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Dr. Seokgi Lee is an Assistant Professor in the Department of Industrial and Systems Engineering at Youngstown State University (YSU), part of the Rayen School of Engineering. Previously, he served as an Assistant Professor in Industrial Engineering at the University of Miami. He holds a Ph.D. in Industrial and Manufacturing Engineering from Pennsylvania State University and B.S./M.S. degrees in Industrial Engineering from Hanyang University, South Korea. His research focuses on distributed control systems, predictive analytics, and simulation-optimization for service engineering processes in supply chains, transportation, and healthcare. Notable expertise includes energy-aware manufacturing systems, prevention service analytics, and applications of machine learning in logistics and healthcare. Dr. Lee’s educational background includes teaching roles at YSU, the University of Miami, and Penn State, covering courses such as Decision Analysis for Engineering, System Analysis and Design, Operations Research, and Production Systems. He has contributed to institutional service through committees like the New Faculty Search in ISEN (YSU) and PhD recruitment in Industrial Engineering (University of Miami). His research trends emphasize interdisciplinary solutions, blending control theory with machine learning to address energy efficiency, healthcare optimization, and sustainable logistics. Recent publications highlight work on dynamic scheduling algorithms, opioid prescribing disparities, and green transportation systems. While no specific awards are listed, his active engagement in academic service underscores his commitment to institutional advancement. Teaching and advising form a core part of his role, with a focus on graduate and undergraduate engineering curricula. His work integrates real-world applications, such as optimizing infusion center operations and modeling energy costs in manufacturing, demonstrating a strong practical orientation.

• Distributed Control Systems
• Predictive Analytics
• Artificial Neural Networks
• +6 more

Dr. Maarouf Saad is a Lecturer in the Department of Electrical Engineering at École de Technologie Supérieure (ÉTS), Université du Québec. He holds a B.Sc.A. and M.Sc.A. from Polytechnique Montréal and a Ph.D. from McGill University. His research spans robotics, control systems, and sustainable energy applications, with a focus on nonlinear control, UAVs, exoskeleton rehabilitation systems, and smart grid technologies. Education: B.Sc.A., M.Sc.A. (Polytechnique Montréal), Ph.D. (McGill) Research Units: GREPCI (Power Electronics), SYNCHROMEDIA (Telepresence), INIT Robots (Haptic Interfaces) His work integrates adaptive control algorithms with artificial intelligence in robotics, particularly for rehabilitation and aerospace systems. He has pioneered fixed-time sliding mode controllers for quadrotors and developed impedance-based methods for distribution system monitoring. In smart grids, he focuses on voltage stability analysis and distributed generation optimization. Key research trends include nonlinear control of electrohydraulic systems, modeling of flexible manipulators, and cooperative control strategies for multi-agent systems. His publications emphasize practical implementations in real-world scenarios, from autonomous airships to exoskeletons for upper-limb rehabilitation. Dr. Saad supervises numerous graduate students across diverse projects, including UAV control, cable robot rehabilitation systems, and energy management in hybrid microgrids. His collaborations extend to institutions like CAE Inc. and laboratories in telepresence and robotics.

• Robotics
• Control Systems
• Nonlinear Control
• +5 more

Roland YAP Hock Chuan serves as an Associate Professor in the Department of Computer Science at the School of Computing, National University of Singapore (NUS), and previously held the role of assistant director at The Logistics Institute Asia Pacific (TLI-AP). His academic foundation was built at Monash University, Australia, where he earned comprehensive qualifications in Computer Science. Education: Ph.D. in Computer Science, Monash University, Australia M.Sc. in Computer Science, Monash University, Australia B.Sc. (Honours) in Computer Science, Monash University, Australia Research Focus: Renowned for pioneering the CLP(R) system that revolutionized Constraint Programming, Prof. Yap's work now spans artificial intelligence, security, programming languages, and social networks. His research bridges theoretical rigor with real-world applications, particularly in data analytics and knowledge compilation for industrial systems. Publication Evolution: His scholarly output reveals a strategic progression from foundational constraint logic programming (1990s) to cutting-edge security mechanisms and AI-driven solutions (2010s). Recent works demonstrate expertise in robust search algorithms, memory protection systems, and social network defense strategies, reflecting continuous adaptation to emerging computational challenges. Scientific Recognition: Best student paper award at SCA 2011 for social network research Best student runner-up paper at CIKM 2009 1995 Australian Computer Science Ph.D. Prize (1996) Praxa Computer Prize for theoretical computing achievements (1985) Academic Leadership: Prof. Yap directs the Tier 1 research project 'Investigating Product Configuration as Knowledge Compilation,' developing accelerated industrial solvers through innovative KC integration. His teaching portfolio includes advanced security courses (CS4239/5439/6231), shaping next-generation cybersecurity expertise. Mentoring excellence is evidenced by multiple student award-winning publications in top-tier conferences. Collaborative Infrastructure: Through his leadership role at TLI-AP, he contributes to NUS's interdisciplinary logistics research ecosystem, connecting computer science with supply chain innovation and operational optimization challenges.

