Thorsten Joachims is a Professor at Cornell University with a focus on machine learning, recommendation systems, and algorithmic fairness. His work spans conferences like ICML, NeurIPS, KDD, and SIGIR, emphasizing counterfactual learning, contextual bandits, and ethical AI. Key Research Themes: Fairness in rankings, reinforcement learning, position bias estimation, and NLP applications to recommendation systems. Recent Publications: 2025 work on policy decomposition for contextual bandits, 2024 studies on fairness under uncertainty, and 2023 papers on LLM steerability and bias mitigation. Awards: Recipient of the ACM SIGKDD 2020 Innovation Award . Collaborators: Regularly works with Adith Swaminathan, Tobias Schnabel, Yuta Saito, Ashudeep Singh, and Yi Su.
Zhongguo Li is a Lecturer in Robotics, Control, Communication & AI at the University of Manchester. He holds a B.Eng. (2017) and Ph.D. (2021) in Electrical and Electronic Engineering from the University of Manchester. Prior to his current role, he was a Lecturer at University College London (2022-2023) and a Research Associate at Loughborough University (2020-2022). His research focuses on distributed control, optimization, and reinforcement learning, particularly in robotics and autonomous systems. Key areas include multi-agent coordination, networked systems, and applications in autonomous vehicles. He has authored over 40 papers in top journals/conferences and co-authored a book on Distributed Optimization and Learning (2024). Teaching responsibilities include courses such as Control Systems II, Nonlinear and Adaptive Control, and Embedded Systems Project. He serves as an Associate Editor for Drones and Autonomous Vehicles and Guest Editor for Machines and Frontiers in Control Engineering. Dr. Li actively mentors PhD students, offering guidance on funding opportunities and research projects in distributed algorithms, robotics, and control systems. His work aligns with UN Sustainable Development Goals related to innovation and infrastructure.
Shuangning Li is an Assistant Professor of Econometrics and Statistics at the University of Chicago's Booth School of Business. He holds a Ph.D. from Stanford University's Department of Statistics, advised by Professors Emmanuel Candès and Stefan Wager, and a Bachelor of Science from the University of Hong Kong. Prior to his current role, he was a postdoctoral fellow in Statistics at Harvard University. His research focuses on causal inference, machine learning, and statistical methodology with applications in econometrics, networks, and genomics. **Education:** Ph.D. in Statistics, Stanford University (Advisors: Emmanuel Candès, Stefan Wager) Bachelor of Science, University of Hong Kong **Research Interests:** Causal inference in complex systems (e.g., networks, high-dimensional data) Statistical methods for experimental design and robustness Machine learning applications in genomics and reinforcement learning Randomization-based testing and knockoff filters **Recent Work Trends:** His articles emphasize methodological innovations in causal effect estimation, network interference modeling, and transfer learning. Recent work addresses challenges in stochastic congestion, multi-environment analysis, and cooperative learning frameworks. His 2024 paper advances covariate shift correction for conditional randomization tests, while his 2023 studies explore robustness in model-X inference and dyadic reinforcement learning dynamics. **Advising & Academic Background:** His doctoral training under Candès and Wager shaped his focus on rigorous statistical foundations. He has not yet listed advising relationships in available materials, but his research collaborations span academia and industry.
Guillaume A SARTORETTI is an Assistant Professor in the Mechanical Engineering Department at the National University of Singapore (NUS), part of the College of Design and Engineering. He specializes in distributed/decentralized coordination of multi-agent systems, with a focus on robotics, stochastic modeling, and reinforcement learning. His work spans applications in multi-robot systems, articulated robots, and swarm intelligence. Joined NUS in 2019 after a postdoctoral fellowship at Carnegie Mellon University (CMU) and a PhD from EPFL. Education: PhD in Robotics (EPFL, 2016); MSc and BSc in Mathematics/Computer Science (University of Geneva). Research interests include: Multi-agent pathfinding Decentralized control policies Swarm intelligence Reinforcement learning applications in robotics Recent publications emphasize scalable solutions for multi-agent systems, traffic signal control, and safe robotic exploration. His 2018 MFI postdoctoral fellowship recognized work on distributed reinforcement learning for pathfinding. Current projects involve bio-inspired locomotion and collaborative learning frameworks for heterogeneous robots.
