Giancarlo Ferrari Trecate is an Adjunct Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) , affiliated with the School of Engineering and the SCI-STI-GFT department. He is also involved in teaching and research through the STI-SGM and EDRS-ENS programs. Research Interests : Automatic control, state estimation, system identification, machine learning, distributed control, hybrid systems, microgrids, biochemical networks, voltage and frequency stabilization in AC/DC microgrids. Publications Trends : His recent work focuses on integrating Neural ODEs and Hamiltonian structures for stable control systems, regret minimization in distributed control, and robust state estimation under uncertainty. Applications include autonomous mobility-on-demand , power grid optimization , and secure microgrid control against cyber-attacks. Scientific Awards : No specific awards mentioned in the provided data. Teaching & Advising : He supervises PhD students in mechanical engineering and teaches courses on Multivariable control and Networked control systems . His lab, DECODE , specializes in Dependable Control and Decision systems.
Keigo Kobayashi is an Associate Professor at Waseda University's Faculty of Science and Engineering, School of Creative Science and Engineering, Department of Architecture. He has served in this position since 2016, previously holding the rank of Assistant Professor from 2012 to 2016. His professional journey includes significant experience at the Office for Metropolitan Architecture (OMA) in Rotterdam, where he worked as a Senior Architect and Project Architect from 2007 to 2012. His educational background includes a Master of Architecture II from Harvard University's Graduate School of Design (2005), a Master of Architecture from Waseda University's Graduate School of Science and Engineering (2003), and a Bachelor's degree in Architecture from Waseda University's School of Science and Engineering (2002). Kobayashi's research focuses on architectural planning and city planning, with particular interests in food-related architecture, spatial behavior, and sustainable design. His work explores the intersection of architecture with everyday life, examining how spatial configurations influence human behavior and community formation. Recent research has investigated food systems in urban contexts, playful approaches to spatial design, and the relationship between architectural form and environmental performance. His publications demonstrate a strong focus on practical applications of architectural theory, with projects spanning from food truck design in Shodoshima to zero-energy housing solutions and innovative approaches to architectural education. His work shows a consistent interest in how architecture can respond to contemporary social challenges while maintaining theoretical rigor. Excellence in Concrete Construction Award 2019, American Concrete Institute (ACI), Qatar National Library / OMA Kobayashi actively contributes to architectural education through various committee memberships, including serving as Chief of the Subcommittee on Strategies for International Compatibility in Architectural Education at the Architectural Institute of Japan and leading the Caberra Accord Correspondence Subcommittee at the Japan Accreditation Board for Engineering Education. His teaching portfolio at Waseda University is extensive, covering architectural design studios from undergraduate to doctoral levels. He leads several research initiatives, including the Wakusei Project in Tosa, Kochi, NPO PLAT (Platform for Architectural Thinking), and the European Thinkbelt/McDonald's Radio University project which represents an innovative approach to architectural education and community engagement, particularly in contexts of migration and cultural exchange.
Aaron Roth is the Henry Salvatori Professor of Computer and Cognitive Science at the University of Pennsylvania, affiliated with the Department of Computer and Information Science in the School of Engineering and Applied Science. He holds a secondary appointment in the Department of Statistics and Data Science at the Wharton School and is associated with several research centers including PRiML, the Warren Center for Network and Data Sciences, and the AMCS program. He received his PhD from Carnegie Mellon University under Avrim Blum and was a postdoc at Microsoft Research New England. His research focuses on algorithms and machine learning, particularly in private data analysis, fairness in machine learning, game theory, mechanism design, and learning theory. His work bridges theoretical computer science with societal concerns, advocating for ethically aware algorithm design. He co-authored the book The Ethical Algorithm with Michael Kearns, which explores how to embed social values like privacy and fairness into algorithmic systems. His recent publications show a strong trend toward uncertainty quantification, multicalibration, conformal prediction, and fairness in reinforcement learning and high-dimensional settings. He frequently publishes in top-tier venues such as STOC, FOCS, ICML, NeurIPS, and COLT, often with a focus on rigorous theoretical foundations with practical implications. Hans Sigrist Prize Presidential Early Career Award for Scientists and Engineers (PECASE) Alfred P. Sloan Research Fellowship NSF CAREER award Google Faculty Research Award Amazon Research Award Yahoo Academic Career Enhancement award Roth has advised numerous PhD students and postdocs, many of whom now hold academic or industry research positions. He is also an Amazon Scholar at AWS and has served in advisory roles for companies like Apple, Facebook, Leapyear, and Spectrum Labs. He has been active in organizing workshops and tutorials on differential privacy, fairness, and adaptive data analysis, and has given keynotes at major conferences and institutions worldwide. He leads research groups and collaborates widely across Penn, focusing on responsible AI, privacy, and algorithmic fairness. His lab produces foundational work on calibration, unlearning, privacy-preserving learning, and equitable decision-making systems.
