جست‌وجوی استاد و پژوهشگر

Katie Morrisonمشاهده پروفایل

استاد

• Mathematical Neuroscience
• Neural Networks
• Neural Coding
• +۳ مورد دیگر

Katie Morrison is an Associate Chair and Professor in the Department of Mathematical Sciences at the University of Northern Colorado (UNC), part of the College of Natural and Health Sciences. Her research focuses on mathematical neuroscience, neural networks, and algebraic coding theory, with a particular emphasis on applying matrix codes and graph-based algorithms to understand neural dynamics. She has been funded by NSF DMS-1951599 and NIH R01 EB022862 grants. Education: Ph.D. in Mathematics (University of Nebraska-Lincoln, 2012), M.S. in Mathematics (2008), B.A. in Mathematics and Psychology (Swarthmore College, 2005), and studies at Budapest Semesters in Mathematics. Her work bridges abstract mathematics with applied neuroscience, including projects like the CTLN initiative. Research trends in her publications emphasize graph-theoretic approaches to neural network dynamics, pattern completion in threshold-linear networks, and algebraic analysis of neural codes. Collaborations with computational topologists and neuroscientists highlight interdisciplinary strengths. Grants and advising: Active in securing federal research funding and mentoring students in interdisciplinary projects. Her lab explores connections between network connectivity and emergent behaviors, with applications to both theoretical and applied neuroscience.

• Machine Learning
• Statistical Learning Theory
• Algorithmic Fairness
• +۱۲ مورد دیگر

Massi Pontil is a part-time Professor of Computational Statistics & Machine Learning in the Department of Computer Science at University College London (UCL). He joined UCL as a lecturer in 2003 and was promoted to Professor in 2010. Since 2016, his primary appointment has been at the Istituto Italiano di Tecnologia (IIT), where he leads the CSML research group. His work bridges theoretical machine learning with practical applications in physical sciences. His research interests span a wide range of topics in machine learning theory and algorithms: Machine Learning Theory and Statistical Learning Algorithmic Fairness and Ethical AI Kernel Methods and Reproducing Kernel Hilbert Spaces Transfer Learning, Multitask Learning, and Meta-Learning Operator Learning and Dynamical Systems Sparsity Regularization and Optimization Pontil's recent work focuses on the intersection of machine learning with numerical simulations of physical systems, particularly in molecular dynamics and climate science. His publications demonstrate a strong emphasis on theoretical foundations while addressing practical challenges in high-dimensional systems, symmetry-aware learning, and uncertainty quantification. Among his notable honors are: Best Paper Runner Up Award from ICML 2013 EPSRC Advanced Research Fellowship (2006-2011) Edoardo R. Caianiello Award for the Best Italian PhD Thesis on Connectionism (2002) Professor Pontil has served on program committees for major machine learning conferences (COLT, ICML, NeurIPS) and on editorial boards of prestigious journals including Machine Learning Journal, Statistics and Computing, and JMLR. He teaches Advanced Topics in Machine Learning at UCL, with a focus on convex optimization and statistical learning theory.

• 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.

• Bioinformatics
• Protein Structure Prediction
• Machine Learning
• +۳ مورد دیگر

Gianluca Pollastri is an Associate Professor in the School of Computer Science at University College Dublin (UCD). He leads a research group focused on machine learning applications in bioinformatics, particularly protein structure prediction and analysis. His academic roles include Associate Professor since 2016, Senior Lecturer from 2008, and Lecturer from 2003. He earned an MSc from the University of Florence and a PhD from the University of California, Irvine. His research integrates deep learning and neural networks to address challenges in protein subcellular localization, secondary structure prediction, and intrinsically disordered regions. Key tools developed include SCLpred, PaleAle, Porter, and PUNCH2. He has secured grants from Science Foundation Ireland, the Health Research Board, and UCD. Pollastri’s work emphasizes rigorous validation of machine learning methods in biology, as outlined in the DOME framework. His lab has produced over 100 peer-reviewed articles, with recent focus on leveraging pre-trained language models (PLMs) for protein analysis. Education: MSc, University of Florence PhD, University of California, Irvine Awards: Best M.Sc. Thesis in Artificial Intelligence (1999) Best Student Project in Artificial Intelligence (1997) His teaching includes modules on Bioinformatics, Connectionist Computing, and Programming. He coordinates research collaborations and maintains a lab with postdoctoral fellows and graduate students.

