Find Academic Supervisors

Kuan-Hsun ChenView profile

Assistant Professor

• Computer Architecture
• Embedded Systems
• Real-Time Systems
• +5 more

Kuan-Hsun Chen is an Assistant Professor specializing in Computer Architecture Design for Embedded Systems Real-Time Systems Non-Volatile Memory (NVM) Optimization Machine Learning and Edge AI His research focuses on bridging hardware-software gaps in real-time embedded applications through innovative architectural solutions. Research highlights include: Decision Tree Optimization for Real-Time Inference Wear-Leveling Techniques in NVM Probabilistic Timing Guarantees GPU-Accelerated Graph Algorithms Security-Enhanced NVM Designs He explores theoretical foundations while emphasizing practical implementations in autonomous systems and cyber-physical platforms. Scientific achievements: Best Paper Award (2023) Outstanding Paper Award (2023) Outstanding Reviewer Award (2024) His work spans algorithm design, hardware-software co-optimization, and rigorous system validation through fault injection and simulation frameworks.

• Power Systems
• Smart Grids
• Renewable Energy
• +3 more

Eklas Hossain is a researcher with extensive contributions to power systems, smart grids, and renewable energy. His work spans power electronics, machine learning applications, and biomedical imaging, reflecting interdisciplinary expertise. Research Interests include: Power quality and grid stability in distributed generation systems Advanced DC-DC and inverter designs for renewable energy Optimal PMU placement and network optimization IoT-based load classification and demand response Publication Trends highlight: Innovations in multilevel converters and GaN devices (2023) Hybrid hierarchical networks and PMU analytics (2022) Smart grid optimization, cardiac imaging, and ceiling fan drives (2021)

Weiyang LiuView profile

Assistant Professor

• Machine Learning
• Inductive Bias
• Generative Modeling
• +3 more

Weiyang Liu is a machine learning researcher and incoming Assistant Professor in the Department of Computer Science and Engineering at The Chinese University of Hong Kong (CUHK), where he will lead the Scalable Principles for Learning and Reasoning Lab (SphereLab). He has previously held postdoctoral and research roles at the Max Planck Institute for Intelligent Systems, Georgia Tech, Google Brain, Nvidia Research, and MERL. His research focuses on principled modeling of inductive bias in learning algorithms, emphasizing geometric invariance, symmetry, and formal reasoning in large language models. His work spans generative modeling (diffusion models, 3D mesh synthesis), foundation models (parameter-efficient training), and symbolic/mathematical reasoning evaluation. Publications include top-tier conferences like NeurIPS, ICML, CVPR, and ICLR, with Spotlight/Notable Top-25% designations. He mentors PhD students including Zeju Qiu, Longhui Yu, and Zhouliang Yu, with alumni transitioning to institutions like Technical University of Munich, University of Tübingen, and Caltech. His lab emphasizes curiosity-driven research , theoretical rigor, and open collaboration.

Jesús Martínez FrutosView profile

Professor

• Computational Mechanics
• Soft Robotics
• Topology Optimization
• +5 more

Jesús Martínez Frutos is a Professor specializing in computational mechanics and soft robotics, with a strong focus on integrating machine learning with multiphysics simulations. His research bridges constitutive modeling, topology optimization, and electroactive polymers to advance soft robotic systems and smart material design. His primary research domains include: Computational Mechanics (finite strain electromechanics, hyperelasticity) Physics-Informed Machine Learning (neural networks, Gaussian processes) Topology Optimization (lattice structures, band gap engineering) Soft Robotics (shape-morphing actuators, dielectric elastomers) Multiphysics Simulation (thermo-electro-mechanics, flexoelectricity) He pioneers methods for embedding physical constraints into data-driven models, particularly for nonlinear material behavior under large deformations. Analysis of his 15 most recent publications (2022-2025) reveals three dominant trends: (1) Physics-augmented neural networks for constitutive modeling that enforce thermodynamic consistency; (2) Multi-material topology optimization for programmable electroactive structures; (3) Gradient-enhanced machine learning frameworks that capture material uncertainties. His work consistently targets real-world applications in soft robotics and energy-efficient actuation systems. Professor Martínez Frutos actively supervises graduate theses as indicated by his profile's dedicated section, though specific student names aren't listed. His research is supported by institutional funding referenced in the profile's funding section, with emphasis on computational methods for next-generation soft robotic systems. Current projects focus on 3D-printed dielectric elastomer actuators and probability-based optimization for safety-critical applications.

