Assoc. Prof. Nhien An Le Khac is an Associate Professor at the School of Computer Science, University College Dublin. He serves as Programme Director for the MSc in Forensic Computing & Cybercrime Investigation, which has trained over 1,500 law enforcement officers globally. His research focuses on cybersecurity, digital forensics, AI security, and secure healthcare IT systems. He holds a PhD from Institut National Polytechnique de Grenoble (France) and has supervised 9 PhD students. His work includes pioneering contributions to electromagnetic side-channel analysis (EM-SCA) for IoT forensics, blockchain forensics, and AI-based fraud detection. Education: BSc/MSc: Vietnam National University, Ho Chi Minh City PhD: Institut National Polytechnique de Grenoble, France Professional Certificate in University Teaching & Learning: UCD Research Interests: Cybersecurity, Digital Forensics, AI Security, Machine Learning, Cloud Computing, Big Data Analytics, Healthcare IT Security. Recent Article Trends: Focus on EM-SCA for IoT device forensics, illicit Bitcoin transaction tracking, and cross-device ML portability. His work bridges theoretical AI advancements with practical forensic applications, emphasizing privacy preservation and explainable AI. Awards & Recognition: World’s Top 2% Scientists (2024) UCD Teaching Excellence Awards (2022, 2018) Best Paper Awards at Elsevier, AI-2022, and DFRWS conferences Grants & Advising: Principal Investigator on grants like Cloud Atlas, CERBERUS, and Urban ARK. Advised 9 PhD students who now work in academia/research globally. Active in funding initiatives like ML-Labs (SFI-funded). Labs & Teams: Leads ASEADOS Lab and maintains datasets like EM-SCA and InSDN. Collaborates globally on forensic frameworks and cybersecurity tools.
Prof. Dr. Anne Lauscher is an Associate Professor of Data Science at the University of Hamburg Business School, specializing in fair, inclusive, and sustainable conversational AI systems. Her research focuses on improving algorithmic fairness through demographic factors in NLP systems and exploring ethical implications of large language models. She holds a PhD from the University of Mannheim, where her work on computational argumentation was awarded summa cum laude, and has conducted research at Grammarly and the Allen Institute for AI. Key contributions include gender-fair machine translation datasets (e.g., Building Bridges), bias detection frameworks (e.g., SHADES), and multilingual benchmarking tools like MultiQ. Her work has been recognized with the Maria Gräfin von Linden-Award and inclusion in the '100 Brilliant Women in AI Ethics' list. Research spans ethical NLP, multilingual AI, and societal impacts of AI technologies. Education: PhD in Data and Web Science (University of Mannheim, 2021), Postdoc at Bocconi University's NLP group (2021-2022). Academic roles include adjunct positions and international collaborations across Europe and the US. Research Interests: Conversational AI fairness, multilingual NLP systems, ethical AI evaluation, bias mitigation in LLMs, and interdisciplinary applications of machine learning in scientific discovery. Publications (select highlights): Over 55 peer-reviewed works in top-tier venues like ACL, EMNLP, and AAAI. Recent focus on LLM hallucination analysis, cross-cultural NLP benchmarks, and gender-neutral language resources. Awards: 2021 Maria Gräfin von Linden-Award (Baden-Württemberg), 2023 '100 Brilliant Women in AI Ethics', 2022 Dissertation Award Nominee (GI). Labs/Teams: Leads the UHH Data Science Research Group, collaborating with industry partners like Grammarly and academic institutions worldwide. Active in initiatives promoting gender equity in STEM and sustainable AI development.
