Find Academic Supervisors

Tobias GrosserView profile

Associate Professor

• Compilers
• Programming Languages
• Performance Programming
• +7 more

Tobias Grosser is an Associate Professor in the Department of Computer Science and Technology at the University of Cambridge. His research focuses on rethinking performance programming by bridging the gap between developers and compilers. He holds a PhD from École Normale Supérieure Paris and has held positions including Reader at the University of Edinburgh and Ambizione Fellow at ETH Zurich. His research interests span compilers, programming language design, static/dynamic analysis, and the integration of machine learning into compiler development. He emphasizes making compilation more modular, automatic, and trustworthy, with applications in quantum computing, climate science, and open-source hardware. Key projects include xDSL (a Python-native compiler framework), LoopOpt, and the Open Earth Compiler for climate simulations. Recent publications highlight advancements in multi-level intermediate representations (IR), formal verification in MLIR, and performance optimization for GPUs and FPGAs. His work often addresses barriers between programmers and compilers, aiming for intuitive collaboration between developers and automated systems. Tobias mentors a dynamic team of PhD students, postdocs, and researchers, including notable contributors like Siddharth Bhat, Arjun Pitchanathan, and Mathieu Fehr. His lab focuses on compiler toolchains for domain-specific hardware accelerators, quantum computing ecosystems, and verified compilation techniques.

• Materials Science
• Nanotechnology
• Condensed Matter Physics
• +3 more

Aleksi Leino is a University Researcher at the Department of Physics, University of Helsinki. He is also a supervisor for the Doctoral Programme in Materials Research and Nanosciences. His research focuses on radiation effects in materials, nanoparticle dynamics, and computational modeling of ion irradiation processes. Leino's work explores phenomena such as ion track formation in diamond, nanoporous graphene creation, and shape transformations of embedded nanoparticles under swift heavy ion irradiation. These studies bridge materials science with quantum technology applications. He has collaborated internationally on projects involving ion beam processing and radiation effects in insulators and semiconductors. Recent publications highlight advancements in predictive simulations of electronic/atomic phenomena and the formation of functional nanostructures. His activities include peer reviews for journals like Applied Surface Science and Journal of Vacuum Science , and invited talks on topics like ion impact modeling in diamond (2024) and functional nanostructure simulations (2024). Leino has organized academic events such as the 2023 European Conference on Surface Science and contributed to the 2023 International Conference on Radiation Effects in Insulators. His research also extends to biomedical applications via development of the ValoMC software for light transport simulations in biological tissues. Key research areas include material phase transitions, nanoparticle behavior under irradiation, and the interplay between electronic/thermal effects in nanoscale systems. His work combines experimental insights with advanced computational techniques like molecular dynamics and Monte Carlo simulations.

Seenivasan HariharanView profile

Researcher

• Heterogeneous Catalysis
• Quantum Computing
• Computational Chemistry
• +3 more

Seenivasan Hariharan is a Researcher at the Department of Algorithms and Complexity, Universiteit van Amsterdam. His work focuses on advancing computational methods for heterogeneous catalysis and quantum computing applications. He holds a PhD in Chemical Engineering and has over a decade of experience in simulating molecule-surface interactions using density functional theory (DFT) and quantum dynamics approaches. His research interests bridge traditional computational chemistry with emerging quantum technologies. Key areas include quantum algorithm development for catalytic interfaces, optimization of alloy catalysts, and modeling surface reactions under extreme conditions. Current projects involve applying variational quantum eigensolvers (VQE) and quantum phase estimation (QPE) algorithms to address limitations of classical DFT methods in capturing spin-related phenomena and strong correlation effects. Publications highlight interdisciplinary contributions, including a 2024 review on quantum computing's role in catalysis modeling and foundational work on surface reaction dynamics since 2013. His work explores material systems ranging from graphene to bimetallic alloys, addressing topics like CO oxidation mechanisms, water dissociation pathways, and coating material characterization through electrodeposition methods. Active in both academic and industrial collaborations, his research emphasizes embedding quantum computing strategies within traditional chemical modeling frameworks to enable large-scale simulations. Recent efforts focus on qubit routing optimization for quantum hardware and developing fault-tolerant algorithms for future quantum systems.

