Steve Hailes is a Professor of Wireless Systems at the Department of Computer Science, University College London. He has served as Head of Department since 2019 and Deputy Head from 2005. His research spans wireless networks, computational trust, AI, and sensor systems for health, ecological, and environmental applications. Education: PhD and undergraduate degree from Cambridge University Appointment: Joined UCL in 1991 (postdoc), Lecturer in 1992 Research interests include: Trust and security in networked systems (co-founder of computational trust) Security of industrial control systems AI/ML applications in security and causal discovery Multi-agent reinforcement learning and moral behavior modeling Gas sensor fabrication and deployment for diverse applications Recent publications focus on feature selection for cybersecurity , moral alignment in LLM agents , trust-based consensus algorithms , and causal discovery using reinforcement learning . Collaborations include co-authors like Westphal, Musolesi, and Tennant. Applications span healthcare (dementia, JIA), ecology (endangered species in Botswana), and environmental monitoring (CO distribution, meth lab detection).
Xugui Zhou is an Assistant Professor jointly appointed in the Department of Electrical and Computer Engineering and the Department of Computer Science at Louisiana State University (LSU). He holds a Ph.D. in Electrical and Computer Engineering from the University of Virginia (2024), where he worked under Prof. Homa Alemzadeh. Prior to his Ph.D., he earned an M.Eng in Control Science and Engineering and a B.Eng in Automation from Shandong University. Before joining UVA, he was a Senior Engineer at NR Research Institute, State Grid of China. His research focuses on the intersection of system dependability, security, and control theory, with applications in autonomous systems and healthcare. Key interests include engineering resilient autonomous vehicles, mitigating adversarial attacks on cyber-physical systems (CPS), and enhancing safety through formal methods and machine learning. He leads a research lab with openings for Ph.D. students and interns. Zhou is actively involved in academic service, serving on program committees for DSN'26, AsiaCCS'26, and ICCPS'25, among others. He also chairs workshops like DSML'25 and organizes conferences such as ICCPS'24. His recent awards include the UVA Engineering Endowed Graduate Fellowship (2023) and selection as a Rising Star in Cyber-Physical Systems (2023). His teaching includes courses like AI-Driven Autonomous Driving Systems and ML for CPS Security at LSU. He has extensive experience in teaching and research supervision, emphasizing hands-on projects and interdisciplinary collaboration.
Angel Xuan Chang is an Associate Professor at Simon Fraser University's School of Computing Science, where she leads research at the intersection of natural language processing, computer vision, and 3D scene understanding. She holds the prestigious Canada CIFAR AI Chair position and is affiliated with multiple research groups including 3DLG, GrUVi, SFU NatLang, SFU AI/ML, and VINCI. PhD in Computer Science, Stanford University MSc in Computer Science, Stanford University M.Eng in Electrical Engineering and Computer Science, MIT BSc in Computer Science and Engineering, MIT Professor Chang's research primarily focuses on connecting language to 3D representations of shapes and scenes, with particular emphasis on grounding language for embodied agents in indoor environments. Her work spans natural language processing and understanding, linking natural language with visual and 3D representations, multimodal grounding of language, embodied AI, and machine learning applications for biodiversity monitoring through the BIOSCAN project. She has developed methods for synthesizing 3D scenes and shapes from natural language and created various datasets for 3D scene understanding. Her recent publications reveal a strong trend toward integrating language understanding with 3D scene generation and manipulation, with increasing focus on practical applications in embodied AI and biodiversity monitoring. The research shows progression from foundational work on text-to-3D scene generation to more sophisticated approaches for evaluating semantic coherence in generated scenes and developing efficient methods for zero-shot scene modeling. Canada CIFAR AI Chair TUM-IAS Hans Fischer Fellow (2018-2022) Best paper award at 3DV 2025 for 'An Object is Worth 64x64 Pixels: Generating 3D Object via Image Diffusion' Professor Chang actively advises numerous graduate students who appear as first authors on her publications, indicating a strong mentoring program. Her research is supported through multiple channels including the CIFAR AI Chair position and likely various research grants supporting her BIOSCAN-related work and 3D scene understanding projects. She has been involved in organizing multiple workshops at major conferences including ICML, CVPR, and ICLR. Her research is conducted through several interconnected groups: 3DLG (3D Language and Graphics), GrUVi (Graphics, Vision, and Interaction), SFU NatLang (Natural Language Processing), SFU AI/ML, and VINCI. These groups work collaboratively on problems spanning language grounding, 3D scene understanding, embodied AI, and biodiversity applications, creating a rich interdisciplinary research environment.