Dr Sudip Mittal is an Assistant Professor in Computer Science & Engineering at Mississippi State University and Associate Research Director of the PATENT Lab. His research spans cybersecurity, artificial intelligence, and cyber-physical systems, with a focus on building self-protecting systems and predictive security for unmanned vehicles. He leads the SECRETS Lab and has published over 70 papers in top venues, with work featured in The LA Times and WIRED. Research interests include: Autonomous intrusion response systems AI-driven threat detection in IoT/CPS Adversarial machine learning His publications (2019-2025) show a strong emphasis on AI security applications, particularly in malware detection, healthcare compliance, and anomaly detection using large language models. Recent articles explore MLOps security, adaptive cyber defense, and synthetic data generation for critical systems.
Marco Valtorta is a Professor and Graduate Director in the Department of Computer Science and Engineering at the University of South Carolina’s Molinaroli College of Engineering and Computing. He specializes in Artificial Intelligence, with a focus on normative reasoning under uncertainty, Bayesian networks, causal models, and computational complexity. His work includes developing algorithms for structure learning in graphical models, causal inference, and applications in multiagent systems. Education: Ph.D., Computer Science, Duke University (1987) M.A., Computer Science, Duke University (1984) Laurea, Electrical Engineering, Politecnico di Milano (1980) Research Interests: Dr. Valtorta’s work integrates logical and probabilistic reasoning, with contributions to causal models, chain graphs, and adversarial machine learning. His funded projects include collaborations with the Office of Naval Research (ONR), IARPA, and the U.S. Department of Agriculture (USDA). Notable collaborations include applying Bayesian networks to healthcare and developing frameworks for trustworthiness assessment in AI systems. Grants & Collaborations: Multi-institution IARPA project on Wigmorean/Bayesian networks for argumentation ONR-funded research on Markov properties of directed hypergraphs with Dr. Linyuan Lu Causal analysis for performance modeling of configurable systems His recent publications emphasize causal inference in AI, automated evaluation of text and sentiment analysis systems, and robustness of foundation models. He has pioneered algorithms for learning chain graphs and addressing adversarial attacks in probabilistic models.
Yang Wang is an Associate Professor in the Department of Computer Science and Software Engineering at Concordia University, holding an adjunct position since 2022. Previously, he served as an Associate Professor at the University of Manitoba (2012–2022) and worked as Chief Scientist in Computer Vision at Huawei Canada (2020–2022). He holds a PhD from Simon Fraser University, MSc from the University of Alberta, and BEng from Harbin Institute of Technology. His research focuses on computer vision, machine learning, and deep learning, particularly in meta-learning, test-time training, and continual learning. Key areas include crowd counting, anomaly detection, video highlight detection, and gaze estimation. His work has been recognized with awards such as the Falconer Emerging Researcher Rh Award (2017) and a Faculty of Science Research Chair (2019–2022). Recent research emphasizes AI models that are personalized and adaptable, leveraging techniques like meta-learning and few-shot learning. He has published extensively in top venues (CVPR, ICCV, ECCV) and holds patents in related fields. His group collaborates with industry partners like Huawei and Sightline Innovation.
Abdulrahman Takiddin is an Assistant Professor in the Department of Electrical & Computer Engineering at the Florida A&M University–Florida State University College of Engineering. He holds a Ph.D. in Electrical Engineering from Texas A&M University (2023), an M.S. in Data Analytics from Hamad Bin Khalifa University (2020), and a B.Sc. in Information Systems from Carnegie Mellon University (2014). His research focuses on cybersecurity in smart grids and cyber-physical systems, leveraging machine learning and graph neural networks to detect adversarial attacks such as false data injection and electricity theft. Key areas include resilient power systems, adversarial evasion attack mitigation, and spatio-temporal analysis of power distribution networks. Recent work emphasizes graph-based approaches for enhancing cyber resilience, including eigenvector centrality-enhanced networks and transfer learning solutions for small data scenarios. His publications address both foundational cybersecurity challenges and applied solutions for electrified transportation systems and smart grid infrastructure. Notable trends in his articles include advancements in unsupervised learning for voltage stability protection and recurrent graph networks for replay attack detection. His research also intersects with bioinformatics and artificial intelligence applications in healthcare, though the majority of his work centers on energy systems and cybersecurity.
