جست‌وجوی استاد و پژوهشگر

• Probability Theory
• Random Walks
• Markov Chains
• +۳ مورد دیگر

Denis Denisov is a Senior Lecturer in the Department of Mathematics at the University of Manchester. His research focuses on Probability Theory, with a particular emphasis on Random Walks, Markov Chains, and Asymptotic Analysis. He has made significant contributions to the study of random walks in cones, boundary-crossing problems, and limit theorems for stochastic processes. Denis actively supervises postgraduate students, with a record of guiding six supervised works. His research explores topics such as harmonic measure in multidimensional gambler’s problems, Green functions for asymptotically stable random walks, and probabilistic approaches to risk processes with level-dependent premiums. His recent work includes studies on Markov chains in Brownian motion domains, Berry-Esseen inequalities for conditioned random walks, and expansions for conditioned random walks. These contributions highlight his expertise in advanced probability theory and its applications to stochastic processes. Denis is open to mentoring PhD students interested in probability, asymptotic methods, and stochastic modeling. His research has been published in high-impact journals such as the Annals of Probability and Transactions of the American Mathematical Society.

Pranabendu Misraمشاهده پروفایل

استادیار

• Algorithms and their applications
• Graph Theory
• Parameterized Complexity
• +۱۰ مورد دیگر

Pranabendu Misra is an Assistant Professor in the Department of Computer Science at Chennai Mathematical Institute (CMI), India. He previously held postdoctoral positions at the Max Planck Institute for Informatics, Germany, and the University of Bergen, Norway. His research is centered in theoretical computer science with strong contributions to algorithms and complexity. Current Affiliation: Assistant Professor, Department of Computer Science, Chennai Mathematical Institute, India Previous Positions: Postdoctoral Fellow, Max Planck Institute for Informatics; Researcher, University of Bergen Education: PhD from Institute of Mathematical Sciences (IMSc), Chennai, advised by Prof. Saket Saurabh; M.Sc. and B.Sc. from CMI in Computer Science and Mathematics Pranabendu's research interests span a broad spectrum of algorithmic theory, including Graph Theory , Parameterized Complexity , Approximation Algorithms , Matroids , Algebraic Methods , Derandomization , Algorithmic Game Theory , Streaming and Dynamic Graphs , and increasingly, AI/ML and Deep Learning . His work combines deep theoretical analysis with practical algorithmic insights. His recent publications in top-tier venues like SODA, STOC, ICALP, and EC reveal a strong trend toward developing meta-theorems, kernelization techniques, and FPT approximation algorithms for fundamental graph problems such as vertex cover, feedback vertex set, connectivity augmentation, and disjoint paths. He also explores fair division in algorithmic game theory and fault-tolerant network design. His scientific honors include: Google India Research Award (2022) SERB Startup Research Grant (2022) Fujitsu Research Grant (2023) Pranabendu actively contributes to academic advising and teaching, offering courses such as Advanced Machine Learning , Approximation Algorithms , and Parameterized Algorithms . He has collaborated extensively with leading researchers including Daniel Lokshtanov, Saket Saurabh, Meirav Zehavi, and Kurt Mehlhorn. While specific grant details are limited, his funded projects suggest strong support for research in algorithms and AI/ML. He teaches at CMI and has previously taught at the Max Planck Institute. He is affiliated with a vibrant research group focusing on algorithms and complexity, working on both foundational and applied algorithmic problems. His recent interest in AI/ML indicates an expanding research scope bridging theoretical guarantees with modern machine learning applications.

