational Complexity
• Approximate Counting
• +۵ مورد دیگر

Heng Guo is an Associate Professor in Algorithms and Complexity at the School of Informatics, University of Edinburgh. He leads the ERC starting grant project New Approaches to Counting and Sampling (NACS), which runs from 2021 to 2026. Previously, he has worked and studied at Berkeley, London, Madison, and Beijing. His research lies at the intersection of theoretical computer science, combinatorics, and statistical physics. Guo's research focuses on algorithms from a complexity perspective, particularly computational counting and sampling. He is renowned for his work on the Lovász local lemma, Markov chain Monte Carlo methods, phase transitions in computational complexity, and complexity classifications. His approach often involves discovering unseen links between different areas of theoretical computer science. Key contributions include confirming a conjecture of Gorodezky and Pak through partial rejection sampling, establishing rapid mixing for Swendsen-Wang dynamics, and developing a polynomial-time approximation algorithm for all-terminal network reliability. His publication record shows a strong trajectory of impactful research in top venues including FOCS, STOC, SODA, J. ACM, and SIAM Journal on Computing. His work on the all-terminal network reliability problem won the Best Paper Award at ICALP 2018. Guo has organized several significant workshops including JerrumFest 2025, MCMC 2.0 (Shonan seminar), and a STOC 2020 workshop on new frontiers of approximate counting. These events highlight his leadership role in the theoretical computer science community. Best Paper Award at ICALP 2018 EATCS Distinguished Dissertation Award 2016 Guo has advised several PhD students including Giorgos Mousa, Jiaheng Wang, and Graham Freifeld, and mentored postdocs such as Weiming Feng, Vishvajeet Nagargoje, and Konrad Anand. His ERC grant supports multiple research associates working on counting and sampling problems. He has taught courses including Computational Complexity, Randomness and Computation, and Algorithmic Game Theory at the University of Edinburgh.

Sami Daviesمشاهده پروفایل

پژوهشگر

• Theoretical Computer Science
• Combinatorial Optimization
• Algorithm Design
• +۵ مورد دیگر

Sami Davies is a Research Scientist in Theoretical Computer Science at the Simons Institute and the Department of Electrical Engineering and Computer Sciences (EECS) at the University of California, Berkeley. He earned his PhD in Mathematics from the University of Washington in 2021 under the supervision of Thomas Rothvoss, with work spanning the Department of Mathematics and the Allen School theory group. Prior to Berkeley, he was a postdoctoral researcher in the theory group at Northwestern University. His research lies at the intersection of theoretical computer science and combinatorial optimization. Key interests include scheduling algorithms, clustering, hypergraph matchings, approximation algorithms, and algorithmic applications in machine learning. His work often addresses fundamental problems in algorithm design with practical implications in resource allocation, data analysis, and network optimization. The most recent publications reflect a strong trend in developing efficient combinatorial algorithms for clustering and scheduling problems, particularly under complex constraints such as communication delays or fairness objectives. There is a growing integration of machine learning concepts into traditional algorithmic frameworks, as seen in works on predictive flows and trace reconstruction. His research is consistently published in top-tier venues including NeurIPS, ICML, SODA, FOCS, and COLT, indicating high impact and recognition within the theoretical CS community. Sami is actively engaged in academic service and outreach. He has served as a mentor in the Washington Directed Reading Program and co-taught the 'Math in Society' course through FEPPS, which provides education to incarcerated individuals. He has also taught yoga at the University of Washington. He enjoys bouldering, running with his dog, vegan cooking and baking, and practicing yoga. He completed a 200-hour yoga teacher training in 2019. Contact: Email: samidavies@berkeley.edu , davies@berkeley.edu