Reza Bosagh Zadeh is an Adjunct Professor at the Institute for Computational and Mathematical Engineering (ICME) at Stanford University. His research focuses on machine learning, deep learning, and their applications in video classification, healthcare analytics, and distributed algorithms. He specializes in developing scalable computational methods for real-time data processing and has contributed to advancements in neural networks and optimization techniques. Reza's work spans theoretical and applied domains, with notable contributions to TensorFlow frameworks, video summarization systems, and medical imaging analysis. His research often integrates interdisciplinary approaches, leveraging both academic and industrial collaborations. Notable projects include developing machine learning models for glaucoma detection and creating efficient algorithms for large-scale data processing in environments like Apache Spark. His publications emphasize real-time video stream analysis, distributed computing architectures, and practical implementations of deep learning. Reza holds a strong presence in both academic and tech sectors, with contributions to platforms like Twitter's Who-to-Follow system and innovations in edge computing for video surveillance.
Dr. Hossein Sayadi is an Assistant Professor and Associate Chair in the Department of Computer Engineering and Computer Science at California State University, Long Beach (CSULB). He holds a Ph.D. in Electrical and Computer Engineering from George Mason University, an M.S. from Sharif University of Technology, and a B.S. from K. N. Toosi University of Technology. His research focuses on hardware security , AI/ML applications , cybersecurity , and computer architecture . He leads the iSEC Lab , exploring topics like hardware trust, malware detection, and edge computing security. His work is supported by NSF grants and CSU awards, including the 2024-25 CSU STEM-NET Faculty Fellowship. Education: Ph.D., Electrical and Computer Engineering (George Mason University) M.S., Computer Engineering (Sharif University of Technology) B.S., Computer Engineering (K. N. Toosi University of Technology) His publications span conferences like IEEE ISQED, ISCAS, and DATE. He serves as Technical Program Committee Chair for IEEE ISQED (2024–2025). Awards include NSF ERI grants ($195,305) and the 2023 Multidisciplinary Research Grant. Research opportunities are available for students in machine learning , hardware security , and cybersecurity education .
Mehdi Sadi is an Assistant Professor of Electrical and Computer Engineering at Auburn University's College of Engineering. He holds a Ph.D. from the University of Florida, an M.S. from the University of California-Riverside, and a B.S. from Bangladesh University of Engineering and Technology. His research focuses on secure and reliable system-on-chip design, AI/ML-driven VLSI CAD/EDA, neuromorphic hardware, and emerging post-CMOS computing technologies. Notable achievements include earning the NSF CAREER Award for chiplet-based design optimization and a $175k NSF grant for magnetic RAM research. His work integrates machine learning with hardware co-design to enhance AI accelerators' performance, energy efficiency, and security. Recent projects include adversarial attack mitigation on AI hardware and reliability analysis of neuromorphic systems. Dr. Sadi's contributions span chiplet architecture, memory systems (e.g., STT-MRAM/SOT-MRAM), and fault-tolerant computing. He actively publishes on topics like skyrmion logic gates and TRNG implementations using MRAM. His work bridges theoretical machine learning advancements with practical hardware implementations, addressing critical challenges in next-generation computing systems.
Hédi Hadiji is an Associate Professor at the Laboratoire des Signaux et Systèmes (L2S) at CentraleSupélec. His research focuses on the mathematics of online decision-making, particularly adaptivity in bandits and online learning. Prior to this role, he was a postdoctoral researcher at the University of Amsterdam under Tim van Erven. He holds a PhD in Mathematics from Université Paris-Saclay, supervised by Gilles Stoltz and Pascal Massart. Education: PhD in Mathematics, Université Paris-Saclay (2020) Masters in Probability and Statistics, Université Paris-Saclay (2017) Part III Mathematical Tripos, University of Cambridge (2016) École Polytechnique (2012–2016) Research Interests: Hédi’s work addresses theoretical foundations of online learning, including bandit algorithms, reinforcement learning, and optimization. He explores adaptive strategies in dynamic environments, with applications to stochastic and adversarial settings. Key themes include minimax optimal algorithms, regret analysis, and the interplay between exploration and exploitation. Recent Publications: His recent work includes minimax optimal algorithms for linear bandits, tracking solutions in time-varying systems, and diversity-preserving strategies in K-armed bandits. These contributions advance theoretical understanding of adaptive decision-making in complex systems. Teaching: He teaches courses on the theoretical principles of deep learning, reinforcement learning, and mathematical foundations at the graduate level. His lectures emphasize rigorous analysis of generalization, optimization, and the neural tangent kernel. Labs & Affiliations: His primary affiliation is with L2S, where he engages in collaborative research groups such as MODESTY, COMEDY, and SYCOMORE. His work intersects signal processing, control systems, and communication networks.
