Sanghyun Hong is an Assistant Professor at Oregon State University's School of Electrical Engineering and Computer Science , focusing on Trustworthy AI and Cybersecurity . He holds a Ph.D. in Computer Science from the University of Maryland, College Park (2021) and a B.S. in Electrical Engineering and Computer Science from Seoul National University (2015). His research bridges machine learning , security , and privacy-preserving systems . Current research themes: Robustness of AI systems to adversarial attacks Privacy-preserving machine learning Security of pre-trained and large language models Hardware fault vulnerabilities in neural networks Cybersecurity workforce development Publication Trends (15 most recent): Focus on adversarial machine learning (jailbreaking LLMs, membership inference) Advances in physics-informed neural networks and time series forecasting Key contributions to AI security and malware detection Interdisciplinary work in visualization design and tsunami warning systems Scientific Accolades : Google Faculty Research Award (2023) Samsung Global Research Award (2022, 2023, 2024) DARPA Riser (2022) NSF SFS Award (co-PI, 2023) USENIX Enigma Speaker (2021) Academic Leadership : Mentors 5 Ph.D. students and has graduated 8 M.S. and B.S. students. Currently developing next-generation auditing frameworks for AI systems while on medical leave until Winter 2026.
Michael P. Wellman is a Professor of Computer Science and Engineering at the University of Michigan, specializing in computational game theory and its applications to economics and finance. He has advised 28 PhD graduates and currently mentors 6 students, emphasizing independent research and tailored advising approaches. His work focuses on multi-agent systems, strategic interactions, and agent-based modeling of financial markets. He holds the endowed Lynn A. Conway Professorship and created the Morris Wellman Faculty Development Professorship. His research group meets weekly for progress reports, paper discussions, and practice presentations. Wellman encourages internships, teaching experience, and conference participation (e.g., ICAIF, AAMAS, EC) to foster career readiness. His scientific contributions span empirical game-theoretic analysis (EGTA), market manipulation detection, and cybersecurity strategies. He prioritizes student independence, collaborative problem-solving, and ethical considerations in AI-driven financial systems.
Dr. Feng Cheng is a Senior Researcher and head of the IT Security Engineering (Sec-Eng) Team at the Hasso Plattner Institute (HPI), Germany, and serves as the Representative of the Chair "Internet Technologies and Systems" at the Digital Engineering Faculty, University of Potsdam. He holds a PhD from the University of Potsdam, an MEng from Beijing University of Technology, and a BEng from Beijing University of Aeronautics and Astronautics. His research is centered on data-driven security engineering, security analytics, network security, cloud security, firewall, IDS/IPS, attack modeling, and penetration testing. Dr. Cheng's research interests span a broad range of cybersecurity domains, with a strong emphasis on security analytics , data-driven threat detection , cloud and network security , and authentication . His work integrates advanced data engineering and machine learning techniques to enhance security operations, including intrusion detection, threat intelligence, and security information and event management (SIEM). Key application areas include IoT security, mobile security, and web security. The recent publications of Dr. Cheng and his team reflect a significant trend towards leveraging big data analytics , machine learning , and graph-based methods for cybersecurity. Their work focuses on developing frameworks for blockchain-based public key infrastructures, advanced SIEM analytics, malware detection using NLP, and chaos engineering for cloud security. This demonstrates a strong commitment to creating practical, scalable, and intelligent solutions for modern security challenges in complex, distributed environments. Dr. Cheng is actively involved in the academic community as a member of ACM and IEEE, a program committee member for numerous international conferences, and an organizer of workshops on cloud computing and cybersecurity. He has also served on editorial boards for international journals. He supervises a large group of PhD, master's, and bachelor's students, guiding research on topics such as security analytics, cyber threat intelligence, digital credentials, and behavioral authentication. He leads the Sec-Eng team, which conducts research on a diverse portfolio including the Security Analytics Lab, HPI-VDB (vulnerability database), Lock-Keeper (physical separation technology), and the Security Lab Generator for scenario-driven security training.