Overview Amir Rahmati is an Assistant Professor in the Department of Computer Science at Stony Brook University , where he directs the Ethos Security & Privacy Lab and is a member of the National Security Institute. His work focuses on securing emerging technologies like IoT, AR, and ML systems. Education PhD in Computer Science & Engineering, University of Michigan (2017) Research Focus His research addresses security threats in emerging systems, including: Cyber-Physical System vulnerabilities Adversarial ML attacks Hardware security Privacy-preserving frameworks Notable contributions include work on cryptocurrency scam detection, neural network robustness, and AR system security. Grants & Awards Supported by the Air Force Office of Scientific Research (AFOSR), Office of Naval Research (ONR), Meta, NVIDIA, and IBM. His research has been featured in MIT Technology Review , Washington Post , and Bloomberg . Advising Seeking students with expertise in hardware, software, ML, UX, or network protocols passionate about security/privacy. Apply via the graduate program and fill out his interest form. Labs & Collaborations Leads the Ethos Lab focusing on securing IoT, AR, and ML systems. Collaborates on projects like Erebus (AR access control) and Valve (serverless computing security).
Han Wang is an Assistant Professor in the Electrical Engineering and Computer Science department at the University of Kansas School of Engineering. His research develops privacy-preserving frameworks for distributed systems, machine learning, and data analytics. Core research areas include differential privacy implementations for federated learning environments, privacy-preserving outsourcing of anomaly detection, and adversarial attack mitigation. Recent work focuses on developing staircase randomized response mechanisms that enhance privacy without compromising data utility in location services and video analytics. Publications demonstrate consistent innovation in privacy engineering with applications spanning vehicle trajectory protection, energy trading systems, and video recognition security. Methodological approaches combine theoretical privacy guarantees with practical system implementations.
Casey Reas is a Professor in the Department of Design Media Arts at the University of California, Los Angeles (UCLA), where he also co-directs UCLA Social Software. He holds a Master's in Media Arts and Sciences from MIT and a Bachelor's from the University of Cincinnati's College of Design. A pioneering figure in generative art and digital media, Reas co-founded the open-source programming language Processing, revolutionizing computational creativity for artists and designers. His work spans software installations, prints, and collaborative projects exhibited globally at institutions like the Centre Pompidou and the Whitney Museum. His research explores intersections of technology, art, and pedagogy, reflected in books such as Compressed Cinema and Making Pictures with Generative Adversarial Networks . Reas' practice emphasizes algorithmic processes and machine learning, exemplified in series like Untitled Film Stills and Compressed Cinema . He actively contributes to initiatives like UCLA’s Game Lab and the Conditional Studio, fostering interdisciplinary innovation. His work balances technical rigor with aesthetic exploration, bridging art, science, and technology.
