جست‌وجوی استاد و پژوهشگر

Siegfried Nijssenمشاهده پروفایل

استادیار

• Data Mining
• Pattern Mining
• Exploratory Data Analysis
• +۵ مورد دیگر

Siegfried Nijssen is an Assistant Professor of Data Mining and Artificial Intelligence at the Catholic University of Louvain (UCLouvain) in Belgium, working within the ICTEAM research institute's Artificial Intelligence and Algorithms group. He has been at UCLouvain since 2016, previously serving as an Assistant Professor at University Leiden (2012-2016) and completing postdoctoral work at KU Leuven (2006-2015). He earned his PhD in Computer Science from University Leiden in 2006. His research focuses on making data analysis simpler through intersections between pattern mining, exploratory data analysis, and programming paradigms in Artificial Intelligence, particularly constraint programming and probabilistic programming. He has developed techniques for analyzing diverse data types including graphs, networks, and multi-relational data. His work bridges theoretical foundations with practical applications in decision tree learning, probabilistic networks, and source code analysis. Nijssen's recent publications demonstrate a strong focus on optimal decision trees, constraint-based pattern mining, and applications in bioinformatics and education. His research shows consistent evolution from foundational graph mining work (including the Gaston algorithm developed in 2004) to current work integrating machine learning with constraint programming for interpretable AI solutions. As an educator, he teaches courses including Mining Patterns in Data, Databases, and Artificial Intelligence and Machine Learning seminars at UCLouvain. He has advised numerous PhD students and postdocs, primarily in collaboration with Pierre Schaus, with former students like Tias Guns now holding professorships.

• Polymer Chemistry
• Energy Storage
• Organic Electronics
• +۴ مورد دیگر

Kenichi Oyaizu is a Professor in the Department of Applied Chemistry at the Faculty of Science and Engineering, Waseda University, Tokyo. His research spans polymer chemistry, energy storage, and materials science, with a focus on functional polymers for batteries, hydrogen storage, and high refractive index applications. He maintains active collaborations across academia and industry. Professor Oyaizu's research centers on designing polymers with tailored redox properties for energy storage systems, including organic radical batteries and hydrogen carriers. He pioneers high refractive index materials through molecular engineering of hydrogen-bonded networks and sulfur-rich frameworks. His group integrates machine learning with experimental synthesis, utilizing lossless data platforms for materials discovery and optimization in electrochemistry and optical applications. Analysis of his 15 most recent publications (2020-2025) reveals three dominant research trajectories: (1) High-refractive-index polymers leveraging hydrogen bonding and sulfur incorporation for optical devices, (2) Energy storage systems using redox-active polymers for batteries and hydrogen carriers, and (3) Materials informatics approaches applying generative models and quantum-inspired algorithms to accelerate polymer design. These streams demonstrate consistent innovation in structure-property relationships for functional materials. No scientific awards or major honors are documented in the provided materials. Professor Oyaizu leads an active research group mentoring graduate students and postdoctoral researchers in polymer synthesis and characterization. His work receives funding from Japanese national agencies supporting sustainable energy materials and advanced polymer research, though specific grant details are not disclosed in the source text. Current projects emphasize machine learning-driven development of solid-state electrolytes and hydrogen storage polymers. His laboratory operates within Waseda University's advanced materials infrastructure, utilizing specialized facilities for polymer synthesis, electrochemical testing, and optical characterization. The team collaborates with international researchers on battery technologies and participates in university-industry consortia focused on sustainable materials development, with recent projects highlighted in Waseda University News and EurekAlert!.

Jonathan A. Kelnerمشاهده پروفایل

استاد

• Algorithms
• Complexity Theory
• Spectral Graph Theory
• +۴ مورد دیگر

Jonathan A. Kelner is a Professor of Applied Mathematics at the Massachusetts Institute of Technology (MIT) and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL) . His research bridges pure mathematics and algorithms, focusing on spectral graph theory, combinatorial optimization, and distributed computing.

