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Peter SollichView profile

Professor

• Non-equilibrium statistical physics
• Soft matter rheology
• Active matter
• +7 more

Prof. Dr. Peter Sollich is a Professor of Theoretical Physics at Georg-August-Universität Göttingen, affiliated with the Institute for Theoretical Physics. His research spans non-equilibrium statistical physics with applications to soft matter, active systems, and complex networks. He maintains a small part-time appointment at King's College London. His primary research interests focus on non-equilibrium statistical physics , particularly soft and active matter rheology, jamming transitions, glassy dynamics, dynamical phase transitions, and inference from dynamical data. His work bridges theoretical physics with applications in materials science and network theory, emphasizing both fundamental mechanisms and quantitative modeling approaches. Analysis of his recent publications reveals strong thematic consistency in studying glassy dynamics and active matter systems , with increasing integration of machine learning techniques for network analysis. Key methodological threads include coarse-grained modeling, spectral analysis of complex systems, and non-equilibrium thermodynamics frameworks. His 2023-2025 work shows growing emphasis on nonreciprocal interactions in active mixtures and physics-inspired machine learning applications. Prof. Sollich actively supervises Bachelor's, Master's, and PhD students, welcoming thesis inquiries in theoretical physics. His group develops analytical and computational approaches to complex dynamical systems, with recent grants likely supporting work on network dynamics and active matter modeling (specific grants not detailed in source text). His research group operates within the Institute for Theoretical Physics at Göttingen, focusing on computational and analytical modeling of disordered systems. Current projects involve elastoplastic modeling of amorphous solids, spectral analysis of heterogeneous networks, and theoretical frameworks for active matter phase separation.

• Hydrology
• Soil Erosion and Conservation
• Sediment and Water Interaction
• +7 more

Dr. Fahimeh Mirchooli is a postdoctoral researcher in the Department of Geography at the University of Bonn, Germany, working within the AG Klaus research group. Her work focuses on hydrology, soil erosion, and sustainability of catchment systems. She holds a PhD in Natural Resource Engineering from Tarbiat Modares University, Iran, and has held academic research roles at Sari Agricultural Sciences and Natural Resources University and Hakim Sabzevari University. PhD, Natural Resource Engineering (Watershed Science and Engineering), Tarbiat Modares University, Iran, 2020 Visiting Researcher, University of Salzburg, Austria, 2019 MSc, Natural Resource Engineering (Watershed Management), Isfahan University of Technology, Iran, 2013 BSc, Natural Resource Engineering (Rangeland and Watershed Management), University of Tehran, Iran, 2011 Dr. Mirchooli's research centers on soil erosion and conservation , sediment-water interactions , spatial modeling using machine learning , and catchment health and sustainability . She integrates geospatial analysis, environmental modeling, and data science to assess land degradation, ecosystem services, and environmental risk. Her work often involves remote sensing, GIS, and hybrid modeling approaches combining empirical and machine learning techniques. Her recent publications (2018–2024) reflect a strong trend in environmental risk modeling , land degradation assessment , and machine learning applications in hydrology and soil science . Topics include gully erosion susceptibility, dust emission risk, flood modeling, and watershed sustainability. The interdisciplinary nature of her research spans ecology, geoscience, agricultural science, and public health . Scientific awards and honors include: Ranked 1st among PhD students in Watershed Management Sciences and Engineering (2020) 6th rank in national PhD entrance exam for Watershed Management (2015) George Forster Fellowship (2025) Sabbatical Scholarship from Ministry of Science, Research and Technology, Iran (2019–2020) Dr. Mirchooli has received research grants including the George Forster Fellowship and a Sabbatical Scholarship. While no formal advisees are listed, her collaborative publication record indicates active mentorship and team-based research. She is involved in hydrology and sustainability research within the AG Klaus group, contributing to projects on catchment health, erosion modeling, and environmental monitoring.

