جست‌وجوی استاد و پژوهشگر

• Bioinformatics
• Combinatorics
• Genomics
• +۵ مورد دیگر

Sylvie Hamel is a Full Professor and Department Director at the Department of Computer Science and Operations Research (DIRO), Université de Montréal. She is affiliated with the CRM (Centre de recherches mathématiques) and the LBIT (Laboratoire de biologie informatique et théorique). Her research bridges computer science, mathematics, and molecular biology, focusing on algorithmic problems in genomics, combinatorial analysis, and RNA structure modeling. Education: Her academic background includes a Ph.D. in algorithmic and combinatorial studies, with a thesis on vector algorithms and bioinformatics. Research Interests: Algorithm design for genome rearrangements and sequence analysis Combinatorial optimization and permutation medians RNA structure prediction and computational biology Consensus algorithms and voting theory Software evolution and design patterns Grants & Projects: Leading projects on consensus algorithms (2025–2031), comparative genomics (2016–2024), and CRM strategic initiatives (2022–2029). Funded by CRSNG (Natural Sciences and Engineering Research Council) and FRQNT (Québec research fund). Awards: CRSNG Postdoctoral Fellowship (2003–2008). Supervision: Currently advising M.Sc./Ph.D. students on topics like consensus algorithms, RNA structure analysis, and combinatorial optimization. Over 20+ completed theses under her supervision. Labs: Active in LBIT (bioinformatics) and CRM (mathematical research), fostering interdisciplinary collaboration.

Krystal Taylorمشاهده پروفایل

دانشیار

• Geometric Measure Theory
• Harmonic Analysis
• Analysis on Fractal Sets
• +۴ مورد دیگر

Krystal Taylor is an Associate Professor in the Department of Mathematics at The Ohio State University, Columbus. Her research focuses on geometric measure theory, harmonic analysis, and analysis on fractal sets, with applications to understanding the geometry of fractals through Fourier transforms and projection theory. She explores themes such as finite point configurations, distance problems, and nonlinear projection problems. Taylor has organized major workshops, including a 2024 Banff International Research Station workshop on geometric measure theory and harmonic analysis. Education: PhD in Mathematics from the University of Rochester (2012, advisor Alex Iosevich); postdoctoral fellowships at the Technion-Israel Institute of Technology and the University of Minnesota's Institute for Mathematics and Its Applications. Research Interests: Fractal geometry, harmonic analysis, geometric measure theory, and their applications to combinatorics and number theory. Her work often bridges pure mathematics with practical applications in data analysis and industry. Recent Activities: Editor for the Notices of the AMS and the Ohio State Mathematics Newsletter; co-organizer of the 2023 HAFS (Harmonic Analysis and Fractal Sets) conference. Upcoming speaking engagements include plenary talks at the Real Analysis Exchange Symposium in Madrid (2025) and invited lectures at CRM Barcelona and the Rényi Institute in Budapest (2024). Labs/Teams: Co-founded the Math to Industry seminar and organized the first international HAFS workshop in 2017. Active in mentoring junior mathematicians through workshops and special sessions at the Joint Mathematics Meetings.

• Empirical Corporate Finance
• Household Finance
• Finance
• +۳ مورد دیگر

Poorya Kabir is an Assistant Professor of Finance at the National University of Singapore (NUS) Business School since January 2022. His research focuses on empirical corporate finance and household finance, particularly on how corporate finance impacts real economic outcomes like product markets and investment. He employs economic empirical methods combined with micro-data analysis. Education: Ph.D. in Finance, Columbia Business School (2020) M.Sc. in Applied Mathematics, Sharif University of Technology (2014) B.Sc. in Electrical Engineering and Mathematics, Sharif University of Technology (2012) Research Interests: Empirical Corporate Finance and Household Finance with a focus on real-world economic implications. His work spans topics like consumer credit disparities, export credit agencies, and capital allocation efficiency. Teaching Interests: Corporate Finance No scientific awards or grants explicitly mentioned in the text. No recorded advisees or research teams are listed.