Di Shi is an Associate Professor at the Klipsch School of Electrical and Computer Engineering , New Mexico State University (NMSU), holding the Paul W. and Valerie Klipsch Distinguished Professorship. He previously founded the AI energy startup AInergy, LLC and held leadership roles at GEIRI North America, NEC Laboratories America, and Arizona State University. Education: PhD in Electrical Engineering, Arizona State University (2012) MS in Electrical Engineering, Arizona State University (2009) BS in Electrical Engineering, Xi'an Jiaotong University (2007) His research focuses on power system data analytics , energy storage , artificial intelligence , and IoT applications for grid stability and renewable integration. His work bridges theoretical innovation with real-world deployment, including software adopted by 15 utility companies. Recent publications highlight his leadership in deep reinforcement learning for grid control, blockchain frameworks for energy management, and tensor decomposition for efficient load modeling. He has secured a $6M NSF grant for AI-driven digital twinning to address climate-aware energy resilience. Awards & Recognition: 2025 Paul W. and Valerie Klipsch Distinguished Professorship 2024 University Research Council Mid-Career Award 2024 IET Fellow Multiple IEEE Best Paper Awards (2019–2022) 2019 L2RPN AI Competition Championship He serves as an editor for IEEE Transactions on Power Systems , IET Generation, Transmission & Distribution , and other journals, and leads the IEEE Task Force on IoT for Power Systems . His team’s patents cover AI-driven load modeling , energy storage scheduling , and state estimation , with 42 granted or pending.
Prof. Dr. Sebastian Steinhorst is an Associate Professor (W3-level with tenure) at the Technical University of Munich (TUM) within the Embedded Systems and Internet of Things group at the TUM School of Computation, Information and Technology . His research focuses on advancing the security, predictability, reliability, and interoperability of smart connected and autonomous systems, particularly for applications in Internet of Things (IoT) , Industry 4.0 , and automotive systems . PhD in Computer Science (2011) from Goethe University Frankfurt Postdoctoral roles at TUMCREATE Singapore (2011-2016) and Aarhus University (2016) Joined TUM in 2016 as Rudolf Moessbauer Tenure Track Professor His research areas include decentralized embedded systems, hardware/software co-design, modeling and verification of cyber-physical systems, security protocols for automotive networks, and time-sensitive networking (TSN) for industrial applications. Recent work explores blockchain-based data sovereignty, zero-knowledge proofs for vehicle authentication, and resilient architectures for autonomous systems. Key scientific contributions include the 2019 ACM TODAES Best Paper Award and pioneering work on CyberSecDome , LeapChain , and Simutack frameworks. He serves on editorial boards and conference committees, including co-organizing the Autonomous Systems Design initiative at DATE. His teaching portfolio spans lectures on System Design for IoT , Software Architecture for Distributed Systems , and IoT Security across multiple semesters. He also leads advanced seminars on embedded systems and IoT.
Professor Khin Than Win is a leading academic in health informatics and digital health at the University of Wollongong (UOW), holding appointments as Professor in the School of Computing and Information Technology, Head of Postgraduate Studies, and Deputy Head (Research). She also serves as Academic Program Director for UOW's Master of Health Informatics and Graduate Certificate in Health Analytics programs. Her research focuses on applying information technology to healthcare, particularly in behavior change support systems, persuasive technology, and ethical AI applications. She has supervised over 20 PhD students and holds leadership roles including Deputy Chair of UOW's Health and Medical Research Ethics Committee, and membership in international committees like the Persuasive Technology Steering Committee. Education: MBBS from Rangoon University, Master's and PhD in IT from Assumption University (Bangkok) and UOW (Australia) Research Interests: Health data analytics, AI in healthcare, privacy/security of health systems Leadership: Program/General Chair roles at ACIS and Persuasive Technology conferences Awards: Best Paper Awards (2023, 2018) Her extensive funding portfolio includes ARC grants and NHMRC projects, totaling over 24 funded initiatives. Current research explores blockchain in medical passports, AI ethics, and culturally tailored health interventions.
