Find Academic Supervisors

• Mathematical Analysis
• Functional Analysis
• Quaternionic and Hypercomplex Analysis
• +13 more

Vincenzo Recupero is an Associate Professor in the Department of Mathematical Sciences "GL Lagrange" (DISMA) at Politecnico di Torino, Italy. His research and teaching are centered on advanced topics in mathematical analysis, with a focus on functional, nonlinear, and quaternionic analysis, as well as partial differential equations and hysteresis models. He teaches core mathematics courses such as Mathematical Analysis II and Mathematical Methods for Engineering to undergraduate students in computer, mechanical, and engineering physics programs, and also lectures in PhD programs on variational evolution problems. His research interests span Quaternionic and Hypercomplex Analysis , motivated by applications in quantum mechanics; Partial Differential Equations in Phase Transition Models such as Penrose-Fife and Stefan systems; and Rate-Independent Processes and Moreau Sweeping Processes , with applications in elastoplasticity, hysteresis, and crowd dynamics. His work emphasizes the well-posedness and continuity of solution operators under various topologies, ensuring robustness and applicability of mathematical models. The recent publications (2011–2023) reflect a consistent focus on sweeping processes, BV solutions, and quaternionic operator semigroups, with increasing sophistication in algebraic and functional-analytic frameworks. These works appear in prestigious journals such as Transactions of the American Mathematical Society and Journal of Differential Equations , indicating strong theoretical contributions to nonlinear analysis and mathematical physics. He is actively involved in the academic community, serving on program committees for international conferences like the International Symposium on Hysteresis Modeling and Micromagnetics (HMM 2025) and chairing organizing committees for summer schools on multi-rate processes and slow-fast systems. His research is aligned with ERC sectors in analysis, mathematical physics, and dynamical systems, and contributes to UN SDGs on quality education and reduced inequalities. Scientific Service and Leadership: Program Committee, International Symposium on Hysteresis Modeling and Micromagnetics (HMM 2025) Program Committee, Two-day Workshop on Nonlinear Analysis Chairman, Organizing Committee, Summer School on Multi-Rate Processes, Slow-Fast Systems and Hysteresis (2019) Program Committee, Control of State Constrained Dynamical Systems (2017) Chairman, Organizing Committee, Summer School on Multi-Rate Processes, Slow-Fast Systems and Hysteresis (2017) He has held external research appointments, including as a Researcher at Tongji University (2013). He is a member of the College of Computer, Film and Mechatronics Engineering and part of the Nonlinear Analysis and Calculus of Variations research group at DISMA. While no formal advisees are listed, his collaborative work includes prominent mathematicians such as Riccardo Ghiloni, Pavel Krejci, and Filippo Santambrogio.

Moataz EmamView profile

Professor

• String Theory
• Supergravity
• Gravitational Physics
• +3 more

Moataz Emam is a Professor of Physics at the State University of New York at Cortland. His research focuses on theoretical physics, particularly string theory, supergravity, and gravitational physics. With numerous publications in prestigious journals including Physical Review D and Classical and Quantum Gravity, Dr. Emam has established himself as a notable contributor to the field of theoretical physics and cosmology. Dr. Emam's research interests center around string theory and its applications to cosmology and gravitational physics. His work particularly focuses on supergravity theories, Calabi-Yau manifolds, and brane-world scenarios. He has made significant contributions to understanding the relationship between complex structure moduli of Calabi-Yau manifolds and cosmological models, as well as exploring solutions to the cosmological constant problem through brane-world constructions. His research often involves sophisticated mathematical techniques from differential geometry and symplectic structures applied to physical problems in higher dimensions. An analysis of Dr. Emam's publication record reveals a consistent focus on N=2 supergravity in five dimensions and its cosmological implications. His work frequently examines the universal hypermultiplet and its role in brane-world scenarios, with particular attention to BPS states and their cosmological consequences. The research shows progression from foundational work on calibrations and brane solutions toward more cosmological applications, demonstrating how theoretical constructs in string theory might connect to observable universe phenomena. His publications span both highly technical mathematical physics papers and more accessible discussions about the philosophical implications of string theory. Dr. Emam has authored the book "Are we there yet? The search for a theory of everything," which explores the current state of fundamental physics research and the quest for a unified theory. While specific grant information isn't prominently featured in the available materials, his publication record in high-impact journals suggests successful research funding support for his theoretical investigations.

