Prof. Kui Wu is a Professor in the Department of Computer Science at the University of Victoria, affiliated with the Faculty of Engineering and Computer Science. His research focuses on computer networks, wireless and mobile networking, mobile computing, and network security. He is part of the Parallel, Networking and Distributed Computing (PANDA) research group. Key areas of expertise include distributed learning frameworks, autonomous systems, IoT anomaly detection, and edge computing architectures. His work integrates machine learning techniques with network optimization, addressing challenges in real-time systems, security, and resource allocation. Notable contributions include advancements in federated learning, privacy-preserving distributed systems, and UAV-based monitoring solutions. Prof. Wu's research also explores edge computing innovations, such as smart contract-aided IoT resource sharing and energy-efficient edge data centers. He has contributed to over 50 peer-reviewed publications, with recent work emphasizing AI-driven network design, anomaly detection in IoT, and reinforcement learning applications in autonomous driving safety. His research has practical implications for improving the reliability and efficiency of next-generation communication and computing infrastructures.
Sabur H Baidya is an Assistant Professor in the Department of Computer Science and Engineering at the University of Louisville's J.B. Speed School of Engineering. He leads the Autonomous Intelligent Mobile Systems Lab (AIMS Lab) and is affiliated with the Louisville Automation & Robotics Research Institute (LARRI). His research focuses on autonomous systems, cyber-physical systems, IoT, edge computing, and distributed intelligence. Education: B.S. in Communication Engineering, West Bengal University, 2007 M.S. in Computer Science, University of Texas at Dallas, 2013 Ph.D. in Computer Science, University of California, Irvine, 2019 Research Interests: His work integrates sensing, communication, and computing systems to develop intelligent distributed systems. Key areas include IoT security, optimization of edge computing architectures, and autonomous drone/robotics applications. He explores machine learning techniques for resource-constrained environments and cybersecurity in containerization platforms. Awards & Recognition: No specific awards listed, though his research has been published in top-tier venues across autonomous systems, IoT, and cybersecurity domains. Advising & Grants: While no student names or grant details are provided, his lab actively engages in collaborative projects with industry partners like Nokia Bell Labs and Huawei, and academic collaborations at Rutgers WINLAB and UC San Diego. Labs & Teams: Directs the AIMS Lab, collaborating with the Louisville Automation & Robotics Research Institute (LARRI) to advance robotics and automation technologies. His work also intersects with the Jacobs School of Electrical and Computer Engineering (UCSD) and WINLAB (Rutgers).
Jaan Raik is a Tenured Full Professor and Head of the Centre for Dependable Computing Systems at Tallinn University of Technology (TalTech), Department of Computer Systems. His research focuses on hardware reliability, fault tolerance in embedded systems, and secure computing architectures. Key areas include fault-resilient deep neural network accelerators, approximation methods for DNNs, and vulnerability assessment in hardware-software systems. He leads the Centre for Dependable Computing Systems, which explores advanced reliability engineering techniques for modern computing systems. His work integrates formal verification, machine learning, and hardware design to address challenges in embedded systems, RISC-V processors, and edge AI chips. Notable contributions include frameworks like Saffira for DNN accelerator reliability assessment and innovative methods for assertion-based verification. Research interests span fault diagnosis, approximation computing, and security-aware architectures. He frequently publishes at top venues such as IEEE European Test Symposium and DDECS, with recent focus on resilient neural networks, cache leakage detection (CLD), and hardware Trojan mitigation. Collaborations include industry partnerships for practical deployment of fault-tolerant systems and academic initiatives to standardize reliability assessment methodologies. His work bridges theoretical computer engineering with applied solutions for real-world systems.
