Ali Ghodsi is a Professor at the University of Waterloo and Director of the Data Science Lab, with affiliations at the Vector Institute. His research spans machine learning, deep learning, and artificial intelligence, with applications in natural language processing, bioinformatics, and computer vision. His group develops theoretical frameworks and algorithms for analyzing large-scale datasets, focusing on neural network architectures, knowledge distillation, and model efficiency. Current projects include deep learning for identity control, computational antibody design, and generative AI/large language models. Ghodsi has authored influential tutorials on diffusion models, graph neural networks, and large language models. Notable research contributions include computational methods for de novo peptide sequencing from mass spectrometry data, green simulation-assisted reinforcement learning, and efficient natural language processing models. His lab maintains collaborations with industry partners including Google, Amazon, and Roche.
HaoYu Wang is an Assistant Professor of Computer Science at SUNY Albany. His research focuses on parameter-efficient and data-efficient deep learning, particularly in natural language processing and machine learning, aiming to democratize AI access. He holds a Ph.D. from Purdue University's School of Electrical and Computer Engineering, a B.Eng. from the University of Electronic Science and Technology of China, and an MS from SUNY Buffalo. His work includes innovations like RoseLoRA (sparse low-rank adaptation for knowledge editing), LightLT (lightweight quantization for long-tail data), and FedKC (federated knowledge composition for multilingual NLU). He has received awards such as the Future Leaders in Data Science (2024) and Bilsland Dissertation Fellowship. Key research areas include parameter efficiency, cross-lingual NLU, and model fairness. Recent publications span topics like federated learning optimization, robust retrieval-augmented generation, and mitigating token overfitting in LLMs. He advises students on Ph.D. and intern roles, emphasizing CVs and research interests in applications.
Bruno Volckaert is a Professor in the Department of Information Technology at Ghent University and Senior Researcher at imec. He obtained his Master of Computer Science (2001) and PhD in Grid Computing Resource Management (2006) from Ghent University. His research focuses on distributed cloud systems for Smart Cities and Industry 4.0 applications. Volckaert's expertise spans: Reliable distributed cloud backend systems Autonomous optimization of cloud applications Cybersecurity through machine learning IoT data processing architectures Kubernetes-based container orchestration Edge-to-cloud continuum computing His publications demonstrate strong focus on: cloud-native technologies, Kubernetes optimization, cybersecurity frameworks, and distributed AI systems. Recent work emphasizes reinforcement learning for auto-scaling, secure edge computing, and intrusion detection systems. He has contributed to over 40 national/international research projects and authored 100+ publications. Current affiliations include leadership roles in: IDLab Research Unit (Ghent University) imec Research Center
Gang (Gary) Tan is a Professor at the Pennsylvania State University's College of Engineering, specializing in computer security, formal methods, and programming languages. He co-directs the Institute for Networking and Security Research (INSR) and leads the Security of Software (SOS) Group, focusing on compiler, programming language, and formal method techniques to enhance computer security. Education: B.E. in Computer Science from Tsinghua University Ph.D. in Computer Science from Princeton University His research integrates formal verification with practical security applications, particularly emphasizing: Compiler-based security enforcement Side-channel mitigation in speculative execution Fairness analysis in machine learning systems Formal grammar approaches for software reliability Key article trends show: Security-focused formal methods (15% of publications) ML fairness verification (20% of recent work) Compiler-based security solutions (30% of output) Side-channel defense mechanisms (25% of research) Parser design and formal grammar synthesis (10% of contributions) Scientific achievements include: NSF CAREER Award Google Research Awards (2x) PLDI 2024 Best Paper James F. Will Career Development Professorship Outstanding Research Award at Penn State Ruth and Joel Spira Excellence in Teaching Award Dr. Tan actively contributes to academic communities through: DARPA ISAT study group membership Program committee roles (CGO 2024, ECOOP 2018, etc) Leadership in security research initiatives
Lin Zhong is the Joseph C. Tsai Professor of Computer Science at Yale University, leading the Efficient Computing Lab. He holds a Ph.D. from Princeton University and M.S./B.S. degrees from Tsinghua University. Previously, he served at Rice University from 2005 to 2019. His research focuses on optimizing computing efficiency, quantum error correction, operating systems, and mobile systems. Education: Ph.D., Princeton University M.S., Tsinghua University B.S., Tsinghua University Research Interests: His work spans quantum computing (e.g., decoding algorithms for surface codes), operating systems (safety, correctness, and lightweight kernels), and mobile/networking systems (massive MIMO, energy-efficient designs). Recent trends include integrating large language models (LLMs) into robotics and securing cloud-based AI workflows. Awards: NSF CAREER Award ACM SIGMOBILE RockStar (2014) and Test of Time (2022) Fellowships from IEEE and ACM Best Paper Awards at ACM MobileHCI, IEEE PerCom, ACM MobiSys, and more Lab & Teams: His Efficient Computing Lab explores systems for quantum error correction (e.g., FPGA-based decoders), secure embedded systems, and LLM-driven robotics. Projects include TimelyLLM (real-time LLM serving) and Blindfold (confidential memory management).