• Computer Vision
• Machine Learning
• Internet of Things
• +3 more

Dr. Fernando J. Galetto serves as a Research Officer and Academic casual in the Department of Engineering at La Trobe University, Melbourne, Australia. His research focuses on developing intelligent systems that leverage Computer Vision, Machine Learning, and Internet of Things (IoT) technologies for real-time Edge device applications, with significant contributions in image restoration, defocus estimation, and infrastructure assessment. His expertise centers on practical implementations of deep learning and AI for resource-constrained environments. Key research areas include marine snow analysis, sewer infrastructure monitoring, and real-time PPE detection systems. His methodologies emphasize computational efficiency without sacrificing accuracy, particularly in underwater imaging and civil engineering applications where Edge deployment is critical. Analysis of his 2021-2025 publications reveals a consistent trajectory toward solving complex image processing challenges through novel filter designs and deep learning architectures. His work bridges theoretical advances in inverse problems and defocus estimation with tangible applications in environmental monitoring and infrastructure maintenance, demonstrating exceptional adaptability to Edge computing constraints. Dr. Galetto's scientific recognition includes: IEEE Award for Technical Innovation and Engineering Achievement (2019) for developing a real-time embedded system using Raspberry Pi and Intel Neural Compute Stick to detect personal protective equipment across four HD video streams at 60 fps. No documented information exists regarding graduate student supervision or externally funded research grants. His collaborative work primarily involves co-authors Deng G, Al-nasrawi M, and Stumpf A across multiple publications. While specific laboratory affiliations aren't detailed in available sources, his research outputs indicate active involvement in La Trobe University's engineering projects related to sewage pipe condition assessment and marine environment monitoring systems.

Dimitrios CharalampidisView profile

Professor

• Image Processing
• Signal Processing
• Pattern Recognition
• +6 more

Dimitrios Charalampidis serves as Associate Dean of the College of Engineering and holds the Huntington Ingalls, Inc., Endowed Professorship in the Department of Electrical and Computer Engineering at the University of New Orleans, with contact details including phone 504-280-7415 and email dcharala@uno.edu. His academic credentials comprise: Ph.D. in Electrical Engineering, University of Central Florida (2001) M.S. in Electrical Engineering, University of Central Florida (1998) Diploma in Electrical Engineering and Computer Technology, University of Patras, Greece (1996) Research focuses on Image Processing , Signal Processing , and Pattern Recognition with specialization in texture analysis, medical imaging, remote sensing, and FPGA-based implementations. His work bridges theoretical algorithms with hardware-accelerated solutions for real-time applications in critical domains. Analysis of 15 recent publications (2023-2015) reveals consistent innovation in machine learning for energy systems (smart grid optimization), environmental monitoring (water-level sensors, storm tracking), and underwater imaging (fish detection). Key methodological trends include dimensionality reduction, efficient FPGA implementations, and hybrid neural network approaches. No specific scientific awards beyond his endowed professorship are documented in the source material. Administrative leadership as Associate Dean suggests significant institutional responsibilities, though graduate student mentorship details and research grants remain unspecified in available information. His research infrastructure operates within the Department of Electrical and Computer Engineering, emphasizing embedded systems development and signal processing laboratories for interdisciplinary applications.