Professor Alessandra Russo leads the Structured and Probabilistic Knowledge Engineering (SPIKE) research group at Imperial College London's Department of Computing. With expertise spanning computational logic, symbolic machine learning, and neuro-symbolic AI, she develops foundational AI techniques applied to security, network management, healthcare, and adaptive systems. Professor Russo holds a PhD in Computing from Imperial College London and an MSc in Computer Science from Ionian University. Her research pioneers logic-based learning systems for intelligent adaptive technologies, with projects including declarative networking for security management, privacy-preserving federated learning, and hybrid neuro-symbolic approaches for robust reasoning. Her current work focuses on developing interpretable AI systems through neuro-symbolic integration, creating frameworks that combine neural networks with symbolic reasoning for explainable decision-making. Recent publications explore rule learning from knowledge graphs, transformer-based world models, and formal methods for representation learning. Professor Russo teaches courses on Logic-Based Learning and AI Applications, and has received the Google PhD Fellowship for her research contributions. She mentors numerous PhD students in areas spanning theoretical foundations and practical applications of computational logic and machine learning.
Professor Jun Zhang is a leading academic in cybersecurity at Swinburne University, Australia, where he directs the Cybersecurity Lab. He has been honored as Australia's top cybersecurity researcher and instrumental in establishing Swinburne as a globally recognized cybersecurity research institution. His work includes high-impact papers and multi-million-dollar R&D projects, culminating in awards like the 2021 'Top Cybersecurity Research Institution' accolade. As course director of the Bachelor of Cyber Security, he pioneered an industry-driven teaching model with Deloitte and CSIRO, significantly boosting course enrollment. His collaborations extend to Adobe's Curriculum Innovation Program and the Australian P-TECH initiative, promoting STEM education and cybersecurity awareness. He supervises doctoral candidates and leads grants focused on AI-driven cybersecurity, smart home security, and blockchain-based edge computing. His research spans vulnerability detection, GAN forensics, IoT security, and privacy preservation in OSNs. Research interests include cybersecurity fundamentals, data science applications, and distributed systems. Notable achievements include the PTFix framework for Java vulnerabilities, the IoTFuzz smart home testing system, and CTI mining methodologies. Awards reflect his mid-career research excellence and industry partnerships. His grants with CSIRO and defense organizations emphasize real-world impact, addressing challenges from malware detection to adversarial machine learning. The Cybersecurity Lab and collaborative projects like Artchain demonstrate his commitment to bridging academia and industry. Professional activities include supervising over 20 HDR students and securing grants totaling millions. His work on blockchain-based edge storage (CSEdge) and SDCCP congestion control highlights innovation in networking. Future directions include advancing AI for design collaboration with CSIRO and enhancing privacy in smart energy technologies. His contributions span technical, educational, and community outreach domains, positioning him as a pivotal figure in cybersecurity's evolution.
Dr. Kanchana Thilakarathna is a Senior Lecturer in Distributed Computing at the University of Sydney's School of Computer Science, and a member of the Centre for Distributed and High Performance Computing. They hold a PhD from the University of New South Wales (UNSW) and a B.Sc. Eng (Hons) from the University of Moratuwa, Sri Lanka. Prior to academia, they worked as a Research Scientist at CSIRO/Data61 and had industry experience as a Mobile Radio Network Engineer. Research Interests : Dr. Thilakarathna focuses on cybersecurity, privacy in mobile and IoT systems, mixed reality privacy, and distributed computing platforms. Their work emphasizes user-centric solutions like the Yalut social media app, which enables decentralized data sharing. Key themes include privacy-preserving techniques, edge computing, and secure federated learning frameworks. Recent Work : Recent articles (2023–2025) explore machine unlearning for large language models, federated learning security, and IoT network slicing using P4 programmability. Their work on synthetic video traffic generation (VideoTrain++) and drone detection (DronePrint) demonstrates cross-disciplinary innovation. Awards : Malcolm Chaikin Prize (2015), Meta Research Awards (2020/2022), and Heidelberg Laureate Fellowship (2019). Grants : ARC Research Hub for Future Digital Manufacturing (2024), NSW Defence Innovation Network Projects (2024/2021), and Facebook Research Awards (2022/2020). Students : Advising 4 current PhD students on topics like wireless trust establishment and machine unlearning. Labs/Teams : Part of the Centre for Distributed and High Performance Computing and Sydney Nano Institute.