• Natural Language Processing
• Graph Neural Networks
• Large Language Models
• +7 more

Liang Zhao is a Professor in the Department of Computer Science at Harbin Institute of Technology's School of Computer Science and Technology. His research spans multiple areas of natural language processing, artificial intelligence, and machine learning, with a particular focus on large language models, graph neural networks, and explainable AI systems. His research interests include natural language processing, graph neural networks, large language models, model explainability, federated learning, sparse attention mechanisms, hallucination mitigation, transformer length extrapolation, and uncertainty quantification. His work addresses fundamental challenges in modern AI systems, particularly in improving the reliability, efficiency, and interpretability of large language models. Professor Zhao's research output shows a clear trend toward addressing critical limitations in large language models, with recent work focusing on hallucination mitigation, model attribution, uncertainty quantification, and efficient attention mechanisms. His publications demonstrate strong interdisciplinary connections between NLP, machine learning theory, and practical system implementation. Through his extensive collaboration network with researchers including Xiaocheng Feng, Bing Qin, Weihong Zhong, and Yuntong Hu, Professor Zhao has established himself as a leading researcher in the Chinese NLP community. His work appears consistently in top-tier conferences including ACL, EMNLP, and NAACL.

Dong Han KIMView profile

Professor

• Ergodic theory and dynamical systems
• Metric theory of numbers
• Financial mathematics
• +4 more

Dong Han KIM is a Professor at the Department of Mathematics Education, Dongguk University, and an Associate Member of the Korea Institute for Advanced Study. He serves as an Editor for the Bulletin of the Korean Mathematical Society . His primary research interests include Ergodic theory, dynamical systems, Metric theory of numbers, Financial mathematics, and Random number generators. Research Contributions His recent work explores Diophantine approximation, particularly intrinsic approximation on circles/spheres and parity-constrained rational approximations. He has developed novel frameworks for Sturmian continued fractions and their Lévy constants, connecting number theory with dynamical systems. His Hausdorff dimension studies in inhomogeneous Diophantine approximation reveal deep metric properties of fractal sets. He investigates quasi-Sturmian tree colorings and Romik's dynamical system, bridging symbolic dynamics and number theory. Scientific Collaborations Collaborated with leading researchers like Yann Bugeaud, Seonhee Lim, and Stefano Marmi. Co-authored papers with Byungchul Cha, Seul Bee Lee, and Lingmin Liao on topics spanning continued fractions to market potential models. Academic Engagement Delivered invited talks at institutions across Japan, Italy, France, China, India, and Russia. Participated in international conferences on dynamical systems, number theory, and finance in Italy, France, China, and Singapore.

V. ArvindView profile

Professor

• Computational Complexity Theory
• Structural Complexity
• Randomized Computation
• +5 more

V. Arvind is a Professor in the Theoretical Computer Science faculty at the Institute of Mathematical Sciences (IMSc) , Chennai. His research is centered on computational complexity theory, with a focus on structural complexity, randomized and algebraic computation, and quantum information and computation. He explores the deep connections between theoretical computer science and mathematics. Institution: Institute of Mathematical Sciences (IMSc), Chennai School: Theoretical Computer Science Academic Rank: Professor Arvind's research interests include computational complexity, structural complexity theory, algebraic computation, derandomization, and quantum computing. He is particularly interested in the interplay between mathematical structures and computation. His work often bridges theoretical computer science with algebra, combinatorics, and logic. His recent publications, primarily expository articles in the EATCS Bulletin’s Computational Complexity Column, cover a wide range of topics such as robust oracle machines, the Alon-Roichman theorem, noncommutative arithmetic circuits, graph isomorphism, and quantum computation. These works reflect trends in foundational complexity theory, algebraic methods in computation, and the exploration of quantum models. The articles emphasize structural insights, lower bounds, and connections to mathematical disciplines. Professional Service and Editorial Roles: Associate Editor, ACM Transactions on Computation Theory Editor, EATCS Computational Complexity Column (since June 2011) Editorial Board Member, International Journal of Computer Mathematics (2009–2013) Co-organizer, ICM Satellite Conference on Algebraic and Probabilistic Aspects of Combinatorics and Computing Program Committee Member for WALCOM 2014, STACS 2012, COCOON 2009, FSTTCS (multiple years, including chair roles), CCC 2006, INDOCRYPT (2002, 2005), and others Teaching: Arvind has taught advanced courses including Computational Complexity, Algorithms, Algebra and Computation, and Discrete Mathematics, often based on foundational texts and notes from leading experts. Lecture notes from his courses have been compiled by students and collaborators. Collaborations: He has an extensive list of co-authors, including prominent researchers such as Manindra Agrawal, Eric Allender, Johannes Köbler, Meena Mahajan, Jacobo Torán, and Ramprasad Saptharishi, indicating strong collaborative research networks in complexity theory and algorithms.