Juan David Guerrero Balaguera is a Research Fellow at Politecnico di Torino's Department of Automatic Control and Computer Science (DAUIN), affiliated with the CAD group. He holds a Ph.D. in Computer and Control Engineering from Politecnico di Torino (2024), advised by Prof. Matteo Sonza Reorda and Prof. Ernesto Sanchez. His research focuses on dependable hardware for safety-critical systems, including GPU reliability, fault tolerance, AI accelerators, and functional in-field testing. Prior to his Ph.D., he earned a Master's (2017) and Bachelor's (2013) in Electronics Engineering from Universidad Pedagógica y Tecnológica de Colombia, where he taught digital design, embedded systems, and FPGA-based image processing from 2014 to 2020. His research interests span advanced FPGA design, computational arithmetic for AI, fault effects analysis in GPUs, and reliability assessment of neural networks. Notable contributions include methods for generating self-test libraries (STLs) for GPUs, evaluating fault impacts on TCUs, and enhancing CNN robustness via dropout layer optimization. Education: Ph.D. in Computer and Control Engineering, Politecnico di Torino (2024) M.S. in Electronics Engineering, Universidad Pedagógica y Tecnológica de Colombia (2017) B.S. in Electronics Engineering, Universidad Pedagógica y Tecnológica de Colombia (2013) He has received the Ph.D. Quality Award (2023 and 2024) from Politecnico di Torino and Best Paper recognitions at DATE 2023 and DDECS 2021. His work bridges theoretical fault models with practical GPU testing methodologies, emphasizing real-world applications in AI and edge computing. Current teaching roles include collaborating on GPU programming (Master's level) and computer sciences courses (Automotive Engineering). Research collaborations involve exploring reliability trade-offs in split-computing DNNs and developing fault-aware design flows for AI accelerators.
Yuke Wang is an incoming Assistant Professor at Rice University's Department of Computer Science starting Fall 2025. He earned his Ph.D. in Computer Science from the University of California, Santa Barbara (2024) and B.E. in Software Engineering from the University of Electronic Science and Technology of China (2018). His research focuses on optimizing deep learning systems through compiler and hardware co-design, with expertise in GPU acceleration, parallel computing, and distributed training. Education: Ph.D., UC Santa Barbara (2024); B.E., UESTC (2018). Professional experience includes postdoctoral research at Amazon AWS AI and internships at NVIDIA, Microsoft Research, and Alibaba DAMO Academy. Research interests include accelerating graph neural networks (GNNs), recommendation systems, and large language models (LLMs). He has developed frameworks like GNNAdvisor, MGG, and ZEN to enhance efficiency and scalability in deep learning workloads. His work has been recognized with awards such as the NVIDIA Graduate Fellowship and ACM PACT Student Research Competition. Awards include NVIDIA Graduate Fellowship (2022-2023), UCSB Dissertation Fellowship (2023), and multiple best paper nominations. He actively serves on program committees for top conferences like OSDI, ASPLOS, and ICS. Current hiring: Seeking graduate/undergraduate students for projects in deep learning systems, compiler optimization, and GPU acceleration. Collaborates closely with industry partners including NVIDIA and Amazon.
Yakun Sophia Shao is an Associate Professor in the Department of Electrical Engineering and Computer Sciences (EECS) at the University of California, Berkeley. She holds a Ph.D. (2016) and M.S. (2014) in Computer Science from Harvard University, alongside a B.E. in Electrical Engineering from Zhejiang University, China. Her research focuses on computer architecture , particularly domain-specific accelerators , heterogeneous systems , and agile VLSI design methodologies . Key research centers: Agile Design of Efficient Processing Technologies (ADEPT), Berkeley Emerging Technologies Research (BETR), Berkeley Wireless Research Center (BWRC), SpeciaLIzed Computing Ecosystems (SLICE) Her work explores hardware-software co-design for efficiency in AI and robotics, including projects like Simba (chiplet-based AI accelerators) and Virgo (GPU matrix units). Notable tools developed include WIICA (workload characterization) and Chipyard (SoC frameworks). Selected honors include: 2024 CRA-WP Anita Borg Early Career Award 2023 NSF CAREER Award 2022 IEEE TCCA Young Computer Architect Award 2022 Intel Rising Star Faculty Award She teaches courses including EECS 151 (Digital Design & ICs) and EECS 251A (Advanced Digital Design).