Ivan B. PenkovView profile

Adjunct Professor

• Lie Theory
• Representation Theory
• Algebraic Geometry
• +2 more

Ivan B. Penkov is an Adjunct Professor of Mathematics at Constructor University Bremen, Germany, with a distinguished career in Lie theory, representation theory, and algebraic geometry. He earned his Master's degree from Moscow State University (1982) and Ph.D. from Steklov Mathematical Institute (1987). His academic journey includes tenured professorships at the University of California at Riverside (1991-2004) and a long-standing professorship at Jacobs University Bremen (2004-2023), followed by his current adjunct role. Education: Master in Mathematics, Moscow State University (1982) Candidate of Physical and Mathematical Sciences (PhD), Steklov Mathematical Institute (1987) Penkov's research focuses on representations of finite and infinite-dimensional Lie algebras and superalgebras, generalized Harish-Chandra modules, geometry of supermanifolds, and homogeneous ind-spaces. His work bridges pure mathematics with mathematical physics, emphasizing infinite-dimensional structures and their applications. Recent publications highlight advancements in bounded weight modules for Lie superalgebras at infinity, topological tensor representations, and automorphism groups of ind-varieties of generalized flags. These works reflect his deep engagement with categorification, geometric methods, and infinite-dimensional algebraic structures. Scientific awards and grants include multiple NSF and DFG grants, a Volkswagen Foundation grant, and the Batsheva de Rothschild Fellowship (2023). He has coorganized significant conferences and programs, such as the California Lie Theory Program and the German-Israeli workshop on symplectic geometry and representation theory. Penkov's advisees include PhD and MSc students like Aleksandr Fadeev, Elitza Hristova, Siarhei Markouski, and Todor Milev, many of whom have contributed to Lie theory and algebraic geometry. He has also mentored postdocs and collaborated with institutions worldwide, including Yale University, UC Berkeley, and the Max Planck Institute of Mathematics in Bonn.

• Data Science
• Statistical Learning
• Approximation Theory
• +7 more

Dr Bertrand Gauthier is a Lecturer at the School of Mathematics, Cardiff University , and a member of the Statistics and Data Science Research Group. His work bridges mathematics and data science, focusing on sampling-based approximation, spectral methods, and computational strategies for large-scale machine learning and uncertainty quantification. Current research: Sampling strategies, kernel methods, sparse approximation Past affiliations: KU Leuven (2015-2016), CNRS - Université de Nice (2012-2014), Université de Saint-Étienne (2007-2011) Research Interests include: Statistical learning and approximation theory Kernel-based modeling and integral operator approximation Low-rank matrix techniques and particle-flow methods Publications highlight his work on Nyström sampling optimization, kernel embedding of measures, and spectral decomposition for IMSE-optimal designs, spanning journals like SIAM Journal on Mathematics of Data Science and Positivity . Collaborations include researchers such as Matthew Hutchings and Kirstin Strokorb. Scientific Awards : Fellow of The Higher Education Academy Supervision includes postgraduate students Harry Bond, Alexandra Zverovich, and past supervisees like Matthew Hutchings (2024) and Michela Corradini (2024). His teaching covers Multivariate Data Analysis and Computational Statistics at undergraduate and postgraduate levels. Labs/Teams : Statistics and Data Science Research Group, Cardiff University School of Mathematics.

Daniel AppelöView profile

Professor

• Numerical Analysis
• Computational Mathematics
• Wave Propagation
• +3 more

Professor Daniel Appelö is affiliated with Virginia Tech's College of Science and Department of Mathematics. He also participates in the Computational Modeling and Data Analytics (CMDA) program. Research focuses on numerical algorithms for differential equations in engineering and natural sciences Specializes in wave propagation (acoustic, elastic, electromagnetic) and quantum computing applications Develops artificial boundary conditions for unbounded domains His career includes faculty positions at Michigan State University (MSU), University of Colorado Boulder, and University of New Mexico (UNM), as well as postdoctoral work at Caltech and Lawrence Livermore National Laboratory (LLNL). At LLNL, he co-developed massively parallel methods for seismology and embedded boundary methods for wave equations. Scientific Awards Hans Werthen Prize postdoc at UNM Education: PhD in Numerical Analysis at NADA, KTH, under Gunilla Kreiss. Thesis work on perfectly matched layer (PML) methods established stability criteria for hyperbolic systems.