Renata Trinca ColonelView profile

Associate Professor

• Business Analytics
• Data Visualization
• Sampling Techniques
• +3 more

Renata Trinca Colonel is an Associate Professor of Practice in Decision Sciences & Business Analytics at SDA Bocconi School of Management and an Adjunct Professor of Statistics at Bocconi University. She serves as Academic Director of Executive MBA programs and coordinates open programs in Business Data Analytics. Education: Degree in Economic and Social Disciplines (D.E.S.) from Università Bocconi ITP (International Teachers Programme) from SDA Bocconi School of Management (2009) Her research focuses on business analytics, data visualization, sampling techniques, marketing research, statistical modeling , and predictive analytics . She has authored books and articles in journals such as the Journal of Product Innovation Management , European Journal of Finance , and Economia & Management . She has received the Innovation in Teaching Award from SDA Bocconi (2017-2018) and Bocconi (2019). Her recent publications span applications of quantitative methods in energy, transportation, retail, banking, and automotive sectors. Scientific Awards: Innovation in Teaching Award (2017-2018) Bocconi Innovation in Teaching Award (2019) Renata has conducted consulting projects for Italian and multinational firms across industries and directs the online course Data Analysis for Business: Foundations .

• Mathematical Logic
• Formal Methods
• Computer Science
• +5 more

Marco Maggesi is an Associate Professor at the University of Florence, affiliated with the Department of Mathematics and Computer Science 'Ulisse Dini'. His research focuses on the formal representation of mathematical and computational systems using proof assistants like HOL Light and UniMath. Research interests span mathematical logic, formal methods, computer science, theorem proving, and category theory, with applications in complex systems, quaternionic analysis, and type theory. His work emphasizes rigorous verification of emergent behaviors in adaptive systems and foundational computational structures. Publication trends show consistent contributions to formal verification (e.g., provability logic, universal algebra), interdisciplinary applications (e.g., IoT, blockchain), and educational outreach (e.g., visual machine learning guides). Research is frequently supported by national projects such as MIUR PRIN grants.

Wolf-Dieter RichterView profile

Professor

• Probability Theory
• Mathematical Statistics
• Multivariate Analysis
• +7 more

Wolf-Dieter Richter is a Professor of Probability Theory and Mathematical Statistics at the University of Rostock's Institute of Mathematics. His research focuses on advanced probability theory, statistical distributions, and geometric probability. He has supervised numerous PhD, Master's, and diploma students, with notable advisees including Jana Davids (1992), Uta Boldt (1993), and Klaus Müller (2017). His teaching includes courses on stochastic processes, multivariate statistical methods, and specialized seminars on topics like integral asymptotics and geometric statistics. Research interests: Geometric representations of distributions, generalized complex numbers, large deviation probabilities, and non-Euclidean statistical models. Supervised over 20 students in areas like statistical analysis, stochastic modeling, and geometric probability. Active in curriculum development for mathematics programs, including teacher training and applied statistics. Key contributions include work on (p₁,…,p_k)-spherical distributions, star-shaped distributions, and geometric interpretations of statistical concepts. His research bridges algebraic structures with probabilistic methods, often involving high-dimensional and non-Euclidean frameworks. Recent articles explore complex numbers in higher dimensions, hyperbolic number systems, and advanced distribution theory. He has pioneered methods for analyzing skewed elliptically contoured distributions and their geometric properties.

Travis KowalskiView profile

Professor

• Complex Analysis
• Mathematical Pedagogy
• Formal Power Series
• +2 more

Travis Kowalski is Professor and Department Head of Mathematics at South Dakota Mines. Originally an art student, he transitioned to mathematics while maintaining artistic approaches to mathematical expression. His research spans complex analysis, formal power series, and mathematics pedagogy. Research interests include solving differential equations through infinite polynomials and developing abilities-based assessment methods. Received prestigious awards including the Mathematical Association of America's George Pólya Award and Chauvenet Prize. Teaching covers diverse courses from College Algebra to Game Theory, recognized with institutional and national teaching excellence awards. Mathematical artwork appears in daily 'constrained art' creations.