• Logic
• Well Quasi-Orders
• Verification
• +۷ مورد دیگر

Sylvain Schmitz is a Professor of Computer Science at Université Paris Cité, affiliated with the Institut de Recherche en Informatique Fondamentale (IRIF). He leads the automata and applications team and is a member of the modelling and verification team. His research focuses on logic, well quasi-orders, verification, formal languages, and database theory. He is actively involved in the academic community, serving on steering and program committees for major conferences such as STACS, LICS, and CSL. PhD in Computer Science, defended on September 24, 2007, at I3S Laboratory, Sophia Antipolis. Habilitation titled Algorithmic Complexity of Well-Quasi-Orders , defended on November 27, 2017, at ENS Paris-Saclay. His research interests span logic, well quasi-orders, verification, formal languages, database theory, theoretical computer science, complexity theory, program verification, automata theory, and model checking. His recent work explores complexity bounds in vector addition systems, reachability problems, and logical foundations of verification. His publications exhibit a strong focus on theoretical foundations of computation, particularly in the analysis of infinite-state systems, complexity hierarchies beyond elementary, and logical characterizations of computational problems. Key themes include well-structured transition systems, non-elementary complexity, and applications of ordinal analysis to verification. IUF junior (2018–2023) Sylvain Schmitz has supervised several PhD students, including Hector Buffière, Aliaume Lopez, Anthony Lick, and Simon Halfon, often in joint supervision with researchers such as Jean Goubault-Larrecq, David Baelde, and Philippe Schnoebelen. His research has been funded by multiple ANR projects including BraVAS, PRODAQ, ReacHard, and AVeriSS. He is a key member of the IRIF laboratory, contributing to research in automata, verification, and logic. He has organized major events such as ICALP 2022 and RP 2020, and is deeply involved in the European theoretical computer science community through committee memberships and conference organization.

• Commutative Algebra
• Graph Theory
• Combinatorics
• +۴ مورد دیگر

Katharine Shultis serves as an Associate Professor of Mathematics at Gonzaga University, holding a Ph.D. from the University of Nebraska-Lincoln and demonstrating interdisciplinary expertise through her dual background in mathematics and chemistry. Her extensive teaching portfolio spans foundational courses like Introductory Statistics and Calculus through advanced offerings including Abstract Algebra and Comprehensive Mathematics. Her academic credentials include: Ph.D. in Mathematics, University of Nebraska-Lincoln M.A. in Mathematics, University of California, San Diego B.A. in Mathematics and Chemistry, Scripps College Dr. Shultis maintains exceptionally broad research interests that bridge pure and applied mathematics. While her doctoral work established her in commutative algebra, she actively pursues graph theory, combinatorics, mathematical games, topology, and voting theory. Her signature approach involves collaborative student research, with explicit interest in future chemistry-mathematics interdisciplinary projects. She champions inquiry-based learning where students actively engage in mathematical discovery rather than passive observation. Her publication trajectory reveals distinctive dual threads: rigorous theoretical work in commutative algebra alongside innovative applications in graph theory and recreational mathematics. Recent collaborations with students dominate her output, particularly in power domination and geometric dissections. Simultaneously, she contributes significantly to mathematics education discourse through publications on undergraduate research practices and pedagogical techniques. No scientific awards or fellowships are documented in the available materials. Dr. Shultis demonstrates exceptional commitment to undergraduate mentorship, evidenced by numerous student co-authorships across diverse projects. Her publication 'What Counts as Math Research?' specifically addresses frameworks for meaningful student research experiences. While grant details remain unspecified, her pedagogical philosophy emphasizes active student participation through inquiry-based learning strategies developed for courses ranging from Calculus to Abstract Algebra. Though no formal laboratory infrastructure is described, her collaborative model involves small student teams working on targeted projects in graph theory, combinatorics, and mathematical games, often extending classroom concepts into original research contexts.

• Data Science
• Statistics
• Machine Learning
• +۳ مورد دیگر

William S. Cleveland serves as the Shanti S. Gupta Distinguished Professor of Statistics and Courtesy Professor of Computer Science at Purdue University, a position held since 2014 following 12 years as Department Head and Distinguished Member of Technical Staff at Bell Labs. His academic foundation includes an A.B. in Mathematics from Princeton University (advised by William Feller) and a Ph.D. in Statistics from Yale University (advised by Leonard Jimmie Savage). Cleveland pioneered the modern definition of data science in 2001, establishing its six technical areas spanning statistical theory to live data analysis systems. His research bridges statistics , machine learning , data visualization , and high-performance computing for deep data analysis , with seminal contributions including trellis display frameworks and Divide and Recombine (D&R) methodology. Current work focuses on DeltaRho software implementation for distributed computing challenges. Recent publications reveal a strong emphasis on climate science applications, particularly Arctic atmospheric river dynamics and high-resolution precipitation modeling, while maintaining core contributions to distributed statistical computing. This reflects his career-long approach of applying advanced data analysis to multidisciplinary problems across telecommunications, cybersecurity, and environmental systems. His distinguished honors include: Lifetime Achievement Award for Graphics and Computing (ASA, 2016) Parzen Prize (Texas A&M, 2016) Statistician of the Year (Chicago ASA, 1996) Highly Cited Researcher designation (2002) Multiple journal prizes including two Wilcoxon and one Youden awards Fellowships in ASA, IMS, and AAAS plus ISI Elected Membership Mentoring 18 Purdue doctoral students since 2004 (6 currently active), Cleveland's research has been sustained through major grants supporting the MODSQUAD team and DeltaRho development. His leadership extends to founding the ASA Graphics Section and authoring influential texts like The Elements of Graphing Data , which remains foundational in visualization practice. He directs the DeltaRho project within Purdue's data science ecosystem, collaborating with the MODSQUAD (Data-Driven Discovery of Models) team on applications spanning climate modeling, cybersecurity analytics, and healthcare systems through integrated high-performance computing frameworks.

