Find Academic Supervisors

• Signal Processing
• Machine Learning
• Neural Networks
• +3 more

Professor Danilo P. Mandic, affiliated with Imperial College London, UK, is a leading researcher in signal processing, machine learning, and biomedical signal analysis. His work spans quaternion algebra, tensor networks, and neural networks for real-world applications. 2025: Published 11+ works on EEG/PPG analysis, quantum learning, and tensor-based LLM compression 2024: Active in interpretable transformers, graph learning for financial data, and hearable devices Research focuses on hypercomplex signal processing, graph neural networks, and medical AI applications. Recent work explores quaternion calculus for signal processing, tensor network structures for LLMs, and hearable device optimization. Key publication trends include: 2025 emphasis on quantum-aware learning, 2024 graph-based time series clustering, and 2023 foundational work on graph CNNs and matched filtering approaches. Collaborates extensively with Dongpo Xu, Sayed Pouria Talebi, Clive Cheong Took, and Tobias Reichenbach on projects involving ear-EEG, ECG enhancement, and financial sentiment analysis.

Jonathan A. KelnerView profile

Professor

• Algorithms
• Complexity Theory
• Spectral Graph Theory
• +4 more

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
• +4 more

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.

• Condensed Matter Theory
• Algebraic Topology
• Topological Field Theory
• +6 more

Clément Delcamp is a CNRS Researcher at the Institut des Hautes Etudes Scientifiques (IHES), focusing on theoretical physics at the intersection of condensed matter and algebraic topology. He was previously a Junior Professor at IHES since 2023, following postdoctoral fellowships at Ghent University, the Max Planck Institute for Quantum Optics (Munich), and the Max Planck Institute for the Physics of Complex Systems (Dresden). His academic journey includes a PhD from the University of Waterloo and the Perimeter Institute for Theoretical Physics, preceded by an MSc in Quantum Fields and Fundamental Forces at Imperial College London (2013–2015). Education: MSc in Quantum Fields and Fundamental Forces (Imperial College London, 2015), PhD (University of Waterloo & Perimeter Institute, 2018) His research explores the algebraic structures underlying quantum lattice systems, emphasizing generalized symmetries, dualities, and topological field theory applications. He has pioneered tensor network methods to study electromagnetic duality in (3+1)d systems and non-abelian Kramers-Wannier dualities in generalized Ising models. His work bridges abstract category theory with concrete physical models, particularly in topological phases and renormalization group techniques. Recent publications highlight his contributions to quantum lattice duality frameworks, defect calculus, and tensor network-based renormalization. His 2024 paper with Lootens and Verstraete demonstrates optimized DMRG simulations for gapped phases via dual models. At IHES, he leads research on non-invertible symmetries and solicits postdocs in theoretical physics. Scientific Awards: Walter Zellidja Scholarship (French Academy), FWO Postdoctoral Fellowship Delcamp's affiliations span leading institutions including the Perimeter Institute, Max Planck Institutes, Ghent University, and IHES. He actively promotes collaboration through seminars and lectures at venues like the Collège de France, Les Houches, and Queen Mary University.

Artem KaznatcheevView profile

Assistant Professor

• Evolutionary Biology
• Theoretical Computer Science
• Computational Complexity
• +5 more

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)

Erik CarlssonView profile

Professor

• representation theory
• algebraic geometry
• algebraic combinatorics
• +5 more

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.

• Graph Analytics
• Parallel Algorithms
• Multicore Computing
• +6 more

Kamesh Madduri is an Associate Professor in the Department of Computer Science and Engineering at Pennsylvania State University, with affiliations to the Huck Institutes of the Life Sciences. His research focuses on graph analytics, parallel algorithms, and high-performance computing for large-scale data analysis. NSF CAREER Award (2013) His work contributes to the development of scalable graph partitioning algorithms, extreme-scale sparse data analytics, and heterogeneous computing frameworks. Recent projects include multilayer network analysis (NetSplicer) and GPU-accelerated graph processing (Jet). Key research areas include network science, computational biology, and distributed-memory graph algorithms. His publications highlight applications in genomic workflows, advertising keyphrase recommendation (Graphite/BroadGen), and large-scale hydrology data management. Collaborative Research: CCRI (2021-2023) SHF: Medium: NetSplicer (2020-2024) PPoSS: Extreme-scale Sparse Data Analytics (2018-2022) XPS: Genomic Workflows Acceleration (2014-2020) EAGER: SME Manufacturing Integration (2024-2026)

Aravind N.R.View profile

Associate Professor

• Algorithms
• Graph Theory
• Combinatorics
• +5 more

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.