• Artificial Intelligence
• Constraints
• Data Analytics
• +4 more

Eric Kerrigan is a Professor of Control and Optimization at Imperial College London's Department of Electrical and Electronic Engineering, part of the Faculty of Engineering. He holds a joint appointment in the Department of Aeronautics. His research focuses on Model Predictive Control (MPC), numerical optimization techniques, and their applications in aerospace, renewable energy, and information systems. Key projects include developing real-time optimization algorithms for embedded systems, co-design frameworks for closed-loop systems, and drag reduction in aerodynamics. He has supervised over 30 PhD students and post-doctoral researchers, many of whom have secured academic positions. Education: PhD in Control Engineering from the University of Cambridge and BSc in Electrical Engineering from the University of Cape Town. Research interests emphasize robust control methods, dynamic optimization, and interdisciplinary applications. Notable contributions include the ICLOCS (Imperial College Optimal Control Software) toolbox and frameworks for energy-efficient UAV communication networks. His work is funded by EPSRC, the European Commission, and industry partners like Siemens and ESA. Funding and collaborations include grants from the Royal Academy of Engineering and Royal Society, with consulting roles in industrial control systems. Editorial roles include Associate Editor for IEEE Transactions on Automatic Control and former Senior Editor for IEEE Transactions on Control Systems Technology . Labs and affiliations include the Control and Power Research Group, Energy Futures Lab, and Space Lab at Imperial College.

• Model Predictive Control (MPC)
• Optimization Techniques
• Aerospace Engineering
• +3 more

Gianluca De Nard serves as a Senior Research Associate at the University of Zurich's Department of Economics, Research Fellow at New York University's Volatility and Risk Institute, and Head of Quantitative Research at OLZ AG in Zurich. His career bridges academic research and practical finance applications, with significant contributions across multiple institutions. Dr. De Nard specializes in quantitative finance with expertise in portfolio optimization, covariance matrix estimation, and financial econometrics. His research focuses on developing robust statistical methods for handling large-dimensional financial data, with particular emphasis on shrinkage techniques and factor models. His work addresses fundamental challenges in modern portfolio theory, including nonstandard errors in financial research, climate risk integration, and AI applications in investment management. Analysis of his recent publications reveals a clear evolution toward integrating machine learning with traditional financial econometrics. His 2024-2025 work shows increasing sophistication in applying AI techniques to portfolio construction while maintaining rigorous statistical foundations. The recurring themes across his research include improving covariance matrix estimation, developing factor models for portfolio selection, and addressing methodological challenges in financial research. Dr. De Nard has established collaborations with leading researchers including Nobel laureate Robert F. Engle, Olivier Ledoit, and Michael Wolf. His work appears in top finance journals including the Journal of Finance, Journal of Financial Econometrics, and Journal of Banking and Finance, with his 2024 paper 'Nonstandard Errors' accumulating over 17,000 downloads. At OLZ AG, he applies his academic research to practical investment challenges as Head of Quantitative Research, demonstrating his ability to translate theoretical advances into real-world solutions. His dual academic-industry roles position him at the forefront of quantitative finance research and application.