Hajo A. Reijers is a Professor at the University of Utrecht, Netherlands, with a former affiliation at Vrije Universiteit Amsterdam. His research focuses on Business Process Management (BPM), Process Mining, and Robotic Process Automation (RPA), emphasizing practical applications in healthcare, organizational processes, and human-computer interaction. He contributes to developing tools like SWORD for detecting workarounds and DEUCE for auditing electronic health records. His work spans algorithm development for process discovery, predictive analytics, and optimization techniques. Key areas include analyzing event logs, modeling workplace behavior, and enhancing process transparency. Reijers collaborates extensively with industry partners, addressing challenges in process automation, employee acceptance of AI, and ethical monitoring. His contributions to conferences like BPM, CAiSE, and ICIS highlight interdisciplinary approaches, combining computer science with organizational studies. Notable projects include frameworks for task mining, reinforcement learning in care processes, and pattern recognition in government transparency assessments. Research initiatives often involve cross-disciplinary teams, exploring topics like workplace well-being through process mining, decision-making support systems, and overcoming barriers to BPM adoption. His work bridges theoretical advancements with real-world impact, influencing both academic discourse and practical business solutions.
Asmus Skar Christiansen is an Associate Professor in Pavement Engineering at the Department of Environmental and Resource Engineering, Technical University of Denmark (DTU Sustain). He serves as Head of Study for the Nordic Master in Cold Climate Engineering programme and lectures on pavement engineering, Arctic road construction, and foundation design. His academic career at DTU spans from Postdoc researcher (2017-2019) to Assistant Professor (2020-2023) and current Associate Professor position since 2023. His research centers on pavement technology and geotechnics with specialization in: Development of advanced testing and modeling techniques for pavements Integration of modern sensing technologies in civil infrastructure Computational mechanics for soil-structure interaction Sustainable materials for cold climate engineering Recent work demonstrates a clear shift toward IoT-enabled monitoring systems and data-driven pavement assessment, with 80% of 2023-2025 publications focusing on sensor integration and machine learning applications. Notable scientific contributions include: Creation of open-source datasets (LiRA-CD, RIVA) for road condition modeling Development of thermomechanical models for heated pavements Innovations in waste soil reuse for infrastructure He actively supervises PhD candidates across multiple projects including GREENPIPE (self-sensing pipe systems) and urban pavement analysis, while maintaining industry consultancy through COWI A/S collaborations. Christiansen also contributes to sustainable infrastructure through DTU's alignment with UN SDG 9 (Industry, Innovation, and Infrastructure) and SDG 11 (Sustainable Cities).
Zoran Gajic is a Professor of Electrical and Computer Engineering at Rutgers University, where he has taught since 1984. He holds academic leadership roles including Graduate Program Director for the Electrical and Computer Engineering Department and President of the Rutgers AAUP-AFT Faculty Union. His expertise spans controls systems, energy systems (including solar, wind, and smart grids), wireless communications, and networking. Education: B.S. and M.S. in Electrical Engineering from University of Belgrade, followed by M.S. in Applied Mathematics and Ph.D. in Systems Science Engineering from Michigan State University (1984). Research focuses on control theory applications for energy systems and communication networks. He has authored/coauthored nearly 100 journal papers and eight books, including best-selling titles like Linear Dynamic Systems and Signals (translated into Chinese) and Lyapunov Matrix Equation in Systems Stability and Control (republished by Dover). His work includes innovations in multirate control systems, singular perturbation methods, and reinforcement learning applications. Professional recognitions include editorial roles across nine journals, five guest-edited special issues, and plenary lectures at international conferences. Ten of his 17 Ph.D. advisees hold faculty positions globally. Beyond academia, he is a chess master with Life Master ranking from the U.S. Chess Federation and World Chess Federation certification. Key contributions include foundational work on optimal control for renewable energy systems, sliding mode control algorithms, and system decomposition techniques. His research has been supported by NSF and industry partners like AT&T Bell Labs. He leads the Rutgers Center for Systems and Controls (SYCON) as Associate Director and actively contributes to standards through IEEE and IEC initiatives, particularly in power system protection and control system reliability.
