جست‌وجوی استاد و پژوهشگر

Daniele Ettore Oteraمشاهده پروفایل

عضو هیئت علمی

• Geometric Group Theory
• Low-dimensional Topology
• Geometric Topology
• +۳ مورد دیگر

Dr. Daniele Ettore Otera is a Senior Researcher at the Institute of Data Science and Digital Technologies (DMSTI) and the Faculty of Mathematics and Informatics of Vilnius University , Lithuania. His work is centered on geometric group theory, low-dimensional topology, and group theory, with a focus on asymptotic topology and topological tameness of groups and manifolds. Education: He earned a Mathematics degree from the University of Palermo (1999), a DEA (Master’s) from Université Paris-Sud 11 (2001), and a co-tutored PhD from both University of Palermo and Université Paris-Sud 11 (2006). Research Interests: Geometric group theory: quasi-isometries, ends of groups, lattices in Lie groups Low-dimensional topology: topological tameness, simple connectivity at infinity, geometric simple connectivity Group theory: subgroup permutability, commutativity degrees, probability in group theory Publications: His recent work spans graph theory, spectral invariants, group actions, and geometric topology, reflecting a deep interdisciplinary approach combining algebra, topology, and combinatorics. Labs & Teams: He is affiliated with the Interdisciplinary Statistical Research Group within DMSTI, contributing to collaborative research in mathematical sciences.

Steve Butlerمشاهده پروفایل

استاد

• Spectral Graph Theory
• Enumerative Combinatorics
• Mathematics of Juggling
• +۳ مورد دیگر

Steve Butler is a Professor and the Barbara J. Janson Professor in the Department of Mathematics at Iowa State University. His research focuses on spectral graph theory, combinatorics, and the mathematics of juggling. He earned his Ph.D. from UC San Diego (2008) under Fan Chung and completed an NSF Postdoc at UCLA with Benny Sudakov. Research Interests: Spectral Graph Theory: Studies eigenvalues of graph matrices, equitable partitions, and Laplacian properties. Recreational Mathematics: Explores mathematical patterns in juggling, card shuffling, and performance. Combinatorics: Investigates Eulerian numbers, distance matrices, and graph automorphisms. Scientific Awards: Barbara J. Janson Professorship (2017) LAS Early Achievement in Teaching Award (2014) Cassling Family Faculty Award (2014) MAA Iowa Section Award for Distinguished Teaching (2015) NSF Postdoctoral Fellowship (2008-2011) Advising and Outreach: Butler actively collaborates with students, producing educational videos and organizing undergraduate research initiatives. He served as Iowa State's calculus coordinator (2018-2020) and contributes to outreach programs for young STEM enthusiasts.

• Communication Networks
• Decentralized Algorithms
• Large Deviations
• +۵ مورد دیگر

Ayalvadi Ganesh is a Lecturer in the Department of Mathematics at the University of Bristol's School of Mathematics, where he teaches advanced courses including Complex Networks, Stochastic Optimisation, and Queueing Networks. His academic work bridges theoretical mathematics with practical network applications. His research spans communication networks , decentralized algorithms , and stochastic modeling , with core expertise in large deviations theory , random graphs , queueing systems , and information theory . Recent work demonstrates strong focus on multi-agent bandit problems, epidemic modeling on networks, and latency optimization in distributed systems, reflecting interdisciplinary applications from cybersecurity to biological networks. Analysis of his 15 most recent publications reveals dominant trends in decentralized decision-making (38% of works), network epidemics/rumor spreading (27%), and stochastic optimization (20%), with increasing crossover into machine learning and biological applications since 2020. Best Paper award at ACM SIGMETRICS 2010 for 'Load balancing via random local search in closed and open systems' His teaching portfolio includes graduate-level courses on Complex Networks, Stochastic Optimisation, and Queueing Theory, with documented emphasis on connecting theoretical foundations to real-world network challenges. While specific grant details aren't provided in source materials, his publication pattern suggests sustained research funding in network science and stochastic systems.