John Psarras is a Professor at the National Technical University of Athens (NTUA) in the School of Electrical and Computer Engineering, specifically within the Division of Industrial Electric Devices and Decision Systems. He serves as the Director of the Decision Support Systems Laboratory (DSSlab) and the University Research Institute of Communication and Computer Systems. He holds a Diploma in Mechanical Engineering (1982) and a Ph.D. in Electrical and Computer Engineering (1989), both from NTUA. His research specializes in decision support systems with applications in energy management, environmental analysis, and information systems. Key areas include: Multi-criteria analysis for energy policy and renewable integration AI-driven optimization of smart grids and building efficiency Sustainable finance mechanisms for green projects Blockchain applications in education and data security His recent publications (2023–2025) demonstrate a strong focus on AI-enhanced decision tools for energy transitions, smart infrastructure, healthcare diagnostics, and cross-border renewable cooperation, reflecting interdisciplinary innovation. He has supervised 22 PhD theses and coordinates EU-funded projects in energy policy, clean technology, and capacity building. No scientific awards are listed in available sources. He leads the Decision Support Systems Laboratory (DSSlab), advancing research in energy analytics, and directs the University Research Institute of Communication and Computer Systems, facilitating large-scale interdisciplinary collaborations.
Dr. Adegboyega Ojo is a Professor at the School of Public Policy and Administration (SPPA), Carleton University , holding the prestigious Canada Research Chair (Tier 1) in Governance and Artificial Intelligence since 2014. His work bridges digital government , AI governance , and smart city development , with a focus on public sector innovation and policy analytics . Research Interests Smart Cities and Urban Digital Infrastructure Open Government Data and Transparency Blockchain Applications in Public Administration AI Ethics and Algorithmic Governance Electronic Participation and Citizen Engagement Publication Trends : His research emphasizes interdisciplinary approaches to digital governance, often involving data-driven policy analysis , smart city frameworks , and open data platform design . Collaborations span institutions in Ireland, Poland, and the U.S., with citations exceeding 5,200. Scientific Awards : Canada Research Chair in Governance and AI (Tier 1) Labs & Teams : Collaborates with the Insight Centre for Data Analytics (Ireland), Delft University of Technology , and United Nations University on projects like OpenGovIntelligence and BOLD (Big and Open Linked Data).