Christophoros NikouView profile

Professor

• Image Processing
• Computer Vision
• Pattern Recognition
• +12 more

Christophoros Nikou is a Professor in the Department of Computer Science and Engineering at the University of Ioannina, Greece. He holds a Ph.D. in Image Processing and Computer Vision from Université Louis Pasteur - Strasbourg I, France (1999), a DEA in Photonics and Image Processing from the same institution (1995), and a Diploma in Electrical Engineering from Aristotle University of Thessaloniki, Greece (1994). His research interests include: Image Processing Computer Vision Pattern Recognition Biomedical Image Analysis Medical Imaging Machine Learning His recent publications (2022–2024) reflect a strong trend toward deep learning, generative models, and multimodal analysis, particularly in document image analysis, super-resolution, and biomedical applications. He has made significant contributions to visual tracking, segmentation, and image registration, with recent work extending into virtual reality, hypercomplex neural networks, and model compression. Scientific awards include: Best paper award, IAPR/IEEE International Conference on Biometrics (ICB’16), 2016 Best paper award, Bayesian and Graphical Models for Biomedical Imaging (BAMBI’16), 2016 He has supervised numerous students, many of whom have co-authored publications with him, and his research has been supported by collaborations across multiple institutions. He has also co-edited a book on biomedical image processing and contributed to several book chapters. His work spans both theoretical and applied domains, with consistent publication in top-tier IEEE and Springer venues.

James HendersonView profile

Researcher

• Natural Language Processing
• Machine Learning
• Deep Learning
• +3 more

James Henderson is a Senior Researcher at Idiap Research Institute where he heads the Natural Language Understanding group. He currently serves as Action Editor for Transactions of the Association for Computational Linguistics (TACL) and was recently awarded an ERC Advanced Grant for his project 'Interpretable Beliefs and Programmable Knowledge with Bayesian Attention in Large Language Models' (BALM). Previously, Henderson held positions as Chargé de Cours at University of Geneva's Department of Computer Science and Principal Scientist at Xerox Research Centre Europe (now Naver Labs Europe). Henderson's research focuses on machine learning methods for natural language processing, with pioneering work on recurrent neural networks for syntactic and semantic parsing. His current investigations include representation learning for language semantics, graph-to-graph deep learning models, entity induction, and variational-Bayesian attention-based representation learning. His research bridges Bayesian inference, transformer architectures, and structured prediction for NLP tasks. His publication portfolio demonstrates consistent contributions to core NLP methodologies, with recent emphasis on transformer optimization, Bayesian neural methods, efficient model architectures, and graph-based language representations. Research frequently appears in top venues including ACL, EMNLP, ICLR, and NeurIPS. Honors: ERC Advanced Grant (2023) Henderson leads the Natural Language Understanding group at Idiap, currently recruiting PhD students and postdoctoral researchers for his ERC project. He obtained his PhD and MSc from University of Pennsylvania and BSc from Massachusetts Institute of Technology, all in computer science.

David RestrepoView profile

Associate Professor

• Advanced Materials
• Mechanical Systems
• Bioinspired Design
• +5 more

David Restrepo is an Associate Professor in the Department of Mechanical, Aerospace, and Industrial Engineering at the Klesse College of Engineering and Integrated Design, The University of Texas at San Antonio (UTSA). He serves as the Graduate Advisor of Record for Mechanical Engineering Master's programs (M.S. and M.S. AMEE). His academic foundation includes a Ph.D. from Purdue University. His educational background: Ph.D. from Purdue University The AMMS Lab, directed by Dr. Restrepo, pioneers the design and synthesis of advanced materials through bioinspired and architected approaches. Research integrates computational simulations, theoretical analysis, fabrication, and experimental testing to develop materials with unprecedented properties. Applications span healthcare (medical devices), defense (military equipment), robotics, civil infrastructure, and aerospace engineering. Core expertise includes phase transforming cellular materials, metamaterials, and nonlinear mechanics across scales. His 2023-2026 publications reveal a dominant focus on hypercomplex automatic differentiation for sensitivity analysis in powder bed fusion, hypersonic flow control, and adhesion measurement. Work bridges computational mechanics with bioinspired design, targeting defense and healthcare applications. Key trends include uncertainty quantification (HYPAD-UQ), computational efficiency in thermal modeling, and educational innovations in finite element analysis pedagogy. No scientific awards were documented in the source material. As Graduate Advisor of Record, Dr. Restrepo mentors Master's students in Mechanical Engineering and Advanced Materials. He actively recruits undergraduate researchers, graduate students, and postdocs for the AMMS Lab, prioritizing interdisciplinary candidates from mechanical, materials, civil, biomedical, and electrical engineering backgrounds, plus physics. Recruitment emphasizes curiosity and creativity for projects spanning fundamental mechanics to applied defense/healthcare solutions. The AMMS Lab drives interdisciplinary research combining solid mechanics, material science, and physics to harness nonlinearities across scales. Current efforts focus on phononic metamaterials for hypersonic flow control, bioinspired medical devices (e.g., endotracheal tubes), and computational frameworks for additive manufacturing. The lab maintains strong industry and defense sector collaborations for real-world implementation of architected materials.