• Quantum Magnetism
• Spin-Orbit Coupling
• Kitaev Physics
• +5 more

Dr. Liviu Hozoi is a prominent researcher in condensed matter physics at the Leibniz Institute for Solid State and Materials Research Dresden (IFW Dresden). His work focuses on quantum materials, particularly studying spin-orbit coupled systems, Kitaev physics, and strongly correlated electron systems. He has established himself as a leading expert in the theoretical and experimental investigation of quantum magnetic materials. Dr. Hozoi's research interests span several critical areas in modern condensed matter physics: Quantum Magnetism and Spin-Orbit Coupled Systems Kitaev Physics and Quantum Spin Liquids Strongly Correlated Electron Systems Quantum Materials with Exotic Magnetic Properties Advanced Spectroscopic Techniques including X-ray Scattering His recent publications demonstrate a strong focus on understanding the interplay between spin-orbit coupling, crystal field effects, and electronic correlations in transition metal compounds. Dr. Hozoi has made significant contributions to the understanding of Kitaev materials, particularly ruthenium and iridium-based compounds, where he has elucidated the origins of anisotropic exchange interactions. His work often combines advanced quantum chemical calculations with experimental validation, bridging theoretical predictions with measurable physical phenomena. Dr. Hozoi has been invited to speak at numerous international conferences, including the Lorentz Center workshop "Spin matters! Magnetic order and chiral molecules in electrocatalysis" in Leiden (2023), demonstrating his standing in the international physics community. His research has been published consistently in top-tier journals including Physical Review Letters, Nature Communications, and Physical Review B.

Muhammad Abu Bakar SiddiqueView profile

Assistant Professor

• Natural Language Processing
• Large Language Models
• Machine Learning
• +7 more

Muhammad Abu Bakar Siddique is an Assistant Professor in the Department of Computer Science at the University of Kentucky, part of the Stanley and Karen Pigman College of Engineering. His research focuses on natural language processing, large language models, and machine learning with particular emphasis on zero-shot learning and conversational AI systems that are safe, personalizable, and interpretable. Dr. Siddique earned his Ph.D. in Computer Science from the University of California, Riverside (2017-2021), his M.S. from Lahore University of Management Sciences, Pakistan (2008-2011), and his B.S. from International Islamic University, Pakistan (2003-2008). His research interests include: Natural Language Processing and Large Language Models Zero-shot and few-shot learning for conversational AI Safe, personalizable, and interpretable conversational systems Task-oriented dialog systems with domain generalization Mobile app recommendation systems Scalable machine learning methodologies Dr. Siddique's publications span top venues including WWW, SIGIR, KDD, and IEEE S&P, demonstrating his focus on developing practical AI solutions that can adapt to new domains without extensive retraining. His recent work shows increasing exploration of quantum software and the intersection of AI with mobile applications. His notable achievements include Best Paper Awards at the IEEE International Conference on Quantum Software (2025) and IEEE ICSC (2021). Dr. Siddique has secured significant funding from the National Science Foundation: CPS Medium: Calfhealth: Explainable AI for Pneumonia Detection in Dairy Calves ($941,359) SaTC CORE: Personalized and Trustworthy Mobile App Recommendations ($300,000) III Small: User-Centric Task-Oriented Dialog Systems ($599,898) DCL EPSCOR: Distributed Edge Intelligence ($100,000) He currently advises three PhD candidates (Adib Mosharrof, Moghis Fereidouni, and Muhammad Umair Haider) and has mentored several successful graduates. Dr. Siddique serves on program committees for major conferences including ACL, NeurIPS, ICML, and AAAI, and participates in outreach by hosting high school students through the University of Kentucky's Summer Youth Program.

