Find Academic Supervisors

Florian FrohnView profile

Lecturer

• Program Verification
• Termination Analysis
• Complexity Analysis
• +11 more

Florian Frohn is a tenured lecturer ("Lehrkraft für besondere Aufgaben") in the Programming Languages and Verification research group at the Department of Computer Science, RWTH Aachen University. He holds a Dr. rer. nat. (2018), MSc (2013), and BSc (2011) in Computer Science, all from German institutions, with his bachelor's studies completed part-time. His research interests center on formal methods for software verification, including automated termination and complexity analysis of imperative programs, satisfiability of Constrained Horn Clauses (CHCs), SMT solving with integer exponentiation, loop acceleration, term rewriting systems, and abstract interpretation. He is a key contributor to several influential tools: LoAT (Loop Acceleration Tool), AProVE (Automated Program Verification Environment), and SwInE; he also worked on Astrée and the CAGE toolchain developed under the DARPA STAC program. His recent publications (2023–2024) demonstrate continued leadership in top venues such as FM, IJCAR, FoSSaCS, and SAS, with work advancing loop acceleration, non-termination proofs, and SMT solving. These contributions reflect a strong trend in developing practical, automated techniques for program verification and analysis. IJCAR Best paper honourable mention (2024) EASST Award for best ETAPS paper (2020) iFM Best Tool Paper Award (2017) ISR Best Poster Award (2017) SEFM Recognition Award (2016) CADE Woody Bledsoe Travel Award (2016) Florian Frohn actively contributes to the research community through program committee roles for major workshops and conferences including TACAS, HCVS, WST, SMT, and LPAR. He has advised no listed students but has been involved in mentoring through research collaborations. His teaching portfolio includes courses on verification techniques, satisfiability checking, and advanced programming concepts. He leads the Termination and Complexity Competition (termCOMP) and participates in organizing key events in the formal methods community.

Mikael OlofssonView profile

Associate Professor

• Telecommunications
• Electronics
• Mathematics
• +5 more

Mikael Olofsson is an Associate Professor and Head of Division at the Division of Electronics and Computer Engineering (ELDA), Department of Electrical Engineering (ISY), Linköping University. He is actively engaged in teaching and academic leadership, serving as director of studies for courses in Integrated Circuits and Systems since 2018. His research background lies at the intersection of telecommunications, electronics, and mathematics , particularly focusing on algorithms and architectures for arithmetic operations in finite fields . His PhD thesis, VLSI Aspects on Inversion in Finite Fields (2002), reflects this interdisciplinary focus. Though currently more focused on education, he continues to explore arithmetic-related algorithmic designs when time permits. The recent publications reflect a strong emphasis on signal theory and telecommunications education , including textbooks and pedagogical research. Topics span signal processing fundamentals, digital modulation, and Massive MIMO principles , indicating a consistent thread in communication systems education and foundational theory. Scientific Awards: No awards listed in the provided text. Advising and Grants: While no formal students or grant awards are listed, Mikael Olofsson plays a significant mentoring role through his extensive teaching and leadership as director of studies. He emphasizes the value of student discussions and strives to provide optimal learning opportunities, suggesting strong engagement in academic advising at the course and program level. Labs and Teams: He is affiliated with the Division of Electronics and Computer Engineering (ELDA), which conducts research in analog, digital, mixed-signal electronics, programmable systems, and processor design. This division is part of the broader Department of Electrical Engineering (ISY), known for strong industry and research collaborations.

• Number Theory
• Algebraic Geometry
• Galois Theory
• +3 more

Bernat Plans Berenguer is a researcher in the Department of Mathematics at the Universitat Politècnica de Catalunya (UPC), affiliated with the Escola Tècnica Superior d'Enginyeria Industrial de Barcelona (ETSEIB). His research focuses on algebraic number theory, Galois theory, and arithmetic geometry, with particular emphasis on rationality problems, field extensions, and group actions. He has contributed to foundational studies on Noether’s rationality problem, ramification theory, and special linear group extensions. Plans Berenguer has led and participated in numerous research projects, including those funded by competitive grants from the Catalan Government and Spanish national programs. His work spans pure mathematics, with applications in cryptography and computational algebra. Notable collaborations include projects on algebraic varieties, modular forms, and geometric mechanics through the GEOMVAP research group. He has published extensively in top-tier journals like Proceedings of the American Mathematical Society and Journal of Algebra , and has presented at international conferences such as the Journées Arithmétiques. His research bridges abstract algebraic structures with concrete computational methods in number theory.