Reiko Heckel is a Professor of Software Engineering at the University of Leicester, serving as Director of Postgraduate Teaching for Computing degrees and Data Analytics Lead at the Leicester Innovation Hub. She previously held academic roles at the Technical Universities of Dresden and Berlin before joining Leicester in 2004. Her research focuses on graph transformation systems, model-based development, stochastic modeling, and formal methods in software engineering. She earned her PhD (Dr.-Ing.) in Computer Science from TU Berlin in 1998. Her research interests span software engineering pedagogy, formal specification techniques, and applications of graph grammars in system modeling. Recent work explores stochastic graph transformations for social networks, transparency engineering in AI systems, and blockchain-based smart contract frameworks. Her contributions bridge theoretical foundations with practical applications in cybersecurity, data integration, and human-centric systems design. Key contributions include advancements in automated test case generation via graph transformations, visual contracts for software reverse engineering, and formal methods for complex system analysis. Her work frequently intersects with industry through collaborations via the Leicester Innovation Hub, emphasizing data analytics and technology transfer. Education: MSc Computer Science, Technical University of Dresden PhD (Dr.-Ing.), Computer Science, TU Berlin (1998) Leadership Roles: Head of Department (2014-2018) Director of Postgraduate Teaching (Ongoing) Research Themes: Model-Based Development Stochastic Systems Analysis Graph Neural Networks Trustworthy AI Her publications reflect a focus on formal methods, with recent trends in applying graph transformation techniques to social network modeling, blockchain smart contracts, and educational pedagogy.
Jun Zhuang is an Assistant Professor in the Department of Computer Science at Boise State University. He holds a Ph.D. from Indiana University-Purdue University Indianapolis (IUPUI), M.S. degrees in Computer Science (University at Buffalo) and Finance (Rochester Institute of Technology), and a B.E. in Safety Engineering (South China University of Technology). His research focuses on trustworthy and robust AI systems, Bayesian inference, generative models, quantum computing, and medical imaging. Education: Ph.D., Computer Science, IUPUI (2023) M.S., Computer Science, University at Buffalo (2018) M.S., Finance, Rochester Institute of Technology (2013) B.E., Safety Engineering, South China University of Technology (2011) Research Interests: Jun investigates robust machine learning algorithms, particularly in quantum information, medical imaging, and graph-based systems. He emphasizes mitigating adversarial attacks, enhancing model interpretability, and integrating blockchain for AI security. His work spans theoretical foundations and practical applications, including generative adversarial networks (GANs) and trustworthy AI frameworks. Recent Articles: His recent work addresses jailbreaking vulnerabilities in large language models (LLMs), quantum computing optimization challenges, and robust graph structure learning. These studies highlight interdisciplinary approaches to advancing AI reliability and security. Awards & Grants: Recipient of the SIGIR Student Travel Grant for CIKM 2022. Active in grant activities through research collaborations and institutional funding. Advising & Labs: Advisor to Ph.D. student Maqsudur Rahman and M.S. students Chia-Ying Wu and Shipra Kumari. Leads the T rustworthy and R obust AI L ab (TRAIL), focusing on developing resilient AI systems.
Dr Yong Lin is an Associate Professor in Operations and Supply Chain Management at the Department of Management, Birmingham Business School, University of Birmingham. He joined the university in September 2021, having previously held academic positions at the University of Greenwich and Huazhong University of Science & Technology. He has also served as a visiting researcher at the Institute for Manufacturing (IfM), University of Cambridge, and as a research assistant at the Hong Kong University of Science & Technology. PhD in Management Science and Engineering, Huazhong University of Science & Technology, 2002 MSc in Industrial Engineering and Management, Huazhong University of Science & Technology, 1999 Dr Lin’s research focuses on digital transformation and sustainability strategies in operations and supply chains. His key interests include supply chain risk and resilience, digital logistics platforms, business ecosystems, and manufacturing strategies. He explores how digital technologies such as blockchain and RFID can enhance transparency, efficiency, and sustainability across supply networks. His recent publications reflect a strong trend toward digitalization, resilience, and sustainability in supply chains, with studies on modular resilience in multinational platforms, blockchain in remanufacturing, and digitalization in healthcare logistics. His work frequently appears in top-tier journals such as International Journal of Operations and Production Management , Journal of Cleaner Production , and Technological Forecasting and Social Change . Senior Fellow of the Advance HE (Higher Education Academy) Certified Management and Business Educator, Chartered Association of Business Schools (CABS) Chartered Fellow, Chartered Institute of Logistics and Transport (CILT) Dr Lin has led and coordinated several research projects funded by the National Natural Science Foundation of China (NSFC), the 863 Program, and the European Regional Development Fund. He actively engages in industry consulting to transfer research outcomes into practical business solutions. He welcomes PhD applicants interested in digital transformation, sustainability, logistics platforms, and business ecosystems. He is also involved in advising and mentoring graduate researchers in these domains. Dr Lin is affiliated with the Department of Management at Birmingham Business School, where he contributes to research, teaching, and knowledge exchange in operations and supply chain management. His work bridges academic rigor with real-world application, particularly in digital and sustainable supply chain innovation.
