Find Academic Supervisors

• Reliable and Fault-Tolerant Computing
• RISC-V Architecture
• FPGA-Based Systems
• +12 more

Sarah Azimi is a fixed-term researcher at the Department of Control and Computer Science (DAUIN) within the College of Computer, Film and Mechatronics Engineering at Politecnico di Torino. She actively contributes to research and teaching in the domains of reliable computing, reconfigurable systems, and AI applications for space and smart city security. Research Interests: Reliability and fault tolerance in safety-critical and space systems RISC-V and FPGA-based architectures High-performance computing (HPC) and reconfigurable computing AI resilience and real-time gesture recognition for public safety Radiation effects and hardening techniques for aerospace applications Publication Trends: Her recent publications focus on RISC-V reliability, radiation effects in space missions, AI resilience in reconfigurable platforms, and smart city security through gesture recognition. Her work spans both journal and conference venues, emphasizing practical and mission-tailored solutions in embedded and aerospace computing. Scientific Awards: No awards explicitly mentioned in the provided text. Advising and Grants: Sarah Azimi supervises multiple PhD students including Federico Buccellato, Aobo Cui, and Giorgio Cora. She leads the competitive research project Safe Smart City: Detecting Violence and Requests for Help in Real Time Through Video Surveillance Devices (2024). She is also a member of the RAMSES CubeSat-1 Development project (2025–2026) and led the commercial research project on the Rempro fault-tolerant processor (2022–2023). Labs and Teams: She is a key member of the CAD - Electronic CAD & Reliability Group (DAUIN) at Politecnico di Torino, contributing to cutting-edge research in electronic design automation and system reliability for aerospace and terrestrial applications.

Glenn H. FredricksonView profile

Professor

• Theoretical Polymer Science
• Complex Fluids
• Block Copolymers
• +7 more

Glenn H. Fredrickson is the Mitsubishi Chemical Professor of Functional Materials in the Department of Chemical Engineering at the University of California, Santa Barbara, with additional appointments in the Materials Department. He directs the Mitsubishi Chemical Center for Advanced Materials (MC-CAM) and the Complex Fluids Design Consortium (CFDC), and previously chaired the Chemical Engineering department (1998-2001). He is an elected member of both the National Academy of Engineering and the National Academy of Sciences. Education Ph.D. Chemical Engineering, Stanford University (1984) M.S. Chemical Engineering, Stanford University (1981) B.S. Chemical Engineering, University of Florida (1980) Research Interests Fredrickson leads an internationally recognized program in theoretical and computational polymer science , focusing on self-assembly of block copolymers , complex fluids , and field-theoretic simulation methods . His group pioneered field-theoretic simulations (FTS) —numerical techniques to solve statistical field theories of polymers—enabling predictive design of advanced materials including high-performance plastics, ion-conducting electrolytes, and nanostructured membranes. Current themes include quantum-fluid analogs, machine-learning-accelerated discovery, and sustainable polymer formulations. Recent Publication Trends Between 2022 and 2025, Fredrickson’s group published extensively on block copolymer morphology control , polymer electrolytes for energy storage , phase-separation kinetics , and quantum many-body analogs in soft matter . Notable advances include machine-learning-enhanced self-consistent field theory, molecularly informed models for surfactant and polyelectrolyte systems, and the application of polymer field theory to spin-orbit-coupled Bose-Einstein condensates. Scientific Awards & Honors Election to National Academy of Sciences (2021) Materials Theory Award, Materials Research Society (2017) William H. Walker Award, AIChE (2016) Polymer Physics Prize, American Physical Society (2007) Election to National Academy of Engineering (2003) Alfred P. Sloan Fellow (1992) Camille and Henry Dreyfus Teacher-Scholar Award (1991) Presidential Young Investigator Award, NSF (1991) Research Group & Funding The Fredrickson Research Group comprises ~15 graduate students and several post-doctoral researchers. The group is supported by multi-agency grants including NSF, DOE, and industry partnerships through the Mitsubishi Chemical Center for Advanced Materials and the Complex Fluids Design Consortium. Laboratory & Collaborative Networks State-of-the-art computational facilities are housed in the Materials Research Laboratory (MRL) at UCSB. Collaborative projects extend to leading experimental groups worldwide, integrating theory with synthesis, characterization, and device testing to accelerate materials innovation.