Mahadev Satyanarayanan is the Jaime Carbonell University Professor of Computer Science at Carnegie Mellon University. His multi-decade research focuses on performance, scalability, availability, and trust in distributed systems spanning cloud to mobile edge computing. He pioneered foundational concepts in mobile computing and Edge Computing through his seminal work on VM-based cloudlets. His current research explores cloudlet-based Edge Computing for latency-sensitive applications, wearable cognitive assistance systems integrating augmented reality, and edge-based machine learning frameworks for efficient training data discovery. He collaborates with Dan Siewiorek, Martial Hebert, and Bobby Klatzky on transformative applications. Dr. Satyanarayanan received his PhD from Carnegie Mellon University after completing Bachelor's and Master's degrees at the Indian Institute of Technology, Madras. His honors include ACM and IEEE Fellowships recognizing his contributions to distributed systems and mobile computing. ACM Fellow IEEE Fellow
Diogo Carbonera Luvizon is a Researcher at the Max-Planck-Institut für Informatik (MPI-INF) in Saarbrücken, Germany, and a member of the Visual Computing and Artificial Intelligence (VIA) Research Center. He holds a PhD in Computer Vision and Machine Learning from CY Cergy Paris University (2019), and Bachelor's and Master's degrees in Engineering and Applied Computing from UTFPR, Brazil. His research focuses on solving complex problems in Computer Vision, Computer Graphics, and Deep Learning, particularly in human modeling and real-time systems. Education: PhD (2019) - CY Cergy Paris University; M.Sc. (2015) - UTFPR; B.Sc. (2011) - UTFPR. Research interests include 3D human pose estimation, action recognition, multitask learning, and novel view synthesis. He has contributed to patents on multiplane image generation (Samsung) and vehicle speed measurement systems. His work has been recognized with awards like the Best Paper Honorable Mention at GCPR-VMV 2022 and Best Presentation Award at ETIS Lab (2018). He has developed open-source tools, including the deephar repository for human action recognition and pose estimation. His current affiliations include MPI-INF and the VIA Research Center, a partnership between MPI-INF and Google.
Dr. Krishnendu Guha is an Assistant Professor and CONNECT Funded Investigator at the School of Computer Science and Information Technology, University College Cork. His research bridges embedded systems, cybersecurity, and quantum-safe hardware design with AI and bio-inspired strategies. PhD: University of Calcutta (Department of Science and Technology, Government of India) Postdoctoral: University of Florida Past Roles: Research Fellow at Intel India, Visiting Scientist at Indian Statistical Institute, Temporary Assistant Professor at NIT Jamshedpur His research focuses on embedded systems security , real-time security mechanisms , and quantum-safe hardware . He integrates AI (e.g., neural networks) and bio-inspired strategies (e.g., gecko crypsis behavior) into security frameworks for FPGAs and edge platforms. Recent publications highlight trends in blockchain for supply chains , quantum machine learning , secure FPGA architectures , and distributed AI systems . His work addresses energy efficiency, fault detection, and decentralized security in hardware. As a CONNECT Centre member, Dr. Guha contributes to advanced research in reconfigurable systems and cybersecurity. Grants and collaborations span quantum-safe design, cloud FPGA security, and hardware trojan mitigation.