• Numerical Analysis
• High-Performance Computing
• Parallel Computing
• +5 more

Carl C. Kjelgaard Mikkelsen is an Associate Professor at the Department of Computing Science, Umeå University. He specializes in numerical analysis and high-performance parallel computing, focusing on robust algorithms that cannot fail. He co-authored the StarNEig library for nonsymmetric eigenvalue problems and works on parallel constraint solvers for molecular dynamics and ultra-sparse linear systems. Research Interests: Numerical analysis, parallel computing, eigenvalue problems, linear systems, algorithm design. His recent publications emphasize robust task-based algorithms, GPU acceleration, and precision requirements in Newton's method. He teaches numerical analysis and scientific computing, emphasizing finite precision arithmetic and software documentation.

Damodaran RadhakrishnanView profile

Associate Professor

• Computer Engineering
• Digital Systems Design
• VLSI Design
• +2 more

Dr. Damodaran Radhakrishnan is an Associate Professor in the Engineering Programs department at the State University of New York at New Paltz. His work focuses on computer engineering and digital systems design. Teaching Interests: Digital Logic Fundamentals, Digital Systems Design, Computer Arithmetic, VLSI Design, Low Power VLSI Design Professional Affiliation: Life member, IEEE

• Dynamical Systems
• Number Theory
• Cryptography
• +3 more

Marcus Nilsson is a Lecturer in Mathematics at Linnaeus University's Faculty of Technology, where he focuses on teaching, research, and administrative responsibilities. He serves as the program manager for the Applied Mathematics program and vice dean for teacher education. Nilsson has taught at UC Berkeley, supported by the STINT Teaching Sabbatical program, and represents Linnaeus University on the National Committee for Mathematics at the Royal Academy of Sciences, addressing school collaboration issues. Teaching: Courses in discrete mathematics, number theory, and cryptography. Research: Specializes in dynamical systems, number theory, and cryptography, with external projects in information security. Commissions: Vice Dean for Teacher Education, project manager for Sonja Kovalevsky Days 2024-2025. Collaboration: Member of the International Center for Mathematical Modeling (ICMM), emphasizing interdisciplinary applications.

Jean Pierre DavidView profile

Professor

• Digital Signal Processing
• Distributed and Concurrent Processing
• Computer Architecture and Design
• +6 more

Jean Pierre David is a Full Professor in the Department of Electrical Engineering at Polytechnique Montréal. He has been with the institution since January 2006, was promoted to Associate Professor in June 2013, and became a Full Professor in June 2021. His research focuses on digital systems design, reconfigurable systems, and hardware implementations of artificial intelligence applications. David received his Electrical Engineering degree (specializing in electronics) from the University of Liège (Belgium) in 1995. He completed his Ph.D. in June 2002 at the Catholic University of Louvain, with research focused on reconfigurable systems (FPGAs). Before joining Polytechnique Montréal, he was a professor at the University of Montreal from August 2002 to January 2006. Jean Pierre David's research spans several key areas in electrical engineering and computer science. His primary focus is on digital systems design, configuration, and programming, with particular expertise in reconfigurable systems such as FPGAs and microcontrollers. He has made significant contributions to Hardware Description Languages (HDL), developing methodologies for fast, safe, and simple design of digital architectures. His work extends to Hardware-in-the-Loop (HIL) simulation, Deep Packet Inspection (DPI) for high-speed communications (10GBE, 40GBE, 100GBE), and applications of digital systems in artificial intelligence, particularly neural network implementations. David's recent research has increasingly focused on energy-efficient AI hardware, RISC-V processor design for neural network acceleration, and specialized architectures for low-precision computation. His publication record shows a clear evolution from foundational work in digital system design and FPGA implementation toward increasingly sophisticated applications in artificial intelligence and neural network acceleration. The most recent publications demonstrate expertise in creating specialized hardware for efficient AI computation, with a strong emphasis on low-precision and binary neural networks that can run efficiently on resource-constrained devices. His work bridges computer architecture, electrical engineering, and artificial intelligence, creating practical hardware solutions for emerging computational challenges. David is affiliated with several important research groups and institutions including the Strategic Microsystems Group of Quebec (ReSMiQ), the Institute of Electrical and Electronics Engineers (IEEE), and the Institute for Data Valorization (IVADO). His work has been recognized through numerous publications in high-impact journals and conferences, with a total of 108 publications to his name. Professor David has supervised an impressive number of graduate students throughout his career, mentoring 9 Ph.D. students and 24 Master's students to completion. His students have worked on diverse topics including FPGA-based neural network acceleration, hardware implementations of deep learning algorithms, energy harvesting systems for IoT devices, and specialized architectures for low-precision computation. His lab appears to maintain strong connections with industry through various research projects and collaborations with researchers like Yves Savaria. His research laboratory focuses on the intersection of hardware design and artificial intelligence, with particular emphasis on creating efficient implementations of neural networks on specialized hardware platforms. The lab maintains strong connections with industry partners and collaborates extensively on projects related to network processing, AI acceleration, and energy-efficient computing systems.