Soheil Salehi is a tenure-track Assistant Professor in the Department of Electrical and Computer Engineering at the University of Arizona, with a joint appointment in Systems and Industrial Engineering. He is the Director of the Privacy-preserving, Intelligent, and Secure Computing (PRISM) Lab, established in August 2022. Prior to this, he was an NSF-Sponsored Computing Innovation Fellow and Postdoctoral Research Fellow at the University of California, Davis. Ph.D., Electrical and Computer Engineering, University of Central Florida, 2020 M.S., Electrical and Computer Engineering, University of Central Florida, 2016 B.S., Isfahan University of Technology, Iran, 2014 Dr. Salehi's research focuses on the intersection of hardware, AI, and security. His work spans hardware and AI-enabled security in IoT , Generative AI for hardware design and security , neuromorphic and biologically-inspired AI hardware , emerging spin-based devices , reconfigurable architectures , low-power VLSI circuits , and digital twins and mixed reality for semiconductor workforce development . He also explores the application of Generative AI in personalized education . His recent publications, spanning 2023–2025, reveal a strong trend toward integrating AI and machine learning into hardware security and design. Key themes include automated secure IC design flows , AI-driven hardware obfuscation , firmware and side-channel attack analysis , security in neuromorphic and spiking neural networks , and educational frameworks using digital twins and generative models . His work appears in top venues like DAC, ICCAD, USENIX Security, IEEE TCAS-I, and ISCAS. Outstanding Reviewer Award, IEEE/ACM Design Automation Conference (DAC), 2023 Best Presentation of the Symposium Award, UC Davis Postdoctoral Research Symposium, 2021 UCF Excellence by a Graduate Teaching Assistant (University-Level), 2016 Nominated for 30-under-30 Award, UCF, 2020 Nominated for Postdoctoral Research Excellence Award, UC Davis, 2022 Dr. Salehi has secured significant research funding as PI and Co-PI, including a $300K NSF SaTC EAGER grant on Generative AI-based Personalized Cybersecurity Tutor, a $174,000 University of Arizona PIF Award, and multiple RII grants totaling over $198K. He has also received industry funding from CHEST. He actively mentors students and leads the PRISM Lab, which focuses on privacy-preserving and intelligent secure computing. His service includes roles as Technical Program Committee (TPC) Member and Session Chair at premier conferences such as DAC, ICCAD, CCS, NDSS, and GLSVLSI. The PRISM Lab, under his direction, conducts cutting-edge research in secure and intelligent hardware systems, with applications in IoT, edge computing, and workforce development. The lab emphasizes interdisciplinary collaboration and innovation in both research and education.
Dr. Xinwen Fu is a Professor in the Miner School of Computer & Information Sciences at the University of Massachusetts Lowell, where he also serves as Director of the iSAFER Center. He received his Ph.D. in Computer Engineering from Texas A&M University (2005), M.S. in Electrical Engineering from University of Science and Technology of China (1998), and B.S. in Electrical Engineering from Xi'an Jiaotong University (1995). Research Interests Dr. Fu specializes in Computer Security and Privacy , with particular focus on IoT Security , Mobile Security , Network Security , and Digital Forensics . His research has been published in all four top computer security conferences: IEEE Symposium on Security and Privacy (S&P/Oakland), ACM Conference on Computer and Communications Security (CCS), USENIX Security Symposium, and Network and Distributed System Security Symposium (NDSS), as well as prestigious journals like ACM/IEEE Transactions on Networking and IEEE Transactions on Dependable and Secure Computing. His work has gained significant media attention from CNN, Wired, Huffington Post, Forbes, Yahoo, MIT Technology Review, and PC Magazine, including multiple appearances on CNN Domestic and International. He has also served as TPC member for numerous prestigious conferences including ACM CCS, IEEE INFOCOM, and IEEE ICDCS. Scientific Awards Best paper award at IEEE International Conference on Industrial Internet (ICII) 2019 Best paper award, International Conference on Wireless Algorithms, Systems, and Applications (WASA), 2017 Teaching Excellence Award of Department of Computer Science, UMass Lowell, 2016 Best paper award, International Performance Computing and Communications Conference (IPCCC) 2016 Award of Excellence, GeekPwn 2016 Ethic Smart Life Hack Competition Erich Spengler Student Cyber Paper Award with students, The Colloquium for Information Security Education (CISSE), 2016 Spotlight Paper of the IEEE Transactions on Mobile Computing (TMC), February 2014 Best paper award, International Conference on Wireless Algorithms, Systems, and Applications (WASA), 2013 Best paper award, IEEE International Conference on Communications (ICC): Communications and Information Security Symposium, 2013 Teaching award, Department of Computer Science, University of Massachusetts Lowell, 2011