• Computer Science
• Algorithms
• Graph Theory
• +۴ مورد دیگر

Virginia Vassilevska Williams is a Professor at the Massachusetts Institute of Technology (MIT) Department of Electrical Engineering and Computer Science (EECS), affiliated with MIT CSAIL. She earned her Ph.D. in Computer Science from Carnegie Mellon University in 2008 and held postdoctoral positions at the Institute for Advanced Study (Princeton), UC Berkeley, and Stanford. Education: B.S. in Mathematics and Engineering from Caltech (2003); Ph.D. in Computer Science from CMU (2008) Her research focuses on combinatorial and graph-theoretic approaches to computational problems, including shortest paths , pattern detection , fine-grained complexity , and computational social choice for analyzing election manipulation and tournament structures. Recent publications highlight advances in sparse graph algorithms , cycle detection , and approximate counting using matrix multiplication techniques. She co-organized programs at the Simons Institute (2023) and Dagstuhl Seminars (2016). Scientific Awards NSF CAREER Award Google Research Fellowship Alfred P. Sloan Research Fellowship Thornton Family Faculty Research Innovation Fellowship Invited Speaker at ICM 2018 She advises current Ph.D. students including John Kuszmaul , Yael Kirkpatrick , and Zixuan Xu . Former students like Amir Abboud (Weizmann Institute) and Nicole Wein (U. Michigan) have achieved academic and industry positions.

Claire Mathieuمشاهده پروفایل

استاد پژوهشی

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

Claire Mathieu is a CNRS Research Director in the Computer Science department at École normale supérieure, specializing in algorithm design and analysis with emphasis on approximation schemes for NP-hard problems. Her academic background includes: Former student at École normale supérieure PhD in Computer Science from Paris-Sud University (1988) Dr. Mathieu's research focuses on theoretical foundations of algorithms, particularly developing local search methods for clustering problems where enlarged neighborhoods yield near-optimal solutions through separability structures. Her work bridges theoretical guarantees with practical applications in combinatorial optimization, advancing approximation techniques for computationally intractable problems. Prior to her current position, she held research and faculty appointments at CNRS, Paris-Sud University, École Polytechnique, and Brown University, demonstrating extensive international experience across French and American academic institutions.

• Supply Chain Optimization
• Supply Chain Analytics
• Freight Transportation
• +۳ مورد دیگر

Michael Hewitt is Professor of Supply Chain Management at Loyola University Chicago's Quinlan School of Business, holding the Ralph Marotta Chair in Free Enterprise. He concurrently serves as Executive Director of the Quinlan Business Leadership Hub and Director of the Supply Chain and Sustainability Center. His academic credentials include a PhD in Industrial and Systems Engineering from Georgia Institute of Technology, complemented by dual MS degrees in Financial Engineering and Industrial Engineering from the University of Michigan, plus a BS in Mathematics & Economics from the same institution. Dr. Hewitt's research pioneers advanced optimization techniques for freight transportation and supply chain networks, integrating mathematical programming with real-world logistics challenges. His work bridges theoretical innovation and industrial application, particularly in time-dependent scheduling and stochastic network design where algorithmic breakthroughs directly impact transportation efficiency. Analysis of his recent publications reveals consistent focus on dynamic discretization methods and decomposition algorithms for complex network problems, with increasing emphasis on sustainability integration in freight systems since 2017. The research trajectory demonstrates evolving sophistication in handling time-dependent variables and stochastic elements within transportation networks. His award-winning contributions include: CSoNet Best Paper Award (2023) for time-dependent traveling salesman research INFORMS TSL Freight Transportation & Logistics SIG Award (2021) Glover-Klingman Prize for Networks journal publication (2019) INFORMS TSL Best Paper Award (2018) Loyola Faculty Researcher of the Year (2015) Funded by the National Science Foundation, Material Handling Institute, and New York State Health Foundation, his research has directly influenced decision systems at Bayer Crop Science, Exxon Mobil, and major freight carriers. Professional service includes past presidency of INFORMS Transportation Science and Logistics Society and editorial board memberships. Through the Supply Chain and Sustainability Center and Quinlan Business Leadership Hub, he drives industry-academic collaboration on sustainable logistics innovation and leadership development, connecting theoretical advances with practical business transformation.

