Find Academic Supervisors

Kevin IgaView profile

Professor

• Mathematical Physics
• Supersymmetry
• Differential Topology
• +4 more

Kevin Iga is a Professor of Mathematics at Pepperdine University's Seaver College, part of the Natural Science Division. He has been teaching since 1998 and holds a PhD from Stanford University (1998) and dual BS degrees in Mathematics and Physics from MIT (1992). His research focuses on supersymmetry, particularly using Adinkra diagrams, differential topology, general relativity, and cryptography. He has contributed to geometric interpretations of supersymmetry algebras and combinatorial analysis of Adinkras. Iga serves as Public Information Officer for the MAA's Philosophy of Mathematics SIG and is active in professional organizations like the AMS, ASA, and MAA. His work spans over 25 years, with significant contributions to mathematical physics, including structural theory of Adinkras, cohomological applications, and cryptographic hardness problems. He teaches courses such as Algebraic Structures, Automata Theory, and Real Analysis. Iga is also involved in community activities, singing in the Pepperdine Concert Choir and participating in Malibu Presbyterian Church.

David StrongView profile

Professor

• Applied Mathematics
• Image Processing
• Numerical Linear Algebra
• +2 more

Roles and Affiliations: David Strong is a Professor of Mathematics at Pepperdine University, affiliated with the Natural Science Division within Seaver College. He holds a PhD in Applied Mathematics from UCLA (1997). Education: - PhD in Applied Mathematics, UCLA, 1997 - MS in Applied Mathematics, UCLA, 1994 - BS in Mathematics, Brigham Young University, 1992 (Summa Cum Laude). Research Interests: Focuses on mathematical software development, image processing, numerical linear algebra, and undergraduate research. His work bridges theoretical mathematics with practical applications, including contributions to computational methods in imaging and iterative solvers for linear systems. Teaching: Teaches courses such as Linear Algebra, Numerical Analysis, and Differential Equations. Develops innovative educational tools like online tutorials for numerical methods. Publications: Active in publishing research on topics like total variation regularization, fixed-point algorithms, and computational mathematics. His work emphasizes interdisciplinary applications of mathematics in science and engineering.

• Mathematics
• Combinatorics
• Number Theory
• +3 more

Orli Herscovici is an Assistant Professor in the Department of Mathematics and Computer Science at St. John’s College of Liberal Arts and Sciences, St. John’s University. Her research focuses on advanced mathematical analysis, combinatorics, and special functions, with notable contributions to fractional calculus, polynomial theory, and combinatorial identities. She has published extensively on topics including Gaussian principal frequencies, deformed fractional transforms, and degenerate polynomials. Her work bridges pure mathematics and applications in probability theory and nonlinear systems. Dr. Herscovici’s academic affiliations include the Mathematics and Computer Science department, where she contributes to teaching and research initiatives. Her publications reflect a deep engagement with interdisciplinary areas such as spectral geometry and umbral calculus. While no specific scientific awards or grants are highlighted in the provided text, her scholarly output demonstrates sustained academic engagement in theoretical and applied mathematics.

• Quantum Chemistry
• Uncertainty Quantification
• Numerical Analysis
• +5 more

Virginie Ehrlacher Galland is a Professor at CERMICS (Centre d'Enseignement et de Recherche en Mathématiques et Calcul Scientifique) within École des Ponts ParisTech. Her expertise lies in applied mathematics, numerical analysis, and computational physics, with a focus on multiscale problems, quantum chemistry, and uncertainty quantification. She holds a PhD from CERMICS (2012) and a Habilitation (2020) from Université Paris-Dauphine. Her research interests include cross-diffusion systems, reduced basis methods, and optimal transport applications. Key contributions involve numerical methods for electronic structure calculations, homogenization techniques, and adaptive algorithms. She leads the ERC Starting Grant HighLEAP (2023-2028) and contributes to major projects like the ERC Synergy project EMC². Awards: Irène Joliot-Curie Prize (2023), Chevalier de l’Ordre National du Mérite (2025). Grants/Projects: ERC Starting Grant HighLEAP (PI), ERC Synergy EMC² (Member), ANR JCJC COMODO (PI). Her work bridges theoretical analysis and computational methods, addressing challenges in materials science, fluid dynamics, and machine learning applications.

