Find Academic Supervisors

• Computational Algebra
• Commutative Algebra
• Applied Algebraic Geometry
• +3 more

Daniele Taufer is a postdoctoral researcher affiliated with the NUMA research unit at KU Leuven (Belgium) since 2022, supported by the FWO (Flemish Fund for Scientific Research) under project 12ZZC23N. Previously, he worked at CISPA (Germany) from 2020 to 2022 as a postdoc on elliptic curve cryptography within the ERC-669891 project, supervised by Antoine Joux. Education: Ph.D. in Mathematics (2016–2020), University of Trento (IT), cum laude, thesis: "Elliptic Loops" (supervised by Massimiliano Sala) Master in Mathematics (2014–2016), University of Duisburg-Essen (DE) and University of Leiden (NL) via the ALGANT double-degree program, thesis: "Algebraic aspects of the Number Field Sieve" (supervised by Hendrik W. Lenstra) Bachelor in Mathematics (2011–2014), University of Padova (IT), thesis: "Gröbner bases and applications" (supervised by Alberto Tonolo) Research interests span computational and commutative algebra, applied algebraic geometry, and cryptographic applications. Key areas include symmetric tensor decomposition (Waring, tangential, Chow, cactus ranks), effective decomposition algorithms (apolarity, Hankel operators), and algebro-geometrical properties of apolar schemes. His work bridges theoretical algebra and practical cryptography, particularly focusing on elliptic curves and their applications in blockchain and isogeny-based systems. Recent scientific contributions examine decompositions of symmetric tensors, elliptic curve discrete logarithm problems (ECDLP), and group structures over discrete rings. His algorithmic developments leverage apolarity and Hankel operators for computational efficiency. Scientific accolades: Maître de conférences qualification (2025) in Mathematics and Applied Mathematics sections Member of the SIAM Activity Group on Algebraic Geometry Labs and teams include the NUMA group at KU Leuven and the ERC-669891 project at CISPA.

• Embedded Systems
• Virtualization of Systems
• Data Processing
• +4 more

Glück Florent is an Associate Professor at the Geneva School of Landscape, Engineering and Architecture (HES-SO), affiliated with the Technical and IT school's Computer Science and Communication Systems department. He specializes in embedded systems, system virtualization, and interdisciplinary projects at the intersection of engineering and healthcare. Affiliations: HES-SO Geneve, hepia inIT, and collaborations with medical and industrial partners. Education: Not explicitly listed, but implied through roles and projects involving systems engineering and computer science. Research Interests: His work spans embedded systems design, real-time data processing, and applied machine learning. Key focuses include secure hardware-software co-design (e.g., FPGA-based security), medical device development (e.g., neonatal monitoring systems), and IoT infrastructure for smart buildings and recycling. Project Trends: Florent leads projects combining engineering with societal impact, such as automated recycling systems (LusTra), secure medical diagnostics (BrainCheckX), and educational virtualization platforms (Nexus VDI). Recent work emphasizes AI-driven solutions for healthcare (e.g., cochlear implant support) and decentralized energy management. Grants and Funding: Multiple projects funded by HES-SO Rectorat, CTI, and industry partners, totaling over CHF 400,000 since 2014. Labs/Teams: Active in distributed embedded systems research, leading teams on projects like DPESI (distributed storage) and HERVA (random number validation platforms).

• Spintronics
• Nanoscale Materials
• Spin Logic Devices
• +7 more

Luqiao Liu is an Associate Professor in the Department of Electrical Engineering and Computer Science (EECS) at MIT. He is affiliated with the Research Laboratory of Electronics (RLE) and leads the Spintronic Material and Device Group. His research focuses on spin electronics (spintronics), nanoscale materials, and spin-based devices for low-power computing, non-volatile memory, and microwave applications. His work explores spin-orbit interactions in topological insulators, antiferromagnets, and hybrid systems. Liu holds a BS in Physics from Peking University and a PhD in Applied Physics from Cornell University. Before joining MIT in 2015, he worked at IBM’s T.J. Watson Research Center. Notable awards include the Aravind V. Subramanium T.L. Memorial Award and IBM’s Patent Application Achievement Award. His research group investigates spin-orbitronics, aiming to leverage pure spin currents for energy-efficient devices. Recent work includes magnon-magnon coupling in magnetic garnet bilayers, superparamagnetic tunnel junctions for true randomness, and coherent spin dynamics in antiferromagnetic systems. Collaborations span quantum engineering, hybrid magnonic systems, and neuromorphic computing. Education: PhD in Applied Physics, Cornell University BS in Physics, Peking University Awards: Aravind V. Subramanium T.L. Memorial Award (Cornell) IBM Patent Application Achievement Award Labs/Teams: Spintronic Material and Device Group (MIT)