Fang Kongمشاهده پروفایل

استادیار

• Online Learning
• Reinforcement Learning
• Machine Learning
• +۴ مورد دیگر

Fang Kong is an Assistant Professor in the Department of Statistics and Data Science at the Southern University of Science and Technology (SUSTech). He earned his PhD in Computer Science from Shanghai Jiao Tong University under the supervision of Prof. Shuai Li and received his Bachelor's degree in Software Engineering from Shandong University. Education: PhD in Computer Science, Shanghai Jiao Tong University (2020-2024) Bachelor's Degree in Software Engineering, Shandong University (2016-2020) Dr. Kong is broadly interested in developing theoretically guaranteed algorithms for sequential decision-making problems, with particular focus on multi-armed bandits and reinforcement learning, as well as their applications in online experimentation and recommendation systems. His research spans theoretical foundations of bandit algorithms, matching markets, influence maximization, and online learning under various feedback structures. He has made significant contributions to the understanding of best-of-both-worlds algorithms that perform well in both stochastic and adversarial environments. His publication record shows a strong trajectory of high-impact work in top-tier conferences including NeurIPS, ICML, ICLR, AAAI, WWW, and AAMAS. His research demonstrates expertise in theoretical machine learning with a focus on bandit algorithms, particularly in matching markets and sequential decision-making problems. His work often bridges theoretical guarantees with practical applications in recommendation systems and online experimentation. Scientific Awards: CCF Doctoral Dissertation Award in Agent and Multi-Agent Systems (2025) Baidu Scholarship (2024) National Scholarship for PhD students (2023, 2022) AAMAS Student Scholarship (2023) Microsoft Research Asia Excellence Award (2022) Dr. Kong actively mentors students at various levels, including PhD and Master's students at SUSTech, visiting students from other institutions, and undergraduate researchers. He serves as a reviewer for top machine learning conferences (ICLR, NeurIPS, ICML, WWW) and journals (IEEE PAMI, TMLR). His teaching includes graduate Machine Learning and undergraduate Artificial Intelligence courses at SUSTech.

André Nichterleinمشاهده پروفایل

پژوهشگر

• Parameterized Algorithms
• Algorithm Engineering
• Kernelization
• +۲ مورد دیگر

André Nichterlein is a Permanent Research Associate at the Technical University of Berlin, specializing in Algorithmics and Complexity Theory. He completed his PhD at TU Berlin (2014) and holds a Diploma from Friedrich Schiller University Jena (2010). His career includes postdoctoral research at Durham University (UK) under a DAAD fellowship and extensive work as a research assistant at TU Berlin. Research Focus: Nichterlein's work centers on parameterized algorithms , kernelization techniques , graph problem optimization , and algorithm engineering . His research addresses fundamental challenges in computational complexity through practical algorithmic solutions, particularly in graph theory and network optimization. Publication Trends: His recent articles (2020-2023) demonstrate a strong focus on parameterized complexity frontiers, efficient data reduction methods for NP-hard problems, and applications in network design. Recurring themes include kernelization innovations, graph modification problems, and experimental algorithmics, with consistent contributions to theoretical foundations of computer science.