Ashutosh Raiمشاهده پروفایل

استادیار

• Theoretical Computer Science
• Parameterized Algorithms
• Fixed-Parameter Tractability
• +۷ مورد دیگر

Ashutosh Rai is an Assistant Professor in the Department of Mathematics at the Indian Institute of Technology Delhi (IIT Delhi). He previously served as an Assistant Professor in the Computer Science Department at IIIT Delhi. His academic journey includes postdoctoral research at Charles University in Prague and the Hong Kong Polytechnic University. He completed his PhD and Master’s at the Institute of Mathematical Sciences (IMSc), Chennai, under the supervision of Prof. Saket Saurabh and Prof. Venkatesh Raman. His research focuses on Theoretical Computer Science, especially parameterized algorithms, fixed-parameter tractability, kernelization, and the complexity of NP-complete problems. His work bridges classical and parameterized complexity, exploring hardness and algorithmic solutions. The 15 most recent publications highlight consistent contributions to parameterized complexity, graph editing, coloring, and optimization problems. His research spans journals like Algorithmica , SIAM Journal on Discrete Mathematics , and Theoretical Computer Science , and top conferences including ICALP, IPEC, ESA, and MFCS. Key themes include kernelization, approximation in parameterized settings, and structural graph problems. He has taught courses such as Analysis and Design of Algorithms, Theory of Computation, Parameterized Algorithms, Operating Systems, and Combinatorics at IIT Delhi, IIIT Delhi, and Charles University. He has also mentored students and collaborated extensively with leading researchers in the field. Email: ashutosh.rai@maths.iitd.ac.in

• Geometric and probabilistic functional analysis
• Random matrices, random graphs, and random tensors
• Free probability and high dimensional phenomena
• +۱۰ مورد دیگر

Djalil CHAFAÏ is a University Professor of Mathematics at Université Paris-Dauphine - PSL, with dual affiliation at CEREMADE (Centre de Recherche en Mathématiques de la Décision) and DMA (Département de Mathématiques et Applications) at École normale supérieure (Paris) - PSL. He currently serves as Directeur des études du DMA (2021-2026) and Directeur scientifique du RNBM (2021-2025). His extensive research spans multiple areas of probability theory, mathematical physics, and applied mathematics. CHAFAÏ's research interests center around geometric and probabilistic functional analysis, random matrices, random graphs, free probability, and high-dimensional phenomena. His work connects mathematical theory with applications in biology, physics, and data science. He has made significant contributions to understanding cutoff phenomena in high-dimensional diffusions, Riesz energy problems, and Coulomb gases. His research often combines theoretical analysis with visual illustrations created using computational tools like Octave, Python, and Julia. Analysis of his recent publications reveals a strong focus on cutoff phenomena in various stochastic processes, equilibrium measures in potential theory, and the mathematical properties of random matrix ensembles. His work demonstrates a consistent pattern of bridging abstract mathematical concepts with concrete physical phenomena, particularly in statistical physics. The interdisciplinary nature of his research is evident in the diverse applications ranging from mathematical biology to data science. CHAFAÏ has successfully advised numerous doctoral students whose work continues to influence the field. His current doctoral students include Samuel Chan-ashing, Rémi Bonnin, and Kewei Pan, while his former students have gone on to positions at prestigious institutions worldwide. He is actively involved in the mathematical community through organizing conferences, seminars, and workshops, including the Matrices Et Graphes Aléatoires (MEGA) project and the Conviviality project funded by ANR.

• Algorithms
• Optimization
• Mathematical Programming
• +۲ مورد دیگر

Federico Della Croce Di Dojola is a Full Professor at the Department of Management and Production Engineering (DIGEP) of Politecnico di Torino. He is also a member of the Interdepartmental Center Ec-L - Energy Center Lab. His academic career spans over two decades with international collaborations including a Visiting Researcher position at Université Paris Dauphine (2004-2005). Research Interests focus on algorithm development and optimization techniques, particularly in combinatorial optimization. His work applies to diverse areas including operations research, production scheduling, and mathematical programming, with connections to sustainable cities and communities (SDG Goal 11). Publications demonstrate expertise in exact algorithms, heuristic methods, and scheduling problems across multiple domains. Key contributions include advancements in transportation optimization, bilevel scheduling, and fuel treatment planning. Scientific Recognition : Member of Editorial Board, European Journal of Operational Research (2016-) Member of Editorial Board, Computers & Operations Research (2016-) Principal investigator in multiple research projects Academic Leadership includes: Director of Department of Control and Computer Science (2012-2015) Doctoral College Member (2003-2025) Course Lecturer at multiple degree levels PhD Supervision includes Quentin Schau (2024-in progress) focusing on Industry 4.0 scheduling problems.