• Probability Theory
• Mathematical Physics
• Random Matrices
• +3 more

Dr. Theo Assiotis is a faculty member at the School of Mathematics, University of Edinburgh, joining in the 2020-21 academic year. His research focuses on probability theory, mathematical physics, random matrices, and branching graphs. He completed his undergraduate studies at the University of Cambridge and pursued further academic training through a PhD and postdoctoral work. His work bridges abstract algebraic structures with probabilistic methods, particularly in stochastic processes and integrable systems. Research interests include random matrices and their applications in statistical mechanics, as well as branching graphs linked to representation theory and Markov chains. Recent collaborations include a paper co-authored with two undergraduate students on characteristic polynomials of random matrices. He emphasizes the importance of perseverance in academic research, advising students to embrace challenges inherent in exploring uncharted mathematical domains. Teaching and supervision are central to his role, with particular enjoyment in mentoring students through research projects. While no specific awards are highlighted in the text, his contributions to probability theory and mathematical physics are evident through his extensive publication record.

• Theoretical Computer Science
• Computational Complexity
• Cryptography
• +4 more

Manuel Blum is a renowned computer scientist and winner of the 1995 ACM Turing Award. He holds the title of Bruce Nelson University Professor of Computer Science at Carnegie Mellon University's School of Computer Science. Previously, he was a faculty member at the University of California, Berkeley. His work spans theoretical computer science, computational complexity theory, cryptography, and program checking. He has supervised 35 doctoral students, many of whom are leading figures in computer science. Education: Ph.D. in Mathematics (MIT, 1964), M.S. in Electrical Engineering (MIT, 1961), B.S. in Electrical Engineering (MIT, 1959). Research focuses on foundational aspects of computing, including cryptographic protocols (e.g., CAPTCHA), program correctness mechanisms, and computational complexity. His contributions to secure human-computer interaction and zero-knowledge proofs are particularly influential. Teaching highlights include courses on theoretical cryptography and complexity theory. Awards include the ACM Turing Award, ACM Fellow, and UC Berkeley's Distinguished Teaching Award.

Xiaodong WenView profile

Researcher

• Heterogeneous Catalysis
• Materials Science
• Physical Chemistry
• +12 more

Xiaodong Wen is a prominent researcher contributing to heterogeneous catalysis, materials science, and computational chemistry. His work spans fundamental studies of surface dynamics in transition metal nanoparticles and applied research in CO 2 reduction, biomedical applications like maslinic acid's role in colon cancer inhibition, and analytical chemistry innovations such as laser-based nanoparticle identification. Core Expertise : Catalysis (Fischer-Tropsch, electrochemical CO 2 reduction), surface science, DFT/molecular dynamics simulations Recent Trends : Focus on catalyst durability via SiO 2 encapsulation, temperature-driven surface morphology, and machine learning for adsorption prediction Publications highlight interdisciplinary approaches bridging physical chemistry, environmental engineering, and biomedical applications. Key subfields include quantum nanoreactors for coke analysis, multiband laser color modulation, and Fe-based carbide modeling.

Ashok SrinivasanView profile

Professor

• High-Performance Computing
• Supercomputing
• Massively Parallel Computing
• +12 more