Arie E. Kaufman is a Distinguished Professor in the Department of Computer Science at Stony Brook University, serving as Chief Scientist of the Center of Excellence in Wireless and Information Technology (CEWIT) and Director of the Center of Visual Computing (CVC). He additionally holds a Distinguished Professorship in Radiology, with a 40+ year career at Stony Brook since joining in 1985 and chairing the CS department from 1999-2009. His seminal research spans computer graphics, visualization, and virtual reality with biomedical applications, pioneering breakthroughs including 3D Virtual Colonoscopy (FDA-approved colon cancer screening), Cube hardware architectures (commercialized as VolumePro), the Reality Deck (1.5 billion-pixel immersive display), and foundational work in volume visualization. His interests focus on real-time rendering, medical imaging, and immersive analytics, with recent work integrating machine learning for healthcare and environmental risk visualization. Recent publications demonstrate continued leadership in high-resolution immersive displays (Silo), XR analytics with LLMs, storm surge visualization, and neural reconstruction techniques. His work bridges theoretical innovation with practical applications, particularly in pancreatic cancer prognosis and disaster preparedness. Major honors include: IEEE Visualization Career Award (2005) Fellow of the National Academy of Inventors (2017) ACM Fellow (2009) IEEE Fellow (1998) Long Island Technology Hall of Fame (2013) European Academy of Sciences membership (2002) As PI on 100+ research grants, Kaufman's work has generated 300+ refereed papers, 40+ patents, and extensive media coverage (New York Times, Science, Wall Street Journal). He leads the Center of Visual Computing with focus on translational research, including VolVis software (5,000+ installations) and Reality Deck deployments for big data analytics. His lab develops cutting-edge visualization infrastructure for medical diagnostics and environmental modeling, with current projects advancing immersive storm surge analytics, neural structure extraction, and VR-based risk communication systems. Future work emphasizes AI-enhanced visualization for precision medicine and climate resilience planning.
Shahid Raza is a Professor of Cybersecurity at the University of Glasgow's School of Computing Science. He previously led the RISE Cybersecurity Unit in Sweden, establishing it as a leading research group. His expertise spans IoT Security, PKI, AI-driven cybersecurity solutions, and hardware/data security. Raza holds a PhD and Docentship from Uppsala University, alongside a Bachelor's with a Gold Medal for academic excellence. Education: B.Sc. (Computer Science, 3.99/4.0 CGPA, Gold Medal), Licentiate, PhD, and Docentship in Cybersecurity from Sweden. He leads EU-funded projects like H2020 CONCORDIA and Horizon Europe CUSTODES, coordinating initiatives such as the Cyber Node and Cyber Range. Active in cybersecurity policy, he serves on the EU SCCG, ECSO, and EARTO Security & Defence Research working groups. Research Interests Public Key Infrastructure (PKI) for IoT AIAgent-Driven Cybersecurity Solutions IoT Certification Standards Hardware Security for Low-Power Devices Grants & Projects Coordinator: Horizon Europe CUSTODES Technical Leader: H2020 Arcadian-IoT Founder: RISE Cyber Range (Sweden's largest cybersecurity test facility) Awards & Memberships IEEE Senior Member Gold Medal for Academic Excellence (Bachelor's)
Hassan Foroosh is a Professor in the Department of Electrical Engineering and Computer Science at the University of Central Florida (UCF), directing the Computational Imaging Laboratory (CIL). He holds a Ph.D. in Computer Science from INRIA-UNSA, France (1996). Prior to UCF, he worked as a Senior Research Scientist at UC Berkeley (2000–2002) and an Assistant Research Professor at the University of Maryland, College Park (1997–2000). Research Interests: His work focuses on Computer Vision, Image Processing, Machine Learning, and Signal Processing. Notable contributions include LiDAR-based perception, adversarial attacks on detectors, medical imaging analysis, and dataset design for action recognition. His research is supported by NASA, NSF, ONR, and industry partners. Publications & Impact: Over 130 peer-reviewed papers, including influential work on super-resolution techniques, transformer networks for 3D object detection, and adversarial machine learning. His recent work explores analytical reasoning in LLMs and multimodal fusion in sports analytics. Awards: Pierro Zamperoni Award (2004), Best ICPR Paper (2004), Sun Microsystems Academic Excellence Award (2004). Labs/Teams: Director of the Computational Imaging Lab (CIL), UCF. Grants: Active funding from NASA, NSF, and industry collaborators.