• Algebra and Number Theory
• Fibonacci Sequences
• Quaternion Algebra
• +3 more

Kübra GÜL is an Associate Professor at the Faculty of Engineering and Architecture , Kafkas University , specializing in Computer Engineering . Her academic work focuses on Algebra and Number Theory , with particular emphasis on Fibonacci-type sequences, quaternions, and matrix groups. Education: PhD in Algebra and Number Theory (2008-2014), BA in Secondary School Science and Mathematics Education (2003-2008), both from Atatürk University. Leadership Roles: Chair of the Department of Computer Engineering (2021-present), Chair of the Academic Field (2015-2021). Her research spans mathematical structures like hybrid quaternions, bicomplex numbers, and trigintaduonions. She has presented at international conferences including the International Scientific Research Congress and the International Conference on Pure and Applied Mathematics. Key collaborations include work with Nurullah Ankaralıoğlu and Abdullah Çağman . Metrics from YÖKSİS and Google Scholar highlight her scientific impact, with 57 citations and an h-index of 5.

• Artificial Intelligence
• Medical Imaging
• Radiomics
• +5 more

Salvatore Vitabile is a Full Professor at the University of Palermo , affiliated with the Department of Biomedicine, Neuroscience and Advanced Diagnostics . His research focuses on Artificial Intelligence , Medical Imaging , and Radiomics , with significant contributions to AI explainability , organoid analysis , and biometric authentication .

Lance E. ChampagneView profile

Associate Professor

• Operations Research
• Military Simulation and Modeling
• Machine Learning
• +3 more

Lance E. Champagne serves as an Associate Professor in the Graduate School of Engineering and Management at the Air Force Institute of Technology (AFIT), where he applies operations research and computational modeling to defense systems optimization. His work bridges military applications with advanced analytics, focusing on simulation-driven solutions for complex operational challenges. His academic foundation includes: Doctor of Philosophy (Ph.D.) in Operations Research from AFIT (2004) Master of Science (M.S.) in Operations Research from AFIT (1999) Bachelor of Science (B.S.) in Biomedical Engineering & Mathematics from Tulane University (1991) Champagne's research centers on military simulation, machine learning, and logistics optimization. He develops agent-based models for combat scenarios, UAV operations, and personnel training systems, while pioneering applications of deep learning in defense contexts. His work integrates Bayesian methods, digital twin frameworks, and quaternion neural networks to solve problems in threat avoidance, supply chain resilience, and real-time decision-making under cyber constraints. Recent publications reveal a pronounced shift toward AI-enhanced defense analytics, particularly in video/text classification for situational awareness and multi-objective optimization for wartime logistics. His 2022-2025 output shows consistent innovation in adapting commercial AI techniques (IndoBERT, GANs) to military-specific challenges while maintaining rigorous validation standards through probabilistic agreement metrics. His scholarly recognition includes: 2022-2023 Graduate School of Engineering and Management Dean's Distinguished Teaching Professor 2014 Winter AFIT/ENC Mathematics and Statistics Instructor of the Quarter As an advisor, Champagne mentors numerous graduate students evidenced by asterisk-marked co-authorships across 15+ publications annually. His research is supported by defense contracts focused on simulation validation, autonomous systems, and logistics optimization. He actively contributes to military operations research through book chapters on simulation history and validation methodologies, while maintaining operational relevance through direct collaboration with Air Force training and maintenance units.

