Find Academic Supervisors

Per Vidar Barth LiljeView profile

Professor

• Cosmology
• Astrophysics
• Large-Scale Structures in the Universe
• +5 more

Per Vidar Barth Lilje is a Professor at the University of Oslo , affiliated with the Institute of Theoretical Astrophysics . His research focuses on Cosmology , Large-Scale Structures , and Cosmic Microwave Background (CMB) , with leadership in the Norwegian participation in the Euclid space mission. Academic Background: M.Sc., University of Oslo, 1985 Ph.D., University of Cambridge, 1988 Postdoctoral research at Canadian Institute for Theoretical Astrophysics (1988–1989) NORDITA Fellow (1989–1992) Research Interests: Dr. Lilje investigates the universe's large-scale structure, cosmic background radiation, and space-based observational techniques. His work spans modified gravity theories, galaxy evolution, and Euclid mission data analysis, including intracluster light and high-redshift galaxy detection. Recent Publications: His 2025 articles with the Euclid Collaboration explore photometric pipelines, galaxy morphology, and relativistic distortions in clustering analysis, reflecting his role in advancing cosmological simulations and observational frameworks. Leadership & Collaborations: As former Head of the Institute of Theoretical Astrophysics (2005–2012, 2017–2024), he contributes to ESA Science Programme Committee and journals. He is a member of the Nordic Optical Telescope project and collaborates on Euclid ’s structure formation studies.

Xingde LiView profile

Professor

• Neurophotonics
• Translational Biophotonics
• Optical Coherence Tomography (OCT)
• +3 more

Xingde Li, PhD, is a Professor at Johns Hopkins University with joint appointments in Biomedical Engineering, Electrical & Computer Engineering, and Oncology. He leads the Laboratory of Biophotonics Imaging Technologies (BIT) and focuses on advancing neurophotonics and translational biophotonics for medical applications. Education : PhD in Physics and Biomedical Optics from University of Pennsylvania (1998); BS in Physics from University of Science and Technology of China (1990). Research Interests : Develops cutting-edge imaging technologies like Optical Coherence Tomography (OCT), two-photon fiberscopy, and AI-assisted imaging for disease diagnostics (e.g., cancer, COPD, preterm birth). His work bridges optics, micro-nano technology, and clinical medicine. Grants & Awards : Received grants from the Bisciotti Foundation Translational Fund (2021 and 2018) and has been featured in high-impact media for innovations like the MAGIC imaging capsule. Labs & Teams : Directs the BIT Lab, collaborating with interdisciplinary teams to translate technologies into clinical tools for disease detection and therapy guidance.

Sarah HarrisView profile

Professor

• Computer Architecture
• FPGA Design
• RISC-V Systems
• +3 more

Sarah Harris is Professor and Undergraduate Coordinator in the Department of Electrical and Computer Engineering at University of Nevada, Las Vegas. Her research focuses on computer architecture education, FPGA-based systems, and hardware implementations of neural networks. Research areas include: RISC-V architecture education tools FPGA-based hardware design Embedded AI systems Computer engineering pedagogy Her publication portfolio shows strong focus on educational innovation with 7 publications (2021-2024) on RISC-V teaching tools and MOOCs, alongside hardware implementations of neural networks and contributions to bioinformatics tools. Earlier work includes digital design fundamentals and memory systems.

• Computer Architecture
• Embedded Systems
• High-Performance Computing
• +9 more