Monique Laurentمشاهده پروفایل

استاد

• Discrete Mathematics
• Optimization
• Polynomial Optimization
• +۵ مورد دیگر

Monique Laurent is a Tilburg University professor and senior researcher at CWI (Centrum Wiskunde & Informatica), focusing on discrete mathematics and optimization . Her work bridges algebra, geometry, and computer science to solve complex combinatorial and polynomial optimization problems. Part-time full professor at Tilburg University (since 2009) Group leader of Networks and Optimization at CWI (2005-2016) Current member of CWI Management Team Research Focus: Semidefinite programming hierarchies, noncommutative polynomial optimization, quantum information theory, and matrix factorization. Her recent work explores applications in quantum entanglement , graph parameters , and combinatorial data analysis . Key Publications: 15+ articles from 2017-2024 address topics like copositive matrices , sum-of-squares convergence , and hypergraph optimization . Collaborations span institutions in the Netherlands, France, Germany, and the U.S. Awards: 2023 Khachiyan Prize SIAM Fellow (2017) KNAW member (2018) EUROPT Fellow (2021) Grants & Projects: Leads EU-funded initiatives TENORS (2024) and POEMA (2019), with prior NWO and Marie Curie grants. Organizes international workshops on polynomial optimization and quantum information.

Anna Puskasمشاهده پروفایل

پژوهشگر ارشد

• Number Theory
• Algebraic Combinatorics
• Representation Theory
• +۳ مورد دیگر

Dr. Anna Puskas holds the position of Honorary Research Fellow at the School of Mathematics and Physics, The University of Queensland, where she maintains active research collaborations within Australia's premier mathematical sciences institution. Her scholarly work bridges abstract algebraic structures with concrete number-theoretic applications, reflecting deep engagement with both theoretical frameworks and cryptographic implementations. Her research program centers on four interconnected pillars: Number Theory & Cryptography : Developing Ramanujan graph applications for post-quantum cryptography and analyzing class field theory in multiquadratic extensions Algebraic Combinatorics : Constructing crystal bases through combinatorial representation theory and Kashiwara operator frameworks Representation Theory : Investigating metaplectic covers of Kac-Moody groups and their Whittaker function decompositions Lie Theory : Exploring t-deformed root multiplicities and correction factors in infinite-dimensional Lie algebras Analysis of her 2012-2019 publications reveals an evolving trajectory from classical invariant theory toward cutting-edge metaplectic representation theory. Her work demonstrates increasing sophistication in handling p-adic groups and automorphic forms, particularly through novel constructions of Whittaker functions on metaplectic covers. The cryptographic applications of her number-theoretic work—especially Ramanujan graphs—show practical impact beyond pure mathematics, while her crystal basis research provides essential combinatorial tools for representation theorists. No scientific awards or honors were documented in available institutional records. Similarly, details regarding graduate student supervision, research grant funding, or laboratory affiliations were not provided in the source materials, suggesting her honorary fellowship may focus primarily on independent research collaboration rather than formal teaching or administrative duties.

• Machine Learning
• Recommender Systems
• Data Privacy
• +۷ مورد دیگر

Mihaela Curmei is a researcher active in machine learning, recommender systems, and algorithmic fairness, with significant contributions to privacy-preserving methods, behavioral modeling, and optimization. Her collaborative work spans institutions and co-authors like Benjamin Recht, Georgina Hall, and Sarah Dean. Research Themes: Shape-constrained regression using polynomial optimization Privacy in matrix factorization with public features Dynamic preference modeling grounded in psychology Multi-learner systems under participation dynamics Temporal impacts of recommendation algorithms Technical Domains: Sum-of-squares programming Federated and distributed learning Stochastic reachability analysis Gradient flow-based dataset modeling Key Venues: Published in Operations Research , NeurIPS, ICML, RecSys, FAccT, and preprint archives. Her recent work (2023-2025) focuses on temporal effects in recommendations, private computation methods, and equilibrium analysis in multi-agent games. Earlier projects (2017-2021) include search engine indexing techniques (BitFunnel) and foundational studies on offline/online metric correlations in recommendation systems.