• Environmental Geochemistry
• Atmospheric Aerosols
• Trace Element Analysis
• +۳ مورد دیگر

Daniela Varrica serves as an Associate Professor in the Department of Earth and Marine Sciences at the University of Palermo, specializing in environmental geochemistry and atmospheric pollution studies. Her research focuses on trace element analysis across multiple environmental matrices including soils, aerosols, biological tissues, and water systems. Her primary research interests encompass Environmental Geochemistry , Atmospheric Aerosols , Trace Element Analysis , Soil Pollution , and Biogeochemical Cycles . Her work frequently examines the spatial distribution of contaminants, anthropogenic versus natural sources of pollution, and human exposure pathways through biomonitoring approaches. Recent publications demonstrate consistent focus on Sicilian environmental systems with particular attention to Palermo urban areas and industrial zones. Analysis of her 15 most recent publications reveals strong emphasis on trace element characterization in diverse environmental compartments. Her research spans from baseline geochemical studies of Sicilian soils to sophisticated analyses of atmospheric particulate matter, with recurring themes of source apportionment, health risk assessment, and biomonitoring using human hair, lichens, and edible mushrooms. The temporal pattern shows increasing sophistication in modeling approaches while maintaining field-based empirical validation. Her scientific contributions include methodological advancements in environmental monitoring and significant documentation of pollution patterns in Mediterranean environments, particularly regarding platinum group elements from vehicular emissions, Saharan dust influences on urban air quality, and industrial impacts on local ecosystems.

• Nonlinear Partial Differential Equations
• Calculus of Variations
• Geometric Measure Theory
• +۳ مورد دیگر

Dr. Miroslav Chlebik is an Associate Professor in Mathematics at the School of Mathematical and Physical Sciences , University of Sussex, UK, since August 2007. He previously held positions as a Research Associate at the Max Planck Institute for Mathematics in the Sciences (2001–2007) and as a Lecturer in Mathematics at Comenius University, Bratislava (1988–2001). His academic credentials include a PhD in Mathematics (CSc) from the Academy of Sciences, Prague, and a RNDr degree in Mathematics from Charles University, Prague. PhD in Mathematics (CSc), Academy of Sciences, Prague RNDr in Mathematics, Charles University, Prague Miroslav Chlebik's research spans nonlinear partial differential equations , calculus of variations , and geometric measure theory , with significant contributions to combinatorial optimization and computational complexity . His work includes rigorous analysis of blow-up phenomena in PDEs and foundational studies on the inapproximability of NP-hard problems like the Travelling Salesman Problem and Steiner Tree Problem . His 15 most recent publications (2023–2012) demonstrate two major research threads: (1) Blow-up analysis in parabolic equations , focusing on rate estimates and boundary condition effects, and (2) Approximation hardness in combinatorial optimization, particularly through weighted amplifiers and graph theory. These works were published in journals such as Nonlinear Analysis: Theory, Methods and Applications , Theoretical Computer Science , and Journal of Combinatorial Optimization . Professional activities include: External REF Assessor for Swansea University (2023–2027) in Analysis and PDEs Editorial Board Member of Acta Mathematica Universitatis Comenianae since 1992 His teaching interests include Topology and Advanced Analysis (Autumn term) and Dynamical Systems (Spring term), reflecting his analytical expertise.

