Find Academic Supervisors

• Causal Inference
• Machine Learning
• Social Media Analysis
• +3 more

Prof. Drew Dimmery is the Professor of Data Science for the Common Good at the Hertie School's Data Science Lab. He holds a PhD in Politics from New York University (2016), with a focus on causal inference and machine learning. Prior to joining Hertie, he served as Scientific Coordinator at the University of Vienna’s Data Science Research Network (2021–2023) and worked on Facebook’s Core Data Science team (2016–2020), developing experimentation tools and methods. His research emphasizes methodological innovation in causal inference and machine learning, particularly applied to social media and internet studies. Key areas include experimental design, algorithmic transparency, and policy applications of data science. He has published in top-tier journals like ICML, KDD, and Science, and his work bridges computational methods with societal challenges such as electoral integrity, digital governance, and public welfare provision. Recent publications analyze the impact of social media platforms on elections, user behavior, and mental health, leveraging large-scale experimentation and causal analysis. His endowed professorship, supported by the Dieter Schwarz Foundation, includes scholarships for the Master of Data Science for Public Policy program, reflecting his commitment to applying data science for societal benefit.

Alex BlackView profile

Researcher

• Algebraic Combinatorics
• Geometric Combinatorics
• Optimization
• +3 more

Alex Black is currently a Hermann Weyl Instructor at ETH Zurich, focusing on algebraic and geometric combinatorics and optimization. He will join Bowdoin College as a tenure-track assistant professor in the mathematics department starting Fall 2025. His research emphasizes the simplex method and its applications in optimization and polytope theory. Black’s research explores the interplay between algebraic and geometric combinatorics, with a particular focus on optimization and the simplex method. His work addresses topics such as polytopes, matroid theory, and pivot rules, aiming to advance understanding in discrete and computational geometry. He investigates issues like exponential lower bounds for pivot rules, monotone paths on polytopes, and the structure of flag polymatroids. INFORMS 2022 George Nicholson Paper Competition Finalist for Small Shadows of Lattice Polytopes No advising roles or grants are explicitly mentioned in the provided information.

• Numerical Linear Algebra
• Differential-Algebraic Equations
• Control Theory
• +3 more

Prof. Tatjana Stykel is a Professor of Computational Mathematics at the University of Augsburg, affiliated with the Faculty of Mathematics, Natural Sciences, and Materials Engineering. She holds positions in the Institute of Mathematics and leads the Computational Mathematics research group. Her academic journey includes a PhD from Technische Universität Berlin (2002) and a Habilitation (2009), both in numerical mathematics. Prior to her current role, she led a junior research group at TU Berlin’s MATHEON Research Center and held visiting professorships at TU Berlin and Humboldt-Universität zu Berlin. Stykel’s research focuses on Numerical Linear Algebra (e.g., generalized eigenvalue problems, matrix equations), Differential-Algebraic Equations (numerical methods and stability), and Control Theory (descriptor systems, model reduction). She has pioneered energy-adaptive optimization methods and Riemannian geometry techniques for complex systems, including applications in quantum chemistry and electromagnetism. Her awards include the Richard von Mises Prize (2007), Leslie Fox Prize (2003), and multiple fellowships from the Pacific Institute for the Mathematical Sciences and Soros programs. She has led major projects like the EU-funded EU-MORNET (2014–2018) and contributed to BMBF networks in model reduction for coupled systems. Stykel teaches advanced numerical methods, including courses on differential equations, optimization, and model reduction. Her work emphasizes interdisciplinary collaboration, with partnerships in robotics, semiconductor technology, and fluid dynamics. She advises several students, including alumni who have become prominent in applied mathematics and engineering.

• Geometry
• Mathematical Physics
• Complex Systems
• +4 more

Jürgen Jost is an Honorary Professor in the Department of Mathematics at the University of Leipzig and a retired Director at the Max Planck Institute for Mathematics in the Sciences. He is a member of multiple prestigious academies including the Leopoldina and the Santa Fe Institute for the Sciences of Complexity. His research focuses on geometric analysis, complex systems, network science, and applications in chemistry and neuroscience. Key areas include Ricci curvature in networks, Dirac-harmonic maps, and the evolution of chemical knowledge systems. Jost has received major awards such as the Leibniz Prize (1993) and an ERC Advanced Grant (2010). His work bridges pure mathematics with interdisciplinary applications, including topological data analysis and mathematical biology. He leads the Max Planck School of Cognition and has contributed to foundational studies on minimal surfaces, geometric flows, and information geometry.