Borislav Radkov Draganovمشاهده پروفایل

استاد

• Mathematical Analysis
• Approximation Theory
• Harmonic Analysis
• +۱ مورد دیگر

Borislav Radkov Draganov is a Professor of Mathematical Analysis at the Faculty of Mathematics and Informatics, Sofia University St. Kliment Ohridski. His research focuses on approximation theory, harmonic analysis, and operator theory. Key research areas include: Trigonometric polynomial approximation Smoothness moduli and K-functionals Weighted approximation in function spaces Converse inequalities and saturation classes Sampling operators and multivariate approximation His recent publications (2023-2025) analyze Voronovskaya estimates for convolution operators, saturation classes for generalized exponential sampling operators, and convergence rates in variable exponent Lebesgue spaces. Collaborators include Tuncer Acar and I. Gadjev. He has extensively studied Peetre K-functionals, Bernstein-type operators, and Szasz-Mirakjan operators over two decades, establishing precise equivalence relations and Jackson-type inequalities in both homogeneous Banach spaces and weighted Lp spaces.

• Mathematical Optimization
• Stochastic Processes
• Control Theory
• +۳ مورد دیگر

Rodolfo Antonio Rios Zertuche is a Research Fellow in the Department of Mathematics and Statistics at UiT The Arctic University of Norway, actively contributing to the research groups Applied and Computational Algebra and MASCOT. His work bridges theoretical mathematics with computational applications across optimization and stochastic systems. His research focuses on Mathematical Optimization, Stochastic Processes, and Dynamical Systems, with significant contributions to variational problem relaxations, sums-of-squares hierarchies, and subgradient method dynamics. Recent investigations examine convexity in measure-theoretic frameworks and convergence properties in polynomial optimization. Publications from 2022-2024 reveal a concentrated trend toward optimization theory, particularly in occupation measure relaxations, correlative sparsity convergence rates, and stochastic approximation techniques. These works demonstrate increasing sophistication in handling nonsmooth functions and probabilistic frameworks through measure-theoretic approaches. Rios Zertuche maintains strong international collaborations with researchers from institutions including LAAS-CNRS (Toulouse) and Czech Technical University, evidenced by co-authorships with Didier Henrion and Milan Korda. His involvement in the MASCOT research group supports interdisciplinary projects connecting algebraic methods with computational mathematics.

• Mathematics
• Optimization
• Natural Sciences
• +۳ مورد دیگر

Prof. Fatma İnci Albayrak is a Professor at the Department of Mathematics Engineering within the Faculty of Chemical and Metallurgical Engineering at Yildiz Technical University . She holds a doctorate, postgraduate, and undergraduate degree in Mathematics Engineering from the same institution. Her research spans Mathematics , Optimization , Natural Sciences , and Fuzzy Modeling , with a focus on applying fuzzy logic to complex engineering and operational problems. Education: Doctorate (1993–1997), Yildiz Technical University Postgraduate (1990–1993), Yildiz Technical University Undergraduate (1986–1990), Yildiz Technical University Her publications reflect expertise in fuzzy systems , multi-objective optimization , differential equations , and nanostructured materials . She has contributed to journals like Physica Scripta , Canadian Journal of Chemical Engineering , and Cellulose , often addressing challenges in biomedical applications, transportation networks, and nanotechnology. She has served as a peer reviewer for Soft Computing and Promet journals. She is affiliated with the Multidisciplinary Nanoscience Technology working group at Yildiz Technical University since 2021, integrating nanomaterials with mathematical modeling. Her work bridges theoretical mathematics and practical engineering, demonstrated through collaborations in polymer nanocomposites, heat exchanger optimization, and traffic assignment problems.