Fraser KingView profile

Assistant Professor

• Climate and Climate Change
• Radiation and Remote Sensing
• Synoptic Meteorology
• +13 more

Fraser King is an incoming Assistant Professor in the Department of Atmospheric and Oceanic Sciences (AOS) at the University of Wisconsin–Madison, starting in Winter 2026. He holds a PhD in Machine Learning and Remote Sensing of Precipitation from the University of Waterloo (2022) and is currently a postdoctoral research associate at NASA Goddard Space Flight Center. His research integrates machine learning with atmospheric physics to advance precipitation and snowfall retrieval, cloud microphysics, and climate modeling. He has held research positions at the University of Michigan and NASA Jet Propulsion Laboratory. His research interests include: Climate and Climate Change Radiation and Remote Sensing Synoptic Meteorology Atmospheric and Cloud Physics Large Scale Dynamics Machine Learning and Model Interpretability Arctic Snowfall Prediction His recent publications reflect a strong trend in applying deep learning (e.g., U-Net, CNNs) and unsupervised methods (PCA, t-SNE, UMAP) to radar and satellite data for precipitation and snow microphysics. Key themes include radar gap inpainting, melting layer detection, and dimensionality reduction for physical interpretation. His work bridges geoscience and AI, aiming for interpretable models that enhance physical understanding. Scientific awards and professional service include: Finalist for the 2023 Governor General's Gold Medal, University of Waterloo Associate Editor, Journal of Atmospheric and Oceanic Technology (AMS) Member, AMS Committee on Artificial Intelligence Applications to Environmental Science Executive Council Member, AGU Precipitation Technical Committee Executive Member, Eastern Snow Conference Research Board Fraser King has mentored students through research projects and led educational initiatives such as a 12-week course on machine learning for land cover classification. He has secured research experience through internships at Aquanty Inc. and multiple NASA-affiliated institutions. He founded MapsByFraser, a company combining cartography and satellite data, and has collaborated with Google's Quantum AI team. His technical skills span Python, deep learning frameworks, and high-performance computing platforms. He leads several major research projects: Towards Interpretable Physical Models : Using sparse autoencoders and nonlinear dimensionality reduction to interpret geoscience models. Microphysical Dimensionality Reduction : Applying PCA, t-SNE, and UMAP to identify physical modes in precipitation data. BlindPaint : A U-Net for radar gap inpainting in spaceborne systems. DeepPrecip : A deep learning model for surface precipitation retrieval. iPhone LiDAR : Using consumer smartphones for snow depth measurement via drones. NRCan Machine Learning Land Cover Classifier : Training ML models on Sentinel-2 data. Climate Model Calibration : Using ML to correct biases in snow-related climate variables. CloudSat Snowfall Validation : Validating high-latitude snowfall estimates. Snow Modelling : A Rust-based physical/temperature-index snow model.

• Theoretical Neuroscience
• Statistical Physics of Neural Networks
• Dynamics of Neuronal Networks
• +6 more

Prof. Dr. Moritz Helias is a University Professor and leads the Theory of Multi-Scale Neuronal Networks group at the Institute for Advanced Simulation (IAS-6), Computational and Systems Neuroscience, Forschungszentrum Jülich. His research bridges biological and artificial neural networks, focusing on dynamics, information processing, and the physics of AI. The group is part of a larger interdisciplinary institute that integrates theory, simulation, and data analysis to understand the brain. Institution: Forschungszentrum Jülich School: Institute for Advanced Simulation Department: IAS-6, Computational and Systems Neuroscience Position: Professor and Group Leader Email: m.helias@fz-juelich.de His research interests lie at the intersection of statistical physics and neuroscience. He investigates how structure shapes dynamics in both biological and artificial networks, aiming to uncover general principles of information processing. Using methods from statistical physics, his work enables a unified framework for understanding collective phenomena, learning, and generalization. Key areas include spiking neural networks, renormalized field theory, and the theoretical foundations of AI. The recent publications reflect a strong trend toward multi-scale modeling of neural systems, integrating statistical physics with neuroscience. Topics include spiking network dynamics, mean-field theory, renormalization, and applications of machine learning in physics. The work spans biological realism and artificial intelligence, with implications for neuromorphic computing and brain-inspired AI architectures. While no scientific awards are listed in the provided texts, his group actively contributes to open science through tools like NEST and theoretical frameworks that influence both neuroscience and AI. Prof. Helias supervises a research group focused on theoretical and computational approaches, contributing to collaborative projects involving large-scale simulations and data analysis. His team works closely with experimentalists and theorists to validate models and advance understanding of brain function. The group is also involved in developing simulation technologies and theoretical tools that support reproducible neuroscience. The Theory of Multi-Scale Neuronal Networks group is embedded within a vibrant research environment at IAS-6, collaborating with teams in statistical neuroscience, computational neurophysics, and future simulation architectures. This fosters a loop between data, theory, and simulation, enabling cutting-edge research on brain function and artificial intelligence.