John Paul ShenView profile

Professor

• Modern Processor Design
• Instruction-Level Parallelism
• Dependable Computing
• +3 more

John Paul Shen is a Distinguished Service Professor of Electrical and Computer Engineering at Carnegie Mellon University (CMU), based at the Silicon Valley campus. With over three decades of experience in academia and industry, he has held prominent roles including founding director of Nokia Research Center-North America Lab (2007–2012) and director of Intel’s Microarchitecture Research Lab (2000–2006). His academic career includes a tenured professorship at CMU’s ECE Department from 1990–2000 and a return to CMU in 2015 as a tenured professor. Education: Ph.D. in Electrical Engineering, University of Southern California M.S. in Electrical Engineering, University of Southern California B.S. in Electrical Engineering, University of Michigan Research Focus: Modern Processor Design: Specializes in superscalar processors, instruction-level parallelism (ILP), and microarchitecture validation tools like VMW (Visualization-Based Microarchitecture Workbench). Neuromorphic Computing: Develops energy-efficient temporal neural networks (TNNs) and CMOS implementations for sensory processing. Edge AI: Focuses on low-precision accelerators, sparsity exploitation, and real-time data analytics for edge devices. Dependable Computing: Leverages idle processor resources for error-checking and fault tolerance. Awards & Impact: Recipient of multiple teaching awards during his CMU tenure. Supervised 17 Ph.D. students and dozens of M.S. students, with notable contributions to compiler design and parallel computing. Authored two influential textbooks, including Modern Processor Design: Fundamentals of Superscalar Processors , used at Stanford. Generated over 100 patents and 200+ papers at Nokia and Intel, with a focus on mobile computing and hardware architecture innovations. Labs & Collaborations: Founded and led the Nokia Research Center-NAL, advancing mobile Internet and computing. Directed Intel’s Microarchitecture Research Lab, advancing IA32/IA64 processor designs. Current research at CMU Silicon Valley focuses on neuromorphic systems, wearable computing, and real-time data analytics.

Chua Tat SengView profile

Professor

• Multimedia Information Retrieval
• Social Media Analytics
• Unstructured Multimodal Data Analytics
• +4 more

Professor Chua Tat Seng is a distinguished academic at the National University of Singapore's School of Computing, serving as KITHCT Chair Professor and Director of the NUS-Tsinghua Extreme Search Center (NExT). He also holds Distinguished Visiting Professorships at Tsinghua and Zhejiang Universities in China. PhD in Computer Science (University of Leeds, 1983) Founding Dean of School of Computing (1998-2000) Co-founded ViSenze and 6Estates technology startups His research focuses on unstructured multimodal data analytics, with particular emphasis on multimedia information retrieval, social media analytics, recommendation systems, and trustworthy AI. He has pioneered work in computational wellness and fintech applications, establishing the Lab for Media Search and leading NExT++ research initiatives. Over 300 publications in leading venues (CVPR, SIGIR, WWW, AAAI) Recipient of ACM SIGMM Technical Achievement Award (2015) Supervised 37 PhD students since 2004 Editorial leadership in ACM Transactions and IEEE Multimedia Recent work explores multimodal LLMs, knowledge editing techniques (AlphaEdit), and 3D generation frameworks, reflecting his commitment to advancing web intelligence and user empowerment.

• Distributed Artificial Intelligence
• Sensor Networks
• Multi-Agent Systems
• +3 more

Dr. Conor Muldoon is a Senior Lecturer in Software Engineering at the Department of Computing and Mathematics, Manchester Metropolitan University. His research focuses on distributed artificial intelligence, sensor networks, and multi-agent systems with applications in environmental monitoring and smart home technologies. Previously, he held postdoctoral positions at University College Dublin (UCD) and the University of Oxford, supported by prestigious fellowships including the INSPIRE Marie Curie and Government of Ireland Embark awards. He earned a Ph.D. in Computer Science from UCD, along with a first-class B.Sc. in Computer and Software Engineering and a Postgraduate Certificate in Higher Education Teaching. His research portfolio includes developing water quality forecasting systems, citizen science platforms (e.g., COBWEB), and autonomic energy management solutions. He has published extensively in journals and conferences, addressing topics such as sensor network optimization, incentive mechanisms for crowdsourcing, and adaptive middleware for ambient intelligence. Dr. Muldoon is a Fellow of the Higher Education Academy and supervises PhD candidates in areas like multi-agent systems and environmental informatics. Education: Ph.D. in Computer Science, University College Dublin B.Sc. (Honours) in Computer and Software Engineering, University College Dublin Postgraduate Certificate in Teaching and Learning in Higher Education His work bridges theoretical computer science with real-world applications, emphasizing sustainability and participatory technologies. Key projects include the Dublin Bay Water Quality Modelling and the MERA Data Extraction Toolkit. Awards include recognition for his contributions to agent-based systems and environmental data science.