Zapater Sancho Marina is an Associate Professor at the ReDS Institute (Institute of Reconfigurable and Embedded Digital Systems) within the School of Engineering and Management Vaud (HEIG-VD), part of the University of Applied Sciences and Arts Western Switzerland (HES-SO). She holds dual master's degrees in Electronic and Telecommunication Engineering from Universitat Politècnica de Catalunya (2010) and a PhD in Computer Science from Universidad Politécnica de Madrid (2015). Her career includes postdoctoral work at EPFL (2016-2020) and assistant professorship at Universidad Complutense de Madrid (2015-2016). Education BSc & MSc in Electronic Engineering (UPC 2010) PhD in Computer Science (UPM 2015) Research Focus spans cross-layer optimization of heterogeneous architectures for performance and energy efficiency, with emphasis on: Embedded systems (IoT/edge computing) High-performance compute architectures Analog in-memory computing for AI Thermal/power management in 3D chips Cloud-edge AI workload orchestration Publication Trends show expertise in RISC-V simulation frameworks, analog computing tiles for CNNs, virtual memory redesign, and AI-driven cloud performance prediction. Her recent work explores thermal-aware 3D chip management, hybrid-cache reliability optimization, and open-source teaching platforms for radio theory. Awards include a Spanish government PhD fellowship. She has led 4 European H2020 projects since 2016 and currently serves as PI for 4 industrial collaborations (Facebook/Intel/Huawei), Innosuisse projects, and HES-SO initiatives. Labs & Teams include the ReDS Institute, EPFL's Embedded Systems Laboratory, and collaborations with Yale/Edinburgh. She co-developed the ALPINE simulation framework and SO3 operating system modifications for Midgard project validation.

• Energy-Efficient Computing
• Hardware Acceleration
• Machine Learning
• +3 more

Dr. David Boland is a Senior Lecturer at the School of Electrical and Computer Engineering, University of Sydney. He holds an MEng and PhD from Imperial College London. His research focuses on energy-efficient hardware acceleration, particularly using FPGAs and application-specific integrated circuits (ASICs), to optimize computational efficiency in domains like machine learning and optical communications. He has contributed to projects involving custom hardware accelerators, federated learning for edge computing, and real-time signal processing. Education: MEng, Imperial College London, 2007 PhD, Imperial College London, 2012 Research Interests: Dr. Boland’s work emphasizes reducing computational overhead through customized hardware solutions. He explores techniques for minimizing unnecessary computations while maintaining accuracy, leveraging FPGA-based designs for parallelism and energy efficiency. Key areas include: Hardware acceleration for machine learning FPGA optimization for neural networks Energy-efficient algorithms for edge computing Online arithmetic and latency-accuracy trade-offs Grants & Collaborations: 2022: On-Board Federated Learning in Orbital Edge Computing (NSW Department of Industry) 2017: Fast Automated Anomaly Detection in Communication Networks (Defence Science & Technology Group) Affiliations: Member of the Net Zero Institute, collaborating on sustainable computing solutions.

• Computer and Network Security
• Computer Networking
• Network Modeling and Performance Evaluation
• +4 more

Cliff C. Zou is a Professor at the Department of Computer Science, University of Central Florida (UCF), and Program Coordinator for Master's degrees in Digital Forensics and Cyber Security and Privacy. He serves as a UCF Cybersecurity and Privacy Cluster member, Point-of-Contact for UCF CAE-CD/CAE-R designations, and leads the Data Systems Group. With a Ph.D. from University of Massachusetts, Amherst (2005), and B.S./M.S. from USTC, he has received numerous awards including the UCF-TIP Award (2013, 2024), Best Paper Awards, and Fellowships. Ph.D., Electrical & Computer Engineering, University of Massachusetts, Amherst (2005) M.S., Automation, University of Science & Technology of China (USTC) (1999) B.S., Automation, USTC (1996) His research spans Computer and Network Security , IoT Security , Blockchain Systems , Wireless Network Security , and Malware Analysis . Recent work focuses on adversarial machine learning, side-channel attacks, encrypted traffic classification, and secure IoT architectures. His publications (2025-2020) highlight trends in deep learning-based defense , WiFi fingerprinting , TrustZone security , and malware propagation . Scientific Awards: UCF Teaching Incentive Program (TIP) Award (2024, 2013) Best Paper Award (2023) Best Student Paper Award (ACSAC 2007) Guo Moruo Scholarship (USTC, 1996) Graduate Fellowships (University of Massachusetts, 1999-2001) Prof. Zou has advised 14 PhD graduates and numerous Master's/BS students, many now holding academic/industry roles. His funded projects include NSF grants (DGE-2325452, DGE-2042996, DGE-1723587, DGE-1915780), US Army PEO STRI collaborations, and Florida Center for Cybersecurity seed grants. He leads the cyberforensic.net educational platform and has contributed to free IoT security labs for professionals.