Carina Curtoمشاهده پروفایل

استاد

• Theoretical Neuroscience
• Computational Neuroscience
• Applied Algebra
• +۴ مورد دیگر

Carina Curto is a Professor of Applied Mathematics and Brain Science at Brown University, affiliated with the Division of Applied Mathematics and the Carney Institute for Brain Science. She holds a B.A. in Physics from Harvard University (2000) and a Ph.D. in Mathematics from Duke University (2005). Her research focuses on theoretical and computational neuroscience, applying algebra, topology, and dynamical systems to study neural networks and neural coding. She has held faculty positions at Penn State (2014–2024) and the University of Nebraska-Lincoln (2009–2014), and was a postdoctoral researcher at Rutgers and NYU. Research Interests : Neural network theory, topology in neuroscience, algebraic structures of neural codes, and their applications to understanding brain circuits and cognition. Her work bridges mathematical theory and experimental neuroscience, with emphasis on threshold-linear networks, combinatorial coding, and circuit dynamics. Grants & Awards : NIH R01, NSF grants (DMS-1951165, DMS-1516881), Simons Fellowship (2021–22), Penn State Faculty Scholar Medal (2020), Sloan Research Fellowship (2011–15). Labs & Collaborations : Leads the Curto Lab for Mathematical Neuroscience at Brown, focusing on interdisciplinary research connecting network dynamics to brain function. Collaborators include Anda Degeratu, Chad Giusti, and Katherine Morrison. Her lab includes postdocs, PhD students, and affiliate members working on projects like CTLN dynamics and clique topology.

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

Professor Igor Shparlinski is a faculty member at the University of New South Wales (UNSW) within the School of Mathematics & Statistics, holding the rank of Professor with an active research profile as evidenced by publications dated 2025. His work bridges pure number theory and cryptographic applications, maintaining international collaborations with institutions like the University of Sydney and Max Planck Institute. Research interests include: Analytic number theory (exponential/character sums) Cryptographic security (Diffie-Hellman, RSA, and pseudorandom generators) Finite field arithmetic and polynomial dynamics Arithmetic combinatorics and additive number theory Distribution of primes/smooth numbers modulo p Elliptic curve structures over finite fields His publications consistently address foundational problems with computational implications, particularly focusing on sum bounds and modular arithmetic. Recent trends (2023-2025) show intensified work on multilinear character sums, matrix statistics over finite fields, and dynamical system orbits. Key journals include Journal of Number Theory and Finite Fields and their Applications , with frequent co-authorship on exponential sum bounds and cryptographic vulnerabilities. Scientific awards: No awards, prizes, or fellowships are documented in the provided text. Advising and grants: No student listings or grant information appears in the source material. Research output suggests sustained funding but lacks explicit details.

Edon Kelmendiمشاهده پروفایل

مدرس

• Dynamical Systems
• Logics in Computer Science
• Game Theory
• +۳ مورد دیگر

Dr. Edon Kelmendi is a Lecturer in Computer Science at Queen Mary University of London. He holds a doctorate from Laboratoire Bordelais de Recherche en Informatique and completed postdoctoral research at the University of Warsaw, University of Oxford, and Max Planck Institute. His research focuses on theoretical computer science, particularly dynamical systems, logics in CS, and game theory. Kelmendi's work examines algorithmic verification of dynamical systems, automata theory, and game-theoretic models. His recent publications demonstrate strong focus on linear dynamical systems and injective piecewise affine maps. He received the Distinguished Paper Award at LICS 2022 for his computational contributions to linear recurrence sequences.

• Uniform Distribution and Discrepancy
• Diophantine Equations
• Analytic Combinatorics
• +۶ مورد دیگر

Robert F. Tichy is a Professor at the Institute of Analysis and Number Theory , Graz University of Technology, Austria. His career spans over four decades with significant contributions to Diophantine equations , uniform distribution , and quasi-Monte Carlo methods . He has held multiple editorial board positions and administrative roles , including Dean of the Faculty of Mathematics, Physics and Geodesy (2018-2019). Research Interests : Uniform distribution, Diophantine equations, algorithmic number theory, fractal structures, quasi-Monte Carlo methods, and mathematics in finance/insurance. Awards : Austrian Mathematical Society award (1985), Honorary Doctorate (University of Debrecen, 2017), Jean-Morlet Chair (2020), and memberships in prestigious societies. Publications : Over 200 papers with recent works (2024) focusing on Diophantine problems , pseudorandom sequences , and Carlitz coefficients . Teaching : Regular courses for engineering and mathematics students, including annual lectures on insurance mathematics .

