Prof. Dariusz Dereniowski serves as a Professor in the Department of Algorithms and Systems Modelling at the Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology. His active research profile is evidenced by recent publications and ongoing projects, with contact maintained via darderen@pg.edu.pl and phone 583471956. His research centers on theoretical computer science, specializing in graph algorithms and computational complexity. Key areas include evacuation strategies in graphs, noisy binary search under error probability, energy-constrained exploration methods, and bicriteria tree-depth optimization. This work addresses fundamental challenges in algorithm design under constraints like congestion avoidance and query inaccuracies. Analysis of his 2023-2025 publications reveals a cohesive research trajectory focused on optimizing graph traversal under physical and informational constraints. These studies advance theoretical foundations while enabling applications in network routing, multi-agent coordination, and combinatorial optimization through novel algorithmic approaches. Prof. Dereniowski leads OPUS-funded projects including "Graph modeling of search processes" (2019-2022, UMO-2018/31/B/ST6/00820) and "Przeszukiwanie struktur grafowych" (2016-2019, UMO-2015/17/B/ST6/01887), demonstrating sustained grant acquisition within the Department of Algorithms and Systems Modelling. Though formal advisees aren't listed, his project management role indicates research team leadership.
جستوجوی استاد و پژوهشگر
استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشیتان را پیدا کنید و با او ارتباط بگیرید.
نمایش ۷۰۶-۷۲۰ از ۱٬۰۶۰ استاد راهنما
اطلاعات این پروفایلها از صفحات عمومی دانشگاهها جمعآوری شده و ممکن است بهروز نباشد.
Caio Corroمشاهده پروفایل
دانشیار
Caio Corro is a Maître de Conférences (Associate Professor) at INSA Rennes, conducting research in the LinkMedia team of the IRISA laboratory. His work bridges theoretical computer science with practical applications in natural language understanding, with particular expertise in structured prediction problems for NLP systems. Dr. Corro's research program spans multiple interconnected domains: Natural Language Processing, with special focus on named entity recognition (including nested and discontinuous entities) Machine Learning algorithms for linguistic structure modeling Combinatorial optimization approaches to NLP challenges Application of AI techniques to linguistic research (AI for science) Development of efficient algorithms balancing theoretical soundness with practical performance His recent publication trajectory shows increasing emphasis on domain adaptation and multilingual capabilities while maintaining rigorous theoretical foundations. Corro has pioneered approaches that reduce computational complexity in structured prediction tasks without sacrificing accuracy, with significant contributions to named entity recognition methodologies. His work on EuroBERT demonstrates commitment to European language technologies, while his insurance domain research shows practical application focus. Dr. Corro maintains an active supervision portfolio: Current PhD students: Ayoub Hammal (co-supervised with Pierre Zweigenbaum and Miguel Couceiro), Benno Uthayasooriyar (co-supervised with Franck Vermet and Antoine Ly) Former students include Alban Petit (PhD graduate), Nicolas Devatine (now PhD student at IRIT), and several master's students who have contributed to his research program As a core member of the LinkMedia team at IRISA, Dr. Corro collaborates across institutional boundaries on cutting-edge NLP challenges, with particular emphasis on adapting techniques to specialized domains where data constraints present unique challenges.
Daniil Nikolaevich Tyapkinمشاهده پروفایل
پژوهشگر ارشد
Daniil Nikolaevich Tyapkin is a Junior Research Fellow and Postgraduate Student at the National Research University Higher School of Economics (HSE), affiliated with the Faculty of Computer Science. He works within the Institute of Artificial Intelligence and Digital Sciences and the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis under the supervision of Alexey Alexandrovich Naumov. Education: 2021: Bachelor's degree in Applied Mathematics and Computer Science from HSE Currently in 2nd year of postgraduate studies with dissertation topic: "Randomized trial for reinforcement learning" Research Interests: His work focuses on reinforcement learning and stochastic optimization, including exploration strategies, generative flow networks, and theoretical analysis of learning algorithms. He develops efficient methods for complex decision-making problems with applications in AI and data science. Publications: His research appears in top venues (ICLR, NeurIPS, ICML, AISTATS) covering reinforcement learning, optimization, and probabilistic modeling. Recent works explore GFlowNets, entropy regularization, and high-probability bounds for learning algorithms. Awards & Honors: Letter of gratitude from HSE Faculty of Computer Science (2022) Publication bonus for List A journal articles (2023-2024) HSE Personnel Reserve - New Researchers category (2022-2023) Teaching & Advising: Teaches courses in Mathematical Statistics and Matrix Computations. No formal student advising mentioned. Laboratory Affiliation: Member of the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis, focusing on algorithmic development for machine learning challenges.