• Computer Science
• Bioinformatics
• Remote Sensing
• +7 more

Ying Liang is a multidisciplinary researcher with significant contributions across computer science, bioinformatics, and remote sensing. Their work spans AI-driven medical diagnostics, environmental monitoring, and computational biology, with recent focus on precision nutrition, skin lesion recognition, and urban heat island analysis. Research Interests : Machine learning for biomedical applications (e.g., skin cancer detection, pancreatic segmentation), remote sensing (landslide mapping, urban structure dynamics), and computational biology (RNA-protein interactions, microRNA regulation). Notable Article Trends : 2025 publications highlight self-supervised remote sensing classification, fractal reservoir modeling, and biomedical AI. 2024 works emphasize cross-domain gait recognition, panoptic segmentation, and health informatics via Wi-Fi sensing. Grants & Collaborations : Co-authored studies with institutions like Open University and Chinese universities, focusing on thermal cycling reliability, multi-omics data fusion, and wireless health monitoring. Labs & Teams : Collaborated on interdisciplinary projects involving neural network architectures, hybrid AI models for environmental and biomedical applications, and biomedical signal processing systems.

Thomas SchierlView profile

Researcher

• Video Coding
• Wireless Communication Protocols
• Multimedia Systems
• +3 more

Dr. Thomas Schierl is the Head of the Video Communication and Applications Department at Fraunhofer Heinrich Hertz Institute (HHI) in Berlin. Since 2010, he has led research groups in multimedia communications and video coding, co-developing key video coding standards such as H.264 SVC and HEVC. He currently heads the Video Coding & Analytics department since 2015, focusing on video compression, wireless transmission, and standardization. Education: Diplom-Ingenieur (Computer Engineering) from Berlin University of Technology, 2003 Dr.-Ing. in Electrical Engineering and Computer Science, Berlin University of Technology, 2010 Research interests span video over wireless networks, system integration of video codecs, and cellular network protocols. He contributed to MPEG-2 Transport Stream standards and co-authored IETF RFCs for video payload formats. In 2014, he received the Emmy Award for MPEG-2 Transport Stream development. Active in standardization bodies: JCT-VC, MPEG, IETF, 3GPP, and DVB. His work includes high-level syntax for HEVC parallelism and V2X resource pooling for 5G NR. Labs/Teams: Leads the Video Coding & Analytics team at HHI, specializing in cutting-edge video compression and communication technologies.

• IoT and Sensor Networks
• Quantum Computing
• RFID Systems
• +4 more

Bartolomeo Montrucchio is a full Professor at the Polytechnic University of Turin's Department of Electrical, Computer and Biomedical Engineering. His research focuses on IoT systems, quantum computing applications, RFID technologies, and sensor networks. He leads interdisciplinary projects involving environmental monitoring, cybersecurity, and embedded systems design. Key research areas include: Development of low-cost air quality monitoring systems leveraging IoT and sensor fusion Quantum algorithm design for financial risk analysis and game theory applications RFID security protocols and anti-collision mechanisms for dense networks Energy-efficient image processing techniques for mobile displays Recent work highlights quantum computing's potential in vulnerability analysis and financial modeling, while maintaining strong contributions to sensor network reliability and environmental applications. His publications span prestigious journals like IEEE Internet of Things Journal and IEEE Transactions on Dependable and Secure Computing. Collaborations with industry partners and academic institutions emphasize practical implementation of theoretical advancements, particularly in smart city technologies and healthcare monitoring systems.

Chinmoy BhattacharjeeView profile

Professor

• Stochastic Geometry
• Distributional Approximations
• Stein's Method
• +4 more

Prof. Chinmoy Bhattacharjee is a Professor of Mathematical Stochastics at the University of Hamburg's Department of Mathematics, part of the Faculty of Mathematics, Computer Science and Natural Sciences. His research focuses on stochastic geometry, distributional approximations via Stein's method, stochastic analysis, random graphs, continuum percolation, and nonparametric inference. He holds a PhD from the University of Southern California and previously held postdoctoral positions at the University of Luxembourg and University of Bern. His work is supported by the German Research Foundation (DFG) through Project No. 531540467. Education: PhD in Applied Mathematics, University of Southern California (2013–2018) M.Stat. and B.Stat. (Hons.), Indian Statistical Institute, Kolkata (2008–2013) Teaching: Recent courses include Stochastic Processes, Probability and Statistics, and seminars on percolation theory and probabilistic methods. Research Highlights: Prof. Bhattacharjee's work bridges stochastic geometry and Stein's method, with applications in spatial random structures, non-Gaussian limit theory, and noise sensitivity in percolation models. Key contributions include Gaussian and Dickman approximation techniques, spectral analysis of Poisson functionals, and statistical applications in random forests. Upcoming Events: Co-organizing session on Spatial Stochastics at German Probability and Statistics Days (2025) Invited talks at Aarhus University and workshops in Germany/Croatia (2025)

