Gordon Geoffrey J. is a Professor at Carnegie Mellon University, specializing in Machine Learning , Artificial Intelligence , and Computer Science . His research spans Reinforcement Learning , Domain Adaptation , Graph-based Planning , and Relational Learning , with significant contributions to Adversarial Learning , Deep Learning Dynamics , and Mobile Sensor Networks . He has pioneered algorithms like ARA* and Anytime Dynamic A* for efficient planning under constraints. Key research areas: Transfer Learning , Bayesian Knowledge Tracing , Multi-agent Systems , Database Optimization His work has been cited thousands of times across foundational topics in AI and robotics, demonstrating sustained impact in algorithm design and applications to complex systems.
Edouard Oyallon is a CNRS Researcher at Sorbonne University's MLIA team within the Institute of Intelligent Systems and Robotics (ISIR). His research focuses on machine learning foundations, particularly the symmetries of deep neural networks, and large-scale distributed/decentralized training algorithms. He has contributed to frameworks like Kymatio for wavelet scattering transforms and collaborates on projects such as SHARP (Frugal Learning) and ADONIS (ANR-funded). He advises multiple PhD and postdoctoral researchers and teaches advanced deep learning courses at Institut Polytechnique de Paris (IPP). Grants include the ADONIS project (ANR/Sorbonne) and participation in VHS and CoCa4AI initiatives. His work spans theoretical and applied aspects, with recent emphasis on optimizing LLM training at exascale. He maintains active roles in academic service, including organizing workshops on federated learning and graph machine learning.
Ke Wang is a Professor in the School of Computing Science at Simon Fraser University . His research focuses on Data Mining , Database Systems , Data Privacy , and Graph and Network Data . He holds a Ph.D. and M.Sc. from the Georgia Institute of Technology (1986 and 1984, respectively). Teaching includes courses like Database Systems II , Introduction to Data Mining , and Special Topics in Databases . He has advised numerous students and alumni, many of whom now work in tech, academia, and industry. Notable awards include the 2013 Faculty of Applied Sciences Research Excellence Award and the ECIR 2019 Best System Paper . His work emphasizes privacy-preserving techniques and has led to contributions like the Introduction to Privacy-Preserving Data Publishing textbook. He has served as a conference chair for major data mining events like SDM 2015/2016 and holds editorial roles in journals like ACM TKDD. His lab, the Database and Data Mining Laboratory , focuses on actionable solutions for real-world data challenges.
Gian Antonio Susto is an Associate Professor at the Department of Information Engineering , University of Padova . With a Ph.D. in Information Technology and post-doctoral experience at National University of Ireland, Maynooth, he leads research in Machine Learning , Semiconductor Manufacturing , and Industrial IoT . His work bridges Anomaly Detection , Continual Learning , and Algorithmic Fairness with applications in Hydroelectric Power Plants , Particle Accelerators , and Smart Mobility . B.Sc. and M.Sc. in Controls Engineering, University of Padova (cum laude) Ph.D. in Information Technology, University of Padova (2013) Post-Doc at National University of Ireland, Maynooth (2012-2013) Assistant Professor at University of Padova (2013-2021) His research focuses on Explainable AI , Virtual Metrology , and Deep Learning for manufacturing and infrastructure monitoring. Recent projects include the AIMS5.0 (AI for Manufacturing Sustainability) and MICS (Circular Economy in Italy) initiatives. His publications span Engineering Applications of Artificial Intelligence , IEEE Transactions , and Information Processing & Management , with 15+ recent papers on topics like Fault Diagnosis , Continual Learning , and Fair Ranking . Key scientific awards include: IEEE CCTA Best Student Paper Award (2021) IP&M 2020 Ph.D Paper Award Best Industry Paper Award, European Workshop on Advanced Control and Diagnosis (ACD 2019) He has supervised Ph.D. students on projects involving Particle Accelerators , Plant Behavior Modeling , and Explainable AI , with alumni now at institutions like Max Planck Institute , IBM , and Scripps Research . Current teaching includes Reinforcement Learning and Explainable Machine Learning at graduate and Ph.D. levels.