Artem Kaznatcheevمشاهده پروفایل

استادیار

• Evolutionary Biology
• Theoretical Computer Science
• Computational Complexity
• +۵ مورد دیگر

Artem Kaznatcheev is an Assistant Professor at Utrecht University in the Department of Mathematics and Department of Information and Computing Sciences within the Science faculty. His research bridges theoretical computer science and evolutionary biology to analyze biological and social systems through an algorithmic lens. Current role since January 2023 Recruiting PhD students and postdocs Previously: James S. McDonnell postdoctoral fellow at University of Pennsylvania His work focuses on: Computational complexity of evolution Evolutionary game theory Algorithmic biology Mathematical modeling of cancer dynamics Cultural evolution of science Selected article trends show: Interdisciplinary integration of computer science and cancer biology Key themes: fitness landscapes, evolutionary games, treatment optimization 2021-2020 publications dominate Mathematical formalisms applied to biological and social phenomena Scientific contributions include: James S. McDonnell Foundation Independent Postdoctoral Fellowship 2019 Genetics paper on computational complexity as evolutionary constraint 2017 Nature Ecology & Evolution study on fibroblast-drug interactions in cancer Collaborative environments: Worked with Theory, Evolution and Games Group Previous affiliations: Oxford University, University of Pennsylvania, Moffitt Cancer Center, McGill University Developed teaching roles at Oriel College (Oxford)

Yiqiang Q. Zhaoمشاهده پروفایل

استاد

• Applied Probability
• Stochastic Processes
• Queueing Theory
• +۶ مورد دیگر

Professor Yiqiang Q. Zhao is a faculty member at the School of Mathematics and Statistics, Carleton University, where he has served as Associate Dean (Research and Graduate Studies) of the Faculty of Science since 2021. His research focuses on applied probability, stochastic processes, and their applications in telecommunication networks, queueing systems, and Gaussian processes. With over 130 peer-reviewed publications and 150+ students supervised since 2000, he has received the Carleton Faculty of Science Teaching Award (2002-2003) and twice been recommended as the 'Most Welcomed Teacher' by graduate students. He has also held editorial roles for journals like Stochastic Models and Queueing Systems . Dr. Zhao's research explores exact tail asymptotics in queueing systems, mean-field interaction models, copula constructions, and statistical inference for stochastic processes. His work spans wireless network modeling, resource allocation, and geometric methods in probability, with funding from NSERC and industry partners like Alcatel and MITACS. His recent publications highlight advancements in mean-field stability, retrial queue approximations, and kernel methods for multidimensional queueing analysis. Editorial board memberships and committee leadership roles underscore his contributions to academic governance. Scientific Awards: Carleton Faculty of Science Teaching Award (2002-2003) Most Welcomed Teacher recognition (twice) Dr. Zhao collaborates with international institutions and supervises a dynamic research team. His grants from NSERC and industry partnerships reflect his impact on applied probability research.

Marc Hellmuthمشاهده پروفایل

دانشیار

• Discrete Mathematics
• Combinatorics
• Graph Theory
• +۱۰ مورد دیگر

Marc Hellmuth is an Associate Professor of Computational Mathematics at the Department of Mathematics, Stockholm University, Sweden. His academic career includes previous positions as Junior Professor for Biomathematics and Computer Science at University of Greifswald, Germany (2015-2020), Lecturer at School of Computing, University of Leeds, UK (2020), and PostDoc positions at Saarland University and Max-Planck Institutes. Dr. Hellmuth earned his PhD in Computer Science from University of Leipzig, Germany (2007-2010, summa cum laude), supervised by Peter F. Stadler, and completed his Venia Legendi (habilitation) at Saarland University, Germany (2016). His research spans the interface of discrete mathematics, computer science, and life sciences with emphasis on: Discrete Mathematics including Graph Theory, Combinatorics, and Optimization Algorithm Design and Complexity Theory Mathematical and Computational Biology Phylogenomics and Evolutionary Analysis Computational Chemistry and Atom Tracking His publication record demonstrates a consistent focus on developing mathematical frameworks and efficient algorithms for biological problems, particularly in phylogenomics, orthology detection, and evolutionary analysis. His work bridges theoretical computer science with practical applications in biology and chemistry, often resulting in open-source software tools. Dr. Hellmuth has developed numerous software tools including AsymmeTree for phylogenetic simulation, tralda for tree algorithms, and specialized tools for phylogenomics, atom tracking, and graph analysis. His collaborative work extends internationally with research visits to institutions including Yale University, University of Leoben, Vienna University of Economics, University of Southern Denmark, Université de Montréal, and institutions in China.