• Combinatorics
• Discrete Mathematics
• Positional Games
• +7 more

Dennis Clemens is a researcher in the Institute of Mathematics at Hamburg University of Technology, where he leads and contributes to research in discrete mathematics, particularly in the Chair of Discrete Mathematics. His work centers on combinatorial game theory, extremal combinatorics, and probabilistic methods in graph theory. Research Interests: His research spans positional games (Maker-Breaker, Waiter-Client, Walker-Breaker), Ramsey theory , random graphs , and graph packing . He investigates structural properties of games on random and perturbed graphs, size-Ramsey numbers, and extremal problems in hypergraphs. Publications Trends: His recent publications (2021–2024) show a strong focus on game-theoretic models over random structures, with frequent collaborations. Key themes include fast winning strategies, universality, connectivity, and Ramsey-type problems. Scientific Awards: No specific awards mentioned in the provided texts. Advising and Grants: While no explicit list of advisees or grants is given, his role as a research leader suggests involvement in mentoring students and securing research funding, likely supported by institutions such as the Simons Foundation (acknowledged in arXiv). Labs and Teams: He is affiliated with the Discrete Mathematics group at TUHH, contributing to collaborative research in combinatorics. He participates in academic networks such as the SIAM & GAMM Chapter Hamburg and workshops on Operator Theory, indicating active engagement in the mathematical community.

Cristina G. FernandesView profile

Associate Professor

• Algorithms
• Combinatorial Optimization
• Graph Theory
• +7 more

Cristina G. Fernandes is an Associate Professor in the Department of Computer Science at the Institute of Mathematics and Statistics, University of São Paulo (IME-USP), where she conducts research in theoretical computer science, combinatorial optimization, and graph theory. She teaches advanced courses such as Advanced Data Structures and Topics in Algorithm Analysis. Her research interests include algorithms, combinatorial optimization, graph theory, approximation algorithms, data structures (persistent, retroactive, kinetic, succinct), and computational geometry. She applies theoretical methods to solve complex problems in network design, clustering, and discrete structures. The recent publications reflect a strong focus on structural and extremal graph theory, approximation algorithms, and combinatorial optimization. Key themes include tree and path packing, dominating sets, Steiner-type problems, and combinatorial properties of graphs. Her work often involves deep structural analysis and algorithmic design for NP-hard problems. Cristina G. Fernandes has not been mentioned with any specific scientific awards in the provided texts. She has supervised numerous postdoctoral researchers, PhD, MSc, and undergraduate students, many funded by FAPESP, CAPES, and CNPq. She leads significant research projects, including CAPES/MATH/STIC/CLIMAT-AMSU on energy efficiency in distributed computing and CNPq Universal projects on partitioning and connectivity. She is actively involved in academic advising and grant-funded research. She leads or participates in the Research Group in Theoretical Computer Science, Combinatorics and Combinatorial Optimization at IME-USP, fostering collaborative research in algorithms and discrete mathematics.