Dr. Ashok Srinivasan is the William Nystul Eminent Scholar Chair and Professor in the Department of Computer Science at the University of West Florida, within the Hal Marcus College of Science and Engineering. He is actively engaged in research, teaching, and professional leadership in high-performance computing and its applications to public policy and health. Education: Ph.D. in Computer Science, University of California, Santa Barbara, 1996 M.S. in Polymer Engineering, University of Akron, 1992 B.S. Tech (Honours) in Chemical Engineering, Regional Engineering College, Tiruchirapalli, 1987 Dr. Srinivasan's research focuses on high-performance computing, supercomputing applications, and simulation-based public policy analysis, particularly in modeling infection spread through air travel. His work integrates computational methods from quantum Monte Carlo, molecular dynamics, and GPU programming with real-world challenges in epidemiology and transportation. He leads Project VIPRA, a multi-institutional effort using supercomputers to analyze public health policies. His recent publications emphasize multiscale and parallel modeling of pedestrian and infection dynamics in transportation systems, leveraging massive computational power for policy-relevant insights. These works span disciplines including computer science, physics, public health, and urban planning. Scientific Awards and Recognitions: Fulbright Senior Research Scholar Best Paper Award at International Conference on Parallel Processing (ICPP) Featured among 12 major breakthroughs using Blue Waters supercomputer Dr. Srinivasan has served as PI or co-PI on approximately $4 million in grants from NSF, DOE, and DoD. He is a founding co-chair of the Student Research Symposium at HiPC, has organized over 15 professional events, and served on more than 40 international conference technical program committees. He actively mentors students and collaborates with researchers at institutions like IBM, Argonne National Lab, and Oak Ridge National Lab. He leads the Project VIPRA research team, which involves interdisciplinary collaboration across universities and domains such as biochemistry, mechanical engineering, and urban planning. His lab focuses on large-scale simulations for public policy impact.

Lenore BlumView profile

Professor

• Computational Complexity
• Real Number Algorithms
• Mathematical Logic
• +5 more

Lenore Blum is a Professor at the Department of Computer Science within the School of Computer Science at Carnegie Mellon University . She has held significant roles such as Founding Director of Project Olympus and Co-Director of the NSF-ITR ALADDIN Center . Her career spans institutions including UC Berkeley, Mills College, ICSI, and MSRI. Research Interests : Blum's work bridges computational complexity and real computation, focusing on algorithms over continuous domains, polynomial equation solving, and transfer principles between discrete and continuous models. She also advocates for diversity in STEM through initiatives like Women@SCS and Expanding Your Horizons . Scientific Awards : Recognized with an NSF Career Advancement Award, AAAS Fellowship (1979), and an honorary Doctor of Laws from Mills College (1999). Grants & Outreach : Led the NSF-funded ALADDIN Center for algorithm adaptation and co-founded the Association for Women in Mathematics. Her outreach includes organizing symposia on mathematics' societal impact and advising the President's Diversity Advisory Council at CMU.

• Logic in computer science
• linear dynamical systems
• automated verification
• +3 more

James Worrell is a Professor of Computer Science at the University of Oxford , with a focus on logic in computer science , linear dynamical systems , and automated verification . He is also a Fellow of Green Templeton College. His research spans theoretical computer science and formal methods , including work on metric temporal logic , probabilistic semantics , and category theory . His publications address decision problems , verification of linear dynamical systems , and automata theory . James has received the EPSRC Established-Career Fellowship for his work in linear dynamical systems verification . His recent papers include polynomial invariants , polyhedral escape problems , and skolem problem solutions . He has advised students such as Mehran Hosseini , Pascale Gourdeau , and Ventsislav Chonev , and teaches courses like Computational Learning Theory and Logic and Proof . His work appears in venues like ICALP , LICS , and SODA .

Laurent DonzéView profile

Professor

• Applied Statistics
• Econometrics
• Survey Methodology
• +7 more

Laurent Donzé is a Professor of Applied Statistics and Modelling at the Department of Informatics, Faculty of Economics and Social Sciences, University of Fribourg. He is also a Research Professor at KOF ETH Zurich and a Professor of Econometrics at the University of Neuchâtel. He leads the ASAM research group and has extensive experience in teaching mathematics, econometrics, and statistics. His educational background includes a Ph.D. in Econometrics from the University of Fribourg, followed by research roles at IRE and KOF ETH Zurich. His academic journey reflects deep engagement with statistical methodology and applied economic research. Donzé's primary research interests lie in applied statistics, particularly survey methodology, fuzzy statistics, imputation, causal inference, matching techniques, and wage discrimination analysis . He has made significant contributions to the development and application of fuzzy statistical tools, especially in defuzzification and fuzzy regression. His recent publications (2019–2025) demonstrate a consistent focus on fuzzy confidence intervals, fuzzy p-values, fuzzy ANOVA, and fuzzy regression models , often applied to real-world datasets like SHARE and Swiss SILC. These works emphasize robust statistical inference under uncertainty and contribute to both theoretical and applied advancements in fuzzy statistics. Among his scientific recognitions is the Best Student Paper Award at IJJCI 2020 . He has also edited special issues and contributed to leading journals and conferences in computational intelligence and fuzzy systems. Donzé has been involved in numerous research and teaching initiatives, including grants from the Swiss National Science Foundation, mentoring at the Swiss Study Foundation, and leadership in statistical societies. He has supervised research projects and collaborated with institutions such as Nestlé, the Swiss Federal Statistical Office, and pharmaSuisse. He is actively involved in academic and professional communities, serving as president of the Education and Research section of the Swiss Statistical Society and contributing to the development of statistical infrastructure in social sciences.