Samantha Allenمشاهده پروفایل

استادیار

• Low-dimensional topology
• Geometric topology
• Knot theory
• +۱ مورد دیگر

Dr. Samantha Allen is an Assistant Professor in the Department of Mathematics and Computer Science at Duquesne University. Previously, she held postdoctoral positions at the University of Georgia (2021-2022) and Dartmouth College (2018-2021) before earning her PhD in Mathematics from Indiana University in 2018 under Charles Livingston. PhD: Mathematics, Indiana University BS: Mathematics and Computer Science, High Point University Dr. Allen specializes in low-dimensional topology, particularly knot theory in dimensions 3 and 4. Her work leverages knot invariants from Heegaard Floer theory to analyze knot concordance, unknotting operations, and other knot-theoretic problems. She has developed algebraic methods for knot primality detection and explored nonorientable surfaces bounded by knots. Her research includes advancements in untwisting numbers, Upsilon torsion functions, and empirical studies linking knot polynomials to causality in spacetimes. Recent publications focus on primality tests for knots, cobordism constraints, and the interplay between Betti numbers and Euler classes in nonorientable surfaces. At Duquesne University, Dr. Allen teaches courses such as Abstract Algebra, Topology, and Foundations of Higher Mathematics. She has also taught calculus sequences and advanced topics at the University of Georgia, Dartmouth College, and Indiana University.

Paul David Nelsonمشاهده پروفایل

استاد

• Number Theory
• Automorphic Forms
• L-functions
• +۳ مورد دیگر

Paul David Nelson is a Professor at Aarhus University, affiliated with the Department of Mathematics. His research focuses on automorphic forms, L-functions, and spectral theory in number theory. University: Aarhus University Department: Mathematics Academic Rank: Professor His work addresses analytic aspects of automorphic representations, including subconvex bounds for L-functions, equidistribution, and theta functions. Recent publications explore spectral decompositions, Hecke sums, and applications of the orbit method. Key trends in his research include automorphic forms on GL(2), spectral theory of modular forms, and connections to arithmetic geometry. Publications span journals like Acta Mathematica, Inventiones Mathematicae, and Forum of Mathematics, Pi. Scientific Awards: Villum Investigator Contact: paul.nelson@math.au.dk

Pedro Felzenszwalbمشاهده پروفایل

استاد

• Computer Vision
• Artificial Intelligence
• Machine Learning
• +۲ مورد دیگر

Pedro Felzenszwalb is a Professor of Engineering and Computer Science at Brown University , with a research focus spanning computer vision, artificial intelligence, machine learning, and algorithms. Born in Rio de Janeiro, Brazil, he earned his BS in Computer Science from Cornell University (1999) and MS/PhD in EECS from MIT (2001/2003). He previously held a faculty position at the University of Chicago (2004-2011) before joining Brown in 2011. Education : PhD in EECS, MIT (2003) MS in EECS, MIT (2001) BS in Computer Science, Cornell University (1999) His research integrates computer vision and AI, emphasizing scalable algorithms for object recognition, image segmentation, and probabilistic modeling. Key methodologies include deformable part models, belief propagation, and dynamic programming. His work has significant applications in early vision tasks, scene understanding, and geometric constraints in 3D object recognition. Pedro’s publications demonstrate a trajectory from foundational graph/image algorithms (2004-2006) to advanced machine learning approaches (2010-2023), with recurring themes in optimization, clustering, and multiscale modeling. Notable journals include Journal of the ACM , IEEE Transactions , and Communications of the ACM . Scientific Awards : ACM Grace Murray Hopper Award IEEE Technical Achievement Award PASCAL Visual Object Challenge Lifetime Achievement Prize Longuet-Higgins Prize NSF CAREER Award He has received NSF funding for projects including Graph Cut Algorithms (2012-2015) and Object Recognition with Hierarchical Models (2008-2013). At Brown, he teaches graduate courses in machine learning, linear systems, and pattern recognition.

• Anesthesiology
• Intensive Care
• Pediatric Anesthesia
• +۳ مورد دیگر

Jacek Karpe serves as a Lecturer ( adiunkt dydaktyczny ) at the Department of Neonatal and Paediatric Anaesthesiology and Intensive Care within the Faculty of Medical Sciences in Zabrze at the Medical University of Silesia. His institutional affiliation is actively maintained with current contact information and publication activity. His research spans the medical sciences discipline with 100% focus in anesthesiology and intensive therapy, specializing in neonatal and pediatric critical care. Key research domains include: Pediatric and neonatal pain management Postoperative analgesia protocols Chronic pain in pediatric populations Sex-specific treatment outcomes Risk factor analysis for infants and adults Prospective multicenter clinical studies With 261 documented publications and significant bibliometric impact (Scopus h-index: 13; WoS h-index: 12), his work demonstrates consistent contribution to anesthesiology literature. His research leverages multi-center collaborations and focuses on evidence-based clinical practice. Karpe has promoted 3 doctoral theses, indicating active mentorship responsibilities within his department. His research integrates clinical practice with academic investigation across age groups from neonates to adults. His work operates within the clinical infrastructure of SPSK Nr 1 im. prof. S. Szyszko ŚUM hospital in Zabrze, facilitating direct translation of research into practice through the university-hospital partnership.