Nathanaël Fijalkowمشاهده پروفایل

پژوهشگر

• Games
• Machine Learning
• Automata Theory
• +۹ مورد دیگر

Nathanaël Fijalkow is a Researcher at CNRS in LaBRI (Bordeaux) and a Research Fellow at The Alan Turing Institute in London. His primary research fields include games , machine learning , automata theory , and dynamical systems , with a focus on synthesizing programs from logical specifications and probabilistic models. Research Interests span program synthesis (programming by example), controller synthesis (temporal logic specifications), games on graphs (parity/mean payoff games), probabilistic automata (bounded ambiguity), and invariants for linear dynamical systems. He bridges formal methods with machine learning through projects like DeepSynth . Scientific Contributions include: Undecidability results for probabilistic automata Advances in parity game algorithms (quasi-polynomial lower bounds) Foundations of probabilistic modal logics Efficient synthesis techniques using SMT solvers and distributional learning Supervision involves guiding postdocs and PhD students such as Guillaume Lagarde, Antonio Casares, and Pierre Ohlmann. He has secured grants like the Momentum DeepSynth project (2019-2021) , aiming to merge formal methods with ML for program synthesis.

• Probability Theory
• Branching Processes
• Products of Random Matrices
• +۷ مورد دیگر

Prof. Dr. Sebastian Mentemeier is a permanent Professor (W2) in the Department of Mathematics 2 at the Institute for Mathematics, Mathematics Education and Computer Science Education, University of Hildesheim, Germany, a position he has held since October 2019. He also holds significant administrative roles as Vice Dean and Dean of Studies in Faculty 4 (Mathematics, Natural Sciences, Economics & Computer Science), and is a member of the Institute's Board and the Central Commission for Studies and Teaching. His research is centered on advanced probability theory, with a focus on branching processes, products of random matrices, and extreme value theory for time series. His research interests include: Non-Gaussian limit theorems Branching processes, particularly Multitype Branching Random Walks Products of random matrices Extreme value theory for time series Heavy-tailed random variables Conditional limit theorems His recent publications, spanning from 2012 to 2022, demonstrate a consistent and high-impact research trajectory in theoretical probability. The articles reveal a strong trend in analyzing the asymptotic behavior of complex stochastic systems, particularly those defined by recursive equations and random matrix products. His work frequently intersects with statistical mechanics and time series analysis, often investigating the tail behavior and limit laws of solutions to stochastic fixed-point equations, with a particular emphasis on heavy-tailed and multivariate settings. Prof. Mentemeier has been a Principal Investigator on two major DFG projects: 'Nonlinear stochastic fixed-point equations with applications in statistical mechanics' (2017-2023) and 'Products of Random Matrices, Noncommutative Branching Random Walks, and Multitype Branching Random Walks in Random Environments' (since 2021). He is an active member of the academic community, serving as a referee for journals such as Stochastic Processes and their Applications and Journal of Theoretical Probability , and has co-organized international conferences on recursive stochastic processes and branching models. He supervises PhD and Master's students, although specific names are not listed on his profile. He has led a research group within the Department of Mathematics 2 and is a key member of the Institute's academic board. His work is conducted within the broader context of the Faculty of Mathematics, Natural Sciences, Economics & Computer Science at the University of Hildesheim.

Jonathan Lee Merzelمشاهده پروفایل

استاد

• Valuation Theory
• Ordered Fields and Quadratic Forms
• Ducci Sequences
• +۳ مورد دیگر

Jonathan Lee Merzel is a Professor of Mathematics and Undergraduate Director of the Science and Mathematics Program at Soka University of America since 2000. Previously, he held academic positions at Holy Names College (now Holy Names University) and Mills College. He earned his PhD, MA, and BS from the University of California, Berkeley, and the University of Maryland, respectively. His research focuses on Valuation Theory, Ordered Fields, Quadratic Forms, and Ducci Sequences. Notable collaborations include work with Ron Brown on Ducci sequences and real places in algebraic function fields. He has contributed to journals such as Journal of Algebra , Homology, Homotopy and Applications , and Fibonacci Quarterly . Education: PhD (UC Berkeley), MA (UC Berkeley), BS (University of Maryland) Courses Taught: Liberal Arts Mathematics, Calculus I, Probability and Statistics, Linear Algebra Awards: Faculty Mentor Award (1988) Outside academia, Merzel enjoys bridge, skiing, biking with family, and practicing Chen Man Ching Style Tai Chi Chuan under teacher Lenzie Williams. He resides in Mission Viejo with his family.