• Complex Systems
• Chaos Theory
• Network Science
• +16 more

Murilo da Silva Baptista is a Reader at the Institute for Complex Systems and Mathematical Biology , within the School of Natural and Computing Sciences at the University of Aberdeen . He has been with the university since 2009, initially as a Senior Lecturer and promoted to Reader in 2014. He is actively accepting PhD students in Physics, Mathematics, and Engineering, and his research is internationally recognized in the fields of complex systems and chaos theory. His research focuses on understanding the relationship between function—such as information processing, collective behavior, and synchronization—and structure in large networked complex systems. He applies analytical methods, data science, nonlinear time series analysis, and machine learning to model systems in neuroscience, smart engineering, and Earth sustainability. He is a leading scientist in chaos-based communication, demonstrating how chaotic signals can enable smart and secure wireless and underwater communication systems. His work includes theoretical developments in phase definition in chaotic oscillators, chaos-based cryptography using Poincaré return times, and the discovery of phenomena like Collective Almost Synchronization, which enhances machine learning for EEG signal prediction. His recent publications (2023–2025) span a wide range of applications, including chaotic image and 3D model encryption, UAV surveillance using chaotic paths, causal feature selection in health systems, modeling neurological disorders, and socio-environmental analysis in Brazil. These works reflect a strong trend toward applying nonlinear dynamics and network science to real-world engineering, biomedical, and societal challenges. Scientific Contributions and Recognition: Proved a conjecture on the analytical calculation of Poincaré first return times using unstable periodic orbits. Contributed foundational work to chaos-based cryptography. Proposed a formula linking mutual information to Lyapunov exponents, supporting the Infomax theory of brain evolution. Discovered the phenomenon of Collective Almost Synchronization in complex networks. Demonstrated that causality is a space-time phenomenon, not purely temporal. Advising and Research Support: He is currently supervising PhD students in Physics, Maths, and Engineering, indicating active mentorship. His research is supported by analytical developments and data-driven modeling. He leads work on optimal wireless chaos communication, synapse modeling, brain network changes post-surgery, and socio-economic causality in Brazil. His collaborations span institutions in the USA, Brazil, Germany, and Portugal. Labs and Research Groups: He is affiliated with the Institute for Complex Systems and Mathematical Biology at Aberdeen, a hub for interdisciplinary research in nonlinear dynamics, network theory, and their applications across physical, biological, and social systems.

Marko LindnerView profile

Professor

• Functional Analysis
• Numerical Analysis
• Operator Theory
• +8 more

Marko Lindner is a Professor at the Institute of Mathematics, Hamburg University of Technology (TUHH), where he holds the Chair of Applied Analysis. His research lies at the intersection of functional analysis and numerical analysis, particularly focusing on the spectral and Fredholm theory of infinite matrices and linear operators. He has led significant research projects funded by the European Union through Marie-Curie grants and has collaborated with institutions and industries including BAE Systems, UK Met Office, and Schlumberger Ltd. Hamburg University of Technology (TUHH), Institute of Mathematics Chair of Applied Analysis Email: lindner@tuhh.de Office: Room 3,094, Building E, Am Schwarzenberg-Campus 3, D-21073 Hamburg His research interests include operator theory, spectral theory, pseudospectra, finite section methods, discrete Schrödinger operators, and applications in mathematical physics and wave scattering. He investigates the stability and convergence of numerical approximation schemes for infinite-dimensional operators, with deep theoretical contributions to limit operator theory and collective compactness. The recent publications highlight a strong trend in spectral approximation of non-selfadjoint operators, particularly Schrödinger-type operators with periodic, aperiodic, and random potentials. His work combines rigorous functional analytic foundations with computational insights, often involving pseudospectra, condition number asymptotics, and subword-based approximation techniques. The integration of numerical methods with spectral theory is a consistent theme, especially in the context of wave propagation and scattering problems. Scientific awards include: Individual Marie-Curie Fellowship (EU, 2005–2007) Individual Marie-Curie Grant (EU, 2008–2011) Marko Lindner has supervised various research projects, particularly in the areas of wave scattering in unbounded domains and the spectral analysis of infinite matrices. His collaborations span applied mathematics, mathematical physics, and engineering, with funding from both public and private sectors. He is actively involved in the academic community, organizing events such as the Operator Theory Workshop and participating in SIAM & GAMM Chapter activities. He leads and contributes to research teams working on applied operator theory, with a focus on developing stable and efficient numerical methods for complex physical systems. His work bridges theoretical operator algebras and practical computational techniques.