Gioele Zardini is the Rudge (1948) and Nancy Allen Assistant Professor at MIT's Department of Civil and Environmental Engineering (CEE), with affiliations to the Laboratory for Information and Decision Systems (LIDS) and the Institute for Data, Systems, and Society (IDSS). He holds a PhD from ETH Zurich and previously worked as a postdoctoral scholar at Stanford University. His research focuses on co-design of complex systems, autonomous systems, and game-theoretic modeling of transportation networks. Education: BSc and MSc in Mechanical Engineering and Robotics from ETH Zurich (2017–2019), PhD in 2023. He has held visiting roles at nuTonomy Singapore, Stanford, and MIT. Research interests include co-design methodologies, autonomous vehicle systems, compositionality in engineering, and strategic interactions in mobility networks. Recent work emphasizes scalable fleet coordination, safety-critical robotics, and user-centric transportation solutions. Notable awards include the 2024 ETH Doctoral Dissertation Award (Silver Medal), Best Paper at ITSC 2021, and federal grants for enhancing urban transit equity. He leads the Zardini Lab, fostering interdisciplinary collaboration in systems engineering and autonomy. Grants and advising: Received federal grants for transit accessibility projects. His work on Autonomy Talks has produced over 180 recorded lectures, promoting knowledge exchange in autonomous systems. Labs/Teams: Principal Investigator at LIDS, affiliate at IDSS, and founder of the Zardini Lab, focusing on systems co-design, mobility innovation, and game-theoretic frameworks.
Claudio Canizares is a University Professor and Hydro One Endowed Chair in the Department of Electrical and Computer Engineering at the University of Waterloo. He also serves as Executive Director of the Waterloo Institute for Sustainable Energy (WISE). With a career spanning over 30 years, his research focuses on power systems stability, smart grids, microgrids, and renewable energy integration. He has secured nearly $118 million in grants and supervised 180+ researchers/students. Education: PhD (1991) and MSc (1988) in Electrical Engineering from University of Wisconsin-Madison; Electrical Engineering Diploma (1984) from Escuela Politécnica Nacional, Ecuador. Research Interests : Nonlinear systems theory, FACTS/HVDC applications, energy storage systems, microgrid stability/control, renewable integration in remote communities, and smart grid analytics. His work emphasizes bridging academic research with industrial applications through collaborations with utilities and tech firms. Key Achievements : IEEE Transactions on Smart Grid Editor-In-Chief; multiple IEEE Fellowships (IEEE, Royal Society of Canada, Canadian Academy of Engineering); 2017 IEEE PES Outstanding Educator Award; 2016 IEEE Canada Electric Power Medal. His publications (370+) include landmark papers on microgrid stability definitions and control frameworks, cited over 29,000 times. Teaching: Recently taught ECE 140 (Linear Circuits), ECE 467 (Power Systems Analysis), and graduate courses ECE 6601PD/ECE 6613PD on power systems modeling and analysis.
Mike Kirby is a Professor at the Kahlert School of Computing, University of Utah. He also holds adjunct professorships in the Department of Bioengineering and the Department of Mathematics. His current roles include leadership in scientific computing and informatics initiatives, including former directorships of the Utah Informatics Initiative (2019-2023) and the Multi-Scale Multidisciplinary Modeling of Electronic Materials (MSME) Collaborative Research Alliance (2016-2022). He has extensive experience in strategic research initiatives, including serving as Assistant Vice President for Research (2024-2025). Education: Dr. Kirby earned a PhD in Applied Mathematics (2002) and MS in Computer Science (2001) from Brown University, and a BS in Applied Mathematics and Computer Science from Florida State University (1997). Research Interests: Focus on large-scale scientific computing, physics-informed machine learning, computational science and engineering, high-order numerical methods, and visualization. His work bridges applied mathematics and computer science to address real-world engineering challenges. Publications: Over 150 peer-reviewed articles, including high-impact contributions in journals like Journal of Computational Physics and SIAM Journal on Scientific Computing . Recent work emphasizes machine learning for differential equations, topology optimization under uncertainty, and multi-fidelity modeling. Awards: Recognized for leadership in computational science and informatics, including contributions to University of Utah’s Clery Compliance Program. Advising & Grants: Supervised over 50 graduate students and postdocs. Secured funding from NSF, DOE, and industry partnerships, totaling millions in research grants. Active in interdisciplinary collaborations across engineering, materials science, and medicine. Labs/Teams: Scientific Computing and Imaging (SCI) Institute, Utah Informatics Initiative, and the Center for Multiscale Modeling of Electronic Materials (MSME).