• Partial Differential Equations
• Variational Methods
• Nonlinear Analysis
• +5 more

Giuseppe Devillanova serves as Associate Professor of Mathematical Analysis (MAT/05) in the Department of Mechanics, Mathematics and Management at Polytechnic University of Bari, Italy. His research bridges pure mathematical theory with engineering applications through advanced analysis of nonlinear systems and variational structures. His primary research focuses on nonlinear partial differential equations, variational methods for critical problems, and symmetry analysis in elliptic equations. He investigates wave propagation in elastic media with nonlinear adhesion, geometric constraints in structural mechanics, and fractal properties of irrigation networks. His work extends to foundational topics in set theory and historical analysis of Richard Dedekind's contributions to mathematics. Analysis of his 2019-2023 publications reveals three dominant research trajectories: (1) Min-max methods for parametrized elliptic problems on unbounded domains with symmetry constraints; (2) Mathematical modeling of nonlinear adhesion phenomena in thermo-elastic waves and flexural beams; (3) Geometric analysis of singular structures in variational problems, including irrigation networks and elastic multi-blisters. His interdisciplinary approach consistently connects theoretical mathematics with mechanical engineering and biomechanical applications.

Matthias KellerView profile

Professor

• Graph Theory
• Spectral Theory
• Discrete Geometry
• +6 more

Prof. Dr. Matthias Keller is a leading researcher in discrete spectral theory and graph analysis, affiliated with the Institute of Mathematics at the University of Potsdam since 2015. His work bridges geometric properties of graphs with spectral theory, focusing on Dirichlet forms, Schrödinger operators, and functional inequalities. Key Collaborations : Daniel Lenz, Radoslaw Wojciechowski, Yehuda Pinchover Books Authored : Graphs and Discrete Dirichlet Spaces (Springer, 2021) His research explores non-positively curved graphs, stochastic completeness, and magnetic sparseness. Recent projects include optimal Hardy inequalities and spectral analysis of fractional Laplacians. Scientific Awards : Swiss Fellowship (2023) Golda Meir Fellowship (2012-2013) Klaus Murmann PhD Fellowship (2007-2010) He advises PhD and Master’s students such as Yannik Thomas , Matti Richter , and Philipp Bartmann , while maintaining active roles in DFG-funded projects and international workshops.

David MénardView profile

Professor

• Magnetism
• Microwave Devices
• Magnetic Materials
• +5 more

David Ménard is a Full Professor in the Department of Engineering Physics at Polytechnique Montréal . He leads the Magentism Laboratory and manages the Superconductivity and Magnetism Laboratory (LSM) , while also participating in research groups like the Quebec Group on Advanced Materials (RQMP) and Thin Film Physics and Technology Research Group (GCM) . Expertise spans Magnetism , Microwave Devices , Mesoscopic Physics , and Nanofabrication Research Trends (2023-2025): Focus on CoFeB thin films , SmCo-Al permanent magnets , molecular radical magnetism , and biomimetic malaria pigment synthesis . Key methodologies include microwave magnetic resonance , spin transport , and Giant Magnetoimpedance (GMI) . Scientific Awards : 2019 Excellence in Teaching Award, Polytechnique Montréal Advising Legacy : Supervised 16 PhD and 12 Master’s theses since 2006, including groundbreaking work on magnetic nanowires , hydrogen embrittlement , and magnetic water treatment .

Nur AhmedView profile

Assistant Professor

• Innovation
• Artificial Intelligence
• Responsible AI
• +3 more

Nur Ahmed is an Assistant Professor at the Walton College of Business , University of Arkansas, and a Digital Fellow at MIT Sloan and MIT CSAIL. His research focuses on the intersection of innovation, artificial intelligence (AI), and responsible AI, examining how firms balance knowledge spillovers and resource acquisition to maintain competitive advantage. His work employs big data, machine learning (including NLP methods like word embeddings and transformers), and qualitative interviews to build testable models for managerial and policy decision-making. Prior to academia, he worked as a Software Engineer at Samsung R&D Lab in Bangladesh, India, and South Korea, informing his research on corporate R&D and technological dynamics. His publications span top-tier journals like Science and Strategic Management Journal , with working papers recognized at conferences including SMS, EGOS, and AMCIS. His research has been cited in prestigious AI reports such as the National Security Commission on Artificial Intelligence and the Stanford AI Index . Media outlets like the Financial Times , Nature , and The New York Times have covered his work. Best PhD Paper Prize (nominated, SMS) Max Boisot Award (finalist, EGOS) Best Paper in Strategy (won, AMCIS) Honorable Mention (ASAC) Best Paper Award (Israel Strategy Conference, finalist) Awardee of multiple grants and scholarships, including the Plan for Excellence Doctoral Fellowship (Ivey Business School) and Maurice Young Centre Travel Grant (UBC). He co-organizes professional development workshops at AOM and SMS conferences, including panels on machine learning in management research and corporate R&D disclosure strategies.