• Control Theory
• Robust Control
• Nonlinear Control Systems
• +۳ مورد دیگر

Richard Seeber is a Researcher at the Institute of Control and Automation (Institut für Regelungstechnik und Automation) at TU Graz. His academic titles include Privatdozent (Priv.-Doz.), Diplom-Ingenieur (Dipl.-Ing.), Doctor of Engineering (Dr.techn.), and Bachelor of Science (BSc). His work focuses on advanced control theory, nonlinear systems, and observer design. He holds a teaching authorization in system dynamics and control theory, reflecting his dual role in research and education. Research interests include robust exact differentiators, sliding mode control, observer design for linear and nonlinear systems, and applications in thermal management and energy systems. He has contributed to methodologies addressing actuator saturation, discretization challenges, and real-time control implementation. His recent work emphasizes predefined-time convergence algorithms and stability analysis under bounded noise or disturbances. Selected projects involve heater modeling for fuel cell stacks, frequency converter nonlinearity compensation, and control strategies for biomass furnaces. His contributions are often applied to industrial systems requiring precise control under uncertainty, such as gas engine power plants and transient operating conditions. He collaborates within TU Graz's Institute of Control and Automation, contributing to both theoretical advancements and practical tool development. His research portal (https://irt.tugraz.at/) hosts publications and tools related to robust control methodologies.

• Machine Learning
• Reinforcement Learning
• Neural Networks
• +۳ مورد دیگر

Ruosong Wang is an Assistant Professor at Peking University , focusing on Reinforcement Learning , Neural Networks , and Optimization . His research bridges theoretical analysis with practical implementations in AI, particularly in deep learning and reinforcement learning frameworks. Research Interests : Wang’s work explores the intersection of Machine Learning Theory , Function Approximation , Generalization , and Algorithm Design . He investigates how overparameterized neural networks and infinitely wide deep nets can be optimized for small-data tasks, while addressing challenges in offline RL , preference-based learning , and graph neural networks . Article Trends : His publications (2019–2021) emphasize provably efficient algorithms for reinforcement learning, fine-grained theoretical analysis of neural network optimization, and hybrid models combining graph kernels with neural architectures. Key themes include sample efficiency , representation learning , and bounded eluder dimension in RL. Collaborations and Access : Wang collaborates with leading researchers at institutions like Carnegie Mellon University and UC Berkeley. All his articles are publicly accessible through arXiv or conference proceedings, with citations reflecting significant influence in machine learning and AI communities.

Thore Husfeldtمشاهده پروفایل

استاد

• Theoretical Computer Science
• Algorithms
• Graph Algorithms
• +۳ مورد دیگر

Thore Husfeldt is a Professor of Theoretical Computer Science at the IT University of Copenhagen, leading the Algorithms Research Group. He holds a PhD from the University of Aarhus (1997), a Docent title from Lund University (2007), and has been a Professor at the University of Lund since 2012. His research focuses on algorithms, with a concentration on exponential time algorithms, combinatorial optimization, and graph theory. Education Bachelor (B.Sc.) in Computer Science and Mathematics, University of Aarhus (1993) Cand. scient. (M.Sc.) in Computer Science, University of Aarhus (1994) Ph.D. in Science (Computer Science), University of Aarhus (1997) Docent, Lund University (2007) Professor of Computer Science, Lund University (2012) Research Interests Husfeldt’s work explores foundational aspects of algorithms, including exponential time algorithms for hard problems, graph algorithms, and combinatorial optimization. His recent contributions address topics like cycle detection, path enumeration, and efficient algorithm design for NP-hard problems. His research bridges theoretical computer science with practical algorithmic challenges. Awards Best Paper Award at ICALP 2014 (Track A: Algorithms, Complexity, and Games) Excellence in Teaching Award (2018) Grants & Projects Husfeldt leads major initiatives such as the BARC2: Basic Algorithms Research Copenhagen (Villum Foundation), focusing on theoretical algorithm research, and DIREC , developing algorithms education through animated videos. He also collaborates on projects like Unifying Theories for Graph Modification Problems (Independent Research Fund Denmark). Labs & Teams He oversees the Algorithms Research Group at ITU and contributes to the BARC (Basic Algorithms Research Copenhagen) initiative, fostering collaborations in algorithmic theory and applications.