Federica MucciView profile

Associate Professor

• International Law
• Cultural Heritage Protection
• European Union Law
• +4 more

Federica Mucci is an Associate Professor of International Law at the University of Rome Tor Vergata, affiliated with the Department of History, Humanities and Society. She specializes in international protection of cultural heritage, European Union law, and treaty law. Her teaching includes courses on cultural heritage protection and EU law for programs in Education and Tourism. Legal Expert: Italian Ministry of Foreign Affairs UNESCO Delegation Member: Contributed to the 2005 UNESCO Convention on cultural diversity and its implementation. Her research focuses on international law frameworks for cultural heritage, maritime law, treaty interpretation, and environmental protection. Key publications include monographs on cultural heritage law (2012) and a PhD thesis on EU copyright law (1998). Publications span interdisciplinary topics such as topological data analysis, though the majority of her work aligns with legal and humanities disciplines. Awards: No specific prizes are mentioned, but her scholarly output includes influential books and articles on international law. Advising/Grants: No formal student advisees or grant details provided in text. Labs/Teams: No specific lab affiliations mentioned, though her role at UNESCO implies collaboration with international bodies.

Don SannellaView profile

Professor

• Computer Science
• Formal Methods
• Algebraic Specification
• +5 more

Don Sannella is a Professor of Computer Science at the University of Edinburgh, affiliated with the Laboratory for Foundations of Computer Science (LFCS) within the School of Informatics. His research focuses on formal methods, algebraic specification (notably the CASL language), security, and theoretical computer science. He has contributed to foundational work in specification languages and their implementation, including the CoFI initiative. Sannella has authored influential books on formal software development and algebraic specifications. He holds the rank of Fellow of the Royal Society of Edinburgh. His work spans academic leadership, editorship of Theoretical Computer Science , and projects like Mobility and Security (MRG) and App Guarden for secure mobile applications. His teaching includes courses on functional programming, computability, and formal methods. He has advised students such as Nikita Samarin and collaborated on research grants, including those in resource analysis and concurrency. Sannella’s contributions integrate rigorous formal techniques with practical software development challenges, emphasizing modularity, correctness, and security.

Anna HuberView profile

Senior Lecturer

• Algebra
• Graph Theory
• Randomized Methods
• +3 more

Anna Huber is a Senior Lecturer in Mathematics and Computing at Teesside University's Department of Computing & Games Centre for Sustainable Engineering. She holds a PhD from Saarland University (2010) and completed postdoctoral research at Durham University (2010-2013) on EPSRC-funded submodular optimization projects. Her research focuses on algebraic structures, graph theory, and combinatorial algorithms with applications in discrete optimization. She has published influential work in SIAM journals on bisubmodular functions and valued constraint satisfaction problems. Education: PhD in Mathematics and Informatics, Saarland University & Max Planck Institute (2010) Previously affiliated with University of Derby as Lecturer in Mathematics Research Themes: Explores algebraic methods for solving combinatorial optimization problems, particularly through randomized algorithms and graph-based approaches. Current work emphasizes tractability analysis in constraint satisfaction frameworks and bisubmodularity applications. Grants & Projects: EPSRC Project: 'Submodular Optimization, Lattice Theory and Maximum Constraint Satisfaction Problems' (2010-2013) Labs/Teams: Active in Teesside University's Computing & Games research groups, collaborating on sustainable engineering computing applications.

Maxim L. YattselevView profile

Professor

• Meromorphic Approximation
• Orthogonal Polynomials
• Integrable Systems
• +3 more

Maxim L. Yattselev is a Professor in the Department of Mathematical Sciences at Indiana University–Purdue University Indianapolis (IUPUI) . He has held prior positions as a Visiting Assistant Professor at the University of Oregon and a Visiting Scholar at Vanderbilt University. Education: Ph.D. and M.S. in Mathematics from Vanderbilt University (2007, 2004), M.S. and B.S. from Dnepropetrovsk National University (2001, 2000). Research Interests focus on meromorphic approximation, orthogonal polynomials, integrable systems, random polynomials, and spectral theory. He explores connections between these areas and applications to Painlevé equations, random matrix theory, and convergence of rational approximants. Publications highlight advancements in strong asymptotics for Angelesco systems, spectral theory of Jacobi matrices on trees, universality in random polynomial roots, and convergence properties of Padé and Hermite–Padé approximants. His work often employs Riemann–Hilbert techniques and potential theory. Advising: Mentored Ph.D. students Hanan Aljubran and Ahmad Barhoumi on random polynomials and multiple orthogonal polynomials. Teaching includes advanced courses in complex analysis, real analysis, orthogonal polynomials, and differential equations at IUPUI, University of Oregon, and Vanderbilt University.