Lexin Li is a Professor in the Department of Biostatistics and Epidemiology at the University of California, Berkeley School of Public Health, with additional affiliations at the Helen Wills Neuroscience Institute, the UC Berkeley-UCSF Joint Program on Computational Precision Health, and the Center for the Theoretical Foundations of Learning, Inference, Information, Intelligence, Mathematics and Microeconomics at Berkeley (CLIMB). He received his BE in Electrical Engineering from Zhejiang University (1998) and PhD in Statistics from the University of Minnesota (2003), followed by postdoctoral training at UC Davis School of Medicine. He joined North Carolina State University as Assistant Professor in 2005, was promoted to Associate Professor in 2011, and served as visiting faculty at Stanford University and Yahoo Research Labs (2011-2013) before joining UC Berkeley as Associate Professor in 2014, where he was promoted to Full Professor in 2018. Dr. Li's research spans statistical methodology development for neuroimaging data analysis, tensor statistics, and machine learning applications to biomedical problems. His work focuses on brain connectivity and network analysis, imaging causal inference, tensor regression, dimension reduction, and statistical machine learning with applications to Alzheimer's disease, Parkinson's disease, and other neurological disorders. His methodological innovations bridge theoretical statistics with practical neuroscience applications, particularly in multimodal neuroimaging analysis and brain network modeling. His recent publications demonstrate a strong trajectory in integrating deep learning with classical statistical inference, particularly in tensor analysis, functional data modeling, and causal inference. The research shows increasing sophistication in handling high-dimensional, complex neuroimaging data while developing rigorous statistical frameworks for inference. His work increasingly focuses on multimodal data integration and developing methods that can handle the complexity of real-world neurological data. Dr. Li has received numerous prestigious honors including being elected as a Fellow of the American Statistical Association (2017), Fellow of the Institute of Mathematical Statistics (2021), Elected Member of the International Statistical Institute, and Fellow of the American Association for the Advancement of Science (2024). Fellow, American Statistical Association (2017) Fellow, Institute of Mathematical Statistics (2021) Elected Member, International Statistical Institute Fellow, American Association for the Advancement of Science (2024) Editor-in-Chief, Annals of Applied Statistics (2025-2027) As an academic leader, Dr. Li serves as Co-Director of the Biostatistics Program (2019-) and Director of Graduate Admissions (2015-) at UC Berkeley. He is an active editor, currently serving as Editor-in-Chief of the Annals of Applied Statistics (2025-2027), and has held associate editor positions at multiple top statistical journals including the Journal of the American Statistical Association and Journal of Computational and Graphical Statistics. He also serves as a Standing Member of the NIH Emerging Imaging Technologies in Neuroscience Study Section (2023-2027). His research has been supported by various NIH grants focused on statistical methodology for neuroimaging analysis. Dr. Li leads a vibrant research group focused on statistical neuroimaging and machine learning methodology, with strong connections to the Helen Wills Neuroscience Institute and collaborations across multiple departments at UC Berkeley. His team develops innovative statistical methods that address real challenges in neuroscience research while maintaining rigorous theoretical foundations. The group maintains active collaborations with neuroscientists and clinicians working on Alzheimer's disease, Parkinson's disease, and other neurological conditions.
Zachary E. Ross is a Professor of Geophysics at the California Institute of Technology (Caltech) and holds the William H. Hurt Scholar distinction since 2021. His research integrates machine learning, computational mathematics, and seismology to analyze earthquakes and fault zones using large seismic datasets. Education B.S., University of California, Davis (2009) M.S., California Polytechnic State University, San Luis Obispo (2011) Ph.D., University of Southern California (2016) His research focuses on high-resolution imaging of fault zones, understanding earthquake sequences in space and time, and applying artificial intelligence to seismic data analysis. He develops scalable algorithms for waveform inversion, ground-motion synthesis, and real-time seismic monitoring. Recent publications highlight his work on neural operators for wave propagation, AI-driven seismicity analysis, and induced earthquake dynamics. He teaches advanced courses including Ge 264 – Machine Learning in Geophysics and Ge 271 – Dynamics of Seismicity . Scientific Awards William H. Hurt Scholar (2021–present)
Professor Marius Portmann is the UQ-Cisco Chair of Network Security at the School of Electrical Engineering and Computer Science (EECS), University of Queensland. His expertise spans Cybersecurity, IoT, and Applied AI. He holds a PhD from ETH Zurich (2003) and has led research in Software Defined Networking (SDN), blockchain, and energy-harvesting IoT systems. Education: PhD in Electrical Engineering from Swiss Federal Institute of Technology (ETH Zurich), 2003. Research focuses on securing IoT networks, AI-driven intrusion detection, and sustainable sensor systems. He has pioneered self-powered IoT systems using energy harvesters and developed frameworks like FlowTransformer for network analysis. His work bridges theoretical advancements with practical applications in smart tourism, energy efficiency, and edge computing. Recent publications highlight innovations in DDoS detection (P4-Secure), sensor-based environmental monitoring (EcoShower), and graph-based anomaly detection (XG-BoT). His datasets (e.g., NF-ToN-IoT-v3) are widely used in ML-based cybersecurity research. Collaborations include industry partners like Cisco and institutions like RMIT. Grants and leadership roles in interdisciplinary projects underscore his impact. He advises on IoT security standards and contributes to open-source tools for network research. Current projects explore edge-AI integration and sustainable sensor networks.