Dr. Yujie Tang is an Assistant Professor in the Faculty of Computer Science at Dalhousie University, Canada, where she has been serving since September 2022. Prior to this, she was an Assistant Professor at Algoma University (2019–2022) and a Post-Doctoral Fellow at the University of Waterloo (2017–2019). Her academic journey includes a PhD from the University of Waterloo and earlier degrees from Harbin Institute of Technology and Lanzhou Jiaotong University. PhD – University of Waterloo (2017) M.E. – Harbin Institute of Technology, Shenzhen, China B.E. – Lanzhou Jiaotong University, Lanzhou, China Her research focuses on intelligent networking and computing technologies for future IoT and 5G/6G systems. Key areas include Internet of Vehicles (IoV), AI-empowered edge computing, resource management in heterogeneous networks, software-defined networking, and UAV-assisted communications. She employs machine learning and optimization techniques to design energy-efficient and high-performance network protocols. The most recent publications reflect a strong trend in applying AI and machine learning to solve complex problems in vehicular networks, edge caching, and spectrum management. Her work spans top-tier IEEE journals such as IEEE Transactions on Vehicular Technology , IEEE Internet of Things Journal , and IEEE JSAC , with a clear emphasis on real-world deployable solutions for next-generation wireless systems. Faculty Research Startup Fund, Dalhousie University, 2022 NSERC Discovery Grant, 2021–2026 Algoma University Research Fund, 2021 Faculty Research Startup Fund, Algoma University, 2019 Best Speaker Award, University of Waterloo, 2017 Faculty of Engineering Award (4 times), University of Waterloo, 2013–2017 University of Waterloo Graduate Scholarship, 2013–2015 International Doctoral Student Award, 2012–2016 Graduate Research Studentship (twice), 2011–2012 Provost Doctoral Entrance Award for Women, 2011 Dr. Tang actively supervises graduate and undergraduate students and has secured competitive research grants, including the NSERC Discovery Grant. She serves on the technical program committees of major IEEE conferences such as INFOCOM, GLOBECOM, and ICC, and regularly reviews for top journals like IEEE JSAC , IEEE TWC , and IEEE TVT . She currently leads a research group focusing on B5G/6G networks, IoV, and edge computing, and she is actively recruiting new students and visiting scholars. Her research group operates within the Faculty of Computer Science at Dalhousie University, where she leads projects in intelligent resource management, AI-driven networking, and integration of space-air-ground networks. She is a member of IEEE, IEEE Communications Society, and IEEE Vehicular Technology Society.