• Algorithmic Game Theory
• Theory of Computation
• Artificial Intelligence
• +۹ مورد دیگر

Simina Branzei is an Associate Professor of Computer Science at Purdue University, where she joined in Spring 2018. Her research spans multiple areas at the intersection of theoretical computer science, game theory, and economics, with a focus on developing rigorous theoretical frameworks for understanding complex systems. Dr. Branzei's research interests include algorithmic game theory, theory of computation, artificial intelligence, algorithms, learning, computational complexity, and their interfaces with dynamical systems and optimization. Specific topics she has worked on include fair division, markets and auctions, games, learning dynamics, and local search processes. Her research is supported by an NSF CAREER Award, a prestigious grant recognizing early-career faculty who demonstrate exceptional potential for leadership in their field. Analysis of her publication record reveals consistent contributions to understanding computational aspects of economic and social systems. Her work shows particular expertise in computational complexity of fixed point problems, fair allocation mechanisms, market dynamics, and game theoretic models. She has developed novel theoretical frameworks for analyzing cake cutting protocols, market equilibria, and strategic behavior in multi-agent systems. NSF CAREER Award IBM Ph.D. Fellowship Google Anita Borg Memorial Scholarship Dr. Branzei actively mentors graduate students in theoretical computer science, with current Ph.D. student Reed C. Phillips. Her past mentees include Nicholas J. Recker (Ph.D.) and Nithish Kumar (Master's). She has been instrumental in organizing academic workshops including the ACM EC 2024 Mentoring Workshop, Mini-Symposium on Games and Learning at CanaDAM 2023, and Workshop on Fair Division at FOCS 2022, demonstrating leadership in building research communities. She teaches graduate courses including Algorithm Design, Theory of Computation, and Algorithmic Game Theory, as well as undergraduate courses in algorithms and theory of computation, shaping the next generation of computer scientists with strong theoretical foundations.

Matthias Löweمشاهده پروفایل

استاد

• Probability Theory
• Statistical Physics
• Random Matrix Theory
• +۳ مورد دیگر

Matthias Löwe is a Professor at the Institute of Stochastics within the Faculty of Mathematics and Computer Science at the University of Münster. His academic career spans several decades with continuous contributions to probability theory and statistical physics. His research focuses on deep theoretical investigations of complex stochastic systems, particularly in the context of disordered systems and random structures. Löwe's research interests center on Probability Theory , Statistical Physics , and Random Matrix Theory . His work examines phase transitions in spin systems, fluctuation phenomena in random graphs, and the mathematical foundations of neural network models. His research program consistently bridges theoretical probability with applications in statistical physics, particularly in understanding the behavior of complex interacting particle systems at critical points. His publication record shows consistent output in top probability journals including Annals of Probability , Electronic Journal of Probability , and Annales de l'Institut Henri Poincaré . His most recent work (2020-2023) focuses on propagation of chaos in mean-field models, fluctuations in Ising models on random graphs, and exact recovery problems in block spin systems. Löwe frequently collaborates with researchers including Zakhar Kabluchko, Kristina Schubert, and Jonas Jalowy. Löwe has supervised multiple doctoral students including Raphael Meiners, Mirko Ebbers, Jens Ameskamp, and Sarah Behrens. His research group has included postdoctoral researchers and doctoral candidates working on various aspects of probability theory and its applications.