Faegheh MoazeniView profile

Assistant Professor

• Mathematical Optimization
• Design Optimization of Critical Infrastructure
• Cybersecurity of Smart Systems
• +5 more

Faegheh Moazeni is an Assistant Professor in the Department of Civil & Environmental Engineering at Lehigh University. Her research focuses on mathematical optimization, cybersecurity of critical infrastructure systems, and smart city technologies. She leads projects in water-energy nexus systems, resilient microgrids, and data-driven predictive control. Her work integrates machine learning, nonlinear model predictive control (MPC), and cyber-physical security frameworks to enhance infrastructure resilience. Recent efforts include hardware-in-the-loop validation of control systems and stochastic modeling for offshore renewable energy. Key research areas include securing smart water systems against cyberattacks, optimizing energy dispatch in islanded microgrids, and developing adaptive algorithms for real-time operational challenges. Her interdisciplinary approach addresses challenges at the intersection of environmental engineering, control theory, and cybersecurity. Notable contributions include frameworks for detecting cyberattacks in water networks, economic dispatch models for water-energy systems, and stability-guaranteed control architectures for naval and civilian infrastructure.

Sai Manoj Pudukotai DinakarraoView profile

Assistant Professor

• Hardware security
• Adversarial machine learning
• Internet of Things networks
• +9 more

Sai Manoj Pudukotai Dinakarrao is an Assistant Professor in the Department of Electrical and Computer Engineering at George Mason University's College of Engineering and Computing. He leads the HArt (Hardware and AI Research) Group, focusing on cutting-edge research at the intersection of hardware security and artificial intelligence. His educational journey includes a BTech in Electronics and Communication Engineering from Jawaharlal Nehru Technological University (2010), an MTech in Information Technology from International Institute of Information Technology Bangalore (2012), and a PhD in Electrical Engineering from Nanyang Technological University, Singapore (2015). Following his doctoral studies, he completed post-doctoral research at TU Wien, Vienna (2015-2017) and George Mason University (2017-2018). Dr. Dinakarrao's research spans hardware security, adversarial machine learning, IoT networks, and deep learning in resource-constrained environments. His work integrates hardware design with AI techniques to address security challenges in computing systems, with particular focus on side-channel attack detection, malware detection in IoT networks, on-chip security, and hardware accelerator design for machine learning applications. His research has resulted in numerous publications in top-tier conferences and journals including IEEE Transactions, ACM conferences, and Design Automation Conference. Analysis of his recent publications reveals a strong trend toward hardware security solutions using machine learning techniques. His work increasingly focuses on Processing-in-Memory architectures, energy-efficient security solutions for IoT devices, and innovative approaches to hardware Trojan detection. Many publications demonstrate interdisciplinary collaboration across electrical engineering, computer science, and cybersecurity domains. Young Research Fellow Award at Design Automation Conference (DAC) 2013 Best paper award at International Conference on Data Mining (ICDM) 2019 Best paper award at International Conference on Consumer Electronics (ICCE) 2020 Best paper nomination at International Conference on Computer-Aided Design (ICCAD) 2019 Best paper nomination at Design Automation and Test in Europe (DATE) 2018 Dr. Dinakarrao has successfully mentored numerous PhD and MS students, with alumni securing positions at AMD-Xilinx, US Government agencies, and academic institutions. His research has been supported by significant grants from NSF, DARPA, and Virginia Commonwealth Cyber Initiative. Current projects include securing supply chains with UVA, developing novel architectures for machine learning acceleration, and creating energy-preserving cryptography protocols. The HArt Group maintains active collaborations with industry partners including AMD-Xilinx and government agencies. The lab focuses on practical implementations of theoretical security concepts, with particular emphasis on creating deployable security solutions for real-world hardware systems. Current research directions include intermittent computing with energy harvesting, hardware fuzzing techniques, and robust machine learning models resistant to adversarial attacks.

• Cognitive Linguistics
• Sociolinguistics
• World Englishes
• +3 more

Prof. Alexander Onysko is a full-time Professor and Head of the Institute of English and American Studies at the University of Klagenfurt's Faculty of Cultural and Educational Sciences. His academic roles include membership in the Curricular Commission for English and American Studies and the Faculty Conference. His research focuses on cognitive linguistics, sociolinguistics, and world Englishes, with particular interest in language contact, multilingualism, and conceptual metaphors. Recent research projects explore interdisciplinary topics like media literacy, swarm intelligence in robotics, and sustainable business ecosystems. Notable publications in 2025 address online political avoidance, robotic exploration algorithms, and healthcare workforce retention during pandemics. He actively contributes to academic administration and has supervised multiple institutional innovation projects. His work bridges linguistic theory with practical applications in education and technology-driven solutions.