Arnaud Carayol is a Professor of Computer Science at Gustave Eiffel University, where he has been working since September 2020. He is a member of the Models and Algorithms team at the Laboratoire d'informatique Gaspard Monge (LIGM). Prior to his current position, he was a full-time researcher at CNRS. His research focuses on theoretical aspects of computer science, with particular emphasis on: Automata Theory Formal Languages Logic in Computer Science Model Checking Pushdown Systems Games on Infinite Structures Professor Carayol's recent publications demonstrate a consistent focus on automata theory, particularly on infinite trees and pushdown systems, with applications to verification and game theory. His work often explores the connections between formal languages, logic, and computational models, with a particular emphasis on decidability and complexity questions. The trend shows increasing sophistication in handling higher-order systems and probabilistic elements in computational models. He has served on program committees for numerous prestigious conferences including LICS, STACS, ICALP, and FOSSACS. Notably, he was PC Co-Chair and organizer for CIAA 2017 and PC Chair and organizer for FICS 2013. Professor Carayol has led significant research projects: Head of project AMIS (2011-2014) financed by ANR Head of project VAPF (2011-2012) financed by Digiteo Member of project LiFoundations (2018-2022) His research continues to advance our understanding of theoretical models in computer science, with implications for program verification, formal methods, and computational theory.
Shelby Kimmelمشاهده پروفایل
استادیار
Shelby Kimmel is an Assistant Professor of Computer Science at Middlebury College, where she works with undergraduate students in both classroom instruction and research projects. Previously, she was a Hartree Postdoctoral Fellow at the University of Maryland in QuICS (Joint Center for Quantum Information and Computer Science). She earned her PhD in physics from MIT and completed her undergraduate studies in astrophysics at Williams College. Dr. Kimmel specializes in quantum computing research, focusing on the design of quantum algorithms and proving their superiority over classical alternatives. Her work includes creating efficient methods for characterizing errors in experimental quantum computers, as well as exploring quantum complexity theory and quantum information theory. She has made significant contributions to the field of quantum query complexity, developing algorithms that solve problems with fewer queries than classical approaches. Her research spans multiple areas within quantum computing, with a particular emphasis on graph problems, connectivity algorithms, and process tomography. Analysis of her publication record shows a consistent focus on developing practical quantum algorithms with provable advantages over classical methods, while also addressing the experimental challenges of implementing these algorithms on real quantum hardware. Her work bridges theoretical computer science and experimental quantum physics. Dr. Kimmel actively mentors students and has advised numerous undergraduate researchers who have gone on to pursue PhDs at prestigious institutions. She also serves as an advisor to Middlebury College Women in Computer Science and has mentored women in physics through programs at University of Maryland and MIT. She maintains an active speaking schedule, presenting her research at major quantum computing conferences and institutions worldwide, from the Simons Institute to IBM research facilities. Her talks cover diverse aspects of quantum algorithms, including path detection, quantum connectivity, and error characterization techniques. Beyond her academic work, Dr. Kimmel enjoys playing and teaching traditional Korean drumming, demonstrating her commitment to cultural engagement alongside her scientific pursuits.
Rui Duarte Neves serves as a Professor in the Department of Civil Engineering at the School of Technology of Barreiro, Polytechnic Institute of Setúbal, Portugal. Previously, he conducted research at the National Laboratory for Civil Engineering (LNEC-Portugal), establishing his expertise in concrete technology and structural assessment before transitioning to his current academic role. His research specializes in concrete durability and sustainable construction materials, with primary focus on service life prediction of reinforced concrete structures and development of eco-friendly alternatives like recycled aggregate concrete. He pioneers non-destructive testing methodologies for on-site evaluation of carbonation and chloride penetration, directly addressing infrastructure longevity challenges in aggressive environments. Analysis of his publication portfolio reveals consistent innovation in sustainable concrete technologies, particularly CO 2 sequestration in recycled concrete and AI-driven predictive modeling for carbonation coefficients. His work bridges experimental validation with computational approaches, including genetic programming and tree-based algorithms, while advancing circular economy principles through industrial waste reuse in cement production. Dr. Neves actively contributes to the Resilience R&D Unit, where his research integrates field testing, statistical modeling, and materials innovation to enhance concrete performance under environmental stressors like carbonation and chloride exposure.