• Artificial Intelligence
• Constraint Programming
• Max-SAT
• +۲ مورد دیگر

Sami Cherif is a Lecturer at the University of Picardie Jules Verne, specializing in Optimization and Cryptography, AI - OCIA . His research focuses on advanced applications of Constraint Programming and Max-SAT in solving complex industrial and cryptographic problems. PhD in Computer Science (2022, Aix-Marseille University) Key collaboration networks: Chu-Min Li, Sorina Ionica, Corinne Lucet Research Interests Dr. Cherif’s work bridges Artificial Intelligence with Cryptography , emphasizing: Max-SAT resolution techniques Energy-efficient manufacturing systems Conference scheduling optimization Cryptographic attack modeling Publication Trends His recent publications (2025–2024) highlight Max-SAT as a central method for: Self-stabilization analysis Assembly line energy optimization Cryptographic problem solving Conference scheduling Academic Collaborations Active collaborations in constraint programming include co-authors such as Chu-Min Li , Sorina Ionica , and Corinne Lucet , with applications spanning from industrial engineering to cryptanalysis.

Sergei Avdoninمشاهده پروفایل

استاد

• Control Theory
• Inverse Problems
• Partial Differential Equations
• +۵ مورد دیگر

Sergei Avdonin is a Professor of Mathematics at the University of Alaska. His work spans control theory, inverse problems, and partial differential equations, with applications in engineering and physics. Education: Ph.D. in Mathematics (1977) from St. Petersburg (Leningrad) State University. Avdonin’s research focuses on developing algorithms for distributed parameter systems, leveraging connections between nonharmonic Fourier series, control theory for PDEs, inverse problems, and signal processing. He collaborates globally to advance boundary control methods and solve inverse problems on metric graphs. Recent publications highlight his contributions to inverse problems in quantum trees, Schrodinger equations with non-self-adjoint potentials, and wave equations on metric graphs. These works intersect applied mathematics, physics, and engineering, addressing challenges in material science, acoustic imaging, and quantum circuits. Avdonin mentors graduate students at the University of Alaska, including Y. Zhao and J. Park, who contributed to his research on distributed systems and inverse theory. His methods are applied to mechanical vibrations, optical fiber networks, heat flow, and quantum circuits.

Etienne Vougaمشاهده پروفایل

دانشیار

• Physical simulation
• Geometry processing
• Discrete differential geometry
• +۴ مورد دیگر

Etienne Vouga is an Associate Professor in the Department of Computer Science and the Institute for Computational Engineering and Sciences (ICES) at the University of Texas at Austin. His interdisciplinary research spans physical simulation, geometry processing, and computer graphics, with applications ranging from special effects in films like The Hobbit and Tangled to Artec's Shapify 3D portrait service. Education: PhD from Columbia Computer Graphics Group Postdoctoral research at Harvard SEAS Professor Vouga's research focuses on physical simulation of thin, elastic materials like cloth, hair, and paper, as they deform and collide. He has made significant contributions to discrete differential geometry, developing algorithms that better understand how geometric properties affect physical behavior of deformable objects. His work bridges computer graphics, computational mechanics, and scientific computing, with applications in special effects, 3D printing, architectural design, and biological modeling. He has pioneered approaches in inverse problems where desired outcomes drive the design process rather than simply simulating physical phenomena. Award Highlights: Best Paper Award at GECCO (2017) for Evolutionary Decomposition for 3D Printing Professor Vouga teaches Computer Graphics: Honors (CS 378H/CS 384G) and Physical Simulation (CS 395T) graduate courses every spring semester. He also serves as head coach of UT's competitive programming teams. His research group has developed several important software tools including Discrete Shell Code for shell simulation and Asynchronous Contact Mechanics Code for robust collision handling. While he is not actively expanding his research group significantly, he remains open to working with exceptional graduate students whose research interests align closely with his work in physical simulation and geometry processing.