• Renewable Energy
• Solar Thermal Energy
• Energy Storage
• +9 more

Dr Leok Lee is a Lecturer in the School of Electrical and Mechanical Engineering at the University of Adelaide . He is also an active member of the Centre for Energy Technology , contributing to cutting-edge research in renewable energy systems. Research Interests: Renewable energy systems, with a focus on solar thermal energy and energy storage. System integration and optimisation of complex transient energy systems. Computational fluid dynamics (CFD) and experimental design for energy applications. Decarbonisation of heavy industry through clean energy technologies. His research spans from fundamental studies in heat transfer and fluid mechanics to applied engineering solutions for decarbonising industrial processes. He has led and contributed to projects funded by ARENA and HILT CRC, targeting the integration of concentrated solar thermal energy into industrial applications such as the Bayer Alumina process. Supervision & Mentorship: Dr Lee is eligible to supervise Masters and PhD students and actively mentors undergraduate, Masters, and PhD candidates. He encourages prospective students to contact him via email to discuss research opportunities. Contact: Email: leok.lee@adelaide.edu.au Location: Room 3, Engineering South, North Terrace Campus

• Photonics
• Optical Communications
• Integrated Photonics
• +4 more

Stephen E. Ralph is Professor and Glen Robinson Chair in Electro-Optics within Georgia Tech's School of Electrical and Computer Engineering, serving as Director of the Georgia Electronic Design Center (GEDC) and founder of the Terabit Optical Networking Consortium. His leadership spans cross-disciplinary research in electronics, photonics, and signal processing for revolutionary system performance. Educational background includes a BEE with highest honors from Georgia Tech (1980) and PhD in Electrical Engineering from Cornell University (1988), followed by postdoctoral work at AT&T Bell Laboratories and IBM Watson Research Center. His research integrates integrated photonics , machine learning , and aerospace applications to develop ultra-high-capacity optical communication systems. Current focus areas include photonic topology optimization, radiation-hardened space systems, and converged optical/mm-wave technologies, emphasizing the synergistic development of electronic-photonic components for next-generation interconnects. Analysis of 2024-2025 publications reveals dominant themes in foundry-compatible photonic design (topology optimization, inverse design), aerospace photonics (radiation testing, analog/digital signal transport), and machine learning applications for nonlinear equalization. The work bridges fundamental device engineering (grating couplers, waveguide bends) with system-level implementations for 5G/6G networks and space communications. Key recognition includes: Fellow of the Optical Society (OSA) Professor Ralph has mentored over 20 PhD students and secured significant research funding including the IUCRC Phase I EPICA project (2021) for aerospace photonic integration. His industry partnerships through the Terabit Optical Networking Consortium drive translational research in high-speed communications. He leads the Georgia Electronic Design Center's multidisciplinary team developing photonic-electronic co-design methodologies, with recent emphasis on topology-optimized devices for commercial foundries and radiation-tolerant systems for space applications.