Laxman DhulipalaView profile

Assistant Professor

• Parallel Algorithms
• Graph Processing
• Parallel Clustering
• +7 more

Laxman Dhulipala is an Assistant Professor in the Department of Computer Science at the University of Maryland, College Park, and a research scientist at Google Research in the Graph Mining team. He holds a Ph.D. from Carnegie Mellon University, advised by Guy Blelloch, and was a postdoctoral researcher at MIT with Julian Shun. Ph.D., Carnegie Mellon University Postdoctoral Research, MIT His research focuses on efficient parallel algorithms, particularly for graph processing and clustering. He explores theoretical and practical models of parallel computation aligned with modern hardware. His work spans parallel graph algorithms, computational geometry, and scalable systems for massive datasets. The recent publications demonstrate a strong trend in scalable and dynamic graph algorithms, with a focus on hierarchical clustering, connectivity, and benchmarking. Key themes include batch-dynamic updates, memory-efficient data structures, and practical parallel implementations for massive-scale problems. Many works appear in top venues such as SPAA, VLDB, NeurIPS, and SIGMOD. Best Paper Award at VLDB'25 Best Paper Award at SPAA'22 Best Paper Runner Up at VLDB'22 Distinguished Paper Award at PLDI'19 Best Paper Award at SPAA'18 Memorable Paper Award Finalist at NVMW'20 Honorable Mention, CMU SCS Dissertation Award Nominated for ACM Dissertation Award Dhulipala has advised and collaborated with numerous students and researchers, including Quinten De Man, Shangdi Yu, Jessica Shi, and others, contributing to influential projects such as Aspen, ParGeo, GBBS, and ParlayLib. He has received recognition for both theoretical and practical contributions to parallel computing. He teaches courses such as CMSC858N (Scalable Parallel Algorithms and Data Structures) and CMSC451 (Design and Analysis of Computer Algorithms). He is actively involved in building tools and frameworks for parallel algorithm development and evaluation, including benchmark suites and graph processing systems. His dual affiliation with academia and Google Research enables impactful, scalable research bridging theory and practice.

• Materials Science
• Nanotechnology
• Thermoelectrics
• +7 more

Dr. Yolita Eggeler is an Assistant Professor in the Department of Physics at the Karlsruhe Institute of Technology (KIT), leading the LEM (Laboratory for Experimental Materials) research group. Her work focuses on advanced materials science, nanotechnology, and additive manufacturing, with a particular emphasis on laser-based fabrication techniques, thermoelectric materials, and micro/nanostructural characterization. She is affiliated with the KIT Physics Department and collaborates on projects involving energy harvesting, semiconductor devices, and high-temperature alloy behavior. Research interests include: Laser microprinting of semiconductors and metals Thermoelectric generator design and optimization Nanoparticle synthesis and applications High-temperature material degradation and creep mechanisms Recent publications highlight innovations in photothermal laser printing of crystalline materials, novel thermoelectric modules, and core-shell nanocrystal engineering. Her work bridges fundamental materials science with applied technologies like flexible electronics and energy conversion systems. Key collaborations involve advanced TEM analysis for microstructural studies and correlative microscopy techniques. She is actively developing new methodologies for in-situ pyrolysis of 3D-printed materials and exploring AI-driven materials discovery through concept graph analysis. Laboratory facilities include state-of-the-art laser printing systems, electron microscopy setups, and thermal characterization equipment. Her team focuses on translating lab-scale innovations into scalable manufacturing processes for next-generation electronic and energy systems.

• Data Analytics
• Distributed Systems
• Query Optimization
• +5 more

Jorge-Arnulfo Quiané-Ruiz is a researcher at Qatar Computing Research Institute , with affiliations at institutions like Technical University of Berlin and University of Nantes (PhD, 2008). His work focuses on data analytics , distributed systems , and query optimization , particularly in cross-platform environments and trusted execution frameworks . Research Interests : Data analytics across heterogeneous platforms Secure and compliant data processing in trusted environments Efficient query allocation and optimization Scalable algorithms for big data and streaming graphs Machine learning in data management Table extraction and data cleaning Publications : Recent work includes benchmarks for TEE-based joins and cross-database query frameworks (2023). Explores space-efficient graph algorithms and ML-driven revenue optimization (2022). Developed RHEEMix , a cost-based optimizer for cross-platform systems (2018). Collaborations : Regularly partners with Volker Markl and Zoi Kaoudi on unified analytics frameworks. Contributed to projects like Apache Wayang and AgoRA .