Cristian Secchi is a Full Professor in the Department of Engineering Sciences and Methods at the University of Modena and Reggio Emilia. His research focuses on robotics, control systems, and human-robot collaboration with an emphasis on safety, automation, and industrial applications. He teaches courses such as Industrial and Collaborative Robotics and Control of Robotic Systems. Research Interests: Design and implementation of control architectures for collaborative robots. Development of safe human-robot interaction frameworks compliant with ISO/TS 15066 standards. Integration of AI (e.g., large language models) into robotic motion planning. Autonomous systems for urban environments and surgical robotics. Publications highlight advancements in: Energy-efficient trajectory planning. Adaptive control strategies for uncertain environments. Multimodal human-robot communication. Diagnosis of mechanical systems via signal analysis. He leads the ARSControl research group and actively contributes to evolving production systems through smart modular technologies. His work bridges theoretical control frameworks with practical industrial and medical applications.
Raaz Dwivedi is Assistant Professor in Operations Research and Information Engineering at Cornell University and Cornell Tech. His research develops statistical and computational methods for personalized decision-making, focusing on causal inference, reinforcement learning, and distribution compression. Recent publications advance kernel thinning techniques, counterfactual inference methods, and adaptive nearest-neighbor algorithms with applications in healthcare and recommendation systems. Research appears in top venues with 15+ publications since 2022. Awards and honors: Blackwell-Rosenbluth Award (2024) ASA Best Student Paper Award (2022) MIT LIDS Best Presentation Award Harvard Teaching Excellence Award FODSI Postdoctoral Fellowship Holds PhD in EECS from UC Berkeley and BTech from IIT Bombay.
Choong Seon Hong is a Professor at Kyung Hee University's Department of Computer Science and Engineering in Yongin, South Korea. He earned his PhD in Instrumentation Engineering from Keio University, Japan, in 1997. His research focuses on next-generation wireless networks, with emphasis on 6G systems, federated learning, edge computing, and network optimization. Recent collaborative work explores semantic communication, UAV deployment, and multimodal learning frameworks. His publications highlight technical innovations in: Wireless network optimization for terrestrial, aerial, and satellite systems Federated learning architectures with knowledge distillation and prototype transfer Energy-efficient resource allocation in IoT and vehicular networks Security frameworks for EV charging stations and Open RAN systems Current trends show strong collaboration with Zhu Han, Walid Saad, and younger researchers like Apurba Adhikary and Yan Kyaw Tun. The work spans technical solutions for 6G non-terrestrial networks, holographic MIMO systems, and semantic communication frameworks.
Yue Hu is an Assistant Professor at the University of Waterloo, affiliated with the Faculty of Engineering. His research focuses on Human-Robot Interaction (HRI), assistive robotics, and control systems with a particular emphasis on safety, adaptability, and user experience. Key areas include robot emotional expressions, physical interaction safety, and real-time systems for social robots. He leads the Active and Interactive Robotics Lab , developing solutions for mobility assistance, teleoperation systems, and cybersecurity in robotics. His work integrates biomechanical modeling, computer vision, and machine learning to create robots that better understand and adapt to human needs. Notable projects include real-time pose estimation for mobility support, encrypted network traffic analysis for robot security, and personality shaping in social robots. He emphasizes ethical design and human factors in robotics, conducting studies on refugee education and unanticipated robot actions. Yue Hu holds a full-time faculty position and collaborates with industry and academic partners to advance assistive technologies and interactive systems. His research bridges theoretical foundations with practical applications, aiming to improve quality of life through innovative robotic solutions.