• Mathematics Education
• Cognitive Development in Early Mathematics
• Integer Understanding
• +7 more

Laura Bofferding is a Professor of Mathematics Education in the Department of Curriculum and Instruction at Purdue University's College of Education. Her career spans over a decade of progressive academic appointments, including Assistant Professor (2011-2017), Associate Professor (2017-2024), and current Professor (2024-present). She maintains active research and teaching roles focused on early mathematics cognition. Her educational background includes: Ph.D. in Curriculum Studies and Teacher Education from Stanford University (2011) M.A. in Learning, Design, and Technology in Education from Stanford University (2007) B.S. in Elementary Education from University of Wisconsin, River Falls (2002) Bofferding's research centers on cognitive development in early mathematics, particularly children's understanding of negative numbers, spatial reasoning through tangram puzzles, and the application of contrasting cases in instruction. Her work bridges theoretical cognitive science with practical classroom applications, emphasizing game-based learning and emergent bilingual education. Recent projects explore AI-generated word problems and programming debugging in elementary contexts, demonstrating her commitment to innovative pedagogical approaches. Her publication trends reveal sustained focus on integer conceptualization (2014-2023), expanding into spatial reasoning (2022-2023) and AI integration (2024). Key thematic clusters include measurement misconceptions, tangram-based shape transformation, and debugging strategies in computational thinking. Notable awards include: Purdue Faculty Engagement Scholar (2021) Purdue Faculty Scholar (2019) AMTE STaR Fellow (2012) Bofferding secures significant grant funding including an NSF CAREER award ($680,504) for integer understanding research and a Purdue Launch the Future Grant ($25,000) investigating AI-generated word problems. Her advising focuses on mathematics education doctoral students through courses like EDCI 62000 (Developing as a Mathematics Researcher), with grant collaborations spanning computational thinking (NSF ITEST) and emergent bilingual education (Purdue Small Research Grant).

• Computational Chemistry
• Catalysis
• Density Functional Theory (DFT)
• +4 more

Dario Duca is a Full Professor in the Department of Physics and Chemistry at the University of Palermo . His research focuses on computational chemistry and catalysis, particularly using Density Functional Theory (DFT) and microkinetic modeling to study reaction mechanisms on nanoscale catalysts. He teaches General and Inorganic Chemistry (10 CFU) and Higher Inorganic Chemistry (8 CFU) for the Chemistry degree program. Fields of Interest: Computational chemistry, catalysis, DFT, materials science, nanotechnology, surface chemistry, reaction kinetics His recent work includes DFT studies of CO/H2 purification over MnO2 catalysts, biomass conversion using halloysite nanotubes, and platinum particle growth in zeolites. He has developed computational tools like the Empathes code for transition state analysis. Contact: dario.duca@unipa.it | Office hours: Mon-Fri 1:00-2:00 pm, Sat 10:00-13:00 at Ed.17, University of Palermo.

Gergely ZábrádiView profile

Associate Professor

• Algebraic Number Theory
• Iwasawa Theory
• Elliptic Curves
• +5 more

Gergely Zábrádi is an Associate Professor and Head of the Department of Algebra and Number Theory at the Institute of Mathematics, Eötvös Loránd University (ELTE), Budapest. He earned his PhD from Trinity College, University of Cambridge, and conducted postdoctoral research at Westfälische Wilhelmsuniversität Münster. His academic focus lies at the intersection of algebraic number theory, Iwasawa theory, and the p-adic Langlands programme. Education: PhD in Mathematics, Trinity College, University of Cambridge (2008) Habilitation, Eötvös Loránd University (2016) Research Interests: Zábrádi's research spans algebraic number theory , particularly noncommutative Iwasawa theory for elliptic curves, and the p-adic Langlands programme . He explores deep connections between Galois representations , automorphic forms , and L-functions , utilizing tools like (φ,Γ)-modules and perfectoid spaces . Publications Overview: His recent work includes studies on generalized Montréal functors , matrix Kloosterman sums , and sup-norm problems for automorphic forms. These publications reflect a consistent engagement with foundational questions in arithmetic geometry and representation theory. Advising and Grants: Zábrádi has supervised numerous PhD, Master's, and Bachelor's theses, guiding students through topics ranging from Iwasawa theory to p-adic Galois representations . His mentorship has produced award-winning research, including first-prize recognition at the Hungarian Student Research Competition (OTDK). Research Groups and Labs: He leads a vibrant research group at ELTE, fostering collaboration with international scholars and participating in networks like the CENTRAL programme. His team focuses on advancing the p-adic Langlands correspondence and related arithmetic questions.