Georges Kaddoum is a Professor at École de technologie supérieure (ÉTS) , specializing in Electrical Engineering. He holds the Canada Research Chair in Unlocking the Power of IoT 6G-Networks and the LACIME – Communications and Microelectronic Integration Laboratory affiliation. His work bridges wireless communications, IoT, and machine learning. Research Interests include wireless communication systems, physical layer security, machine learning for networking, and 6G technologies. He focuses on optimizing network performance in challenging environments (impulsive noise, underwater, non-terrestrial networks) and developing AI-driven solutions for jamming mitigation, resource allocation, and secure IoT frameworks. Recent Publications highlight 6G-enabled vehicular networks, quantum-safe blockchain integration, federated learning for transportation systems, and deep learning-based receivers for chaotic communication systems. His work emphasizes semantic communication, interference management, and digital twin applications. Awards IEEE TCSC Award for Excellence in Scalable Computing (2022) Prix d’excellence de la relève (Université du Québec, 2018) Prix d’excellence en recherche (ÉTS, 2018) Multiple IEEE Exemplary Reviewer and Best Paper Awards (2014–2022) Supervision includes 15+ PhD/Master’s students working on topics like index modulation, physical layer security, UAV communications, and intelligent resource management. Research Units Ultra Research Chair on Intelligent Tactical Wireless Networks LACIME Laboratory Canada Research Chair in IoT 6G-Networks
Eric Talley is the Marc and Eva Stern Professor of Law and Business at Columbia Law School , where he has served as Full-time Faculty since 2015. Prior to Columbia, he held tenured positions at the University of California, Berkeley (2006–2015) and the University of Southern California (1995–2006), and visiting appointments at Caltech, University of Chicago, ETH-Zurich, European University Institute, Georgetown, Harvard, RAND Graduate School, and Stanford. Education : J.D. and Ph.D. in Economics from Stanford University; B.A. in Economics & Political Science from UC San Diego His research and teaching focus on Corporate Law , Mergers & Acquisitions , Corporate Governance , Law and Machine Learning , and Economic Analysis of Law . He co-directs the Ira M. Millstein Center for Global Markets and Corporate Ownership , directing research on corporate governance and performance trends. Recent publications analyze topics such as valuation disputes , officer liability in Delaware , and gender dynamics in startups , reflecting his interdisciplinary approach blending law, finance, and data science. His work has been featured in top journals and media outlets like the Wall Street Journal , Bloomberg , and Harvard Law School’s Forum . Scientific Awards : Willis L.M. Reese Prize for Excellence in Teaching (2017, 2022) Elected member of the American Academy of Arts & Sciences (2024) Talley hosts the Beyond “Unprecedented” podcast, exploring economic recovery post-pandemic. He serves on the Executive Committee of the Columbia University Data Science Institute and has held leadership roles in the Society for Empirical Legal Studies and the American Law and Economics Association.