Russell ImpagliazzoView profile

Professor

• Computational Complexity
• Cryptography
• Randomness in Computation
• +5 more

Russell Impagliazzo is a Professor in the Department of Computer Science and Engineering at the University of California, San Diego (UCSD). He has held positions as Assistant Professor, Associate Professor, and Professor at UCSD since 1991 and was a Visiting Professor at the Institute for Advanced Study (Princeton) from 2007 to 2012. His academic journey includes a BA in Mathematics from Wesleyan University and a PhD in Mathematics from UC Berkeley. His research focuses on computational complexity theory , with key contributions to: Randomness in computation Cryptography (e.g., pseudorandom generators) Circuit lower bounds Proof complexity (e.g., polynomial calculus, resolution) Structural complexity (e.g., average-case hardness) Optimization heuristics (e.g., local search) The trends in his publications include foundational work on derandomization, hardness amplification, and algebraic proof systems. His papers often bridge theoretical computer science with mathematics, particularly in analyzing the limits of computational models. Scientific awards and honors include: NSF Young Investigator Sloan Fellow Fulbright Scholar Guggenheim Fellow Simons Investigator Best Paper Award (Computational Complexity Conference) Best Paper Award (STOC) Outstanding Paper Award (SIAM) He actively advises students and has contributed to grants and programs such as the Simons Institute’s Fine-Grained Complexity and Algorithms and the Meta-Complexity program at the Simons Lab in Spring 2023.

Stefano Alberto MalanView profile

Assistant Professor

• Automatic Control
• Control Engineering
• Systems Engineering
• +10 more

Stefano Alberto Malan is a Tenured Assistant Professor at the Department of Electronics and Telecommunications (DET) , Politecnico di Torino, and a member of the CARS@PoliTO - Center for Automotive Research and Sustainable Mobility . His academic rank is Assistant Professor in the scientific disciplinary sector IINF-04/A - Systems and Control Engineering (Area 0009 - Industrial and Information Engineering). Educational Background : Laurea in Electrical Engineering (1988) from Politecnico di Torino. Control Engineer at FIAT Research Center (1988-1990). Research Interests : Stefano's work spans Automatic Control , Control Systems Engineering , and Dynamic Modeling , with a focus on model-based and sensor-based algorithms for combustion and emission control in diesel engines and Direct Virtual Sensors . He contributes to Robust Control via uncertainty model sets identification, Building Energy Management (e.g., WiFi4Energy, EcoThermo projects), and Automotive Systems (chassis dynamics, engine controls). His methodologies include Computer-Aided Control System Design using H-infinity and interval arithmetic, and Multi-objective Robustness Analysis . Article Trends : His publications since 2015 emphasize Control Systems (e.g., LiDAR for UAS precision spraying), Diesel Engine Emissions Management , Virtual Sensor Design , and Sustainable Mobility . Recent works integrate Machine Learning (e.g., recurrent neural networks) and Optimization Techniques (e.g., genetic algorithms) into automotive and energy systems. Teaching Contributions : He has taught Automotive Control Systems (2019–2025) and Automation and Planning of Production Systems (2020–2021) at Politecnico di Torino’s Master of Science in Computer Engineering . He is a member of the College of Computer, Film and Mechatronics Engineering . Patents : Co-inventor of two national patents: Gruppo contabilizzatore per impianti termici di riscaldamento o raffrescamento (2013, heating/cooling metering unit). Metering unit for heating or cooling systems (2010, PCT/IB2009/055912). Laboratories and Teams : Active in the Automatica Research Group at DET and the CARS@PoliTO center, Stefano collaborates on projects like WiFi4Energy and EcoThermo to advance sustainable mobility and energy efficiency.