Stephen PistoriusView profile

Professor

• Medical Physics
• Medical Imaging
• Image Reconstruction
• +4 more

Stephen Pistorius is a Professor in the Department of Physics and Astronomy at the University of Manitoba's Faculty of Science. He serves as Associate Head and Director of the Medical Physics Program, focusing on interdisciplinary research at the intersection of medical physics, biomedical engineering, and artificial intelligence. Role : Professor, Associate Head, Director of Medical Physics Program Contact : Office 205 Allen Building, Stephen.Pistorius@umanitoba.ca , +1 204-474-6205 Research Interests Medical Imaging : Development and optimization of radar-based microwave sensing systems for breast cancer detection. Image Reconstruction : Innovation in computational algorithms for PET and microwave imaging. Artificial Intelligence : Application of machine learning to tumor detection and signal analysis. Publication Trends His recent work emphasizes microwave imaging hardware (antenna arrays, phantom materials), machine learning integration for tumor detection, and image quality metrics across modalities. Key subfields include radar systems , stochastic optimization , and clinical translation of microwave sensing . Additional Contributions He contributes to radiation oncology via EPID-based positioning automation and dose verification systems, while also addressing technical challenges in 3D-printed phantom modeling and microwave propagation in biological tissues.

Fuyuan ZhangView profile

Researcher

• Artificial Intelligence
• Cybersecurity
• Formal Methods
• +7 more

Fuyuan Zhang is a Postdoctoral Researcher at the Max Planck Institute for Software Systems, specializing in advanced software testing methodologies and formal verification techniques. His research focuses on improving the reliability and security of AI systems, quantum computing frameworks, and concurrent systems through innovative testing criteria, adversarial attacks, and compositional reasoning. Key areas of expertise include: Large Language Model (LLM) testing and validation Quantum program analysis and security Adversarial machine learning and neural network robustness Formal verification of concurrent and cyber-physical systems Automated bug detection in complex software systems His work bridges theoretical foundations with practical applications, addressing critical challenges in AI safety, quantum software reliability, and system-wide security certification.

Chengming ZhangView profile

Assistant Professor

• Efficient machine learning systems
• AI algorithm-hardware Co-design
• Effective efficiency algorithms
• +5 more

Chengming Zhang is a tenure-track assistant professor in the Computer Science Department at the University of Houston. He recently completed his Ph.D. in Computer Engineering from Indiana University in May 2024, where he was a member of the HiPDAC group working on building efficient and scalable deep learning systems under the advisement of Prof. Dingwen Tao. He has extensive industry research experience with multiple projects at Microsoft Research, Meta Reality Labs, and Argonne National Laboratory. His educational background includes: Ph.D. in Computer Engineering, Indiana University (May 2024) Dr. Zhang's research focuses on creating efficient machine learning systems that can operate effectively across diverse hardware platforms. His work bridges the gap between theoretical algorithms and practical implementation with particular emphasis on efficient machine learning systems for training and inference on parallel, distributed, and heterogeneous hardware; AI algorithm-hardware co-design, particularly for GPU architectures; effective efficiency algorithms including model compression, data efficiency, and parameter-efficient tuning; and large-scale deep learning applications such as Large Language Models, Agents, and Image/Video Generation systems. His publication record demonstrates a consistent focus on optimizing deep learning systems through hardware-aware approaches. The majority of his work centers around making deep learning more efficient through techniques like model compression, hardware-algorithm co-design, and memory optimization. His research spans both theoretical algorithm development and practical system implementation, with publications in top-tier conferences including the International Conference on Supercomputing, PPoPP, and AAAI. A notable trend in his work is the emphasis on practical efficiency - not just theoretical improvements but solutions that deliver real-world performance gains on actual hardware. Dr. Zhang is actively building his research group at the University of Houston and has secured significant computational resources for his lab, including multiple high-performance computing servers worth a total of $130K (two 8-Ada6000 servers and one dual-4090 servers).