• Image Analysis
• Statistical Methods
• Determinantal Point Processes
• +۶ مورد دیگر

Agnes Desolneux holds dual roles as a Research Professor (CNRS Directrice de recherche) at the Centre Borelli and a Professor attached to the Department of Mathematics at ENS Paris-Saclay. Her affiliations include the Centre Borelli (ex-CMLA) and CNRS laboratories, focusing on advanced interdisciplinary research in mathematical imaging and stochastic methods. Education and Background: PhD in Applied Mathematics (2000), ENS Cachan Habilitation à Diriger des Recherches (HDR) in Applied Mathematics (2010), Université Paris Descartes Research Interests: Her work combines statistical analysis, stochastic processes, and mathematical modeling to address challenges in image analysis. Key areas include: Statistical methods ( a contrario framework, maximum entropy models) Image restoration and texture synthesis Determinantal point processes (DPP) and optimal transport Geometric analysis of random fields (curvature, Euler characteristics) Perceptual modeling of Gestalt principles Recent Contributions: Her research explores cutting-edge topics such as Gromov-Wasserstein distances in Gaussian mixture models, anisotropy analysis of random fields, and federated learning techniques. Her methodologies bridge theoretical probability and applied image processing. Awards and Recognition: No specific awards listed, but her extensive publication record in top journals like SIAM Journal on Imaging Sciences and Annals of Applied Probability highlights her scholarly impact. Grants and Collaborations: Active in projects like StoRI (Stochastic Recipes in Imaging) and the Master MVA program, emphasizing interdisciplinary training in mathematical data science. Her work often involves collaborations with institutions like the EDMH doctoral school and the Université Paris-Saclay. Labs and Teams: Leads research within the Centre Borelli, a CNRS-ENS Paris-Saclay hub for mathematics and computer science, fostering innovation in imaging and stochastic modeling.

David Kahleمشاهده پروفایل

دانشیار

• Statistical Computing
• Data Visualization
• Algebraic Statistics
• +۴ مورد دیگر

David Kahle is an Associate Professor in the Department of Statistical Science at Baylor University. His research focuses on statistical computing, data visualization, algebraic statistics, Bayesian methods, and pharmaceutical statistics. He is known for developing R packages such as ggmap , mpoly , and unmconf , which enhance computational and visualization capabilities in statistical analysis. Dr. Kahle holds a Ph.D. from Rice University (2011) and a B.A. in Mathematics from the University of Richmond (2006). He has authored over 30 peer-reviewed articles and serves as a Research Fellow at Baylor’s Collaborative on Hunger and Poverty. His work spans interdisciplinary collaborations in healthcare policy, environmental science, and data-driven decision-making. His research interests include: Statistical software development and R package creation Bayesian computation and prior elicitation techniques Algebraic approaches to statistical problems Applications in biostatistics and pharmaceutical trials Data visualization methodologies Notable awards include the Mike Kutner Junior Faculty Travel Award (2014–2015) and recognition for outstanding classroom performance (2013). He has secured research funding from institutions like Eli Lilly & Company and the USDA, totaling over $20 million in grants. Dr. Kahle advises graduate students on topics ranging from Bayesian regression to computational algebraic methods. His lab actively contributes to advancing statistical methodologies through open-source software development and collaborative research initiatives.