Shujun Li is a Professor of Cyber Security and Head of the Cyber Security Research Group at the School of Computing, University of Kent. He also holds a Visiting Professorship at the Department of Computer Science, University of Surrey. His research focuses on cyber security, privacy, AI applications, and human-centric computing. He leads the Institute of Cyber Security for Society (iCSS), a university-wide interdisciplinary research centre. Education: PhD in Information and Communication Engineering (Xi'an Jiaotong University, 2003), followed by postdoctoral research at City University of Hong Kong, Humboldt Research Fellowship at FernUniversität in Hagen, and a 5-year Zukunftskolleg Research Fellowship at Universität Konstanz. Research interests include cyber security (usable security, digital forensics, misinformation), AI safety, human factors, and socio-technical systems. He has published over 100 papers, with awards including the IEEE Guillemin-Cauer Best Paper Award and EPSRC recognition. Awards: Includes IEEE Transactions Best Paper Awards, EPSRC peer review recognition, and multiple conference best paper awards. Active in interdisciplinary projects like MACRO (cyber risks in mobility systems) and ACCEPT (reducing human-related cyber risks). Labs/Teams: Directs iCSS, co-founded Kent & Medway Cyber Cluster, and leads the Kent Interdisciplinary Research Centre in Cyber Security (KirCCS). Collaborates with industry and government agencies on cyber resilience and AI ethics.
Yue Gao is a Professor of Wireless Communications at the Institute for Communication Systems, University of Surrey. He holds a PhD from Queen Mary University of London (2007) and previously served as a lecturer, senior lecturer, and reader at QMUL. His research focuses on smart antennas, signal processing, spectrum sharing, millimeter-wave systems, and IoT in mobile/satellite communications. He has authored over 180 papers, two patents, a book, and five book chapters. Current roles include EPSRC Fellow (2018–2023) and editorial roles for IEEE Transactions on Cognitive Communications and Networking, Vehicular Technology, and Internet of Things Journal. Notable awards include the EU Horizon Prize (2016). His work spans interdisciplinary projects like GBSense (GHz Bandwidth Sensing) and contributes to 6G research. Collaborations include leadership roles at IEEE conferences and global spectrum sensing challenges. Research interests emphasize antenna design (e.g., 3D printed, Ka-band), sub-Nyquist sampling, and machine learning for spectrum reconstruction. Affiliated with Surrey’s antenna measurement facilities (NPL) for advanced testing (400 MHz to 110 GHz, THz spectroscopy). Active in mentoring PhD students in antennas/wireless communications and oversees projects like the GBSense Challenge, advancing sensor-driven spectrum management.
Dr. Richard Jiang is a Senior Lecturer (Associate Professor) at Lancaster University's School of Computing and Communications. His research focuses on Artificial Intelligence, Neurocomputing, Quantum AI, Privacy Computing, and Medical Computing. He has pioneered secure pattern recognition in encrypted domains and quantum neuromorphic computing. With over £1M in research grants from EPSRC and others, he has authored 100+ publications and supervised over 20 PhD students. Dr. Jiang's work includes the Face2Brain method for neurodegenerative assessment and explainable models for brain aging analysis. He contributes actively to academic committees, editorial boards, and conferences like the World Conference on eXplainable AI. His research spans ethical AI frameworks, quantum algorithms for medical imaging, and privacy-preserving biometric systems.