José Mairton Barros da Silva Jr. is an Assistant Professor in the Division of Computer Systems at Uppsala University, Sweden, starting April 2023. Prior to this, he was a Marie Skłodowska-Curie Postdoctoral Fellow jointly at Princeton University (Department of Electrical and Computer Engineering) and KTH Royal Institute of Technology (Division of Network and Systems Engineering). He holds a Ph.D. in Electrical Engineering and Computer Science from KTH, supervised by Carlo Fischione and Gábor Fodor, and BSc and MSc degrees in Telecommunications Engineering from the Federal University of Ceará, Brazil. Ph.D. in Electrical Engineering and Computer Science, KTH Royal Institute of Technology, 2019 MSc in Telecommunications Engineering, Federal University of Ceará, 2014 BSc in Telecommunications Engineering (with honors), Federal University of Ceará, 2012 His research lies at the intersection of wireless communications and machine learning, with a focus on federated learning, communication efficiency, full-duplex systems, millimeter-wave communications, and vehicular networks. He investigates how to optimize distributed machine learning over constrained wireless channels, leveraging techniques in signal processing, optimization, and network design to improve efficiency, fairness, and scalability. The recent publications highlight a strong trend toward integrating machine learning with wireless system design, particularly in enabling efficient federated learning over-the-air, reducing communication overhead via quantization and lazy aggregation, and enhancing full-duplex mmWave systems through low-resolution hardware and smart beamforming. His 2022 survey on Wireless for Machine Learning serves as a foundational reference in the field. He has been recognized with prestigious awards, including: Marie Skłodowska-Curie Fellowship (2022–2025) Grant from the Ericsson Research Foundation (2022) He has been actively involved in research leadership and dissemination, having served as Secretary for the Full-Duplex and Self-Interference Cancellation Emerging Technologies Initiatives (2018–2021), co-chaired workshops at IEEE GLOBECOM, and delivered tutorials on 'Wireless for Machine Learning' at major IEEE conferences (ICASSP, PIMRC, ICC, GLOBECOM). He has also taught in the KTH-Ericsson Data Science Micro Degree Program. His research has been supported by competitive grants and collaborative institutions including Ericsson, Princeton, and KTH. He contributes to open science through GitHub, where he shares code for full-duplex mmWave beamforming algorithms. He has been affiliated with research labs and teams at KTH, Princeton, GTEL (Brazil), and Rice University, working closely with leading experts in communication theory and machine learning. His future work is expected to advance intelligent, energy-efficient, and scalable wireless systems for distributed AI.
Dr. Vladimir Golkov is a Postdoctoral Researcher at the Technical University of Munich (TUM) within the School of Computation, Information and Technology, specifically in Informatics 9 (Computer Vision Group). He works under the supervision of Prof. Dr. Daniel Cremers and maintains an active research profile in deep learning applications for medical imaging and biomedical data analysis. His primary research interests focus on deep learning since 2014, with specialization in high-dimensional and geometric data structures, data-processing goals beyond supervised learning (including clustering and anomaly detection), and applications in biomedicine and physics. Dr. Golkov has successfully mentored students who have gone on to pursue PhD studies at prestigious institutions including TUM, LMU, Mila, ETHZ, Cambridge, and Stanford. Analysis of his recent publications reveals a strong trend toward medical imaging applications, particularly in MRI technology, where he combines deep learning approaches with traditional physics-based methods. His work frequently bridges the gap between theoretical machine learning advances and practical medical applications, with significant contributions to diffusion MRI, sequence optimization, and 3D data processing. He has also expanded into emerging areas including large language models for medical applications and equivariant deep learning architectures. Dr. Golkov receives grant support from the Deutsche Telekom Foundation and maintains an active publication record with numerous contributions to leading conferences including ISMRM, MICCAI, and NeurIPS workshops. His research demonstrates a consistent trajectory of innovation at the intersection of computer vision, deep learning, and biomedical applications. He is actively involved in teaching and mentoring, with students from his projects advancing to top PhD programs worldwide. His office is located at Boltzmannstrasse 3, 85748 Garching, Germany (Office: 02.09.061), and he can be contacted at vladimir.golkov@tum.de.
Rosario Milazzo is a PhD candidate (2022-2025) in Computer and Systems Engineering at the Department of Control and Computer Engineering (DAUIN), Polytechnic University of Turin, concurrently serving as an external lecturer and teaching assistant for Innovation Management and ICT Product Development in Management Engineering. His research centers on developing robust deep neural networks resilient to transient faults like radiation-induced errors in satellite and automotive systems. He earned Bachelor's (2020) and Master's (2022) degrees in Computer Engineering from the same institution, joining the GRAINS research group as a scholarship holder before commencing his NODES-funded PhD under advisors Lia Morra, Sophie Fosson, and Luca Sterpone. Research focuses on Artificial Intelligence with specialization in Computer Vision and fault-tolerant deep learning for critical infrastructure. His work integrates self-supervised learning, quantization, and architectural innovations to maintain accuracy/efficiency under hardware-induced faults in space and mobility applications, addressing real-world reliability challenges. Publication analysis (2020-2025) reveals a progression from agricultural photogrammetry to cutting-edge fault-tolerant AI, with recent works emphasizing medical imaging (mammography), satellite systems, and weather-event mitigation through graph/transformer architectures and self-supervised techniques. Funded by NODES (North West Digital and Sustainable), Milazzo contributes to the GRAINS research group's mission without current advisees. His scholarship position since 2022 supports collaborative development of intelligent systems for high-stakes environments. As a GRAINS (GRAphics and INtelligent Systems) member, he operates within a specialized environment advancing graphics, computer vision, and robust AI deployment, directly enabling his investigations into neural network resilience for satellite and automotive domains.