Umberto MichelucciView profile

Professor

• Machine Learning
• Artificial Intelligence
• Astrophysics
• +5 more

Umberto Michelucci is a Professor of Scientific Machine Learning at Lucerne University of Applied Sciences and Arts (HSLU), Switzerland. He holds a PhD in Machine Learning applied to Physics and has over 20 years of industry experience. He is the Subject Head of Applied Data Intelligence in Continuing and Executive Education, Head of Certificates in Machine Learning/Data Engineering, and founder of TOELT LLC and the AI Center of Excellence at Helsana Versicherung AG. His research focuses on machine learning applications in science, astrophysics, uncertainty quantification, and sensor technology. Education PhD in Machine Learning applied to Physics (Portsmouth University) Master in Theoretical Physics (University of Florence) Postgraduate Certificate in Higher Education (Open University, UK) Research Interests Michelucci’s work bridges machine learning and scientific disciplines. Key areas include: Machine learning for astrophysics (INAF collaborations) Uncertainty analysis in high-stakes ML systems Deep learning for optical sensing (e.g., olive oil quality analysis) Foundational mathematical concepts for ML in science Awards & Recognition World’s Top 2% Scientists (Stanford List) Google Developer Expert in Machine Learning AI Global Ambassador (2022) TOP AI Influencer in Switzerland (2021) Grants & Collaborations He collaborates with institutions like INAF (Italy) and NVIDIA/Google, leading projects on AI for agrifood, medical imaging, and astrophysics. His work includes $multi-million industry partnerships and EU-funded research. Labs & Teams Director of the TOELT AI Lab and oversees HSLU’s Applied Data Intelligence programs. Active in open-source initiatives and global AI standardization efforts.

Akimasa MiyakeView profile

Professor

• Quantum Information Science
• Quantum Computation
• Quantum Many-Body Physics
• +3 more

Akimasa Miyake is a Professor in the Department of Physics and Astronomy at the University of New Mexico (UNM). His research focuses on quantum information science, quantum computation, and quantum many-body physics, with particular emphasis on symmetry-protected topological orders and their applications to quantum computing. He leads the Miyake Research Group, which participates in the NSF-funded STAQ project, a major initiative advancing quantum computing co-design. Miyake earned his PhD from the University of Tokyo in 2004. His work bridges theoretical physics and quantum information, exploring novel approaches to quantum metrology, error mitigation, and computational advantage using nonlocal games. He has contributed to foundational studies on topological phases, matchgate circuits, and measurement-based quantum computation. Recent research highlights include hardness results for surface code decoding and group-theoretic error mitigation techniques. Miyake teaches advanced undergraduate and graduate courses in quantum mechanics, mathematical physics, and quantum computation. His educational activities emphasize active learning and preparation for interdisciplinary challenges in quantum technologies. He maintains an active lab focused on theoretical and applied aspects of quantum systems, with collaborations spanning computational physics and condensed matter theory.

• Formal Verification
• Logic
• Cryptography
• +3 more

Rajeev Gore is a Professor in the Department of Data Science & AI at Monash University. His research focuses on formal verification, automated theorem proving, and logic systems. He has contributed significantly to areas including modal logics, voting system verification, and cryptographic protocols. Gore has been actively involved in developing verified decision procedures for modal logics and exploring applications in electronic voting systems. His work bridges theoretical computer science and practical applications, with a particular emphasis on ensuring correctness through formal methods. Key contributions include the N-PAT nested model-checker and verified verifiability frameworks for voting systems like BeleniosVS and ElectionGuard. His research also addresses trust domains and cryptographic applications, reflecting a deep engagement with both foundational and applied aspects of logic and computation. Gore's publications span over two decades, demonstrating sustained contributions in formal systems, automated reasoning, and security-critical applications. His work often combines rigorous mathematical foundations with practical tool development, emphasizing trustworthiness and verifiability in complex systems.