Michael J. LindseyView profile

Assistant Professor

• Computational Methods
• Numerical Linear Algebra
• Optimization
• +19 more

Michael J. Lindsey is an Assistant Professor in the Department of Mathematics at the University of California, Berkeley, and a Faculty Scientist at Lawrence Berkeley National Laboratory. His research focuses on computational methods driven by Numerical Linear Algebra , Optimization , and Randomization , particularly for High-Dimensional Scientific Computing in quantum many-body problems and applied probability. University : UC Berkeley (Assistant Professor since 2022) Lab Affiliation : Mathematics Group at Lawrence Berkeley National Laboratory Email : lindsey@berkeley.edu His work includes Semidefinite Relaxation for quantum and classical problems, Monte Carlo Sampling techniques, and Tensor Networks for high-dimensional functions. He has pioneered Variational Embedding theory with guaranteed energy bounds and scalable solvers for quantum systems. Recent publications span Quantum Chemistry , Machine Learning , and High-Dimensional Probability , with applications to Electronic Structure , Molecular Dynamics , and Optimal Transport . He received the 2024 Hellman Fellowship and the 2019 SIAM Student Paper Prize . Teaching includes graduate and undergraduate courses in numerical analysis and applied mathematics at UC Berkeley and New York University. He also organizes the HDSC Seminar on high-dimensional scientific computing.

Valera VeryazovView profile

Senior Lecturer

• Theoretical Chemistry
• Computational Chemistry
• Quantum Chemistry
• +5 more

Valera Veryazov is a Senior Lecturer in Computational Chemistry at Lund University, Sweden. As Principal Investigator in the eSSENCE: The e-Science Collaboration initiative, he leads multiple research projects focusing on quantum chemical calculations and material science investigations. His work combines advanced electronic structure theory with software development through the OpenMolcas platform. Research Focus: Electronic structure calculations, atomic clusters, and computational method development Key Collaborations: Wallenberg Initiative Materials Science for Sustainability, Crafoord Foundation His research explores molecular and solid-state systems through density functional theory, with particular emphasis on actinide compounds, transition metals, and hydrogen-bonded networks. Recent work demonstrates expertise in embedded cluster approaches for spectroscopy calculations. Current projects include Engineering atoms with tailored properties using physics-informed Kolmogorov-Arnold Networks (2025-2026) and basis set development for electronic structure theory. Scientific achievements include significant OpenMolcas software contributions and grants from Swedish research foundations.

• Cybersecurity
• Network Security
• Security Automation
• +4 more

Fulvio Valenza is an Assistant Professor (Fixed-Term Tenure-Track, RTD-B) at the Department of Control and Computer Engineering (DAUIN) , Polytechnic University of Turin . He is a member of the NETGROUP - Computer Networks Group and teaches courses such as Security of next-generation networks and Data Protection, Privacy, and Anonymity across multiple degree programs including Cybersecurity Engineering and Computer and Systems Engineering. Research Interests: Cybersecurity, Network Security, Security Automation, Distributed Systems, Quantum Cryptography Projects: MIRANDA (EU-funded cybersecurity research), Commercial contracts for "ESCAPE" seminar organization Research Trends in his publications focus on security automation in virtualized/cloud environments, formal verification of network policies, and optimization of security configurations for industrial and automotive systems. His work bridges theoretical methods (e.g., formal verification) with practical deployments (e.g., Kubernetes security, VNF placement). Teaching spans both the Polytechnic University of Turin and the University of Eastern Piedmont (2020-2022), covering topics in network security, cloud technologies, and cybersecurity.