Gleb Pogudinمشاهده پروفایل

استادیار

• Symbolic computation
• Algorithms for differential equations
• Algorithms for difference equations
• +۵ مورد دیگر

Gleb Pogudin is an Assistant Professor at École Polytechnique, Institute Polytechnique de Paris, where he is a member of the MAX team within the Laboratoire d'informatique. His research focuses on the intersection of symbolic computation, differential equations, and algebraic methods with applications across multiple scientific domains. Dr. Pogudin's primary research interests span several interconnected areas: Symbolic computation and computer algebra algorithms Theory and applications of differential and difference equations Nonlinear algebra and polynomial systems Structural identifiability of dynamical models Model reduction techniques for complex systems His recent publications (2024-2025) demonstrate a strong focus on developing theoretical foundations for differential elimination, structural identifiability analysis, and model reduction. These works span applications in systems biology, epidemiology, pharmacology, and optics. Notably, his research bridges pure mathematical theory with practical computational implementations, creating tools that address real-world scientific challenges. Dr. Pogudin has developed several significant software tools that implement his theoretical advances: StructuralIdentifiability.jl: A Julia package for assessing structural identifiability CLUE: Software for exact model reduction of ODE models via constrained lumping SIAN: Software for structural identifiability analysis of ODE models His GitHub repositories contain numerous implementations of algorithms from his papers on differential elimination and related topics, demonstrating his commitment to making theoretical advances practically accessible to researchers across disciplines.

Alejandro Ochoaمشاهده پروفایل

استادیار

• Statistical Genetics
• Computational Biology
• Population Genetics
• +۳ مورد دیگر

Alejandro Ochoa is an Assistant Professor of Biostatistics & Bioinformatics at Duke University , affiliated with the Duke Center for Statistical Genetics and Genomics , Duke Institute for Brain Sciences , and the Center for Combinatorial Gene Regulation . His research focuses on statistical genetics and computational biology , addressing challenges in population structure estimation, genetic association models, and heritability analysis with applications to multi-ethnic studies and admixed populations . Education : Ph.D. in Quantitative and Computational Biology from Princeton University (2013) Research Highlights : Develops scalable statistical methods for genetic studies under arbitrary relatedness, emphasizing equity in multi-ethnic cohorts . Key contributions include software packages like popkin (kinship estimation), ligera (robust association studies), and bnpsd (admixed population simulation). His work addresses biases in principal component analysis , heritability estimation , and GWAS meta-analysis . Grants : National Institutes of Health (2023-2026): Regulators of Calcineurin Pathways as Diagnostic and Therapeutic Targets for Nephrotic Syndrome National Institutes of Health (2020-2026): HLA Risk Alleles in Primary Nephrotic Syndrome National Human Genome Research Institute (2020-2025): Duke FUNCTION Center Labs & Collaborations : Leads the Ochoa Lab , which develops open-source software under GNU General Public License and collaborates with programs like UPGG and CBB .

• Additive Combinatorics
• Number Theory
• Ramsey Theory
• +۳ مورد دیگر

Dr Jonathan Chapman is a Research Fellow at the Department of Mathematics, University of Warwick. His research focuses on additive combinatorics, Ramsey theory, and number theory, with particular emphasis on partition regularity, Diophantine equations, and combinatorial structures in number systems. His work bridges algebraic, analytic, and probabilistic methods to address problems in arithmetic Ramsey theory and additive patterns. Recent publications explore topics such as monochromatic solutions to multiplicative equations, additive Ramsey configurations over primes and non-integer sequences, and generalizations of classical Ramsey criteria like Rado and Roth theorems. His studies often involve intricate interplays between linear algebraic structures, syndetic sets, and hypergraph Ramsey phenomena. No scientific awards or grants are explicitly listed in available records. He currently holds no documented advisees, though his research likely involves collaboration with graduate students and postdocs in the Mathematics Institute. His affiliations include the Mathematics Institute at the University of Warwick, with office B2.28 and contact details available via institutional channels.

• Algebraic Number Theory
• Analytic Number Theory
• Cryptanalysis
• +۸ مورد دیگر

Carlo Sanna is an Associate Professor at the Department of Mathematical Sciences (DISMA) of Politecnico di Torino, Italy. His research focuses on algebraic and analytic number theory, cryptography, and their intersections, particularly in post-quantum and public-key cryptographic systems. Research Interests : Algebraic Number Theory, Analytic Number Theory, Cryptanalysis, Post-Quantum Cryptography, Public-Key Cryptography, Discrete Mathematics. Teaching : He teaches Linear Recurrences (PhD level) and Advanced Cryptography (Master's level) at Politecnico di Torino, while also lecturing Linear Algebra and Geometry for Aerospace and Management Engineering. Publications : His recent work spans Fibonacci partitions, Lucas sequences, post-quantum cryptographic algorithms, and properties of linear recurrences. Supervised PhD Students : Leonardo Errati (Post-Quantum Cryptography, 2025) Giulia Salvatori (2024) Federico Accossato and Enrico Guglielmino (2022) Giuliano Romeo (2021-2025, p-adic continued fractions) Research Projects : Algebraic Methods in Cryptanalysis (2025-2027, Scientific Director) QUBIP (Quantum-oriented browser updates, 2023-2026, Scientific Head) SERICS Spoke 5 (Cybersecurity, 2023-2025, Scientific Manager) Email : carlo.sanna@polito.it