David Gesbert serves as Professor and Director of EURECOM, a leading research institution in Sophia Antipolis, France, specializing in digital sciences. Previously heading the Communications Systems Department, he now directs EURECOM while leading the Foundations & Algorithms research group within the Communication Systems Department. His leadership spans institutional administration and cutting-edge research supervision. Dr. Gesbert's research portfolio demonstrates exceptional depth across communications theory and wireless networking: Communication theory and information theory fundamentals Signal processing for wireless networks with emphasis on robustness Machine learning applications for decentralized network optimization Connected robotics and UAV-enabled flying radio access networks 6G wireless architecture with focus on AI integration Distributed decision making under information uncertainties His publication trajectory reveals a strategic evolution from classical communication theory toward AI-integrated wireless systems, particularly focusing on UAV-aided networks and connected robotics for 6G. Recent work emphasizes learning-based approaches for network optimization under asymmetric information conditions, with growing emphasis on sustainability and green communications. Dr. Gesbert's scientific recognition includes: Fellow of IEEE (2011) and Asia Pacific Artificial Intelligence Association (2021) Thomson-Reuters List of Highly Cited Researchers in Computer Science Multiple Best Paper Awards at IEEE conferences (EW 2017, ICC 2019) ERC Advanced Grant recipient for the PERFUME project on Smart Device Communications 3IA Chair funding for AI for future IoT Networks 2019 Winner of 'Fundamental research project of the year' by French SCS He actively mentors PhD students and researchers, welcoming collaboration in his research domains. His group secures substantial research funding including ERC grants, 3IA Chairs, and participation in major European projects like the WINDMILL ITN Marie Curie project on Machine Learning in Wireless Communications. Dr. Gesbert serves on editorial boards and regularly delivers keynotes at premier international conferences, establishing himself as a thought leader in next-generation wireless communications.
Professor Peter Y. K. Cheung is a Professor of Digital Systems at Imperial College London, holding dual affiliations within the Department of Electrical and Electronic Engineering and the Dyson School of Design Engineering. His work focuses on reconfigurable systems, FPGA architectures, and high-level synthesis tools. He co-founded one of the UK's leading FPGA research groups with Professor Wayne Luk, addressing challenges in variability mitigation, reliability, and application-specific FPGA deployments. His research spans Field-Programmable Gate Arrays (FPGAs) Reconfigurable computing Neural network acceleration Cryptographic protocols Embedded systems He has pioneered techniques such as logic shrinkage for FPGA-based neural networks and developed frameworks like LUTNet for efficient inference. His contributions also include fault-tolerant FPGA designs and methodologies for distributed computation protocols. Key collaborations include work with the Department of Computing on FPGA-based AI acceleration and cybersecurity applications. His recent work explores edge computing, secure decentralized systems, and pandemic modeling using adaptive control strategies. Notable projects include the DSCS protocol for secure distributed computation, acceleration of gravitational wave detection algorithms, and energy-efficient CNN implementations. His research bridges hardware-software co-design with real-world applications in healthcare, finance, and aerospace.
Juan Garay is a Professor in the Department of Computer Science & Engineering at Texas A&M University, affiliated with the College of Engineering. His research focuses on cryptography, information security, and distributed systems, with notable contributions to cryptographic protocols, blockchain technologies, and consensus mechanisms. He holds a leadership role in advancing theoretical and applied aspects of secure computation and network security. Research Interests: Cryptography and Information Security Secure Multiparty Computation Cryptocurrencies and Blockchain Protocols Consensus Algorithms Distributed Computing Game Theory in Cryptography Publications highlight his work on the Bitcoin Backbone Protocol, secure multiparty computation, and post-quantum cryptographic systems. He actively contributes to conferences and workshops in cryptography and distributed systems. He advises graduate students in computer science and engineering, though specific advisee names are not listed. His work is supported by grants from the National Science Foundation (NSF) and other institutions, focusing on secure protocols and distributed systems. Office: Peterson Building (PETR 429) Contact: garay@cse.tamu.edu
Catherine Tucker is the Sloan Distinguished Professor of Management and Professor of Marketing at the MIT Sloan School of Management. She serves as the faculty director of the EMBA program and has chaired the MIT Sloan PhD Program. Her research focuses on the intersection of technology, marketing, and public policy, with expertise in digital privacy, online advertising, and blockchain applications. She co-founded the MIT Cryptoeconomics Lab and has testified before Congress on digital privacy issues. Tucker holds a PhD in Economics from Stanford University and a BA from the University of Oxford. Education: PhD in Economics, Stanford University Bachelor of Arts, University of Oxford Research Interests: Algorithmic bias and fairness Privacy regulation and consumer data Blockchain technology applications Healthcare technology and digital health Marketing strategy in digital ecosystems Awards: NSF CAREER Award (2010s) William F. O’Dell Award (Long-term Impact in Marketing) Garfield Economic Impact Award (2020) Editorial Roles: Senior editor at Marketing Science , former co-editor at Quantitative Marketing and Economics , and associate editor roles at Management Science and Journal of Marketing Research . Labs & Initiatives: Co-director of the NBER Digital Economics and AI program, and co-founder of the MIT Cryptoeconomics Lab studying blockchain applications.