Hima Lakkaraju is an Assistant Professor at Harvard University with dual appointments in the Harvard Business School and the Department of Computer Science. Her research focuses on trustworthy AI, including machine learning interpretability, fairness, privacy, and safety. She holds a PhD from Stanford University and has received accolades such as the Alfred P. Sloan Fellowship and NSF CAREER Award. Her work bridges algorithmic foundations and societal implications of AI, with applications in healthcare, policy, and business. Education: PhD in Computer Science from Stanford University (2013-2017). Academic background includes roles at IBM Research, Microsoft Research, and Adobe. Research Interests: Algorithmic Foundations of AI Interpretability and Explainable AI Fairness and Bias Mitigation Privacy-Preserving ML Generative Models and LLMs Ethical AI Policy and Regulation Key Achievements: Over 100 publications in top venues like NeurIPS and ICML; co-founder of the Trustworthy ML Initiative; featured in MIT Tech Review, Forbes, and Harvard Business Review. Current projects include the AI4LIFE research group and work on regulatory frameworks for AI. Advising and Grants: Supervises over 30 students across PhD, master's, and postdoc levels. Research supported by NSF, Sloan Foundation, Schmidt Sciences, Google, Amazon, and others. Initiatives include the Regulatable ML workshop and NeurIPS ethics co-chair roles. Labs and Collaborations: Leads Harvard's AI4LIFE group and collaborates with industry partners like Fiddler AI. Active in policy discussions on AI regulation and societal impact.
Prof. Dan Jiao is the Synopsys Professor of Electrical and Computer Engineering at Purdue University's Elmore Family School. She leads the Rapid-Heterogeneous Integration (Rapid-HI) Design Institute and serves as Editor-in-Chief of the IEEE Journal on Multiscale and Multiphysics Computational Techniques. Her research focuses on computational electromagnetics, multiphysics modeling, and AI-driven design automation for advanced integrated circuits and quantum systems. She has held academic positions since 2005, progressing from Assistant to Full Professor, and has extensive industry experience at Intel Corporation (2001–2005). Education: PhD in Electrical Engineering, University of Illinois at Urbana-Champaign (2001) Senior Staff Engineer at Intel Corporation (2001–2005) Research Interests: Fast numerical methods for large-scale electromagnetic analysis AI/ML integration in design automation (EDA/MDA) Quantum circuits and spin qubit systems Heterogeneous integration and advanced packaging Multiphysics co-simulation for nano-scale devices Signal/power integrity in high-speed systems Key Projects: Leads the NSTC AIDRFIC program (first NSTC R&D Jump Start project), the DARPA NGMM Rapid-HI Design Institute, and the GENIE-RFIC generative design tool initiative. Also directs the Consortium for Electromagnetic Science and Technology. Awards & Honors: 2022 ACES Computational Electromagnetics Award IEEE Fellow (2016) Intel Outstanding Researcher Award (2019) MTT-S Distinguished Microwave Lecturer (2020–2023) 2013 Schelkunoff Prize Paper Award Advising & Grants: Advised over 30 PhD/master's students and led projects funded by NSF, DARPA, Intel, SRC, and industry partnerships. Key grants include NSF CAREER (2008), ONR Young Investigator (2006), and multiple industry-sponsored initiatives. Labs & Teams: Rapid-HI Design Institute (DARPA NGMM) Quantum device co-design group Multiphysics modeling team
Mohammadreza Karamad is an Assistant Professor in the School of Sustainable Energy Engineering at Simon Fraser University (SFU), with a joint appointment in the Sustainable Energy Engineering department. His research focuses on computational materials discovery, leveraging quantum-mechanical methods (e.g., DFT) and machine learning (ML) to design advanced energy materials for clean technologies like hydrogen storage and catalysis. He holds a Ph.D. from the Technical University of Denmark (DTU) and completed postdoctoral research at Stanford University. His academic background includes leadership roles in the CMD Lab (Computational Materials Discovery), where he explores novel materials for electrochemical energy conversion processes. Key research areas include electrochemistry, heterogeneous catalysis, and material science, with a particular emphasis on CO2 reduction, ammonia synthesis, and sustainable energy storage solutions. Dr. Karamad collaborates with industry and academic partners to advance materials discovery through high-throughput computational screening and AI-driven approaches. He actively seeks motivated students (undergraduate and graduate) to join his research program, focusing on developing next-generation energy materials. His lab is located in room B8220, and he can be reached at mkaramad@sfu.ca. Notable technical contributions include pioneering work on transition metal nitrides for CO2 reduction, single-atom catalysts for ammonia synthesis, and machine learning frameworks for predicting material properties. His research bridges fundamental theory with practical applications, addressing global challenges in sustainable energy and environmental technology.