Dániel Marxمشاهده پروفایل

استاد

• Parameterized Algorithms
• Computational Complexity
• Algorithmic Graph Theory
• +۷ مورد دیگر

Dániel Marx is a tenured Professor at the CISPA Helmholtz Center for Information Security in Saarbrücken, Germany. He leads research in the area of Algorithmic Foundations and Cryptography, with a focus on parameterized algorithms and computational complexity. He has held previous positions at the Max Planck Institute for Informatics and the Institute for Computer Science and Control of the Hungarian Academy of Sciences (MTA SZTAKI). His work is highly theoretical, aiming to understand the precise complexity of algorithmic problems, especially on bounded-treewidth graphs and in parameterized settings. PhD: Budapest University of Technology and Economics, 2005 Postdoc: Tel Aviv University, Humboldt University Berlin Senior Research Fellow: MTA SZTAKI, 2012–2019 Senior Researcher: Max Planck Institute for Informatics, 2019–2020 Faculty: CISPA Helmholtz Center, 2020–present His research interests lie at the intersection of theoretical computer science and discrete mathematics. He is particularly known for his work on parameterized algorithms , fine-grained complexity , and lower bounds . His group investigates algorithmic graph theory problems, including domination, independence, homomorphism, and clustering, especially under structural constraints like bounded treewidth. He has pioneered techniques in dynamic programming, kernelization, and hardness proofs based on the Exponential Time Hypothesis. The recent publications of Dániel Marx reveal a consistent focus on establishing tight complexity bounds for fundamental algorithmic problems. His work spans from exact and parameterized algorithms to approximation and counting problems. A recurring theme is the analysis of problems on bounded-treewidth graphs, where he explores the boundary between tractable and intractable cases. He also contributes to network design (e.g., Steiner problems), clustering, and subgraph counting, often providing complete classifications of complexity based on parameters. European Research Council Starting Grant European Research Council Consolidator Grant Humboldt Research Fellowship for Experienced Researchers Dániel Marx has advised numerous researchers and collaborated widely across institutions. His research is supported by prestigious grants, including ERC grants that funded his group at MTA SZTAKI. He actively leads a research group at CISPA focused on parameterized algorithms and complexity. While specific PhD students are not listed in the provided text, his extensive co-authorship network indicates a strong mentoring and collaborative presence. His work has significant implications for the foundations of computer science and algorithm design. He leads the research group on Parameterized Algorithms and Complexity at CISPA, continuing his long-standing focus on theoretical algorithm design and analysis. His team works on foundational problems in graph algorithms and complexity theory, aiming to develop new algorithmic techniques and understand the limits of efficient computation.

• Public Transportation Optimization
• Electric Vehicle Scheduling
• Operations Research
• +۳ مورد دیگر

Dr. Marcel van Kooten Niekerk serves as an Assistant Professor in the Department of Information and Computing Sciences within the Faculty of Science at Utrecht University. His academic appointment focuses on the Simulation of Complex Systems subgroup, where he applies computational methods to solve challenging transportation problems. He also maintains a professional connection with the transportation industry as a Consultant Logistics and Innovation at Qbuzz BV, demonstrating his commitment to bridging academic research with practical applications in public transport. Van Kooten Niekerk's research spans two primary domains: transportation optimization and theoretical computer science. His more recent work centers on public transportation systems, particularly focusing on the integration of electric vehicles into existing public transit networks. His expertise encompasses complex scheduling challenges including electric bus operations, platform assignments across multiple stations, and the integration of timetabling with vehicle scheduling. Earlier in his career, he made significant contributions to theoretical computer science, particularly in graph theory and combinatorial optimization problems such as partitioning graphs into triangles. His publication record reveals a clear evolution from theoretical computer science toward applied transportation research, with a growing emphasis on sustainability. The most recent publications demonstrate sophisticated approaches to handling uncertainty in electric bus operations, optimizing platform assignments across multiple bus stations in Utrecht, and developing comprehensive frameworks for reliable and sustainable public transport systems. His work consistently applies operations research methodologies, particularly integer linear programming and stochastic optimization techniques, to solve real-world transportation challenges. Van Kooten Niekerk has established himself as a researcher who effectively bridges theoretical computer science with practical transportation applications. His work has gained significant attention, with some publications accumulating over 100 citations and reader captures, indicating substantial impact in the transportation research community. His doctoral thesis on optimizing for reliable and sustainable public transport represents a comprehensive contribution to the field of electric vehicle integration in public transportation systems.