• Number Theory
• Analytic Number Theory
• Multiplicative Number Theory
• +4 more

Cécile Dartyge is an HDR lecturer at the Faculty of Science and Technology, University of Lorraine, France, affiliated with the Institut Élie Cartan de Lorraine (IECL) and actively contributing to the Analysis and Number Theory research team. Her position reflects a permanent academic appointment with research supervision privileges granted through her Habilitation à Diriger des Recherches. Her research centers on analytic number theory with specialized focus areas including multiplicative properties of polynomial values, sums of ellipsephic digits and integers, partitions of integers, and pseudo-random sets. These investigations explore deep structural properties of integer sequences and their arithmetic behavior, often employing sophisticated analytic techniques to uncover distribution patterns and asymptotic properties. Analysis of her publication record since 2018 reveals consistent emphasis on friable (smooth) integers, prime factorization of polynomial values (particularly quartic cases), and exponential sums. Her work demonstrates strong interdisciplinary connections between combinatorial number theory, digital expansions, and cryptographic applications, frequently appearing in high-impact mathematics journals. No scientific awards are documented in available sources. Information regarding doctoral students or grant funding is not specified in current records, though her HDR qualification confirms extensive research supervision experience. Dr. Dartyge maintains active collaboration within the IECL's Analysis and Number Theory group, participating in seminars and research initiatives that advance theoretical frameworks in multiplicative and additive number theory.

Daniel LowdView profile

Professor

• Adversarial Machine Learning
• Statistical Relational AI
• Probabilistic Graphical Models
• +4 more

Daniel Lowd is a Professor in the Department of Computer Science at the University of Oregon . He studies adversarial machine learning , probabilistic graphical models , and statistical relational AI , with applications to security and data poisoning defense. His research has been funded by the DARPA Media Forensics (MediFor) program (2016), and he received tenure in 2017. He co-organizes the annual Workshop on Tractable Probabilistic Modeling , including at ICML and IJCAI. Key research areas: Probabilistic AI, Adversarial Learning, Markov Logic Major collaborations: Javid Ebrahimi, Jonathan Brophy, Zayd Hammoudeh Recent publications include work on robust regression (SaTML 2023), influence estimation (JMLR 2023), and neural score estimation (ICML/NeurIPS 2021-2023). He maintains the Libra Toolkit for probabilistic models. As an active academic, he advises PhD students like Shivvrat Arya (now at UTD), Jonathan Brophy , and Zayd Hammoudeh . He advocates for faculty unionization and open science, and is a unicyclist in his free time.

Uwe NaumannView profile

Professor

• Algorithmic Differentiation
• Computational Engineering
• Scientific Computing
• +6 more

Uwe Naumann is a Universitätsprofessor (University Professor) for Computer Science at RWTH Aachen University, Germany, and Principal Scientist at the Numerical Algorithms Group (NAG) Ltd., Oxford, UK. His research focuses on Algorithmic Differentiation (AD) , including combinatorial problems in AD, integration of AD into numerical methods, AD software development, and AD in parallel high-performance computing. He also explores static and dynamic program analysis, transformation, optimization, and adjoint methods in computational science, engineering, and finance. Education : Dr. rer. nat. (PhD-equivalent in Mathematics, TU Dresden, 1999), Diplom in Mathematics (TU Dresden, 1996). Employment : Principal Scientist, NAG Ltd. (since 2022). Program Director, SIAM Activity Group on Applied and Computational Discrete Algorithms (2021-2022). Professor for Computer Science, RWTH Aachen (since 2004). Postdoctoral Researcher, Argonne National Laboratory (2002-2004). Research Trends : His recent publications emphasize GPU-accelerated optimization, interval arithmetic, Jacobian chaining, differential-algebraic equations, matrix-free methods, and adjoint sensitivity analysis. Contact : naumann@stce.rwth-aachen.de , +49 241 80-28920, IT Center, Room 230, RWTH Aachen.