Jeff Shamma is the Department Head and Professor of Industrial and Enterprise Systems Engineering (ISE) at the University of Illinois at Urbana-Champaign, holding the Jerry S. Dobrovolny Chair. He is also courtesy Professor in Aerospace Engineering and Mechanical Science and Engineering. Formerly, he held the Julian T. Hightower Chair at Georgia Institute of Technology and faculty positions at KAUST. Dr. Shamma earned his PhD in Systems Science and Engineering from MIT (1988) and a BS in Mechanical Engineering from Georgia Tech (1983). He is a Fellow of IEEE and IFAC, recipient of the IFAC High Impact Paper Award, AACC Donald P. Eckman Award, and NSF Young Investigator Award. His research spans Decision and Control , Game Theory , and Multi-Agent Systems , focusing on human-machine networks, distributed autonomy, and adaptive robotic systems. Recent work examines crowd dynamics, risk-sensitive control, and feedback linearization for constrained optimization. Jeff has served as Editor-in-Chief of IEEE Transactions on Control of Network Systems (2020–2024) and held editorial roles in journals like Annual Reviews in Control and IEEE Transactions on Robotics . His 15 most recent publications (2024–2025) analyze learning dynamics, multi-agent optimization, and UAV-crawler systems, reflecting trends in autonomous systems, game-theoretic modeling, and industrial inspection technologies. Scientific distinctions include: Fellow of IEEE and IFAC IFAC High Impact Paper Award (2020) AACC Donald P. Eckman Award (1996) NSF Young Investigator Award (1992) Mohammed Dahleh Distinguished Lecture Award (2013) Dr. Shamma advises current PhD students Hassan Abdelraouf, Aya Hamed, and Nawaf Otaibi, with former advisees including Sarah Toonsi (2025) and Fat-hy Rajab (2025). His lab integrates theoretical research with applied projects like FalconScan, a UAV-crawler system for industrial inspection, and develops magnetic legs for curved surface UAV landing.
Christopher Kruegel is a Professor in the Department of Computer Science at the University of California, Santa Barbara (UCSB), where he conducts research in systems security. He is affiliated with the International Secure Systems Lab (iSecLab) and was a co-founder of Lastline, Inc., which was acquired by VMware in 2020. His work focuses on creating practical security solutions that address real-world problems through system building and experimental validation. Professor Kruegel's research spans multiple areas of computer security including malware analysis, web security, network security, and vulnerability analysis. His work often involves developing systems that analyze programs for malicious behavior, scanning web applications for vulnerabilities, and improving privacy on social networks. He has made significant contributions to firmware security, smart contract analysis, and mobile security through his extensive publication record. His 15 most recent publications (2022-2023) demonstrate a strong focus on practical security solutions across diverse domains including firmware security (Shimware, Fuzzware), blockchain and smart contract security (Confusum Contractum, NFT security), mobile security (TEEzz, Columbus), and vulnerability detection techniques (Actor, Toss a Fault to Your Witcher). His research shows consistent innovation in security tools and methodologies with several papers receiving distinguished awards. Fellow of the Institute of Electrical and Electronics Engineers Outstanding Graduate Mentor Award, UCSB Academic Senate Distinguished Artifact Award for Fuzzware paper Distinguished Paper Award for Ramblr paper Best Student Paper Award for Detecting Spammers On Social Networks Professor Kruegel has advised numerous graduate students and collaborated extensively with researchers at UCSB and beyond. His work has been funded by various research grants supporting his security research initiatives. He is actively involved in the International Secure Systems Lab, which focuses on practical security solutions for real-world problems.