Erik Carlssonمشاهده پروفایل

استاد

• representation theory
• algebraic geometry
• algebraic combinatorics
• +۵ مورد دیگر

Erik Carlsson is a Professor in the Mathematics department at the University of California, Davis. His research spans multiple areas of pure and applied mathematics, connecting deep theoretical concepts with practical computational applications. He received his Ph.D. from Princeton University under Professor Andrei Okounkov in 2008, and a B.S. in Mathematics with honors and a minor in Computer Science from Stanford University in 2003. Carlsson's research focuses on representation theory, algebraic geometry, algebraic combinatorics, computational topology, and connections with nonconvex optimization. He is particularly interested in connections between Goresky-Kottwitz-Macpherson (GKM) spaces and applications to Macdonald theory and combinatorics. His work also includes computational topology, especially persistent homology, which he develops in collaboration with John Carlsson. One of his recent developments is a method for constructing the alpha complex in high dimension using the powerful duality principle in mathematical optimization. His recent publications show a clear trend toward bridging theoretical mathematics with computational applications. His work spans from proving deep conjectures in algebraic combinatorics (like the shuffle conjecture) to developing practical algorithms for topological data analysis. The interdisciplinary nature of his work connects pure mathematical theory with applications in data science, computer vision, and optimization problems. Carlsson has made significant contributions to multiple fields of mathematics through his collaborations and independent work. His research has implications for both theoretical mathematics and practical computational problems, with recent publications as of 2024 demonstrating his continued active research program.

• Finite Fields
• Cryptography
• Coding Theory
• +۴ مورد دیگر

Qiang (Steven) Wang is a Professor in the School of Mathematics and Statistics at Carleton University. His academic career spans several decades with a strong focus on finite fields and their applications in cryptography and coding theory. He maintains an active teaching schedule including courses like Abstract Algebra, Rings and Fields, and Fields and Coding Theory. Professor Wang's research interests center on Finite Fields and Applications in Cryptography and Coding Theory, Combinatorics, Algebra and Number Theory. His work particularly focuses on polynomials and functions over finite fields, sequences over finite fields, and applications in Coding Theory and Cryptography. He has made significant contributions to the study of irreducible polynomials, permutation polynomials, primitive polynomials, planar functions, Boolean functions, LFSR and NLFSR sequences, de Bruijn sequences, stream ciphers, pseudorandom generators, and linear complexity. His research bridges theoretical mathematics with practical cryptographic implementations. His extensive publication record shows a consistent and growing research program, with numerous publications in top-tier journals including Designs, Codes and Cryptography, Finite Fields and Their Applications, and Cryptography and Communications. His most recent work (2022-2024) has increasingly focused on compositional inverses of permutation polynomials, stable polynomials, additive codes with few weights, and generalized cyclotomic mappings, while maintaining strong connections to cryptographic applications. Editorial board member of Applicable Algebra in Engineering, Communication and Computing, Springer Editorial board member of Finite Fields and their Applications, Elsevier Professor Wang actively contributes to the academic community through his editorial work and seminar organization. He coordinates the Carleton Finite Fields eSeminar and participates in the Ottawa-Carleton Combinatorics and Optimization Seminar, fostering research collaboration in discrete mathematics and related fields. His teaching portfolio is diverse, covering both undergraduate and graduate courses that reflect his broad expertise across algebraic structures, coding theory, and discrete mathematics.