Toni MilunView profile

Lecturer

• Mathematics Education
• Financial Mathematics
• Business Statistics
• +3 more

Toni Milun serves as a lecturer in mathematics and statistics at Algebra University of Applied Sciences, where he bridges theoretical concepts with practical business applications. His academic foundation includes a 1999 degree from the Faculty of Science and Mathematics at the University of Zagreb and a 2012 postgraduate specialist degree in Statistical Methods for Economic Analysis and Forecasting from the Faculty of Economics in Zagreb. He is currently completing his doctoral research in Economics at the Faculty of Economics in Rijeka. His research spans Mathematics Education, Financial Mathematics, and Business Statistics, with a distinctive focus on making complex quantitative concepts accessible through innovative pedagogical approaches. Milun's work consistently connects mathematical theory to real-world economic scenarios, particularly in SME financing, investment analysis, and statistical literacy for decision-makers. His publications reveal a strong emphasis on practical applications in Croatian business contexts and educational settings. Analysis of his 15 most recent publications shows dominant themes in financial mathematics (35%), business statistics (30%), and adult education (25%), with recurring subfields including percentage calculations in commerce, regression modeling of socioeconomic factors, and maritime/IT-specific mathematical applications. His research demonstrates consistent engagement with Croatian economic data and educational challenges. His notable recognition includes: Pride of Croatia award for special contribution to education Milun actively disseminates knowledge through public-facing initiatives rather than formal academic advising. He has co-authored a high school mathematics textbook and provides consulting services in applied mathematics and statistics, focusing on business problem-solving. His educational outreach generates significant impact through digital platforms and traditional media. He leads the www.tonimilun.com educational portal and television productions ('Školski sat' and 'Financijalac'), directing teams of educators, video producers, and subject-matter experts to create accessible mathematical and financial literacy content for diverse audiences across Croatia.

Angel Gimenez PastorView profile

Professor

• Differential Geometry
• Lorentzian Geometry
• Dynamical Systems
• +4 more

Angel Gimenez Pastor is a Professor of Mathematics at Universidad Miguel Hernández (UMH) in Spain, specializing in differential geometry and dynamical systems. His research primarily focuses on the intersection of Lorentzian geometry, null curve theory, and applications to fluid mechanics and suspension flows. He completed his doctorate at Universidad de Murcia in 2002 with a thesis on "Geometría de curvas degeneradas" under the supervision of Dr. Pascual Lucas Saorín. His research interests encompass differential geometry, particularly Lorentzian geometry and null curves, dynamical systems theory, ergodic theory, and mathematical physics applications. His work bridges theoretical mathematics with practical applications in fluid mechanics and suspension modeling. He has made significant contributions to understanding the geometry of null curves in Lorentzian space forms and their applications to relativistic particle models. Professor Gimenez Pastor's publication record reveals a clear trajectory of research connecting geometric structures with dynamical behavior. His work on topological entropy of multimodal maps, switching systems, and suspension flows demonstrates his ability to connect abstract mathematical concepts with physical applications. The recurring themes across his publications include null curve geometry, dynamical system analysis, and mathematical modeling of complex physical phenomena. His scientific collaborations are extensive, with significant joint work with José María Amigó, Pascual Lucas, José Valero, and other researchers in Spain and internationally. These collaborations span multiple disciplines, reflecting the interdisciplinary nature of his research. As an academic advisor, he has mentored numerous students who have gone on to make their own contributions in mathematics and related fields, particularly in the areas of dynamical systems and mathematical physics. His research continues to influence both theoretical developments and practical applications in mathematical physics and fluid dynamics.

Carlo AngiuliView profile

Assistant Professor

• Type Theory
• Programming Language Foundations
• Homotopy Type Theory
• +3 more

Carlo Angiuli is an Assistant Professor of Computer Science at the Luddy School of Indiana University . He is a leading researcher in type theory , with a focus on its applications to programming language design and logic . His work bridges theoretical foundations and practical implementations, particularly through dependent types , proof assistants , and homotopy type theory . His research interests include: Type Theory (foundational systems for programs and proofs) Programming Language Foundations (formal semantics and reasoning) Homotopy Type Theory (higher-dimensional structures) Dependent Types (expressive type systems) Proof Assistants (formal verification tools) Computational Logic (algorithmic reasoning) Angiuli actively contributes to the programming languages community as a POPL Program Committee member and co-author of a forthcoming textbook on dependent type theory. His students include Johnson He, Huang Xu, Kelton OBrien, and Ian Ray , who joined in Fall 2025. He has received significant recognition, including the Best Paper Award at FSCD 2019 and the School of Computer Science Distinguished Dissertation Award from Carnegie Mellon University. His current work explores advanced type-theoretic frameworks and their implementation in proof assistants like the red* family of tools. Scientific awards: Best Paper Award, FSCD 2019 (Junior Researchers category) School of Computer Science Distinguished Dissertation Award, Carnegie Mellon University He teaches courses such as Modern Dependent Types (CSCI-B619), Programming Language Foundations (CSCI-B522), and Introduction to Computer Science (CSCI-C211).

Gavin BrownView profile

Professor

• Algebraic Geometry
• Computer-Assisted Mathematics
• Birational Classification
• +1 more

Gavin Brown is Professor of Mathematics at the University of Warwick's Mathematics Institute. His research focuses on birational classification in algebraic geometry using computer-assisted constructions and databases. He investigates complex geometric structures including Fano varieties and Calabi-Yau manifolds through both theoretical frameworks and computational approaches. Research interests center on developing mathematical models for noncommutative functions, deformation theory of algebraic curves, and singularity classifications. This work bridges pure mathematics with computational applications to solve geometric classification problems. Recent publications demonstrate consistent themes in computational algebraic geometry, particularly in developing databases for geometric structures and advancing theoretical understanding of deformation spaces. The research integrates computational methods with deep geometric insights.