Hugo Alves Akitayaمشاهده پروفایل
استادیار
- Algorithms
- Computational Geometry
- Graph Drawing
- +۵ مورد دیگر
Hugo Alves Akitaya is an Assistant Professor at the University of Massachusetts Lowell in the Miner School of Computer & Information Sciences, which is part of the Kennedy College of Sciences. He holds a Ph.D. in Computer Science from Tufts University (2018), an M.Eng. from University of Tsukuba (2014), and a B.Eng. from University of Brasília (2011). His educational background includes: Ph.D. in Computer Science from Tufts University (2018) M.Eng. in Computer Science from University of Tsukuba (2014) B.Eng. in Control and Automation from University of Brasília (2011) Hugo's research primarily focuses on computational geometry and algorithms, with special emphasis on reconfiguration algorithms, discrete geometry, graph drawing, and origami-related folding problems. His work bridges theoretical computer science with practical geometric applications, often exploring the mathematical foundations of folding and unfolding processes. His research has significant implications for robotics, computer graphics, and network design. Hugo's publications demonstrate consistent contributions to major computational geometry conferences including SODA, SoCG, GD, and FWCG. His work shows a clear trajectory from theoretical foundations in geometric algorithms to practical applications in areas such as robot swarm coordination (Freeze Tag problem), paper folding mathematics, and geometric network optimization. The recurring themes across his publications include NP-hardness proofs, geometric optimization techniques, and novel algorithmic approaches to longstanding problems in computational geometry. Hugo has received several prestigious awards including: Loevner Fellowship (2016-2017) LASPAU-administrated scholarship (Science without Borders) (2014-2018) Outstanding Presentation Award at JSST2013 1st Place in ACM Student Research Competition at SIGGRAPH 2013 MEXT scholarship from Japanese Government (2011-2014) Outstanding Student Award from University of Brasília (2011) Hugo teaches courses including COMP 5030 (Algorithms) and COMP 5800 (Computational Geometry) at UMass Lowell, and previously taught Comp 160 (Algorithms) at Tufts University. His research is conducted in collaboration with prominent figures in computational geometry including Erik Demaine, Diane Souvaine, and Csaba Tóth. His work often involves interdisciplinary approaches, connecting computer science with mathematical theory and practical applications. Hugo maintains an active research laboratory focused on computational geometry problems, with particular emphasis on folding algorithms and geometric reconfiguration. His team explores both theoretical foundations and practical implementations of geometric algorithms, often drawing inspiration from origami mathematics. The group maintains strong collaborations with researchers at institutions including Tufts University, Carleton University, and various international partners.
Stephen Melczerمشاهده پروفایل
استادیار
Stephen Melczer is an Assistant Professor in Combinatorics and Optimization at the Cheriton School of Computer Science, University of Waterloo. His research advances analytic combinatorics, lattice path enumeration, and symbolic computation techniques. Research develops multivariate methods for asymptotic enumeration, including work on generating functions, lattice paths, and tree structures. Recent publications focus on SageMath implementations, AVL tree encodings, and singularity analysis. Publications demonstrate consistent innovation in combinatorial algorithms with applications to information theory and data structures. Work integrates symbolic computation with asymptotic analysis for rigorous combinatorial results.
Saul Jacka is a Professor at the University of Warwick and Editor-in-Chief of the journal Stochastics . As a Faculty Fellow at the Alan Turing Institute, his research focuses on probability theory with emphasis on mathematical finance, stochastic processes, stochastic control, optimal stopping, and probability on trees. He actively supervises PhD students in these areas and contributes to the Centre for Doctoral Training in Statistics. His research explores theoretical and applied aspects of stochastic analysis, including applications in financial markets and risk management. Recent work addresses optimal stopping problems, martingale approaches to control, and regime-switching models. Key contributions include studies on Snell envelope decomposition, coherent risk measures, and numerical approximations for Lévy processes. Jacka has held academic roles since the 1990s, publishing extensively in top journals such as Stochastic Processes and their Applications , SIAM Journal on Control and Optimization , and Annals of Applied Probability . His work frequently bridges theoretical probability with practical financial modeling challenges. Awards: Alan Turing Institute Faculty Fellowship Grants: Funded PhD places in Statistics (starting 2025) Labs/Teams: Centre for Doctoral Training in Statistics, Warwick Probabilistic Modelling Group
Matt Roberts is a Professor of Probability and Royal Society University Research Fellow at the University of Bath, leading the Probability Laboratory (Prob-L@B) within the Department of Mathematical Sciences. His research focuses on stochastic processes including branching processes, random graphs, and noise sensitivity, with applications in percolation and Brownian motion. He has held postdoctoral positions at institutions like the University of Warwick, McGill University, and the Weierstrass Institute in Berlin. Education: PhD in Probability (University of Bath, supervised by Simon Harris), postdoctoral fellowships at multiple institutions. Grants: Includes EPSRC and Royal Society-funded projects on geometric-random fitness models, branching structures, and inclusion initiatives. Advising: Supervised six PhD students (e.g., Sam Johnston, Alice Callegaro) and multiple postdoctoral researchers (e.g., Eleanor Archer, Apolline Louvet). Research interests span theoretical probability with emphasis on spatial stochastic systems and their applications. His work has contributed to understanding branching random walks, fragmentation processes, and critical percolation dynamics.