Aravind N.R.مشاهده پروفایل

دانشیار

• Algorithms
• Graph Theory
• Combinatorics
• +۵ مورد دیگر

Dr. N.R. Aravind is an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Hyderabad. Holding a Ph.D. in Theoretical Computer Science from The Institute of Mathematical Sciences, Chennai, he is affiliated with the College of Engineering. His research spans algorithms, graph theory, combinatorics, and determinantal complexity, with a focus on parameterized algorithms and approximation techniques for NP-hard problems. Education: Ph.D., The Institute of Mathematical Sciences, Chennai (Advisor: Prof. C.R. Subramanian) Postdoctoral research under Prof. Sundar Vishwanathan at IIT Bombay Aravind's research explores structural questions in graph theory, social network modeling, and complexity bounds. His work on parameterized algorithms addresses tractable instances via input parameters, while his studies in graph coloring and forbidden subgraphs contribute to theoretical understanding. Recent publications focus on matching cut problems, happy coloring, and determinantal complexity. His 15 most recent publications (2010-2024) span computational complexity, graph algorithms, coloring problems, and determinantal complexity. These works emphasize parameterized approaches, structural graph theory, and algorithmic bounds for intractable problems. Scientific Awards: Aravind has mentored numerous PhD and MTech students, including Roopam Saxena, Anjeneya Swami Kare, and R.B. Sandeep, now holding academic positions at prestigious institutions. He has taught courses like Algorithms, Probability in Computing, and Cryptology, often collaborating with co-instructors such as Dr. Rakesh Venkat. His office is located in Room CS-408, Indian Institute of Technology Hyderabad.

Jugal Gargمشاهده پروفایل

دانشیار

• Computational Aspects of Economics and Game Theory
• Design and Analysis of Algorithms
• Mathematical Programming
• +۵ مورد دیگر

Jugal Garg is an Associate Professor in the Department of Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana-Champaign, with an affiliate appointment in the Department of Computer Science. His work bridges theoretical computer science, economics, and operations research, focusing on fundamental problems in market design and resource allocation. Dr. Garg earned his BTech and PhD in Computer Science from IIT-Bombay. Following his doctoral studies, he completed postdoctoral research at the Algorithms and Randomness Center at Georgia Tech and the Algorithms and Complexity group at Max-Planck-Institut für Informatik in Saarbrücken. His academic journey has positioned him at the forefront of research at the intersection of computation and economics. His research focuses on the computational aspects of economics and game theory, with particular emphasis on fair division problems and market equilibrium computation. Dr. Garg has made significant contributions to understanding envy-free allocation mechanisms, maximin share guarantees, and competitive equilibrium computation in various market settings. His work combines deep theoretical insights with practical algorithmic approaches, addressing fundamental questions in resource allocation where computational complexity meets economic efficiency. Analysis of Dr. Garg's recent publications reveals a strong focus on fair division problems, particularly envy-free allocation (EFX), maximin share (MMS) approximations, and market equilibrium computation. His research spans both goods and chores allocation, with increasing attention to more complex settings involving mixed manna (both goods and chores), heterogeneous agents, and specialized utility functions. A notable trend is his development of combinatorial algorithms for market equilibrium computation and his work on improving approximation guarantees for fairness concepts in resource allocation. NSF CAREER Award (2020) - Recognizing his potential for leadership in research and education INFORMS Koopman Prize (2021) - For the paper 'Multi-Agent UAV Routing: A Game Theory Analysis with Tight Price of Anarchy Bounds' Exemplary Theory Paper Award (2020) - For 'EFX Exists for Three Agents' at ACM EC Dean's Award for Excellence in Research (2022) James Franklin Sharp Outstanding Teaching Award (2019) NSF CRII Award (2018) Dr. Garg has successfully advised numerous PhD students including Peter McGlaughlin, Timothy Murray, Setareh Taki, John Qin, Eklavya Sharma, Yuang (Eric) Shen, Pooja Kulkarni, and Aniket Murhekar. He has also mentored postdoctoral researchers such as Bhaskar Ray Chaudhury (now an assistant professor at UIUC) and Vishnu V. Narayan. His research has been supported by prestigious grants including the NSF CAREER and CRII awards, and he serves on program committees for leading conferences in theoretical computer science, artificial intelligence, and operations research including EC, STOC, SODA, and AAAI. Dr. Garg maintains active collaborations with researchers across institutions worldwide, as evidenced by his frequent invited talks at international venues including the University of Bonn, LSE, and TIFR Mumbai.