Tony HarkinView profile

Associate Professor

• Applied and Computational Mathematics
• Fluid Mechanics
• Dynamical Systems
• +4 more

Anthony A. Harkin is an Associate Professor in the Department of Mathematics and Statistics at the Rochester Institute of Technology (RIT), within the College of Science. His academic roles include teaching and research in applied mathematics, fluid mechanics, and dynamical systems. He holds a B.S. from SUNY Brockport, M.S. from MIT, and Ph.D. from Boston University, with postdoctoral research at Harvard University. His expertise spans computational mathematics, fluid dynamics, and network science, with a focus on mathematical modeling and partial differential equations. Education: B.S. in Math/Physics/Computer Science, SUNY College at Brockport (1993) M.S. in Applied Mathematics, Massachusetts Institute of Technology (1995) Ph.D. in Mathematics, Boston University (2000) Postdoctoral Fellow in Applied Mathematics, Harvard University (2001–2005) Research Interests: Fluid Mechanics: Bubble dynamics, multiphase flow, and nonlinear acoustics Dynamical Systems: Perturbation theory, renormalization group methods, and bifurcation analysis Network Science: Community detection, graph theory, and clustering algorithms Data Science: Hyperspectral image processing and machine learning applications Teaching Contributions: Harkin has taught advanced courses at RIT, Harvard, and Boston University in areas such as mathematical modeling, partial differential equations, dynamical systems, and linear algebra. He received the RIT Provost's Award for Excellence in Teaching (2008) and Harvard's Certificate of Distinction in Teaching (2004). Publications: His research spans fluid dynamics, network theory, and mathematical modeling, with notable contributions on bubble dynamics, graph modularity, and renormalization group methods. Recent work includes studies on Rayleigh collapse of bubbles and community detection in networks. Professional Activities: Co-founder of icitizen.com, a political networking platform. Active in editorial roles, including as editor of the International Journal of Applied Nonlinear Science (2013).

Roger BielawskiView profile

Professor

• Differential Geometry
• Mathematical Physics
• Hyperkähler Manifolds
• +3 more

Prof. Roger Bielawski is a Professor at the Institute of Differential Geometry within the Faculty of Mathematics and Physics at Leibniz University Hannover. He serves as Spokesperson of the Riemann Center for Geometry and Physics and is a member of its leadership. His research focuses on differential geometry, mathematical physics, and hyperkähler manifolds, with contributions to monopole theory, complex geometry, and geometric analysis. He has organized major conferences such as the 2019 'Geometric and Analytic Aspects of Moduli Spaces' and the 2011 'Variational Problems in Differential Geometry.' His work bridges algebraic geometry, geometric analysis, and theoretical physics, emphasizing structures like spectral curves, integrable systems, and geometric moduli spaces. Roles: Spokesperson (Riemann Center), Professor (Institute of Differential Geometry), Deputy Representative (Faculty Council). Research Interests: Hyperkähler metrics, monopole dynamics, geometric structures in algebraic geometry, and integrable systems. Publications span over three decades, with recent works addressing vector bundles on real curves, quiver representations, and hypercomplex limits. He actively contributes to academic governance and maintains collaborative networks through the Riemann Center and Institute initiatives.

Adam ShefferView profile

Associate Professor

• Computational Geometry
• Discrete Mathematics
• Incidence Geometry
• +3 more

Adam Sheffer is an Associate Professor of Mathematics at Baruch College and the Graduate Center of the City University of New York (CUNY). He is a member of Baruch's Discrete Math Group and serves as the Director of the NYC Discrete Math REU and the Baruch College Computer Science Program. His research focuses on computational geometry, discrete mathematics, and additive combinatorics, with notable contributions to incidence geometry and polynomial methods. He is the author of Polynomial Methods and Incidence Theory (Cambridge University Press) and an editor of the Journal of Computational Geometry . Sheffer's academic activities include organizing the Polymath Jr Program and the NYC Geometry Seminar. He has mentored numerous student research projects, several of which have led to published papers. Key research interests include geometric incidences, distinct distances problems, and combinatorial configurations. His work frequently bridges algebraic and geometric techniques to solve problems in discrete mathematics.