Lorrie Faith Cranor is the Director and Bosch Distinguished Professor in Security and Privacy Technologies at the CyLab Security and Privacy Institute, and the FORE Systems University Professor of Computer Science and Engineering and Public Policy at Carnegie Mellon University. She also directs the CyLab Usable Privacy and Security Laboratory (CUPS) and co-directs the MSIT-Privacy Engineering master’s program. She previously served as Chief Technologist at the U.S. Federal Trade Commission and is a co-founder of Wombat Security Technologies. Carnegie Mellon University – CyLab Security and Privacy Institute School of Computer Science – Department of Computer Science College of Engineering – Department of Engineering and Public Policy PhD Program in Societal Computing Human-Computer Interaction Institute (affiliate) Heinz College (affiliate) Dr. Cranor’s research centers on usable privacy and security, privacy engineering, and technology policy. Her work spans authentication systems, privacy policies, IoT security, and human behavior in digital environments. She has pioneered research on password usability, privacy labels (e.g., 'nutrition labels' for privacy), and privacy decision-making. Her interdisciplinary approach integrates computer science, public policy, and behavioral science to create systems that are both secure and user-friendly. Her recent publications reflect a strong focus on privacy interfaces, consent mechanisms, IoT security labels, and user comprehension of digital privacy. Trends include evaluating the usability of privacy controls across platforms, designing tools for developers to generate accurate privacy disclosures, and understanding user behavior in online tracking and advertising contexts. Her work frequently appears in top venues such as CHI, SOUPS, and PETS. ACM CHI Academy inductee ACM Fellow IEEE Fellow 2018 ACM CHI Social Impact Award 2018 International Association of Privacy Professionals Privacy Leadership Award 2018 IEEE Cybersecurity Award for Practice Alumni Achievement Award, McKelvey School of Engineering Top 100 Innovator under 35, Technology Review Dr. Cranor has advised numerous PhD students in interdisciplinary programs including Societal Computing, Engineering and Public Policy, and Human-Computer Interaction. Her research has been supported by grants from the National Science Foundation, DARPA, and industry partners. She serves on the boards of the Computing Research Association, Center for Democracy and Technology, and Electronic Privacy Information Center. She is a frequent media commentator on privacy and security issues. She leads the CyLab Usable Privacy and Security Laboratory (CUPS), a multidisciplinary research group that includes faculty and students from computer science, policy, and HCI. The lab focuses on making privacy and security systems more intuitive, effective, and user-controlled. Key projects include the Usable Privacy Policy Project, privacy nutrition labels, and the Personalized Privacy Assistant Project.
Dr. Emma Li is a Senior Lecturer in Responsible & Interactive Artificial Intelligence at the University of Glasgow's School of Computing Science. She directs the Interactive and Trustworthy AI Lab, developing secure human-robot collaboration frameworks for applications in healthcare, industry, and nuclear decommissioning. Her research pioneers behavioral biometric authentication methods that verify users through robotic motion patterns, enhancing security in teleoperation systems. Recent projects include cyber-physical teleoperation for nuclear decommissioning funded by the RAICo Programme. Li leads the Understandable Autonomous Systems research theme and contributes to the UKRI Centre for Doctor Training in Socially Intelligent Artificial Agents. Her publications demonstrate consistent innovation in human-robot trust establishment, adversarial robustness, and secure communication for robotic systems operating in sensitive environments.