Dr. Vishal Sharma is a Senior Lecturer in the School of Electronics, Electrical Engineering and Computer Science at Queen's University Belfast. His research focuses on Cyber-Physical Systems (CPS), 5G/6G Security, Unmanned Aerial Vehicles (UAVs), Blockchain, and Digital Twins. He has held roles at institutions like Singapore University of Technology and Design (SUTD) and Soonchunhyang University, South Korea. Notable achievements include Best Paper Awards at ICCMIT 2017, IEEE SITE 2024, and HUCAPP/VISIGRAPP 2025. He leads the Innovation-by-Design Lab and is a Fellow of the Higher Education Academy (FHEA). Research Interests: Cyber Defence, UAV Security, Secure Computing, Network Security, and Sustainable Edge Computing. He has collaborated on projects like RapidRANDefender (QRICSec) and Traceable Procurement for Net-Zero Processes. Awards include the Royal Society International Exchanges Committee appointment (2025) and QUB's Individual Performance Award (2024). Grants and Projects: Principal Investigator for projects such as Exploring Operational Capabilities of Arm Morello for UAV Security (2023) and TUDOR: Ubiquitous 3D Open Resilient Network (2023). Active in editorial roles for IEEE Communications Magazine and IET Networks. His work aligns with UN Sustainable Development Goals (SDGs) related to climate action and innovation. Publications span 150+ articles in top journals/conferences, with a focus on secure communication, edge computing, and UAV networks. Supervises PhD students in cyber defence, AI security, and distributed ledger technologies.
Zhibo Pang is an Adjunct Professor at KTH Royal Institute of Technology's Department of Intelligent Systems (EECS) and Senior Principal Scientist at ABB Corporate Research Sweden. His work focuses on digital transformation in industry and healthcare, spanning robotics, AI, control systems, and wireless communication. He leads projects in embodied intelligence, Industry 4.0, and Healthcare 4.0, with 23 granted patents and over 120 journal papers. Education: PhD in Electronic and Computer Systems (KTH, 2013), MBA in Innovation & Growth (University of Turku, 2012). Key Roles: IEEE Technical Committee Chair, Editor of 6 IEEE journals, ABB Inventor of the Year (2016, 2018, 2021). Research Interests: Robotics safety, wireless automation, federated learning, digital twins, and IoT security. Recent Projects: Cloud-fog automation frameworks, robot skin systems for healthcare, and latency-aware industrial control. His work bridges academia and industry through cross-functional collaborations.