Lorenzo GaleottiView profile

Researcher

• Mathematical Logic
• Computability Theory
• Set Theory
• +3 more

Lorenzo Galeotti is a researcher affiliated with the University of Amsterdam, specifically working within the Mathematical & Computational Logic (MCL) group at Amsterdam University College. His research focuses on foundational aspects of mathematical logic, computability theory, and generalized analysis. Research Interests: Galeotti investigates topics at the intersection of logic, computability, and non-standard mathematical structures. His work includes studying transfinite computability, infinitary intuitionistic set theory, and the properties of generalized real numbers and non-Archimedean fields. He also explores the application of logical frameworks in educational contexts, such as game-based learning tools for propositional logic. Publications: His recent articles address advanced topics in logic, including models of Peano arithmetic fragments, symmetry in infinite-time computation, and surreal number systems. These works reflect a consistent focus on extending classical concepts into transfinite and non-standard domains. Contact: Email correspondence can be directed to l.galeotti@uva.nl .

• Optoelectronics
• Photonics
• Semiconductor Materials
• +5 more

Prof. Mattias Hammar is a Professor at the Department of Electrical Engineering, Division of Electronics and Embedded Systems at KTH Royal Institute of Technology. He holds a MSc (1986) and PhD (1993) from KTH's Department of Physics. His research focuses on optoelectronic materials, photonic devices, and nanotechnology. He has led multiple national/international projects in photonics and serves as Program Director for KTH's International Master's in Nanotechnology. He teaches courses in analog/digital electronics, research methodology, and nanotechnology. Research interests include semiconductor lasers (VCSELs, photonic crystal lasers), quantum dot-based photonics, nanomembrane fabrication, and quantum communication technologies. His work spans device fabrication, material characterization, and integration of III-V semiconductors with silicon platforms. Key projects involve developing single-photon sources for telecom applications and hybrid photonic systems. He has advised numerous students through KTH's master's and PhD programs in electrical engineering and engineering physics. His lab focuses on next-gen optoelectronic devices with applications in quantum communication, energy harvesting, and flexible electronics. Recent work includes advances in β-Ga₂O₃-based devices, entangled photon generation at telecom wavelengths, and nanomembrane transfer printing for silicon photonics. He collaborates with institutions like IBM Research and Zarlink Semiconductor, emphasizing industrial-academic partnerships.

Luca DanielView profile

Professor

• Electrical Engineering
• Biomedical Engineering
• Computer Science
• +5 more

Luca Daniel is a Professor at the Massachusetts Institute of Technology (MIT), affiliated with the Research Laboratory of Electronics (RLE) and the Department of Electrical Engineering and Computer Science. His research focuses on computational modeling, simulation, and design of complex systems, with applications in electronics, biomedical engineering, and machine learning. He leads the Computational Prototyping Group, which develops algorithms and software for model order reduction, integral equation solvers, and uncertainty quantification. Professor Daniel holds a Ph.D. from the University of California, Berkeley (2003), and has authored numerous influential papers in top conferences and journals. His work has been recognized with awards such as the APS Best Paper Award (2023), EPEPS Best Conference Paper Award (2020), and the ACM Outstanding Ph.D. Dissertation Award in EDA (2016). His research interests include electromagnetic simulation, biomedical systems, stochastic methods, and machine learning robustness. He has advised over 20 PhD students and developed open-source tools like FastMaxwell and CAPLET for electromagnetic analysis. His teaching spans courses on modeling, simulation, signals and systems, and numerical methods.