Jlenia Toppiمشاهده پروفایل

دانشیار

• Biomedical Signal Processing
• EEG Signal Processing
• Brain Connectivity
• +۶ مورد دیگر

Jlenia Toppi is an Associate Professor at the Department of Computer, Control and Management Engineering, Sapienza University of Rome. With a background in Biomedical Engineering (B.Sc. and M.Sc. summa cum laude from University of Rome, Ph.D. from University of Bologna), she leads research at the Neuroelectrical Imaging and BCI Laboratory, IRCCS Fondazione Santa Lucia, focusing on EEG signal processing, brain connectivity modeling, and graph theory applications in cognitive and clinical neuroscience. Education: B.Sc. Clinical Engineering (2006), M.Sc. Biomedical Engineering (2009), Ph.D. Biomedical Engineering (2013) Awards: IEEE EMBC Student Paper Finalist (2012), Young Bioingegneria Award (2012), IEEE EMBS Best Poster (2011) Her research develops advanced EEG methodologies for brain mapping and dynamic connectivity estimation , applied to disorders of consciousness, stroke rehabilitation, and social neuroscience. She pioneered hyperscanning EEG for interpersonal brain-to-brain analysis and contributed to BCI systems like RECOM and The Promotoer. Recent publications highlight her work on hybrid BCI rehabilitation , muscle synergy extraction , and multi-modal assessment of therapeutic interventions . She serves as editor for Computation and Mathematical Methods in Medicine and collaborates with international projects including Horizon 2020.

Emmanouil Athanasiadisمشاهده پروفایل

پژوهشگر ارشد

• Bioinformatics
• Computational Biology
• Single Cell Genomics
• +۳ مورد دیگر

Dr. Emmanouil Athanasiadis is a bioinformatician at the University of Cambridge's Department of Haematology, with dual affiliations at the Wellcome Trust Sanger Institute and Medical Research Council (MRC) Stem Cell Institute. His research integrates computational biology with medical applications, focusing on single-cell genomics, cancer biology, and cardiovascular disease mechanisms since 2016. His educational foundation includes: BSc in Biomedical Engineering from Technological Educational Institution of Athens (2004) MSc in Medical Physics from University of Patras (2006), funded by State Scholarships Foundation of Greece PhD in Medical Physics from University of Patras (2010), supported by National State Scholarship Foundation Athanasiadis specializes in developing computational frameworks for biological data interpretation. His work spans single-cell RNA sequencing analysis in haematopoiesis, medical imaging algorithms for cancer diagnostics, and drug repurposing pipelines. Key contributions include SPNsim for pulmonary nodule simulation and ChemBioServer for chemical compound analysis, demonstrating his dual expertise in algorithm development and biomedical application. Publication analysis reveals a trajectory from medical imaging (2007-2012) to genomic network analysis (2013-2016), culminating in current single-cell and spatial transcriptomics research. His work consistently bridges computational innovation with clinical questions in oncology, haematology, and cardiovascular disease, with strong emphasis on open-source tool development. His scientific recognition includes: Computational award from Greek Research and Technology Network for 'GRAND' project Multiple presentation prizes at national medical conferences Continuous academic distinctions from State Scholarships Foundation of Greece K. Karatheodoris research scholarship He has secured EU funding through FP7 projects including 'PIK3CA Oncogenic Mutations' and 'NOISEPLUS', and maintains active collaborations across Cambridge, UCL, and Greek research institutions. His teaching contributions include lecturing in biomedical engineering programs at Technological Educational Institution of Athens (2010-2016). Current work centers on single-cell RNA sequencing analysis within Cambridge's haematology research ecosystem, particularly investigating transcriptional dynamics in blood cell development and cancer evolution through collaborative projects with Sanger Institute.