• Algorithms
• Complexity Theory
• Theoretical Computer Science
• +۵ مورد دیگر

Jesper Nederlof is an Associate Professor in the Algorithms and Complexity group at the Department of Information and Computing Sciences, Faculty of Science, Utrecht University. His research focuses on designing efficient algorithms for computationally hard problems, particularly in the areas of parameterized complexity, graph algorithms, and NP-complete problems. He received his M.Sc. in Applied Computing Science from Utrecht University in 2008 and his Ph.D. from the University of Bergen in 2011 with the thesis 'Space and Time Efficient Structural Improvements of Dynamic Programming Algorithms' under supervision of Pinar Heggernes. Nederlof's research interests span multiple areas of theoretical computer science, with a particular focus on designing algorithms for NP-complete problems with small exponential worst-case run time. His work extends to algorithmic game theory, information theory, representation theory, approximation algorithms, and online algorithms. He has made significant contributions to parameterized complexity, particularly in developing algorithms parameterized by structural graph parameters like treewidth and cutwidth. His publication record shows a consistent output of high-quality research in top theoretical computer science venues. His recent work demonstrates trends toward tighter bounds for exponential-time algorithms, improved space complexity, and connections between different complexity hypotheses like ETH. Many papers focus on structural parameters of graphs to develop more efficient algorithms for fundamental problems like Hamiltonian cycle, Steiner tree, and subset sum. EATCS-IPEC Nerode Prize (2023) WG best paper award (2020) Nederlof has been involved in teaching courses on algorithms, (non)-linear optimization, graph theory, (vector) calculus, modeling, and management and product development. His research has been supported by various grants including an NWO open competition project during his postdoctoral period and an EU ERC Starting Grant for the project 'Finding Cracks in the wall of NP-Completeness' (2020-2025). As a member of the Algorithms and Complexity group at Utrecht University, Nederlof collaborates with researchers working on foundational aspects of computing, contributing to the group's reputation in theoretical computer science research.

Daniel Lokshtanovمشاهده پروفایل

استاد

• Theoretical Computer Science
• Discrete Mathematics
• Algorithmic Graph Theory
• +۷ مورد دیگر

Daniel Lokshtanov is a Professor and Vice Chair at the Department of Computer Science at the University of California, Santa Barbara (UCSB) , with a visiting professor affiliation at the University of Bergen . He is renowned for his work in Theoretical Computer Science and Discrete Mathematics , particularly focusing on Algorithmic Graph Theory and Parameterized Complexity . Key roles: Professor (UCSB, since 2020), Vice Chair (UCSB), Visiting Professor (University of Bergen) Research focus: Kernelization, Graph Minors, Exact Algorithms, Treewidth, Subexponential Algorithms Research Trends : His recent publications (2022-2020) demonstrate expertise in applying Parameterized Complexity to Graph Algorithms , including work on Unit Disk Graphs , Graph Reconfiguration , and Subexponential Time Algorithms . Topics span Graph Contraction , Obstacle Removal , and Kemeny Rank Aggregation . Scientific Awards : Outstanding Young Researcher Meltzer Award Best ESA Paper Award (2015) Advising : While not actively seeking new PhD students, he supervises MS students at UCSB and has mentored numerous advisees through Parameterized Algorithms research. He co-organizes the Inter-Collegiate Programming Contest at UCSB and the Norwegian Informatics Olympiad for high school students.

• Spatial statistics
• Computational statistics
• Statistical genetics
• +۵ مورد دیگر

Charles Geyer is a Professor at the School of Statistics, University of Minnesota . His work spans computational statistics, spatial statistics, and statistical genetics, with applications to endangered species conservation and stochastic process modeling. Research Interests: Spatial statistics, Markov chain Monte Carlo methods, likelihood inference in exponential families, constrained optimization, and statistical software development. Software Contributions: Developed R packages mcmc , aster , rcdd , and trust for Monte Carlo methods, life history analysis, computational geometry, and optimization. Teaching: Instructs graduate and undergraduate courses including Stat 5101, Stat 5102, Stat 5421, Stat 5601, and advanced seminars on computational statistics. Publications: Notable works include research on maximum likelihood estimation in exponential families, fuzzy P-values, geometric ergodicity in MCMC, and computational methods for conservation genetics.