Yang Weng is an Associate Professor at the School of Electrical, Computer and Energy Engineering, Arizona State University. He leads the U.S.-Israel International Consortium on Energy Cyber Initiative on Cybersecurity R&D and directs a research lab focused on smart grid resilience and machine learning applications. Previously, he was a TomKat Postdoctoral Scholar at Stanford University. Education: Ph.D. in Electrical and Computer Engineering, Carnegie Mellon University M.S. in Machine Learning, Carnegie Mellon University Research: His interdisciplinary work bridges power systems, machine learning, and cybersecurity, emphasizing renewable integration, grid optimization, and cyber-physical resilience. Key themes include physics-informed AI, adversarial robustness in energy infrastructure, and real-time control algorithms for dynamic grids. Publications: Recent articles (2024–2025) demonstrate strong trends in AI-driven grid security, adaptive control under uncertainty, and climate-impact modeling. Dominant domains include neural network applications for stability guarantees, cyber-attack mitigation, and data-efficient renewable integration. Awards: NSF CAREER Award (2021), Amazon Research Award (2023) Best Paper Awards at IEEE SmartGridComm (2012, 2013), PES GM (2014), PMAPS (2016) IEEE Senior Member, Sun Award (ASU), Centennial Award (ASU) Grants & Leadership: Secured DOE, NSF, and AFOSR funding for projects on AI-enhanced grid resilience. Advises PhD/postdoc candidates and chairs the U.S.-Israel Energy Center consortium. Organized international workshops (e.g., ICRDE 2023) and validated research via hardware-in-the-loop experiments. Lab & Team: Directs a research group developing deployable ML solutions for utilities (e.g., OPAL-RT collaborations). Focus areas: cybersecurity toolchains, reinforcement learning for grid control, and anomaly detection architectures.
Virginia Smith is the Leonardo Associate Professor of Machine Learning at Carnegie Mellon University's School of Computer Science, with a courtesy appointment in the Electrical and Computer Engineering Department. She leads research addressing critical challenges in machine learning systems, particularly focusing on safety and efficiency. Her research interests span federated and collaborative learning, efficient training methods, data privacy, and AI safety. Recent work has explored topics such as model unlearning, LLM security, and resource-efficient distributed learning systems. She has made significant contributions to understanding how to make machine learning systems more robust, private, and efficient while maintaining performance. Professor Smith's publication record demonstrates a clear progression toward addressing practical challenges in deploying machine learning systems at scale. Her recent work shows strong emphasis on large language model safety, privacy-preserving techniques, and efficient distributed learning approaches. The research spans theoretical foundations to practical implementations, with numerous papers appearing in top-tier venues including NeurIPS, ICML, ICLR, and MLSys. AFOSR Young Investigator Award Sloan Research Fellowship 2023 Samsung AI Researcher of the Year Best Paper Award at ICML 2025 Exploration in AI Workshop Outstanding Paper Award at MLSys 2023 As an educator, Professor Smith mentors numerous PhD students and postdocs while teaching advanced machine learning courses at CMU. She serves as Program Chair for ICML 2025 and co-organizes a semester program on Federated and Collaborative Learning at the Simons Institute. Her research group maintains strong collaborations with industry partners including Amazon, where she has received research awards.
Dr. Baijian "Justin" Yang serves as the Associate Dean for Research at Purdue Polytechnic Institute and is a Professor in the Department of Computer and Information Technology at Purdue University. He earned his Ph.D. in Computer Science from Michigan State University, with Master's and Bachelor's degrees in Automation (EECS) from Tsinghua University. Dr. Yang has established himself as a leader in multiple interdisciplinary research domains. Dr. Yang's educational background includes: PhD in Computer Science, Michigan State University (2002) MS in Automation (EECS), Tsinghua University (1998) BS in Automation (EECS), Tsinghua University (1995) His research interests span multiple cutting-edge domains with practical applications: Cybersecurity : Developing novel approaches for threat intelligence, security education, and network defense Big Data : Creating innovative algorithms for dimension reduction, regression with categorical variables, and tensor decomposition Applied Machine Learning : Implementing AI solutions in healthcare, manufacturing, and forestry applications Digital Forestry : Using UAV imagery and remote sensing for forest management and tree species classification Dr. Yang's publication record demonstrates significant impact across multiple disciplines, with recent work focusing on spatial transcriptomics analysis (SiGra), delirium detection using limited-lead EEG, and visual localization technologies. His research bridges theoretical advances with practical applications in healthcare, manufacturing quality control, and