Robb LindgrenView profile

Professor

• STEM Education
• Educational Technologies
• Embodied Learning
• +3 more

Dr. Robb Lindgren is a Professor of Educational Psychology and Curriculum & Instruction at the University of Illinois, Urbana-Champaign , with affiliate appointments at the Beckman Institute, National Center for Supercomputing Applications, iSchool Informatics, Center for Social & Behavioral Science, and Grainger College of Engineering. His research focuses on interactive and immersive digital technologies for learning, particularly in STEM education . PhD in Learning Sciences and Technology Design from Stanford University MA in Psychology from Stanford University BS in Computer Science from Northwestern University Dr. Lindgren explores how body-based interactions with technologies like virtual reality and augmented reality enhance understanding of abstract concepts. His work combines embodied cognition with digital learning environments , examining how physical engagement shapes multimodal learning and collaborative problem-solving . Recent projects include haptic feedback systems and gesture-augmented simulations for molecular visualization and climate change education. His publications analyze trends in XR (Extended Reality) applications, emphasizing gestural agency , collaborative discourse , and multisensory learning . Key findings show how embodied interactions improve conceptual retention in physics and explanatory modeling in thermal conduction, alongside innovative assessment methods using attention tracking and behavioral analytics. 2017 - Jan Hawkins Award for Early Career Contributions Multiple NSF-funded projects on technology-enhanced learning As Director of the Technology Innovations in Educational Research and Design (TIER-ED) initiative, Dr. Lindgren fosters cross-campus collaboration to address educational challenges through emerging technologies. He teaches courses on digital learning environments , educational game design , and embodied learning theories , while developing prototypes for STEM education in partnership with schools and museums.

Paige Randall NorthView profile

Assistant Professor

• Category Theory
• Homotopy Theory
• Functional Programming
• +3 more

Paige Randall North is an Assistant Professor at Utrecht University, holding dual appointments in Mathematics and Information and Computing Sciences. She is also an Honorary Research Fellow at the University of Birmingham and a Guest Researcher at Delft University of Technology. Her research focuses on applying category theory and topology to computer science, particularly in functional programming and homotopy theory. North earned her PhD in Mathematics from the University of Cambridge (supervised by Martin Hyland) and a BS from the University of Chicago. She has held postdoctoral positions at the University of Pennsylvania and Ohio State University. Her research explores connections between type theory and higher category theory, including the univalence principle and categorical diffusion. Recent activities include organizing conferences like TYPES and DutchCATS, serving on program committees for LICS and POPL, and teaching advanced courses on homotopy type theory and interactive theorem provers. Funding has come from the AFOSR (2021-2024). Her work emphasizes formalization in proof assistants like UniMath and Coq, with contributions to categorical semantics and bicategorical type theory.

Anastasia DoikouView profile

Professor

• Mathematical Physics
• Quantum Integrable Systems
• Classical Integrable Systems
• +3 more

Anastasia Doikou is a Professor of Mathematics at the School of Mathematical & Computer Sciences, Heriot-Watt University, and a member of the Maxwell Institute for Mathematical Sciences in Edinburgh. She holds a prominent role as the convener of the Mathematical Physics Group within the Department of Mathematics. Previously, she served as Director of Research (Associate Executive Dean of Research) in her school from 2019-2022. Her academic journey includes a BSc in Physics from the National & Kapodistrian University of Athens and a PhD in Theoretical Physics from the University of Miami (1999). Postdoctoral positions spanned institutions such as Durham University, the University of York (via an EPSRC Fellowship), LAPTH-Annecy, and the University of Bologna before her tenure at Heriot-Watt since 2013. Education: BSc Physics, National & Kapodistrian University of Athens PhD Theoretical Physics, University of Miami (1999) Research Interests: Her research focuses on quantum and classical integrable systems, quantum algebras, Yang-Baxter and reflection equations, and their applications. She explores topics such as integrable quantum spin chains, Bethe ansatz techniques, braid groups, Temperley-Lieb algebras, and soliton solutions in integrable PDEs. Her work bridges algebraic structures (e.g., braces, quandles) with integrability, emphasizing their role in non-local and non-commutative systems. Recent Trends in Articles: Her publications from 2022–2025 highlight advancements in algebraic solutions to the Yang-Baxter equation using braces and skew braces, parametric approaches to reflection and set-theoretic equations, and explorations of quantum group symmetries. These contributions underscore her expertise in connecting abstract algebraic frameworks with integrable models in mathematical physics. Awards: EPSRC Fellowship (2007) Lab/Team Affiliations: Part of the Maxwell Institute for Mathematical Sciences and the Mathematical Physics Group at Heriot-Watt University.

Lap Chi LauView profile

Professor

• Algorithms
• Optimization
• Spectral Graph Theory

Lap Chi Lau is a Professor at the University of Waterloo's Cheriton School of Computer Science. His primary research explores algorithms, optimization, and spectral graph theory. His recent publications focus on graph algorithms, spectral methods, and combinatorial optimization, with consistent applications to network design and experimental methodologies. Publications demonstrate advanced techniques in graph sparsification, eigenvalue methods, and efficient algorithm design for complex computational problems. His work frequently intersects theoretical computer science and applied mathematics, contributing to foundational improvements in graph partitioning, spectral clustering, and randomized algorithms.