Kenneth KentView profile

Professor

• Hardware/Software Co-Design
• Virtual Machines
• Reconfigurable Computing
• +5 more

Dr. Kenneth Kent is a Professor in the Department of Computer Science at the University of New Brunswick (UNB), where he has served for 14 years. He is the Director of the Information Technology Centre (ITC) and heads the Reconfigurable Computing Group. He also serves as Director of the IBM Centre for Advanced Studies - Atlantic and holds an Honorary Professorship at Hochschule Bonn-Rhein-Sieg. His research focuses on hardware/software co-design, reconfigurable computing, virtual machines, and embedded systems. Dr. Kent earned his PhD and Master of Science in Computer Science from the University of Victoria. His work has led to over 100 refereed publications and the supervision of 70+ graduate students. He co-founded WEnTech Solutions Inc., a software firm addressing waste-to-energy optimization. His awards include the IBM Faculty Fellow of the Year and Project of the Year (as Principal Investigator) for contributions to the J9 Java Virtual Machine. His articles span FPGA acceleration, compiler optimization, cloud storage security, and IoT intrusion detection. Recent work emphasizes energy-efficient Node.js systems and advancements in CAD tools like VTR 9 for FPGA architecture. Dr. Kent’s advising and grants include leading the IBM CAS Atlantic and directing industry-academia collaborations. He has pioneered technologies such as the Eclipse OpenJ9 JVM and the CephArmor storage interface, balancing academic research with commercial innovation. He leads the Reconfigurable Computing Group at UNB and collaborates with the Institute for Visual Computing in Germany. His research bridges theoretical computing and practical applications, with a focus on scalable systems and embedded technologies.

Georgios B. GiannakisView profile

Professor

• Data Science
• Power Systems
• Network Science
• +7 more

Georgios B. Giannakis is a Full Professor, Endowed Chair, and Presidential Chair in the Department of Electrical and Computer Engineering at the University of Minnesota since 1999. He directs the Digital Technology Center and has held academic roles at the University of Virginia (1987-1999) and USC (1982-1986). His research spans Data Science, Wireless Communications, Network Science, and Statistical Signal Processing , with applications to IoT and power systems. Diploma in Electrical Engineering, NTUA (1981) MSc in Electrical Engineering, USC (1983) MSc in Mathematics, USC (1986) PhD in Electrical Engineering, USC (1986) His publications (470+ journals, 770+ conferences, 34 patents) focus on fading channel modeling, UWB localization, blind signal estimation, and cross-layer wireless design . Articles emphasize multicarrier systems, time-varying channels, and ultra-wideband communication , with citations exceeding 76,000 (H-index 145). Scientific Awards : EURASIP 'Athanasios Papoulis' Society Award (2020) IEEE Fourier Technical Field Award (2015) Gugliermo Marconi Prize Paper Award (2003) 9 Best Journal Paper Awards (IEEE/SPS & ComSoc) IEEE SPS Technical Achievement Award (2001) He has mentored over 50 PhD students and 25 postdocs, served IEEE as Distinguished Lecturer, and contributed to Greek university accreditation panels. His work bridges theoretical signal processing and practical communication systems .

Matthew JohnstonView profile

Associate Professor

• Integrated Sensors with CMOS
• Stretchable Electronics
• Lab-on-CMOS Platforms
• +3 more

Matthew Johnston is an Associate Professor in the School of Electrical Engineering and Computer Science at Oregon State University. His research focuses on integrating sensors with CMOS circuits, stretchable electronics, and bio-energy harvesting. He holds a B.S. from Caltech and a Ph.D. from Columbia University. Prior to academia, he co-founded Helixis, a biotech instrumentation startup, and worked in venture capital. His awards include the 2020 SRC Young Faculty Award and 2021 Teaching Excellence Award. Education : B.S., Electrical Engineering, California Institute of Technology, 2005 M.S., Electrical Engineering, Columbia University, 2006 Ph.D., Electrical Engineering, Columbia University, 2012 Research Interests : Johnston explores lab-on-CMOS platforms, stretchable sensor systems, and energy harvesting for low-power applications. His work bridges electronics engineering with biomedical and environmental fields, emphasizing practical applications through interdisciplinary collaboration. Awards : 2020 Semiconductor Research Corporation Young Faculty Award 2021 Oregon State University Teaching Excellence Award 2021 Provost Fellowship Advising & Grants : Johnston’s research is supported by grants from industry and federal agencies. His lab, the SIM Lab, develops innovative electronic systems for healthcare and environmental monitoring. Labs & Teams : He leads the SIM Lab , focusing on interdisciplinary projects in integrated circuits and biomedical applications.