Ryan T. WhiteView profile

Associate Professor

• Deep Learning
• Machine Learning
• Artificial Intelligence
• +11 more

Ryan T. White is an Associate Professor at Florida Institute of Technology in the Department of Mathematics and Systems Engineering within the College of Engineering and Science. He serves as Director of the NEural TransmissionS (NETS) Lab, focusing on deep learning, computer vision, and data science. He is also an Affiliate Faculty member in Electrical Engineering and Computer Science. Ph.D. in Applied Mathematics (2015) from Florida Tech His research bridges deep learning and computer vision with applications in autonomous satellite operations , physics-informed neural networks for biomedical and geoscience problems, and NLP in aerospace domains. Projects include real-time edge computing , stochastic process analysis , and generative AI for synthetic data. The NETS Lab he directs has produced 15+ recent publications in conferences like IEEE Aerospace, AIAA SCITECH, and AAS/AIAA, with funding from the U.S. Space Force, Air Force Research Lab, and NSF. His teaching spans graduate/undergraduate courses in deep learning , machine learning , probability , and honors calculus . Current advisees include Ph.D. candidates and M.S. students working on topics like 3D object detection , information-theoretic neural analysis , and geophysical signal processing . The lab’s scientific contributions include real-time satellite feature detection , physics-guided neural networks for blood flow modeling, and entropy-based visual explanations for AI interpretability. Collaborations span Georgia Tech , Mulitscale Cardiovascular Fluids Laboratory , and Engage-AI for global development projects analyzing UNDP Sustainable Development Goals.

• Analogue and Mixed Signal IC Design
• Digital System Design (FPGA)
• CMOS Lab-on-Chip Platforms
• +4 more

Junming Zeng is a Researcher at the Department of Electrical and Electronic Engineering, Faculty of Engineering at Imperial College London. His work focuses on advanced CMOS technologies for biomedical applications, including lab-on-chip platforms and ion imaging systems. He holds a PhD from Imperial College London (2022), following a Master's in Analogue and Digital Integrated Circuit Design (2017) and a Bachelor's in Electronic Engineering (2016), both from UK institutions. His research interests span analogue/mixed-signal IC design, FPGA-based digital systems, and ultra-high-speed ion sensing solutions. He has pioneered CMOS lab-on-chip platforms for real-time chemical monitoring and developed compressed sensing techniques for optimizing sensor array performance. His work integrates deep learning for applications like diabetes glucose prediction and drift compensation in ISFET sensors. Zeng has received the Best Student Paper Award (1st Prize) at ISCAS 2018 and Imperial's Department PhD Scholarship. His research bridges electrical engineering and biomedical engineering, with a focus on scalable, energy-efficient systems for healthcare and diagnostics. He leads projects involving edge computing, temporal fusion transformers, and microfluidic integration, demonstrating expertise in both hardware innovation and algorithmic development. His lab develops cutting-edge systems such as 1000fps ISFET SoCs with programmable gain and high-throughput digital readout architectures. Recent work includes live demonstrations of real-time pH monitoring in 3D-printed microfluidic systems and spatio-temporal ion membrane characterization platforms.

• Power Amplifiers
• Signal Processing
• Radar Systems
• +7 more

Souheil Ben Smida is an Associate Professor at the School of Engineering & Physical Sciences at Heriot-Watt University, affiliated with the Institute of Sensors, Signals & Systems. His research focuses on power amplifiers, signal processing, radar systems, and biomedical applications, with a particular emphasis on integrating neural networks and wireless communication technologies. His work spans areas such as nonlinear systems, 5G technology, and healthcare innovation, leveraging radar and machine learning for non-invasive monitoring. Recent contributions include dual-band radar systems for vital signs detection, low-complexity neural network-based channel estimation, and predistortion techniques for power amplifiers. Key research trends include biomedical applications using radar and neural networks, optimization of communication systems for 5G, and hardware implementations on FPGA platforms. His publications reflect a balance between theoretical advancements and practical applications in electronics, healthcare, and telecommunications. Dr. Ben Smida's expertise contributes to the UN Sustainable Development Goals, particularly in advancing health technologies and sustainable industrial innovation.