Salman Durrani is a Professor and Associate Director Education at the School of Engineering, Australian National University. He holds a PhD in Electrical Engineering from the University of Queensland and a BSc (First Class Honours) from the University of Engineering & Technology, Lahore, Pakistan. His research spans Internet of Things networks, satellite/UAV communications, machine learning applications in wireless systems, early wildfire detection, and backscatter communications. Current projects focus on terahertz communication security, UAV-assisted networks, and IoT-based environmental monitoring. His recent publications demonstrate strong emphasis on wireless security, terahertz technology optimization, UAV network design, and IoT applications for environmental protection. Technological innovations include novel beamforming techniques and lightweight authentication protocols. AI 2000 Internet of Things Most Influential Scholar (2020, 2022, 2023) IEEE ComSoc Asia Pacific Outstanding Paper Award (2016) Australian Council of Graduate Research Excellence Award (2019) ANU Vice-Chancellor's Awards for Supervision (2018) and Education (2012) He has supervised 15 PhD students to completion and secured $1.9M in research funding as chief investigator for six grants. Current projects include participation in the ANU Optus Bushfire Research Centre of Excellence.
Chris Fuller, Ph.D., is the Samuel Langley Distinguished Professor of Engineering at the College of Engineering , Virginia Tech. He leads the Vibrations and Acoustics Laboratory (VAL) , focusing on active/passive noise control systems, metamaterials, and their application to aerospace, medical devices, and industrial machinery. Education: Ph.D. (1979) and B.E. (1974) from the University of Adelaide, Australia. Research Interests: Structural acoustics, adaptive materials, machine learning in noise prediction, and biomedical acoustics (e.g., neonatal incubators). Awards: ASME Rayleigh Award (2017), NASA Team Achievement Award (1996), and Fellow of the Acoustical Society of America. Recent Publications: Highlight advancements in drone noise reduction using neural networks, metamaterials for HVAC systems, and poro-elastic materials for low-frequency noise control.
Professor Li Hui serves as the executive dean of the School of Network and Information Security at Xidian University, where he holds the position of second-level professor and doctoral supervisor. He is nationally recognized as a distinguished teacher and serves in multiple prestigious roles including member of the National Steering Committee for Postgraduate Education in Cryptography, inaugural president of ACM SIGSAC CHINA, and director of several major academic societies related to cryptography and information security. Professor Li's research spans cryptographic information security, privacy computing, information theory, and coding theory, with significant contributions to network and cyberspace security. His work demonstrates a strong focus on both theoretical foundations and practical applications, particularly in developing security protocols for emerging technologies like blockchain, federated learning systems, and IoT environments. His research output shows consistent innovation in balancing security requirements with computational efficiency across diverse application domains. With over 300 publications and more than 15,000 Google Scholar citations (H-index 60), Professor Li's scholarly impact is substantial. His recent publications demonstrate increasing emphasis on privacy-preserving machine learning, secure multi-party computation, and cryptographic protocols for distributed systems, reflecting the evolving security challenges in the AI era. Three second-class national teaching achievement awards Special prize and first-class national teaching achievement awards Four first-class provincial and ministerial science and technology progress awards Privacy Computing Theory award (Qian Weichang Chinese Information Processing Science and Technology Award) Multiple patents with over 80 granted inventions Professor Li leads the Cyber Changan Team and serves as head of the Shaanxi Provincial Innovation Team for Mobile Internet Security. He has successfully supervised numerous doctoral and master's students who have gone on to win prestigious competitions like the National College Student Information Security Competition. His research is supported by major national grants including a National Key R&D Program project and key projects from the National Natural Science Foundation of China.
Ikjot Saini is a Professor at the University of Windsor’s Faculty of Engineering, co-leading the SHIELD Automotive Cybersecurity Centre of Excellence, Canada’s first organization addressing threats in connected transportation. Her research focuses on automotive cybersecurity, vehicular networks, and privacy-preserving technologies. She has supervised doctoral students Shiva Nejati and Kunj Dhonde, and contributed to courses in the University’s Continuing Education program, specializing in cybersecurity education for professionals. Her work includes pioneering studies on blockchain-based security for connected autonomous vehicles (CAVs), machine learning-driven intrusion detection systems, and privacy-enhancing mechanisms like pseudonym-changing strategies. She has been recognized with the K.W. Michael Siu Award from the APMA Institute for Automotive Cybersecurity (2020). Saini’s research bridges theoretical advancements with real-world applications, ensuring vehicles and infrastructure remain secure against evolving cyber threats. Her contributions span academic publications, industry partnerships, and policy recommendations, positioning her as a leader in vehicular cybersecurity. Ongoing projects emphasize eco-efficiency in cybersecurity solutions and adversarial modeling for privacy evaluation.