Gabor Tardosمشاهده پروفایل
استاد پژوهشی
Gábor Tardos is a Research Professor at the Alfréd Rényi Institute of Mathematics, part of the Hungarian Academy of Sciences. He has held academic positions including Professorships at Central European University (2010–2020) and Eötvös University (1992–2003), and visiting roles at institutions like the Institute for Advanced Study (1996–97) and the University of Chicago. His research focuses on combinatorics, discrete and computational geometry, and complexity theory, with significant contributions to graph theory, Ramsey problems, and extremal combinatorics. Education: Diploma in Mathematics, Eötvös University, 1987. Ph.D. in Mathematics, Eötvös University, 1988 (thesis on universal algebra). Doctor of the Hungarian Academy of Sciences, 2005. Research Interests: Combining theoretical depth with algorithmic applications, Tardos explores combinatorics (including extremal graph theory and Ramsey theory), discrete geometry (geometric graphs, arrangements), and computational complexity (algorithm design, lower bounds). His work often bridges abstract combinatorial structures with practical computational challenges. Recent Publications Trends: His recent work emphasizes edge-ordered graphs, extremal functions for forbidden matrices, and geometric applications like necklace cuts and grasshopper problems. These studies advance theoretical foundations while addressing algorithmic efficiency and structural limits in discrete systems. Awards: 2022 ERC Advanced Grant, 2020 Gödel Prize, 2019 Corresponding Member of Hungarian Academy of Sciences. 2018 Invited Speaker at ICM, 1999 Erdős Prize, and 1988 G. Grünwald Prize. Grants and Labs: Leading projects funded by the European Research Council and Canadian Research Chairs, focusing on geometric and algorithmic combinatorics. Collaborations span international teams addressing foundational questions in discrete mathematics. Future Works: Ongoing research includes extremal edge-ordered graph functions, Ramsey-type problems, and applications of combinatorial methods in theoretical computer science.
Youngtak Sohnمشاهده پروفایل
استادیار
Youngtak Sohn is an Assistant Professor in the Division of Applied Mathematics at Brown University since July 2024. Previously, he was a postdoctoral researcher at MIT Mathematics, mentored by Elchanan Mossel and Nike Sun through the NSF/Simons Collaboration on Theoretical Foundations of Deep Learning. He earned his PhD in Statistics from Stanford University (2021) under Amir Dembo and a BSc in Mathematics from Seoul National University (2016). His research focuses on probability theory and its intersections with mathematical statistics, theoretical computer science, and statistical physics. Key areas include high-dimensional statistics, random constraint satisfaction problems, spin glass theory, and the analysis of stochastic networks. Recent work explores phase transitions in estimation problems, universality of max-margin classifiers, and solution geometry of random constraint satisfaction problems. He has taught courses at Brown (APMA 2640) and MIT (Theoretical Computer Science seminar), and served as a teaching assistant for numerous Stanford courses including stochastic processes, statistical inference, and regression analysis.