Prof. Dr. Matthias Rosenthal is a Professor of Multiprocessor and Real-Time Systems at the ZHAW School of Engineering, Zurich University of Applied Sciences (ZHAW), where he also serves as Head of the Research/Focus Area Realtime Platforms. He holds a PhD and MSc in Electrical Engineering from ETH Zurich (1993–1997). His research focuses on multiprocessor systems, hybrid multicore architectures, distributed signal processing, embedded GPU computing, and real-time embedded systems. Key projects include In-Flight GNSS Interference Detection, dAIrector (automated multi-camera live production), and novel AFM techniques for industrial quality control. He has led over 15 industry-focused projects, including collaborations with Innosuisse and companies like Harman International. His work emphasizes real-time systems, FPGA-GPU co-design, and embedded AI solutions. Education: PhD (ETH Zurich, 1997), MSc (ETH Zurich, 1993) Awards: CTI Startup Label (2005) Teaching: Lectures on digital systems, real-time computing, and information theory Notable contributions include advancements in embedded machine learning for food waste management, secure boot concepts for Zynq MPSoC, and low-latency wireless video systems. His research bridges theoretical computer engineering with practical industrial applications.
Luca Magri is a Professor of Scientific Machine Learning at Imperial College London's Department of Aeronautics (Faculty of Engineering), leading the MagriLab . He holds dual roles as Director of Research in Aeronautics and Director of the Research Centre in Data-Driven Engineering. He is also a Professor in Fluid Mechanics at Politecnico di Torino under the PNRR project on Twin Real-time digital twins. His research bridges physics-aware machine learning, data assimilation, and fluid mechanics, with applications in quantum computing, turbulence modeling, and thermoacoustics. Magri completed his PhD in Engineering at the University of Cambridge (2012-2015) and held postdoctoral roles at Stanford University (2015-2016). He has been a Royal Academy of Engineering Research Fellow (2016-2021), Simons Fellow (2022-2023), and Hans Fischer Fellow at TUM (2018-2021). He leads the Alan Turing Institute's Scientific Machine Learning group and collaborates with institutions like Pembroke College and the Isaac Newton Institute. His research interests span quantum reservoir computing, data-driven stability analysis, and real-time digital twins for combustion systems. Key projects include optimizing wind farm layouts and suppressing extreme events in chaotic flows. He has pioneered methods like Proper Latent Decomposition (PLD) and physics-constrained neural networks for turbulence reconstruction. Magri has secured funding from EPSRC, Royal Academy of Engineering, and EU initiatives. His group includes engineers, physicists, and computer scientists addressing net-zero challenges in aerospace propulsion and energy systems. Recent work focuses on quantum computing for nonlinear PDEs and AI-driven process design in manufacturing.
Sayed Ahmad Salehi is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Kentucky's College of Engineering, where he directs the Computing with Unconventional Technologies (CUT) Lab. His research spans energy-efficient VLSI circuits for deep learning, stochastic computing, and DNA-based molecular programming. His research focuses on unconventional computing paradigms to address limitations in traditional electronics. Key areas include stochastic and approximate computing for energy-efficient deep learning hardware targeting IoT and edge devices, and biomolecular computing using DNA for environments incompatible with silicon. His lab develops tools like FUNSC (stochastic computing) and FUNDNA (DNA computing) that enable mathematical function computation through novel encoding schemes. Analysis of his recent publications reveals strong emphasis on in-memory computing architectures (particularly for DRAM and phase-change memory), stochastic-to-binary conversion techniques , and DNA circuit synthesis for mathematical operations. His work bridges theoretical computer science with practical hardware implementations for resource-constrained environments. Award highlights include: BIOMOD competition silver award (2023) Best paper nomination at Asilomar Conference (2022) ESWEEK Student Travel Grant (2019) He actively mentors PhD and undergraduate researchers, with recent graduates joining companies like Applied Materials. His lab secures significant NSF funding for interdisciplinary projects in stochastic computing and DNA-based systems. Current initiatives include CUT Lab's Kentucky iGEM team and collaborations on photonics-integrated computing. The CUT Lab operates at the intersection of computer architecture, molecular biology, and signal processing, with research impacting edge AI hardware, biocompatible computing, and next-generation memory systems.