Wolfgang MulzerView profile

Professor

• Theoretical Computer Science
• Computational Geometry
• Algorithms
• +3 more

Wolfgang Mulzer is a Professor in the Department of Computer Science at Freie Universität Berlin, within the Faculty of Mathematics and Computer Science. He leads the Theoretical Computer Science research group (AG Theoretische Informatik) and holds a PhD from Princeton University (2010), advised by Bernard Chazelle. His research focuses on computational geometry, algorithms, and discrete mathematics, with notable contributions to geometric algorithms, data structures, and combinatorial optimization. Education: PhD in Computer Science, Princeton University (2010) Advisor: Bernard Chazelle Research Interests: Mulzer's work spans theoretical computer science with an emphasis on computational geometry. Key areas include geometric algorithms (e.g., Voronoi diagrams, Fréchet distance), dynamic graph algorithms (e.g., disk graphs, connectivity problems), and algorithmic complexity. His research also addresses discrete geometry challenges like Tverberg theorems and geometric intersection graphs. Grants & Labs: Leading the Theoretical Computer Science group at FU Berlin Active in collaborative projects on geometric algorithms and algorithmic foundations Professional Activities: Mulzer has authored or co-authored over 100 peer-reviewed papers, with work appearing in top venues like Discrete & Computational Geometry , SIAM Journal on Computing , and Algorithmica . His research bridges theoretical foundations with practical algorithm design.

Ullrich KötheView profile

Associate Professor

• Machine Learning
• Neural Networks
• Explainable AI
• +6 more

Prof. Dr. Ullrich Köthe is an Associate Professor and group leader in the Visual Learning Lab Heidelberg at the University of Heidelberg . He focuses on Explainable Machine Learning , leveraging Invertible Neural Networks to enhance transparency and utility in image analysis and medical applications . He also maintains the widely used VIGRA image analysis library. Education: PhD in Informatics, University of Hamburg, 2000 Habilitation in Informatics, University of Hamburg, 2008 His research interests center around machine learning , image analysis , and scientific computing , particularly the development of robust algorithms for medical imaging , computer vision , and life sciences . His work on invertible neural networks and parameter-free segmentation has led to significant advancements in the field. Recent publications highlight his contributions to Bayesian inference , neural network interpretability , and stochastic modeling , with applications ranging from disease outbreak dynamics to connectomics . Key trends include generative models , parameter-free segmentation , and likelihood-free inference . Scientific Awards: DAGM 2003 Main Prize DAGM Best Paper Award 2008 He has supervised numerous Master and Bachelor theses in machine learning and image analysis , with teaching roles in Advanced Machine Learning and Explainable AI . His collaborative research grants include funding from HARMAN International (2024). He leads the Explainable Machine Learning subgroup and has contributed to open-source software projects like ilastik and VIGRA , which are critical tools in bioimage analysis .

• Computational Geometry
• Discrete Geometry
• Convex Optimization
• +6 more

Francisco Criado is a researcher with significant contributions to computational geometry, discrete mathematics, and applied algorithm design. His work spans theoretical and applied domains, including convex optimization, Voronoi diagrams, and geometric modeling in gas dynamics and earthquake decision-making frameworks. Collaborators : Francisco Santos, Sebastian Pokutta, Michael Joswig Key Venues : Discrete & Computational Geometry , Foundations of Computational Mathematics , NeurIPS Research Interests focus on geometric algorithms, polyhedral complexes, and applications of fuzzy logic. His 2025 papers explore convex hulls, zonotopes, and tropical geometry, while earlier works address gas dynamics and decision-making models. Article Trends reveal expertise in high-dimensional geometry, combinatorial optimization, and numerical methods. He frequently employs randomized and linear convergence algorithms in 2022–2025. Advising and Grants : No explicit data provided, but his extensive co-authorship network suggests collaborative research leadership.

• Algorithm Design
• Transportation Systems
• Route Planning
• +3 more

Sabine Storandt is a Lecturer at the Department of Computer Science, University of Freiburg, with a focus on algorithm design and transportation systems. She has contributed significantly to research in route planning, electric vehicle navigation, and public transit optimization. Her work emphasizes practical applications of theoretical algorithms in real-world scenarios. Her research interests include algorithms for vehicle navigation, route optimization, and facility location problems. She has received notable awards, including the Best Paper Award at VLDB 2014 and the INFOS Award for her PhD thesis on 'Algorithms for Vehicle Navigation.' In teaching, she has led courses such as Information Retrieval (as a tutor), Randomized Algorithms (lecture + tutorial), and Information Extraction (seminar). She has also collaborated on projects like DORC (Distributed Online Route Computation) and Enabling E-Mobility, addressing challenges in transportation and energy efficiency. Her recent publications highlight advancements in electric vehicle infrastructure, public transit planning, and efficient route algorithms, reflecting her expertise in bridging theoretical computer science with practical transportation solutions.