Artem SapozhnikovView profile

Professor

• Probability theory
• percolation
• random walks
• +1 more

Artem Sapozhnikov is a Professor at the Mathematisches Institut of Universität Leipzig, Germany. His research focuses on probability theory, particularly percolation, random walks, and random graphs. He maintains an active research program with numerous publications and supervises PhD students and postdocs. Professor Sapozhnikov teaches a range of mathematics courses for physicists, including Mathematics for Physicists 1-4, Probability Theory I & II, and specialized courses on percolation. His teaching spans both undergraduate and graduate levels, demonstrating his commitment to mathematical education. His research interests center around probability theory , with specific focus on: Percolation theory and phase transitions Random walks and their properties on various structures Random interlacements and Brownian motion Boolean models and geometric probability Models with long-range correlations His work bridges theoretical probability with applications in statistical physics. Analysis of his recent publications shows a strong focus on the properties of random interlacements, Brownian motion, and percolation models. His research examines connectivity properties, phase transitions, and geometric aspects of these stochastic processes. Recent work with Yingxin Mu has explored visibility windows and indistinguishability of components in various models. Earlier collaborations with Caio Alves, Deepan Basu, and Yinshan Chang investigated decoupling inequalities, crossing probabilities, and loop percolation. Professor Sapozhnikov has supervised several PhD students and postdocs, including: Deepan Basu (PhD 2013-2017) Caio Alves (postdoc 2016, 2017-2020) Yinshan Chang (postdoc 2013-2016) Lorenzo Taggi (PhD 2012-2015) Yingxin Mu (postdoc 2021-2023) He currently has open positions for PhD students through the IMPRS program.

Siegfried BeckusView profile

Senior Lecturer

• Spectral Theory
• Dynamical Systems
• Operator Algebras
• +6 more

Siegfried Beckus is a Senior Lecturer and Researcher at the Institute of Mathematics , University of Potsdam, Germany, since 2020. His work focuses on spectral theory of Schrödinger operators on graphs with aperiodic ordered potentials, dynamical systems , and operator algebras . He has also contributed to the study of Delone sets , graph limits , and aperiodic tilings . Education : PhD in Mathematics (2016) and Diploma (2012) from Friedrich Schiller University Jena, Germany. Research Interests : Spectral theory of quantum systems, aperiodic order, groupoids, C*-algebras, and applications to quasicrystals. His recent publications address operator families defined by dynamical systems , Sturmian Hamiltonians , and symbolic substitution systems on non-abelian groups. These works establish spectral continuity , Lipschitz/Hölder estimates , and periodic approximation techniques . Scientific Awards include Postdoctoral Fellowships at Technion (Haifa) and Research in Pairs scholarships at Oberwolfach. He has supervised several theses on topics like non-Euclidean estimates , Delone dynamical systems , and primitive substitutions .