Suchendra M. Bhandarkar is a Professor in the School of Computing at the University of Georgia, affiliated with the Franklin College of Arts & Sciences. He holds courtesy faculty positions in the College of Engineering and the School of Environmental, Civil, Agricultural, and Mechanical Engineering. His research focuses on Artificial Intelligence, Computer Vision, Pattern Recognition, and Computational Biology. He has advised PhD students such as Anirban Mukhopadhyay and has been actively involved in interdisciplinary projects spanning robotics, coral reef mapping, and food safety automation. Education: Ph.D. and M.S. in Computer Engineering from Syracuse University (1985-1989), and B.S. in Electrical and Electronics Engineering from Indian Institute of Technology Bombay (1983). His funded projects include advanced GeoAI terrain analytics, underwater robotics, and AI-driven food safety systems. Notable grants include support from USDA, NIH, and the US Army. Research highlights include 3D coral reef mapping via SLAM, deep learning for food safety, and zero-shot learning algorithms. Key collaborations involve the USDA Agricultural Research Service, Sony Corporation, and international networks like CapsNet for medical imaging. His labs and teams focus on applying AI to environmental, biomedical, and agricultural challenges.
Murali Emani is a researcher affiliated with Argonne National Laboratory, IL, USA. His work focuses on High-Performance Computing (HPC), machine learning, and artificial intelligence acceleration. Emani holds a PhD in Computer Science from the University of Edinburgh (2015). He specializes in optimizing large-scale systems, including performance evaluation of AI accelerators, transformer models, and neural architecture search. His research bridges HPC infrastructure with AI applications, addressing challenges in resource allocation, inference efficiency, and cross-architecture benchmarking. Key contributions include frameworks for GPU memory optimization (XUnified) and holistic performance evaluation of large language models across diverse hardware (e.g., BaKlaVa, Centimani). He collaborates extensively with institutions like the University of Chicago, NVIDIA, and MLCommons on initiatives such as DeepSpeed4Science and GenSLMs for genome-scale language models. His work frequently appears in top venues like IPDPS, SC, and Euro-Par. Emani’s research emphasizes practical system-level innovations, with applications spanning bioinformatics (SARS-CoV-2 evolutionary analysis), protein design, and exascale computing. He advocates for FAIR principles in HPC data management and contributes to open benchmarks like MLPerf HPC.
Sarma Vrudhula is a Professor at the School of Computing and Augmented Intelligence, Arizona State University (ASU). He holds a Ph.D. in Electrical Engineering from the University of Southern California (1985). Previously, he was a professor at the University of Arizona and served as the founding director of the NSF UA/ASU Center for Low Power Electronics. He is an IEEE Fellow recognized for contributions to low-power and energy-efficient digital circuit design. Educations: Ph.D. Electrical Engineering, University of Southern California (1985) M.S. Electrical Engineering, University of Southern California (1980) Bachelor's in Mathematics (Computer Science and Mathematical Statistics), University of Waterloo, Canada (1976) Research Interests: His work focuses on design automation, energy management in digital systems, statistical analysis of process variations, threshold logic circuits, and emerging technologies. He has pioneered methodologies for low-power VLSI design, thermal management of multi-core processors, and hardware implementations of threshold logic using spintronic devices. Publications: His recent research includes scalable energy-efficient architectures for AI, in-memory computing, and reconfigurable threshold logic gates. Key topics span energy efficiency in edge computing, neuromorphic systems, and sustainable VLSI design. Awards: IEEE Fellow (2005) Best Paper Award (2008) for macro cell characterization methodology Service & Grants: He led the NSF IUCRC Consortium for Embedded Systems and served on editorial boards. His grants include projects on threshold logic synthesis, energy-aware embedded systems, and hardware acceleration for neural networks. Courses Taught: Algorithmic Foundations of CAD for Digital Systems Computer Architecture Discrete Mathematics for Engineers