Radu-Lucian LUPŞAمشاهده پروفایل

مدرس

• Image Processing
• Computer Graphics
• Graph Algorithms
• +۳ مورد دیگر

Radu-Lucian LUPŞA serves as a Lecturer at Babeş-Bolyai University's Faculty of Mathematics and Computer Science in Romania. His academic career spans over two decades with a consistent focus on computer science, particularly in image processing and graph algorithms. His teaching responsibilities include courses on Graph Algorithms and Parallel and Distributed Programming, demonstrating his expertise across theoretical and applied computer science domains. LUPŞA's research interests center around image processing, with significant contributions to compression methods, dithering algorithms, and multiresolution analysis. His work bridges theoretical computer science with practical applications in graphics and healthcare optimization. The evolution of his research shows a progression from fundamental image compression techniques in the late 1990s to more specialized applications in medical imaging and transportation logistics by the late 2000s. Analysis of his publications reveals strong expertise in computational geometry, algorithm design, and optimization problems. His research demonstrates consistent application of mathematical rigor to practical computing challenges, particularly in the areas of image representation and processing. The interdisciplinary nature of his work is evident in publications spanning computer graphics, healthcare logistics, and mathematical optimization. LUPŞA completed his PhD in 2006 with the dissertation titled Contributions to the analysis, processing and representation of images , which serves as a foundation for his subsequent research trajectory. His academic output shows a period of high productivity between 1996-2008 with publications in respected venues including IEEE conferences and academic journals. As an educator, LUPŞA has developed comprehensive teaching materials for graph algorithms, including lecture notes, practical assignments, and evaluation methods. His teaching approach emphasizes both theoretical foundations and practical implementation, as evidenced by the detailed laboratory assignments and programming tasks he has designed for students.

Arne Meierمشاهده پروفایل

استاد

• Complexity Theory
• Parameterised Complexity Theory
• Logic in Computer Science
• +۳ مورد دیگر

Arne Meier is a Professor at Leibniz Universität Hannover, affiliated with the Faculty of Electrical Engineering and Computer Science and the Institute of Theoretical Computer Science. He heads the Algorithms research group, focusing on theoretical aspects of computer science with applications to artificial intelligence and database systems. Meier obtained all his academic degrees—Bachelor's, Master's, PhD, and Habilitation—at Leibniz Universität Hannover, establishing a strong foundation in theoretical computer science. His academic journey at the same institution reflects his deep commitment to advancing research in computational theory. Meier's research spans several interconnected areas in theoretical computer science. His primary focus is on complexity theory, particularly the parameterized complexity of problems in non-classical logics with applications to AI. He also investigates enumeration algorithms and the logical foundations of artificial intelligence. His work bridges theoretical computer science with practical applications in knowledge representation and reasoning systems. He has a notable interest in LaTeX and typography, having developed the 'timeline' package for creating timelines in LaTeX documents. His recent publications (2023-2025) demonstrate a consistent focus on the intersection of logic, complexity, and artificial intelligence. Meier's work shows progression from foundational research in dependence and team logics toward more applied areas in argumentation theory and database systems. His research increasingly addresses computational challenges in AI systems, particularly in reasoning under uncertainty and handling inconsistent information. Meier actively contributes to the academic community through extensive program committee service for major conferences including AAAI (2021, 2023, 2024, 2025), IJCAI (2021-2025), and FoIKS (2024 as Co-Chair, 2026). He has also served as a reviewer for numerous conferences and journals in theoretical computer science and artificial intelligence. His current research projects include the DAAD-funded 'Applications and Complexity of Logics in Semiring-Team-Semantics' (2024-2025) and the DFG project 'Team Logics: New Bridges to Database Repairs' (2023-2026). Previously, he led the DFG project 'Nonclassical logics: parametrised and enumeration complexity' (2013-2022) and the MWK project 'Innovation Plus: Komplexität von Algorithmen' (2020-2022). Meier leads the Algorithms research group at Leibniz Universität Hannover, which focuses on theoretical aspects of algorithms with applications to logic and artificial intelligence. The group's work spans complexity theory, logical formalisms, and their applications to computational problems in knowledge representation and database systems.

• Matroid Theory
• Graph Theory
• Knot Theory
• +۳ مورد دیگر

Eunice Gogo Mphako-Banda is a Professor in the School of Mathematics at the University of the Witwatersrand in Johannesburg, South Africa. She has held academic positions at the University of Malawi and the University of KwaZulu-Natal, with a clear progression from Lecturer to Professor. Education: BEdSc, University of Malawi (1992) PG Dip in Mathematics, University of Sheffield (1994) MSc in Mathematics, University of Manchester (1995) PhD in Discrete Mathematics, Victoria University of Wellington (2001) Research Interests: Professor Mphako-Banda’s research spans Matroid Theory , Graph Theory , Knot Theory , and Combinatorics . Her work in Matroid Theory focuses on matroid construction and polynomial invariants, particularly the Tutte polynomial. In Knot Theory , she explores knot invariants and constructions, often using combinatorial tools like graph theory to solve topological problems. Publications Overview: Her recent publications (2016–2023) reflect a deep engagement with graph polynomials, chromatic invariants, and usability studies in multilingual interfaces. Notable contributions include studies on the Tutte polynomial, Hosoya polynomial, and rainbow coloring techniques, alongside interdisciplinary work on keyboard usability in African languages. Scientific Awards: No awards are explicitly mentioned in the provided text. Students & Grants: There is no information provided about students supervised or grants received. Labs & Teams: No specific lab or research group affiliations are mentioned in the text.