• Networking and Communications
• Blockchain Technology
• Smart Grids and Energy Systems
• +5 more

Roberto Rojas-Cessa is a Professor in the Department of Electrical and Computer Engineering at New Jersey Institute of Technology (NJIT), affiliated with the School of Applied Engineering and Technology. His research focuses on networking, blockchain applications in smart cities, energy systems, wireless communications, and high-performance switching. He has led multiple National Science Foundation (NSF)-funded projects, including initiatives on controlled delivery power grids and next-generation network quality of service. Notably, his work explores blockchain for energy metering, sustainable environmental measures, and smart grid optimization. He is also a Senior Member of the National Academy of Inventors (2024). His research interests span network protocols, distributed systems, and IoT applications. Recent projects include AMI-Chain (a blockchain-based power metering system) and studies on indirect free-space optical communications for vehicular networks. He has contributed to advancements in medium access control for crowded networks and energy packet switches for digital microgrids. Rojas-Cessa’s work integrates machine learning for network management and flood impact analysis. He has developed tools for time-lapse analysis of urban data and agent-based models to evaluate electric vehicle adoption. His publications emphasize scalability, security, and efficiency in both traditional and emerging technologies. Grants: Collaborative Research on Power Grids (NSF, 2016–2018), NeTS-NR: Quality of Service Networks (NSF, 2004–2008) Awards: Senior Member of the National Academy of Inventors (2024) His lab activities include experimental evaluations of digital microgrids and blockchain implementations for carbon footprint tracking. He actively collaborates on projects addressing emergency communications and resilient energy distribution systems.

Peter BuiView profile

Teaching Professor

• Systems Programming
• Operating Systems
• Parallel Computing
• +6 more

Peter Bui is a Teaching Professor in the Computer Science and Engineering department at the University of Notre Dame , located within the College of Engineering. He teaches courses such as Data Structures, Systems Programming, and Ethical and Professional Issues, while also managing the core Elements of Computing programming sequence for the Computing & Digital Technologies minor. Education: Ph.D. in Computer Science and Engineering from University of Notre Dame (2012) His research interests span systems programming, operating systems, parallel computing, cloud computing, distributed computing, programming languages, compilers, and web services . He actively integrates these domains into his teaching and extracurricular work with the Linux Users Group. Recent publications highlight his work in distributed computing frameworks , including the development of tools like WorkQueue and Madeup for scalable scientific workflows and 3D printing integration. Projects such as ROARS and Weaver demonstrate his focus on robust data management and workflow automation. Outside academia, he stewards the Linux Users Group , engages with open-source communities, and balances personal interests like gaming in RuneScape with family time.

Lars DittmannView profile

Professor

• Software-Defined Networking (SDN)
• Network Function Virtualization (NFV)
• 5G Networks
• +6 more

Lars Dittmann is a Professor at the Department of Electrical and Photonics Engineering at the Technical University of Denmark (DTU), leading the Networks Technology and Service Platforms section. His work bridges advanced networking technologies with real-world applications in healthcare and transportation. Academic Role: Professor, Head of Section University: Technical University of Denmark (DTU) Department: Networks Technology and Service Platforms Research Interests: Professor Dittmann specializes in Software-Defined Networking (SDN) , 5G and IoT technologies , and energy-efficient network design , with a focus on applications in telemedicine and transportation systems . His work integrates machine learning for privacy-preserving traffic analysis and explores optical networks for high-bandwidth scenarios. Scientific Contributions: His recent publications emphasize green cellular networks using SDN/NFV/C-RAN, IoT benchmarking for coverage and mobility, and secure edge architectures for railways. Collaborative projects like the Future Patient telerehabilitation program highlight his interdisciplinary impact. Supervision: He supervises PhD candidates such as Radheshyam Singh, focusing on SDN-based IoT security and 5G network optimization. Labs & Projects: Leads initiatives like EXplorative network PLAnnINg and Broadband Trial Integration , addressing challenges in network reliability , emergency communication , and optical data center scaling .