Matteo Maffei is a Full Professor at TU Wien, leading the Security and Privacy group. He joined in 2017 after 11 years at Saarland University's CISPA. He holds a Ph.D. in Computer Science from Ca’ Foscari University of Venice (2006). He coordinates the TU Wien Cybersecurity Center, the SecInt Doctoral School, and the FWF Special Research Program SPyCoDe. His research focuses on formal methods for security and privacy, blockchain technologies, and web security. Roles: Full Professor, Coordinator of TU Wien Cybersecurity Center, Module Head of Christian Doppler Lab for Blockchain Technologies (CDL-BOT), Board Member of Vienna Cybersecurity and Privacy Research Cluster (ViSP). His work emphasizes formal verification of cryptographic protocols, smart contracts, and decentralized systems. Key achievements include ERC Advanced (2024) and Consolidator (2018) Grants, and leadership roles in conferences like IEEE Computer Security Foundations Symposium (CSF). Research Interests: Formal methods, smart contracts, blockchain scalability, web security, privacy-preserving protocols, and decentralized systems. His recent projects include optimizing Lightning Network channels, secure multi-hop payments, and AI-driven robustness verification. Publications: Over 200 publications in top venues like CCS, IEEE S&P, and CRYPTO. Recent work includes advancements in blockchain interoperability (e.g., Alba bridges), light client protocols (Blink), and neural network verification techniques. Grants & Awards: ERC Advanced Grant (2024), ERC Consolidator Grant (2018), DFG Emmy Noether Fellowship (2009). Led projects funded by EU Horizon, FWF, and industry partners like ABC Research GmbH. Advising & Labs: Supervised over 20 PhD/Master theses. Active in the Christian Doppler Lab for Blockchain Technologies and the SPyCoDe SFB. Collaborates with institutions like SBA Research and Stanford University.
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.
Mathias Funk is an Associate Professor in the Industrial Design department at Eindhoven University of Technology (TU/e), leading the Things Ecology lab. His research focuses on designing with data and systems behavior, particularly in IoT and medical technology contexts. He co-founded UXsuite GmbH and developed the OOCSI communication framework. With a PhD in Electrical Engineering from TU/e (2011), Funk has held academic roles since 2013, including postdoctoral positions in Electrical Engineering and Industrial Design. He teaches courses like 'Data-enabled design' and supervises projects on AI, data foundry, and OOCSI applications. His work bridges engineering and design, addressing sustainability and human values through systemic design approaches. Education: PhD in Electrical Engineering, TU/e (2011) Computer Science background from RWTH Aachen Postdoc at TU/e's Electrical Engineering and Industrial Design departments Research Interests: Data-driven design methodologies, IoT systems, medical technology innovation, sustainable design practices, and interactive systems for musical expression. Recent work emphasizes telemonitoring efficacy in heart failure care and federated learning applications. Awards: Best Paper Award (2014) Honorable Mention (2021) Grants/Projects: Leading initiatives like MEDICAID (medtech solutions), ACACIA (interpretable AI), and Data-Driven Servitizing in Professional Printing. Active in clinical workflow prototyping and cardiovascular disease detection projects. Lab Activities: Manages the Things Ecology lab, focusing on systemic design challenges in smart ecosystems. Collaborates internationally through spin-offs and visiting lectureships.