Divya BhadauriaView profile

Researcher

• Data Profiling
• Data Quality
• Data Preparation
• +3 more

Divya Bhadauria is a Researcher in the Information Systems Group at the Hasso Plattner Institute for Digital Engineering (HPI), Potsdam, Germany, under the leadership of Prof. Dr. Felix Naumann. Her work focuses on advancing data engineering, particularly in data profiling, data quality, and distributed computing. She contributes to projects like Metanome (data profiling), KITQAR (data quality for AI), and Janus (change exploration), addressing challenges in data preparation and integration. Affiliations : HPI, Department of Information Systems Office : F-2.05 Her research emphasizes practical applications of data science, including algorithmic solutions for data cleaning, machine learning integration, and scalable systems. Recent publications explore topics like table dissolution for privacy, graph-based overlap estimation, and AI-driven data quality frameworks. She actively participates in conferences such as SIGMOD and BTW, contributing to the broader data engineering community.

• 3D Integrated Circuits
• Hardware Security
• AI Accelerators
• +4 more

Krishnendu Chakrabarty is the Fulton Professor of Microelectronics in the School of Electrical, Computer and Energy Engineering at Arizona State University (ASU). He also serves as CTO of the SWAP Hub for the Department of Defense Microelectronics Commons and Director of the ASU Center for Semiconductor Microelectronics (ACME). Previously, he was the John Cocke Distinguished Professor and Chair of Electrical and Computer Engineering at Duke University. Education: B.Tech from IIT Kharagpur (1990), M.S.E. and Ph.D. from the University of Michigan (1992, 1995). His research focuses on 3D integrated circuits, hardware security, AI accelerators, and microfluidic biochips. He has authored 27 books, 930 peer-reviewed papers, and holds 24 patents. His work has been supported by NSF, DARPA, NIH, and industry partners like Intel and NVIDIA. Key awards include NSF CAREER Award, IEEE Technical Achievement Awards, Humboldt Research Award, and Distinguished Alumnus from IIT Kharagpur. He advises 44 PhD graduates and collaborates with industry leaders such as Synopsys and Cisco. His leadership roles include Editor-in-Chief of IEEE Transactions and Fellowships in ACM, IEEE, and AAAS. Research highlights include pioneering test methodologies for 3D ICs, developing AI-driven biochip solutions, and securing hardware against cyber-physical threats. His labs and teams focus on advancing semiconductor technology and healthcare applications through interdisciplinary innovation.

Philipp GrohsView profile

Professor

• Mathematical Data Science
• Deep Learning Algorithms
• High-Dimensional Partial Differential Equations
• +7 more

Prof. Dr. Philipp Grohs is a full professor of Mathematical Data Science at the University of Vienna and head of the Mathematical Data Science group at RICAM (Austrian Academy of Sciences). He holds a MSc from TU Vienna (2006) and a PhD from the same institution (2007). After postdoc positions at TU Graz, KAUST, and ETH Zurich, he became an assistant professor at ETH Zurich in 2011 before moving to the University of Vienna in 2016. His research focuses on designing efficient algorithms for signal processing, computational sciences, and finance, with recent contributions to understanding deep learning algorithms and solving high-dimensional PDEs using machine learning. He has received the ETH Latsis Prize (2014) and was selected for an Alexander von Humboldt Professorship (2019). Research Interests: Mathematical foundations of deep learning High-dimensional PDEs and their numerical solutions Signal and image processing (phase retrieval, Gabor systems) Approximation theory and function spaces Computational finance and mathematical modeling Recent Research Trends: His work explores theoretical guarantees for neural network performance, particularly in overcoming dimensionality challenges. Notable contributions include phase retrieval algorithms, analysis of DNN expressivity, and applications of deep learning in quantum chemistry and epidemiology modeling. Awards: ETH Latsis Prize (2014) Alexander von Humboldt Professorship (2019) Advising & Projects: Leads research initiatives such as the 'Data Science' hub at Vienna, and has coordinated projects on hybrid computational sciences and explainable AI models. Active in supervising interdisciplinary research teams across mathematics and computer science. Labs/Teams: Directs the Mathematical Data Science group at RICAM and oversees computational research collaborations with institutions like KAUST and ETH Zurich. Engages in applied projects like group testing strategies for SARS-CoV-2 and neural network-based electronic structure calculations.