Timothy GrantView profile

Assistant Professor

• Cryo-electron microscopy (cryo-EM)
• Structural biology
• Computational imaging
• +4 more

Timothy Grant is an Assistant Professor of Biochemistry at the University of Wisconsin–Madison and an Investigator at the Morgridge Institute for Research , embedded within the John W. and Jeanne M. Rowe Center for Research in Virology . His laboratory, the Grant Lab , focuses on pushing the limits of cryo-electron microscopy (cryo-EM) to visualize ever-smaller and more dynamic biological macromolecules. Education & Academic Home: Faculty appointment: Assistant Professor, Department of Biochemistry, UW–Madison College of Agricultural and Life Sciences. Concurrent appointment: Morgridge Institute Investigator, Rowe Center for Research in Virology. Research Interests: The Grant group develops computational and experimental methods that extend cryo-EM into two major frontiers: size —capturing structures of very small proteins previously invisible to cryo-EM—and motion —resolving conformational changes of molecular machines in real time. These advances are integrated into the open-source software package cisTEM , which provides a user-friendly workflow for single-particle image processing. Publication Trends: Across the 15 most recent papers (2021-2025), Grant’s work spans method-centric algorithmic innovation, high-resolution structural studies of bacterial DNA replication-restart machinery, and integrative technologies that couple native mass spectrometry with cryo-EM. A clear trajectory emerges from tool development toward application in virology and antibiotic-target validation. Scientific Awards: None explicitly mentioned in the provided text. Students & Research Team: Grant currently mentors six graduate students (Colin Hemme, Gan Li, Heidy Elkhaligy, Peter Ducos, Roma Broadberry, Shashwat Shastri) and several postdoctoral researchers and staff, including Alex Duckworth, Raison Dsouza, and Tim Wagner. Laboratory & Collaborations: The Grant Lab is physically located within UW–Madison’s Biochemistry Building and leverages the Center for High-Throughput Computing shared between UW–Madison and Morgridge to perform large-scale cryo-EM data processing.

Didier MeuwlyView profile

Professor

• Forensic Science
• Biometrics
• Probabilistic Evaluation
• +3 more

Didier Meuwly is a Full Professor of Forensic Biometrics at the University of Twente (since 2013) and Principal Scientist at the Netherlands Forensic Institute (NFI). His work focuses on automating and validating probabilistic evaluation of forensic evidence, particularly biometric traces. He has contributed to international standards via ISO Technical Committee 272 and served as Associate Editor for Forensic Science International . PhD in Forensic Speaker Recognition (University of Lausanne, 2000) Research spans forensic biometrics, likelihood ratios, AI validation, and gait/body analysis from surveillance footage. Recent work addresses ISO standards (21043), forensic AI explainability, and multimodal evidence evaluation. His publications emphasize empirical validation and statistical rigor. Key awards include: ENFSI Distinguished Forensic Scientist Award (2022) University of Lausanne Law Faculty Prize (2002) Active in global forensic networks, he chairs the ENFSI R&D Committee and collaborates across disciplines on digital evidence, biometric security, and forensic methodology.

• Bioinformatics
• Structural Biology
• Computational Biology
• +3 more

Martin Steinegger is a researcher affiliated with Johns Hopkins School of Medicine and previously held roles at institutions such as the Max Planck Institute for Biophysical Chemistry and Technical University of Munich. His research focuses on bioinformatics, protein structure prediction, and computational methods for analyzing large genomic datasets. He has contributed to tools like MMseqs2, ColabFold, and Foldseek, advancing fields like metagenomics and structural biology. His work emphasizes scalable algorithms and open-source software development. Education includes a Master of Computer Science from Ludwig-Maximilians-Universität München (2013-2014) and a Ph.D. from Technical University of Munich (2014-2018). He has also held visiting scholar positions at Seoul National University, Centre for Genomic Regulation, and University of California, San Francisco. Key research interests revolve around protein structure prediction, metagenomic analysis, and developing machine learning frameworks for biological data. His publications highlight innovations in protein language models, structural phylogenetics, and database management systems. Notable contributions include the AlphaFold Protein Structure Database, MMseqs2 sequence search tool, and ColabFold for accessible protein folding predictions. His work bridges computational methods with biological discovery, addressing challenges in structural biology and genomic data interpretation.