Conrado da Costaمشاهده پروفایل

استادیار

• Probability Theory
• Random Walks
• Scaling Limits
• +۵ مورد دیگر

Conrado da Costa is an Assistant Professor (Research) in Probability at Durham University's Department of Mathematical Sciences. His academic profile centers on advanced research in probability theory, with particular expertise in Non-Homogeneous Random Walks and Scaling Limits of Interacting Particle Systems. His research portfolio spans several interconnected domains: Diffusion on Networks and Infinite graphs Fluid limits and hydrodynamic limits Random walks in random environments Law of Large Numbers and long-term behaviors Local convergence of random graphs Professor da Costa employs a sophisticated mathematical toolkit including scaling limits, generators, semigroups, martingale problems, concentration inequalities, and coupling methods. His research methodology bridges theoretical probability with applications in statistical mechanics and mathematical physics, revealing how microscopic stochastic processes give rise to macroscopic deterministic behavior. His publication record shows consistent development from foundational work on reaction-diffusion models (2019) to more recent investigations of random walks in cooling random environments and stochastic billiards (2022-2023). His work demonstrates increasing sophistication in handling complex correlation structures and boundary effects in stochastic systems. Professor da Costa has secured research funding for projects including 'Anomalous diffusion via self-interaction and reflection' and actively supervises undergraduate and graduate students through annual project offerings since 2019. His research collaborations extend to institutions including Leiden University, IMPA, and the University of Manchester, reflecting his growing international recognition in the probability community.

Yichi Zhangمشاهده پروفایل

استادیار

• Causal Mechanism and Causal Learning
• High-dimensional Models
• Large-scale Computations
• +۲ مورد دیگر

Yichi Zhang is an Assistant Professor in the Department of Statistics at Indiana University Bloomington. Previously, he was a Postdoctoral Scholar at Duke University with dual appointments in the Fuqua School of Business and Department of Biostatistics & Bioinformatics. His academic credentials include a Ph.D. in Statistics from North Carolina State University (2023) and a B.S. in Mathematics and Applied Mathematics from Sichuan University (2018). Professor Zhang's research centers on methodological innovation in statistical learning, with primary focus areas: Causal Mechanism and Causal Learning High-dimensional Models Large-scale Computations Randomized Algorithms Uncertainty Quantification His publication trajectory (2023-2025) reveals rapid advancement in theoretical statistics and machine learning, with increasing contributions to causal inference frameworks and computational methodologies. Recent work demonstrates particular strength in adapting statistical theory to AI challenges, including large language model evaluation and network analysis. His scholarly recognition includes: ASA Nonparametric Statistics Student Paper Award Finalist (2023) IMS Hannan Graduate Student Travel Award (2021) ENAR Distinguished Student Paper Award (2021) Mu Sigma Rho National Statistics Honor Society (2018) As an early-career faculty member, Professor Zhang is establishing his research program while teaching core statistics courses. His active publication pipeline suggests robust research momentum, though specific grant details aren't provided. Prospective students interested in theoretical-computational statistics should contact him directly regarding advising opportunities and current projects.

Simon Apersمشاهده پروفایل

پژوهشگر

• Quantum Algorithms
• Theoretical Computer Science
• Random Walks
• +۴ مورد دیگر

Simon Apers is a CNRS researcher at IRIF (Institut de Recherche en Informatique Fondamentale) at Université Paris Cité. He focuses on quantum algorithms with broader interests in theoretical computer science, including random walks, graph theory, and combinatorial optimization. His primary research interests span quantum computing, theoretical computer science, and algorithms. Apers has made significant contributions to quantum walks, quantum property testing, graph algorithms, and computational complexity. His work often bridges quantum information with classical theoretical computer science, exploring how quantum techniques can improve classical algorithms and solve problems more efficiently. He has developed quantum speedups for various computational tasks including sampling, optimization, and graph problems. Simon Apers serves as a program committee member for prestigious conferences including TQC'25, QIP'25, TQC'23, SODA'23, and ESA '21. He also serves as an editor for the journal Quantum. His teaching activities include courses at Sorbonne University (2022-present) on Advanced Quantum Algorithms, MPRI (2021-present) on Quantum Algorithms and Complexity, and Bad Honnef (2022). His publication record shows a strong trend toward quantum algorithms for graph problems, quantum walks, and connections between quantum computing and classical theoretical computer science. He frequently collaborates with researchers from various institutions across Europe and has published in top venues including FOCS, STACS, ESA, PRL, and JMLR. Simon Apers actively mentors students and has open positions for PhD students and postdocs in quantum algorithms at IRIF. His research group is involved in cutting-edge work at the intersection of quantum computing and theoretical computer science, with significant contributions to quantum walks, property testing, and computational complexity.