• Digital Signal Processing (DSP)
• Field-Programmable Gate Arrays (FPGA)
• Machine Learning Hardware Acceleration
• +13 more

Gian Carlo Cardarilli is a researcher specializing in digital hardware design and machine learning acceleration. His work focuses on FPGA implementations, Residue Number System (RNS) architectures, and reconfigurable computing for applications in wireless communication, edge AI, and fault-tolerant systems. Key collaborations with institutions like IEEE and ACM through publications. Active in translating theoretical algorithms into practical hardware solutions for real-time systems. Research interests include: Optimizing deep learning models for heterogeneous platforms. Developing radiation-hardened memory systems. Creating energy-efficient signal processing architectures. Advancing reconfigurable functional units for embedded processors. His article analyses span fields like Quantum Cellular Automata , Variable Fractional Delay Filters , and RNS-Based Position Estimation , reflecting a trend toward adaptive, low-power, and domain-specific hardware.

• Parallel Computing Systems
• Multi-core Embedded Systems
• System-level Design
• +5 more

Prof. A.D. Pimentel holds a full professorship at the Informatics Institute of the University of Amsterdam, leading the Parallel Computing Systems (PCS) group within the Systems and Networking Lab. His research focuses on multi-core and multi-processor systems, emphasizing performance, energy efficiency, dependability, and productivity in system design and runtime management. He earned his PhD and MSc in Computer Science from the University of Amsterdam in 1998 and 1993, respectively. Current roles: Chair of PCS group, Board member of Advanced School for Computing and Imaging (ASCI), and ICT Research Platform Nederland (IPN) Teaching: Courses on Multi-core Processor Systems, Embedded Software, and Architecture Research interests span edge AI, sustainable computing, and system-level modeling. Recent work includes innovations in energy-efficient scheduling, thermal management in 3D-stacked systems, and adaptive CNN inference at the edge. Over 25 years of contributions to embedded systems design space exploration and hardware/software co-design have been recognized through awards like the IEEE CEDA Outstanding Service Award (2025). Awards: IEEE DATE Fellow (2025), NWO Knowledge & Innovation Covenant grant lead Active in conference organization, serving as General Chair for Embedded Systems Week (2026) and Design Automation and Test in Europe (DATE 2024). Engages in cross-disciplinary projects like improved secure semiconductor evaluation (ISSE) and energy labeling for digital services.

Fred BeyetteView profile

Professor

• Biomedical Engineering
• Medical Devices
• Neurotechnology
• +3 more

Fred Beyette, Ph.D., is a Professor and Founding Chair of the School of Electrical and Computer Engineering at the University of Georgia College of Engineering. His research focuses on developing point-of-care medical devices for neurologic emergencies, including stroke and traumatic brain injury (TBI), resulting in 12 patent applications. He has trained 55 graduate students and mentored over 125 undergraduates in research and design. Before joining UGA in 2017, Beyette held roles as a Professor of Electrical Engineering and Computing Systems at the University of Cincinnati, serving as Associate Department Head and Graduate Program Coordinator. He earned all three degrees in Electrical Engineering from Colorado State University and completed a postdoctoral fellowship at the University of Sheffield. His research spans biomedical sensor development, wearable health monitoring systems, and adaptive machine learning for medical diagnostics. Recent work includes EEG-based fatigue detection systems, anomaly detection in power grids, and optimization of online laboratory instruction during the pandemic. Publications highlight advancements in brain-computer interfaces (BCI), lightweight neural networks for P300 detection, and pedagogical strategies for remote engineering education. Beyette’s lab focuses on translating engineering innovations into clinical and industrial applications through interdisciplinary collaboration.