environmental monitoring. The interdisciplinary nature of his work is evident in collaborations spanning computer science, healthcare, forestry, and manufacturing domains. His scientific achievements have been recognized with numerous awards: 2023 HRSA Building Bridges to Better Health Competition Winner (Phase 1) and 2nd place ($100,000 prize) in Phase 3 2023 Outstanding Faculty Award in Engagement, Department of Computer and Information Technology, Purdue University 2021 Leadership in Manufacturing Award, Manufacturing Times Digital (MxD) 2021 Good to Great Award, Purdue Polytechnic 2020 Outstanding Faculty Award in Discovery, Department of Computer and Information Technology 2019 University Faculty Scholars, Purdue University As an educator and mentor, Dr. Yang has advised numerous graduate students through their PhD and Master's research. His leadership extends to significant service roles including serving as Faculty Champion for the Holistic Safety and Security research impact area at Purdue Polytechnic from 2018 to 2021, board membership with ATMAE (2014-2016), and participation in the IEEE Cybersecurity Initiative Steering Committee (2015-2017). He holds valuable industry certifications including CISSP, MCSE, and Six Sigma Black Belt, demonstrating his commitment to bridging academic research with industry practice. Dr. Yang leads multiple research projects including "Digital Forestry" for developing tools to quantify forest function, "CHEESE" (Cyber Human Ecosystem of Engaged Security Education), and "CICI" (Supporting Controlled Unclassified Information with a Campus Awareness and Risk Management Framework). His work on "Applied Machine Learning" focuses on solving real-world problems, while his "Dimension Reduction and Memory Amnestic Big Data Regression" project innovates computational algorithms for large-scale data analysis.
Amir Houmansadr is an Associate Professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst. His research focuses on network and AI security, privacy-enhancing technologies, and censorship circumvention. He holds a PhD from the University of Illinois at Urbana-Champaign (2012) and a postdoctoral fellowship at the University of Texas at Austin (2012-2014). Key research areas include secure communication systems, adversarial ML attacks, federated learning defenses, and analyzing censorship mechanisms like the Great Firewall of China. He leads the SPIN research group, which develops tools like CensorLab and MassBrowser. Notable contributions include exposing GFW vulnerabilities and advancing privacy-preserving AI models. Awards include the DARPA Director’s Award (2024), ACM CCS Distinguished Paper (2023), and NSF CAREER Award (2016). His work has been featured in media outlets like The Guardian, MIT Technology Review, and MassLive. He advises over 20 students and has served on program committees for top security conferences (IEEE S&P, ACM CCS, USENIX Security). Current courses include CMPSCI 660: Advanced Information Assurance.
Ruoyu (Fish) Wang is an Associate Professor at the School of Computing and Augmented Intelligence, Arizona State University (Tempe campus). He also holds affiliations as Associate Director of Impact at the Global Security Initiative, Center for Cybersecurity & Trusted Foundations, and with the Biodesign Center for Biocomputing, Security and Society. His educational background includes: Ph.D. in Computer Science, University of California, Santa Barbara Professor Wang's research focuses on system security, with an emphasis on automated binary program analysis and reverse engineering of software. He is the co-founder and core developer of the angr binary analysis platform, which won third place in the DARPA Cyber Grand Challenge (2018). His work spans vulnerability discovery, fuzzing, and security tool development for binary program analysis. His current research interests include: Binary program analysis and reverse engineering Automated vulnerability discovery and mitigation Fuzzing techniques and robust testing Phishing and fraud detection in e-commerce Security of firmware and embedded systems Application of machine learning to security problems His recent publications (2024-2025) demonstrate cutting-edge research in fraud detection for e-commerce using LLMs, advanced fuzzing methodologies, and binary decompilation techniques. Key trends include bridging theoretical program analysis with practical security tools, as evidenced by extensions to the angr platform, and addressing emerging threats in financial ecosystems and client-side security. Dr. Wang has received notable recognition: Third place in DARPA Cyber Grand Challenge (2018) with team Shellphish As an active educator, he supervises graduate research (CSE 599/799) and teaches core cybersecurity courses including Software Security (CSE 545) and Information Assurance (CSE 365). His teaching spans multiple semesters through 2025, covering practicums, internships, and special topics in computing security. Dr. Wang co-founded the angr binary analysis platform and contributes to Arizona State University's security research ecosystem through leadership roles in the Center for Cybersecurity & Trusted Foundations and Biodesign Center for Biocomputing, Security and Society.