• Ultra-low power digital circuit design
• Energy-efficient AI processor design
• Compute-in-memory
• +5 more

Dr. Longyang Lin is an Assistant Professor at the School of Microelectronics, Southern University of Science and Technology (SUSTech) in Shenzhen, China. He received his Ph.D. from the National University of Singapore in 2018 and has been with SUSTech since May 2021. His research focuses on cutting-edge integrated circuit design with emphasis on ultra-low power systems, hardware security, and cryogenic circuits. Education: Ph.D., National University of Singapore, 2018 M.Sc., Lund University, 2011-2013 B.Sc., Umeå University & B.Eng., Shenzhen University, 2007-2011 Dr. Lin's research spans ultra-low power digital circuit design , energy-efficient AI processor design , compute-in-memory , on-chip sensor fusion , and cryogenic CMOS circuit design . His work addresses critical challenges in battery-less systems, hardware security, and energy-efficient computing. He has developed innovative solutions for self-powered sensor nodes, widely energy-scalable VLSI systems, and secure integrated circuits that operate across extreme temperature ranges. His recent publications demonstrate a strong focus on hardware security, ultra-low power design, and biomedical applications. The research shows a clear progression toward more integrated systems that combine sensing, processing, and communication in highly energy-constrained environments. Many papers address the challenge of maintaining performance while drastically reducing power consumption, with several targeting sub-nanowatt operation. Scientific Awards: Takuo Sugano Award for Outstanding Far-East Paper, ISSCC 2022 ISSCC Demonstration Session Certificate of Recognition, 2022 ISSCC Demonstration Session Certificate of Recognition, 2020 IEEE SSCS Singapore Chapter Award, 2017 & 2018 ISSCC Student Travel Grant Award, 2017 Dr. Lin actively mentors students and researchers, with openings for Research Assistant Professors, Postdoctoral Fellows, Research Assistants, and Graduate Students. His research group benefits from ample funding and regular tape-out opportunities (3-5 per year across different process nodes). He serves as Associate Editor for IEEE Transactions on VLSI Systems and has authored or co-authored over 40 publications, including 10 in IEEE JSSC, 5 in ISSCC, and 12 in VLSI Circuits conference. His laboratory focuses on integrated circuit design for ultra-low power applications, with specialized expertise in hardware security, energy harvesting systems, and cryogenic electronics. The research environment supports tape-outs in advanced CMOS processes, enabling practical validation of theoretical designs.

Matthew SatrianoView profile

Associate Professor

• Algebraic Geometry
• Combinatorics
• Number Theory
• +3 more

Matthew Satriano is an Associate Professor specializing in Algebraic Geometry , with a focus on Combinatorics , Number Theory , and Moduli Spaces . His work bridges abstract algebraic structures with applications in coding theory and mathematical biology. Key contributions include: Advancing the theory of Toric Stacks and their intrinsic characterizations. Proving smoothness criteria for Good Moduli Spaces and exploring motivic integration for Artin stacks. Investigating Commuting Matrices and their algebraic properties. His recent publications highlight collaborations in Approximating Rational Points , Translation Varieties , and Noncommutative Surfaces . Despite no explicit awards listed, his prolific output in top journals and preprints suggests significant recognition in his field.

• Side-Channel Analysis
• Neural Networks
• Neuromorphic Hardware
• +3 more

Matthias Probst is a Researcher at the Department of Information Security, Technical University of Munich (TUM), specializing in hardware security and neural network implementations. His work bridges cryptographic engineering with emerging computing paradigms, focusing on vulnerabilities in neuromorphic hardware and side-channel analysis of neural networks. Research Focus: Probst investigates Side-Channel Analysis techniques against neuromorphic systems, particularly Spiking Neural Networks (SNNs) and Physical Unclonable Functions (PUFs). His recent publications reveal expertise in Hardware security countermeasures (DOMREP series) Fault injection methodologies (Switch-Glitch) Secure neural network acceleration Quantum-resistant hardware implementations His work demonstrates how timing and electromagnetic emanations can extract secrets from emerging hardware architectures. Publication Trends: Analyzing Probst's 2019-2025 publications shows increasing focus on neural network security (60% of recent work), with growing emphasis on neuromorphic hardware (40% since 2022). His research uniquely combines traditional side-channel analysis with next-generation computing architectures, revealing critical vulnerabilities in spiking neuron implementations and PUF-based security primitives. Key themes include timing-based attacks on loop PUFs, electromagnetic fault injection sweet spots, and masked neural network accelerators. Collaboration Network: Probst consistently collaborates with TUM's Chair of Information Security team led by Prof. Georg Sigl, particularly with Manuel Brosch and Michael Gruber. His 2024 EMDRIVE Architecture paper involves cross-institutional work spanning embedded diagnostics to edge computing.