Eckehard OlbrichView profile

Researcher

• Computational Social Science
• Text and network analysis for social media data
• Information theoretic approach to complex systems
• +7 more

Eckehard Olbrich is a Group Leader and Researcher at the Max Planck Institute for Mathematics in the Sciences (MiS) in Leipzig, Germany. His work bridges mathematics, information theory, and social science with a focus on complex systems analysis. He has coordinated major European research projects including SoMe4Dem (Social Media for Democracy) and ODYCCEUS (Opinion dynamics and cultural Conflict in European Spaces). His educational background includes a PhD in theoretical solid-state physics from the Technical University Dresden (1995), followed by postdoctoral work at the Max Planck Institute for the Physics of Complex Systems in Dresden and research at the University of Zürich. Since 2004, he has been affiliated with the Max Planck Institute for Mathematics in the Sciences. Olbrich's research spans computational social science, information theory, and complex systems. He applies information-theoretic approaches to analyze social media data, complex networks, and human sleep EEG patterns. His work on information decomposition, multi-level systems, and time series analysis has produced significant contributions to understanding complex phenomena across disciplines. He has developed methods for analyzing polarization, opinion dynamics, and network structures in social systems. His publication record shows a strong trend toward interdisciplinary research combining information theory with social and biological systems. Recent work focuses on computational social science applications, particularly analyzing polarization and issue alignment on social media platforms, while maintaining connections to fundamental information theory and complex systems research. Olbrich has collaborated extensively with researchers including Sven Banisch (Karlsruhe Institute for Technology), Peter Achermann (University of Zürich), David Wolpert (Santa Fe Institute), and Jürgen Jost at MiS. His research has been supported by major funding programs including Horizon Europe, Horizon 2020, and the DFG. He has taught courses on Complex Systems Methods and Data Analysis and Modeling at the University of Potsdam, and has contributed to the development of TISEAN, free software for nonlinear time series analysis. His current research continues to explore the intersection of information theory, network science, and computational social science with applications to understanding democratic processes in the digital age.

• Computational Algebra
• Commutative Algebra
• Applied Algebraic Geometry
• +3 more

Daniele Taufer is a postdoctoral researcher affiliated with the NUMA research unit at KU Leuven (Belgium) since 2022, supported by the FWO (Flemish Fund for Scientific Research) under project 12ZZC23N. Previously, he worked at CISPA (Germany) from 2020 to 2022 as a postdoc on elliptic curve cryptography within the ERC-669891 project, supervised by Antoine Joux. Education: Ph.D. in Mathematics (2016–2020), University of Trento (IT), cum laude, thesis: "Elliptic Loops" (supervised by Massimiliano Sala) Master in Mathematics (2014–2016), University of Duisburg-Essen (DE) and University of Leiden (NL) via the ALGANT double-degree program, thesis: "Algebraic aspects of the Number Field Sieve" (supervised by Hendrik W. Lenstra) Bachelor in Mathematics (2011–2014), University of Padova (IT), thesis: "Gröbner bases and applications" (supervised by Alberto Tonolo) Research interests span computational and commutative algebra, applied algebraic geometry, and cryptographic applications. Key areas include symmetric tensor decomposition (Waring, tangential, Chow, cactus ranks), effective decomposition algorithms (apolarity, Hankel operators), and algebro-geometrical properties of apolar schemes. His work bridges theoretical algebra and practical cryptography, particularly focusing on elliptic curves and their applications in blockchain and isogeny-based systems. Recent scientific contributions examine decompositions of symmetric tensors, elliptic curve discrete logarithm problems (ECDLP), and group structures over discrete rings. His algorithmic developments leverage apolarity and Hankel operators for computational efficiency. Scientific accolades: Maître de conférences qualification (2025) in Mathematics and Applied Mathematics sections Member of the SIAM Activity Group on Algebraic Geometry Labs and teams include the NUMA group at KU Leuven and the ERC-669891 project at CISPA.

Ehrhard BehrendsView profile

Professor

• Functional Analysis
• Probability Theory
• Stochastic Processes
• +4 more

Prof. Ehrhard Behrends is a Professor of Mathematics at the Department of Mathematics and Computer Science, Freie Universität Berlin. His research focuses on functional analysis, probability theory, and stochastic processes. He is also deeply involved in mathematical education and public outreach, having authored numerous popular mathematics books and articles. Key roles include leading the European Mathematical Society's Raising Public Awareness committee (2009–2015) and developing the mathematics portal mathematics-in-europe.eu . Behrends has organized major exhibitions like 'Mathematics for all the Senses' and contributed to initiatives such as the Year of Mathematics 2008. His work bridges advanced research with accessible communication, reflected in his books on Markov chains, analysis textbooks, and the Five Minutes of Mathematics column.