• Stochastic Analysis
• Optimal Transport
• Ricci Flows
• +3 more

Eva Kopfer is an Associate Professor at the University of Bonn's Institute for Applied Mathematics, Department of Stochastic Analysis. Her research focuses on stochastic analysis, optimal transport theory, Ricci flows, and geometric analysis. She has led courses on stochastic analysis, financial mathematics, and advanced calculus. Notable contributions include work on exponential ergodicity for kinetic SDEs, stochastic homogenization of transport problems, and quantum gravity measures on manifolds. Her research interests bridge probability theory, differential geometry, and functional analysis, with applications to geometric flows and metric measure spaces. Recent work explores conformally invariant random fields, polyharmonic structures in quantum gravity, and generalized Ricci flow dynamics. She has collaborated extensively on projects involving discrete-to-continuous limits in optimal transport and Liouville quantum gravity measures. Publications highlight advancements in stochastic differential equations, ergodic theory, and geometric PDEs. Her work often integrates probabilistic methods with geometric analysis, yielding insights into transport phenomena and manifold structures. Current research themes include non-equilibrium systems, functional inequalities, and random geometric structures. Eva Kopfer's academic contributions are evident in high-impact journals like Journal of Functional Analysis and Communications in Pure and Applied Mathematics. She actively engages in seminar series and lecture series at the University of Bonn, covering topics from stochastic calculus to frontiers in economics and mathematics.

Christoph LenzenView profile

Professor

• Distributed Computing
• Clock Synchronization
• Randomized Load Balancing
• +5 more

Christoph Lenzen is a Professor at CISPA Helmholtz Center for Information Security since July 2021. Previously, he served as a Group Leader at the Max Planck Institute for Informatics (2014-2021), and held postdoctoral positions at MIT CSAIL (2013-2014), Weizmann Institute (2012), and Hebrew University (2011). Research Interests Design and analysis of distributed algorithms Clock synchronization and adversarial fault tolerance Randomized and self-stabilizing systems Hardware algorithms for fault-tolerant systems Grants and Awards ERC Proof-of-Concept Grant (2021) ERC Starting Grant (2017-2022) ETH Zurich Medal for Outstanding Thesis (2011) Education Ph.D. in Computer Science (2011), ETH Zurich Diploma in Mathematics (2007), University of Bonn Teaching Clock Synchronization and Adversarial Fault Tolerance (2022 winter) How To Clock Your Computer (2022 summer) Theory of Distributed Systems (2019 winter, 2020 winter, 2018 winter, 2017 winter, 2016 winter, 2015 winter)

Melanie SchmidtView profile

Professor

• Geometric Data Analysis
• k-means clustering
• Combinatorial Optimization
• +3 more

Professor Melanie Schmidt is a faculty member in the Department of Computer Science at Heinrich-Heine-Universität Düsseldorf, where she leads the Algorithms and Data Structures research group. Previously, she was affiliated with the University of Bonn's Institute of Computer Science, where she completed her PhD under Prof. Dr. Heiko Röglin and headed a subgroup on "clustering for big data" within his research group. Current Position: Professor at Heinrich-Heine-Universität Düsseldorf Previous Position: Researcher and lecturer at University of Bonn PhD Advisor: Prof. Dr. Heiko Röglin Her research focuses on geometric data analysis, particularly k-means clustering in data streams, combinatorial optimization, and approximation algorithms. Her work bridges theoretical computer science with practical applications in big data processing. She has made significant contributions to understanding the theoretical foundations of clustering algorithms while developing efficient implementations for real-world applications. Professor Schmidt's publication record shows a consistent evolution from theoretical analysis of k-means to practical implementations for big data environments. Her recent work explores fairness in clustering, privacy-preserving techniques, and efficient algorithms for high-dimensional data. She has published in top-tier conferences including SODA, ICALP, ESA, and ITCS, demonstrating both theoretical rigor and practical relevance. Best Student Paper Award at ESA 2012 (joint work with Martin Groß, Jan-Philipp W. Kappmeier, and Daniel Schmidt) She actively supervises numerous Master's and Bachelor's students, with current advisees including Lena Carta, Lukas Drexler, and Anna Arutyunova. Her research group includes members such as Anja Rey, Julian Wargalla, and Annika Hennes. She teaches advanced courses in algorithms and data structures, with a focus on randomized algorithms and efficient algorithm design for big data problems. Professor Schmidt leads the Algorithms and Data Structures research group at Heinrich-Heine-Universität Düsseldorf, which focuses on developing and analyzing efficient algorithms for fundamental computational problems, with particular emphasis on clustering, geometric data analysis, and big data applications.