Stanislav Volgushevمشاهده پروفایل

دانشیار

• Data Science
• Probability
• Theoretical Statistics
• +۷ مورد دیگر

Stanislav Volgushev is an Associate Professor in the Department of Statistical Sciences at the University of Toronto, with a cross-appointment in the Department of Mathematical & Computational Sciences at the University of Toronto Mississauga (UTM). His work bridges theoretical statistics with practical applications in big data analysis, with a focus on developing robust statistical methods for complex data structures. His educational background includes: Ph.D. in Mathematics from Ruhr University Bochum (2010), advised by Prof. Holger Dette Diploma in Mathematics from Ruhr University Bochum (2007) Volgushev's research spans multiple areas of statistical theory and methodology. His primary interests include quantile regression, empirical process theory, extreme value theory, time series analysis, copulas, and bootstrap methods. He is particularly focused on addressing statistical and computational challenges presented by big data, developing methods that maintain theoretical rigor while being practically applicable to modern data analysis problems. His work often involves the development of nonparametric and semiparametric methods that can handle complex dependence structures. Analysis of his recent publications reveals a strong focus on high-dimensional statistics, with particular attention to change-point detection, extremal graphical models, and quantile-based methods. His work bridges theoretical statistics with machine learning, showing increasing interest in optimization methods for statistical learning and causal inference. There's a clear trajectory toward addressing computational challenges in modern statistics, with many papers developing methods specifically designed for big data contexts. Volgushev serves as an associate editor for several prestigious journals: Electronic Journal of Statistics (since 2022) Canadian Journal of Statistics (since 2022) Scandinavian Journal of Statistics (since 2021) Statistical Inference for Stochastic Processes (since 2014) Previously served at Computational Statistics and Data Analysis (2018-2023) and Bernoulli (2016-2018) He has taught various courses including STA107 (Introduction to Probability and Modelling), STA2211 (Probability Theory II), and STA314 (Introduction to Statistical Learning) at both the St. George and Mississauga campuses. His teaching reflects his research expertise, covering both theoretical foundations and modern applications of statistical methods. Administratively, he serves as Associate Chair, Graduate Studies in his department, demonstrating leadership in academic governance.

• coalgebras
• computer science semantics
• universal algebra
• +۴ مورد دیگر

Dr. Stefan Milius is a Senior Lecturer (Akademischer Direktor) at the Chair for Theoretical Computer Science of Friedrich-Alexander University Erlangen-Nuremberg. His academic career spans theoretical computer science with a focus on categorical and algebraic methods applied to computational problems. Dr. Milius's research program centers on coalgebras and their applications in computer science, semantics of iteration and recursion, universal algebra and category theory, and logic and formal verification of systems. His work bridges abstract mathematical theory with practical computational applications, particularly in verification frameworks for software systems. His approach often combines category-theoretic perspectives with concrete computational problems. His publication record demonstrates consistent contributions to theoretical computer science, with recent work exploring stone duality, bialgebraic reasoning frameworks, automata theory with computational effects, and foundational aspects of programming language semantics. He has developed novel approaches to extending classical algebraic language theory to effectful settings and created new frameworks for reasoning about program equivalence in stateful and higher-order languages. Best Theory Paper at FM 2019 for "Generic Partition Refinement and Weighted Tree Automata" EATCS Best Paper Award at MFCS 2017 for "Eilenberg Theorems for Free" CALCO 2015 Best Paper Award for "Syntactic Monoids in a Category" Ackermann Award 2006 for PhD thesis Braunschweig Preis für hervorragende Leistungen im Studium, 2000 Dr. Milius has held significant editorial responsibilities including Editor-in-Chief of Logical Methods in Computer Science since 2020 and membership on the editorial boards of Applied Categorical Structures and TheoretiCS. He actively contributes to the theoretical computer science community through conference organization, serving as PC co-chair for FoSSaCS 2026 and MFPS 2025, and as a program committee member for numerous top-tier conferences including LICS, MFPS, and CALCO. Earlier in his career, he contributed to practical software development through projects like Alan, a Turing machine simulator developed during a 1997 software engineering practicum, and the VerSyKo project on verification of synchronous software components at TU Braunschweig (2011-2012).