- Constraint Satisfaction Problems (CSP)
- Algebraic Methods in Computer Science
- Computational Logic
- +۵ مورد دیگر
Prof. Manuel Bodirsky is a Professor of Algebra and Discrete Structures at Technische Universität Dresden since August 2014. He leads the Algebra and Discrete Structures group within the Faculty of Computer Science and is affiliated with the International Center for Computational Logic (ICCL). His research focuses on constraint satisfaction problems (CSP), algebraic methods in computer science, computational logic, Ramsey theory, model theory, and discrete mathematics. Notable projects include exploring the algebraic tractability of CSPs and applying universal algebra to classify computational complexity. His work frequently intersects with combinatorics, graph theory, and theoretical computer science. Recent publications address advanced topics like temporal CSPs, spectrahedral shadows, and resilience problems using valued CSP frameworks. He maintains active collaborations in computational algebra and logic, contributing to both theoretical foundations and algorithmic applications. Education: Not explicitly listed in provided texts but inferred to include advanced studies in mathematics and computer science. Research interests span foundational areas such as: Constraint satisfaction problem complexity classification Applications of universal algebra to computational problems Model theory and finite structures Combinatorial properties of graphs and tournaments Algorithmic approaches to algebraic and logical systems His recent articles emphasize methodological innovations, including reductions to semidefinite programming, Ramsey-theoretic techniques, and gadget-based transformations. Despite no listed academic awards in the provided data, his prolific publication record reflects significant contributions to theoretical computer science and discrete mathematics. No student advisees or grant details were explicitly mentioned, though his group likely engages in funded research projects given the institutional context.
Adrien Kasselمشاهده پروفایل
پژوهشگر
- Probability
- Combinatorics
- Mathematical Physics
- +۳ مورد دیگر
Adrien Kassel is a Researcher at CNRS based at École Normale Supérieure de Lyon, working within the Unit for Pure and Applied Mathematics (UMPA) in the Probabilities team. His research spans the intersection of probability theory, combinatorics, and mathematical physics, with particular focus on random structures on graphs and surfaces. Dr. Kassel's research centers on probabilistic combinatorial structures, especially determinantal point processes , random spanning trees and forests , and loop models . His work connects deep mathematical concepts from statistical mechanics with geometric and topological structures. He investigates scaling limits of discrete models, connections to conformal field theory, and applications to mathematical physics. His research often reveals profound connections between seemingly disparate areas of mathematics through the lens of probability. His publications demonstrate a consistent focus on the interplay between combinatorial structures and probabilistic phenomena. The research trajectory shows increasing sophistication in handling geometric aspects of random processes, with recent work exploring connections to quantum gravity and conformal field theory through Schramm-Loewner evolution. Dr. Kassel received the prestigious Paul R. Halmos - Lester R. Ford Award for his article "The Looping Rate and Sandpile Density of Planar Graphs" co-authored with David B. Wilson. This award recognizes expository excellence in mathematical writing. He has advised Héloïse Constantin, who successfully defended her PhD thesis on "Spanning forests and phase transition" in June 2023. Dr. Kassel teaches advanced courses including "Determinantal processes" at the Master's level and "Integration and Probability" at the undergraduate level. He has co-organized numerous academic events including the ICJ-UMPA probability seminar, workshops on random maps and matrices, and meetings between ENS Lyon and SISSA. As coordinator of MathαLyon from 2017-2022, he actively engaged in mathematical outreach, bringing exhibits to middle and high schools across the Lyon region. His commitment to popularization extends to writing for Images des Mathématiques and participating in various math circles and outreach programs.
- Computer Vision
- Machine Learning
- Video Analysis
- +۴ مورد دیگر
Greg Mori is a Professor at the School of Computing Science, Simon Fraser University. He holds a Ph.D. from UC Berkeley and an Hon. B.Sc. from the University of Toronto. His research focuses on computer vision and machine learning, particularly human pose estimation and activity recognition. He has served as Director of the School of Computing Science (2015–2018) and is now Senior Research Director at Borealis AI. Mori has contributed to methodologies like hidden Conditional Random Fields (hCRF), LDA variants, and deep networks. His work emphasizes features capturing visual intuition operationalized via machine learning. Education: Ph.D. in Computer Science, University of California, Berkeley (2004) Hon. B.Sc. in Computer Science and Mathematics, University of Toronto (1999) Research Interests: Computer vision, machine learning, video analysis, human activity recognition, human pose estimation, pedestrian detection and tracking, object recognition. Awards: CIPPRS Research Excellence Award (2008) NSERC Discovery Accelerator Supplement (2008, 2015) ICCV Helmholtz Prize (2017) Outstanding Reviewer Awards (ICCV 2009, CVPR 2010) Grants & Service: Program Chair for CVPR 2020 and General Chair for CVPR 2023 Editorial roles for IJCV and T-PAMI Organizing committee member for CVPR, ICCV, ECCV, NeurIPS, and ICLR Labs/Teams: Vision and Media Lab at Simon Fraser University, leading research in computer vision and machine learning applications.
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