• Computational Intelligence
• Evolutionary Multi-objective Optimization
• Evolutionary Machine Learning
• +۵ مورد دیگر

Professor Hisao Ishibuchi is Chair Professor of Computer Science and Engineering at Southern University of Science and Technology (SUSTech) in Shenzhen, China, a role he has held since April 2017. Previously, he spent nearly three decades at Osaka Prefecture University, progressing from Research Associate (1987-1993) to Assistant Professor (1993), Associate Professor (1994-1999), and full Professor (1999-2017). He is an IEEE Fellow , served as Vice-President of the IEEE Computational Intelligence Society (2010-2013) , and is currently President of the Japan Society for Evolutionary Computation (2016-2018) . He is Editor-in-Chief of IEEE Computational Intelligence Magazine (2014-2019) and the Journal of the Japan EC Society (2014-2018). Education: Ph.D. in Engineering, Osaka Prefecture University, 1992 M.S. in Engineering, Kyoto University, 1987 B.S. in Engineering, Kyoto University, 1985 Research Focus: Professor Ishibuchi is internationally recognised as a pioneer of computational intelligence , with seminal contributions to evolutionary multi-objective optimisation , evolutionary machine learning , fuzzy systems , neural networks , and hybrid intelligent systems . He introduced the first multi-objective memetic algorithm and early methods for multi-objective fuzzy rule-based classifier design that balance accuracy and interpretability. Publications & Impact: With over 100 journal papers in top-tier venues such as IEEE Transactions on Evolutionary Computation and nearly 500 conference papers, his work has attracted more than 24 000 Google-Scholar citations and an h-index of 68. His recent articles concentrate on many-objective optimisation, fuzzy machine learning, and transfer learning techniques. Honours & Awards: IEEE Computational Intelligence Society Fuzzy Systems Pioneer Award 2019 IEEE Fellow 2014 JSPS Prize 2007 (Japan’s most prestigious mid-career award) Multiple Best Paper Awards from GECCO, FUZZ-IEEE, SCIS & ISIS, WAC, ACIIDS, HIS-NCEI, and others Teaching & Mentoring: At SUSTech he teaches Advanced Algorithms and Advanced Optimization Algorithms , covering greedy algorithms, hyper-heuristics, memetic algorithms, multi-objective optimisation, and performance assessment. His research group actively recruits post-doctoral fellows and research assistants in evolutionary computation, fuzzy systems, and neural networks. Labs & Teams: He leads the Computational Intelligence Research Group at SUSTech, maintaining active collaboration networks across Asia, Europe, and North America, and supervising several post-doctoral researchers and graduate students working on next-generation intelligent systems.

Ilai Bistritzمشاهده پروفایل

استادیار

• Game Theory
• Distributed Control
• Multiagent Learning
• +۱۰ مورد دیگر

Dr. Ilai Bistritz is an Assistant Professor at the School of Industrial and Intelligent Systems Engineering and School of Electrical and Computer Engineering , Tel Aviv University , with a PhD in Electrical Engineering (2023) from Stanford University under Nicholas Bambos . His research bridges Game Theory , Distributed Control , and Multiagent Learning , focusing on decentralized decision-making in networked systems like autonomous vehicles , smart grids , and epidemic modeling . His work addresses challenges in Distributed Optimization where agents operate with limited communication and feedback, such as in multiplayer bandits and delayed adversarial environments . He has developed algorithms for max-min fairness , non-myopic informational cascades , and delay-robust regret minimization , achieving theoretical breakthroughs in Networked Artificial Intelligence . Scientific awards include the Best Student Paper Award at IEEE WCNC 2018 and Best Student Paper Finalist at WODES 2020 . His research emphasizes privacy-preserving protocols, scalable architectures, and applications in health monitoring , energy systems , and wireless networks .