Zsolt Kira serves as an Assistant Professor in the School of Interactive Computing at Georgia Institute of Technology's College of Computing, with additional affiliations at the Georgia Tech Research Institute and as Associate Director of ML@GT. He leads the Robotics Perception and Learning (RIPL) Lab, driving research at the intersection of machine learning and robotics. Dr. Kira earned his Ph.D. in 2010 under Professor Ron Arkin, establishing foundational expertise in robotics and AI before transitioning to his current academic role after industry experience at SRI International Sarnoff. His research pioneers beyond supervised learning through unsupervised, semi-supervised, self-supervised, and continual/lifelong learning frameworks, while advancing distributed perception via multi-modal fusion and cross-robot information integration. This dual focus addresses core challenges in robotic autonomy and sensor processing. Analysis of his 15 most recent publications reveals dominant trends in embodied AI systems, robust foundation model adaptation, and multimodal learning architectures. Key themes include neural radiance field applications, reinforcement learning for locomotion, and novel benchmarks for memory evaluation in agents. While specific advisees and grants aren't documented in the source material, his RIPL Lab leadership implies active graduate mentorship and research funding acquisition. The lab's work directly enables next-generation robotic systems through algorithmic innovation in perception and learning. The RIPL Lab operates as a hub for developing machine learning techniques that solve difficult perception problems in robotics, with particular emphasis on unsupervised learning paradigms and distributed multi-robot systems that push the boundaries of autonomous operation.
Dr. Abdelhak Bentaleb is an Assistant Professor in the Department of Computer Science and Software Engineering at Concordia University and founder/director of the IN2GM Lab. His research focuses on optimizing networked multimedia systems using machine learning, with emphasis on video streaming, edge computing, and 5G/6G networks. He holds a PhD from the National University of Singapore (awarded SIGMM and DASH-IF Best Thesis prizes) and completed a postdoctoral fellowship there. Education: PhD in Computer Science, National University of Singapore (2019) Postdoctoral Research Fellowship, National University of Singapore (2019-2022) Research Interests: AI-driven video streaming optimization, low-latency media delivery, network protocols, immersive media technologies, and IoT systems. Current projects explore end-to-end AI-enabled systems for QoE optimization in video delivery using reinforcement learning and deep learning techniques. Awards: SIGMM Award for Outstanding PhD Thesis DASH Industry Forum Best PhD Dissertation Award Multiple DASH-IF Excellence Awards His work includes over 50 publications in top venues (e.g., ACM MMSys, IEEE INFOCOM, USNIX NSDI) and 3 patents. The IN2GM Lab focuses on applied AI/ML solutions for networked systems challenges.
Jan Akmal is an Assistant Professor at Aalto University, holding dual affiliations in the Department of Energy and Mechanical Engineering and the Materials to Products group. His research specializes in additive manufacturing (AM), focusing on defect detection, smart materials, and 4D printing applications. He leads the AIM-Zero project (2023–2026), exploring AI-driven zero-defect AM processes. Akmal has received the Aalto Doctoral Incentive Scholarship (2023) and an Honorary Award (2023). He serves on editorial boards for Frontiers in Manufacturing Technology and Frontiers in Mechanical Engineering , and chairs the Finnish Rapid Prototyping Association (FIRPA). Key research areas include AI-based defect detection in metal AM, self-sensing components, and hybrid materials for dynamic displays. He collaborates globally on topics like optical tomography in powder bed fusion and medical AM applications. His work addresses sustainability, industrial adoption of AM, and legal frameworks for military logistics. Akmal has authored 24 publications and contributed to datasets on AM inaccuracies and defect classification, emphasizing practical applications and industry integration.
Xilin Liu is an Assistant Professor at the Edward S. Rogers Sr. Department of Electrical & Computer Engineering (University of Toronto) and the Center for Advancing Neurotechnological Innovation to Application (CRANIA) . He obtained his PhD from the University of Pennsylvania and previously worked at Qualcomm Inc. in California. Expertise in integrated circuits and systems for brain-machine interfaces , neuromodulation , and edge AI Published in top venues including Nature Electronics , IEEE JSSC , and ISSCC Recipient of multiple best paper awards and IEEE Senior Member His research spans three main themes: High-speed data converters for wireless/wireline communication IC design for neural interfacing Accelerating machine learning via hardware Recent publications focus on closed-loop neuromodulation , ultra-wideband transceivers , and flexible biomedical sensors . These works integrate analog IC design , edge AI , and real-time neural interfacing across medical rehabilitation , parkinson's monitoring , and memory research . Awards include: IEEE Solid-State Circuits Society Predoctoral Achievement Award (2016) Best Paper Award at BioCAS (2015) ECE Department Teaching Award (2022) Multiple conference best paper finalists His lab collaborates with UHN , EMBS , and global institutions while maintaining strong commitments to equity, diversity, and inclusion (EDI) in research practices.