Ke Xu is a Professor in the Department of Computer Science at Tsinghua University's School of Information Science and Technology. With extensive research contributions in network security, privacy-preserving technologies, and machine learning applications for networking, Professor Xu has established himself as a leading researcher in computer science. Professor Xu's research interests span network security, privacy-preserving technologies, machine learning for networking, federated learning, internet protocols, encrypted traffic analysis, blockchain applications, and AI in networking. His work bridges theoretical foundations with practical implementations, focusing on real-world security challenges and network optimization problems. He has developed novel frameworks for secure network operations, privacy-preserving data sharing, and efficient AI deployment in distributed environments. Professor Xu's publication record shows a clear trend toward integrating artificial intelligence with traditional networking challenges. His recent work explores federated learning security, encrypted traffic analysis using deep learning, and novel approaches to network security that leverage machine learning techniques. The interdisciplinary nature of his research spans computer networking, security, privacy, and artificial intelligence. Professor Xu has received recognition for his contributions to network security and privacy-preserving technologies through publications in top-tier venues including IEEE journals, ACM conferences, and security symposia. His work has appeared in IEEE Transactions on Dependable and Secure Computing, IEEE/ACM Transactions on Networking, and security conferences like CCS and NDSS. Professor Xu actively collaborates with researchers across institutions, supervising students and junior researchers in exploring cutting-edge problems in network security and AI. His research has been supported by significant grants focusing on network security, privacy, and intelligent networking infrastructure. He leads projects that address fundamental challenges in secure communication, privacy-preserving data analysis, and intelligent network management. Professor Xu is involved with research laboratories focusing on network security and intelligent systems at Tsinghua University. His team works on developing practical security solutions, privacy frameworks, and AI-enhanced networking protocols that address real-world challenges in today's increasingly connected world.
Petter N. Kolm serves as a Clinical Professor of Mathematics and Program Director at New York University, with his office located in Warren Weaver Hall (520). He can be contacted at petter.kolm@nyu.edu or 212-998-4855, and holds an editorial board position at the Journal of Portfolio Management. His academic qualifications include: Doctorate in Mathematics from Yale University M.Phil. in Applied Mathematics from the Royal Institute of Technology in Stockholm M.S. in Mathematics from ETH Zurich Dr. Kolm's research centers on quantitative finance, with primary focus areas including quantitative trading strategies, delegated portfolio management, financial econometrics, risk management, and optimal portfolio strategies. His work integrates advanced mathematical modeling with practical investment applications, bridging theoretical frameworks and real-world market dynamics through rigorous empirical analysis. Analysis of his 15 most recent publications reveals consistent emphasis on portfolio optimization techniques—particularly Bayesian methods and the Black-Litterman model—alongside significant contributions to algorithmic trading systems, factor-based equity portfolio construction, and machine learning applications for financial sentiment analysis. His scholarly output demonstrates evolution from foundational portfolio theory toward contemporary computational finance challenges. As Program Director, Dr. Kolm oversees academic programming and likely mentors graduate students in quantitative finance, though specific advisee details are not documented. His prior industry role at Goldman Sachs Asset Management provided direct experience in developing hedge fund strategies, informing his applied research approach. Dr. Kolm's professional trajectory includes significant industry engagement through his tenure in Goldman Sachs' Quantitative Strategies Group, where he developed quantitative investment systems. His current academic leadership position leverages this practical experience to shape quantitative finance education and research at NYU.
Akie Iriyama is a Professor at the Graduate School of Business and Finance, Waseda University, with affiliations to the Faculty of Commerce and Graduate School of Advanced Science and Engineering. Their research bridges practical and theoretical aspects of entrepreneurship, international business, and strategic management. Education: PhD, University of Pittsburgh, Joseph M. Katz Graduate School of Business M.A. and B.A., Keio University, Tokyo, Japan Research Focus: Globalization of venture capital and transnational technical communities Strategic responses to informal and foreign rivals in emerging markets Equity ownership dynamics in international joint ventures Technology sourcing and real options theory in MNC subsidiaries Paper Trends: Recent publications analyze competitive actions in non-market spaces, reverse technology transfer mechanisms, and equity ownership evolution in international partnerships. Common themes include cross-border knowledge flows, strategic paradoxes in outsourcing, and institutional factors shaping innovation. Scientific Awards: Waseda University Teaching Award (2016) Best paper award nominee (Academy of Management, 2016) Dean’s Discretionary Award (SUNY Buffalo, 2010) Nominated for Haynes Prize (Academy of International Business, 2010) Nominated for Best Conference Papers (Strategic Management Society, 2008) Research Projects: Current projects include large-scale data analysis of urban startup location strategies in Japan (2020-2024) and studies on multi-partner international joint ventures.