• Quantitative Finance
• Portfolio Optimization
• Covariance Matrix Estimation
• +3 more

Riccardo Bettati is a Professor and Associate Department Head in the Department of Computer Science & Engineering at Texas A&M University, part of the College of Engineering. He holds a Ph.D. from the University of Illinois at Urbana-Champaign (1994) and a DipInf. from ETH Zurich. His research focuses on distributed real-time systems, scheduling algorithms, communication protocols, and security, particularly in privacy and anonymity. He directs the Texas A&M University Center for Information Assurance and Security. Education: Ph.D. in Computer Science (UIUC), DipInf. in Computer Science (ETH Zurich). Research Interests: Distributed real-time systems, scheduling, communication security, privacy in networks, and cyber-physical systems. Notable projects include NetCamo, RT-ARM, and VTech. He has received awards like the Chevron Faculty Fellowship (2008-2009) and Best Paper at EuroMicro (2006). Recent publications span cybersecurity, IoT security, and side-channel attack countermeasures. He teaches courses on operating systems, computer systems, and cryptocurrencies. His work emphasizes secure networked systems and resilience against modern threats.

• Distributed real-time systems
• Scheduling algorithms
• Communication protocols
• +4 more

Lingzhou Xue is a Professor of Statistics at The Pennsylvania State University, affiliated with the Eberly College of Science. He holds dual roles as a faculty member and the Associate Director of the National Institute of Statistical Sciences (NISS). His research focuses on high-dimensional statistics, nonparametric methods, statistical learning, and optimization, with applications in biomedical, environmental, and social sciences. He leads the SLDM (Statistical Learning and Data Mining) Lab and MDS (Microbiome Data Science) Lab. Education: B.Sc. in Statistics from Peking University (2008), Ph.D. in Statistics from the University of Minnesota (2012), postdoctoral training at Princeton University (2012–2013). Professional roles include Associate Editorships at the Journal of the American Statistical Association, Annals of Applied Statistics, and ACM Transactions on Probabilistic Machine Learning. Research Interests: Federated learning, causal inference, graphical models, reinforcement learning, optimal transport, and large-scale optimization. Recent work emphasizes theoretical guarantees for sparse PCA and federated Q-learning algorithms. Awards: IMS Fellow (2024), ASA Fellow (2023), COPSS Emerging Leader Award (2021), and Bernoulli Society New Researcher Award (2019). He has mentored 17 Ph.D. students and 3 postdocs, with four students securing tenure-track faculty positions. Service: Organized multiple NISS writing workshops, co-chaired the Ingram Olkin Statistics Serving Society Forum on Gun Violence, and contributed to the ASA whitepaper 'Discovery with Data' (2014).

• High-Dimensional Statistics
• Nonparametric Statistics
• Statistical Learning
• +9 more

Juan Bazerque Giusto is a Visiting Assistant Professor at the Department of Electrical and Computer Engineering, University of Pittsburgh, within the Swanson School of Engineering. He holds a B.Sc. in Electrical Engineering from Universidad de la República (Uruguay), and M.Sc. and Ph.D. degrees from the University of Minnesota. His research focuses on machine learning, stochastic optimization, and networked systems, with emphasis on reinforcement learning, swarm robotics, and power systems optimization. Education: B.Sc., Electrical Engineering, Universidad de la República, 2003 M.Sc., Electrical and Computer Engineering, University of Minnesota, 2010 Ph.D., Electrical and Computer Engineering, University of Minnesota, 2013 His work bridges theoretical advancements in optimization and signal processing with practical applications in robotics, energy systems, and wireless networks. Notable contributions include multiagent systems for mobile infrastructure, safe reinforcement learning algorithms, and sparse kernel-based methods for signal recovery. Publications: Over 15 peer-reviewed articles in IEEE Transactions and top conferences, emphasizing interdisciplinary research in reinforcement learning, distributed optimization, and cognitive networks. Recent work explores networked robotics and energy-efficient datacenter management. Awards: University of Minnesota Master Thesis Award (2009-2010) Best Paper Award at ICCRON 2007 Professional Experience: Previously served as Assistant Professor at Universidad de la República (Uruguay) before relocating to the U.S. in 2022.

• Machine Learning
• Stochastic Optimization
• Networked Systems
• +4 more

P. N. Karthik is an Assistant Professor in the Department of Artificial Intelligence at the Indian Institute of Technology (IIT) Hyderabad. He holds a Ph.D. and Master of Science in Engineering from the Indian Institute of Science (IISc), Bengaluru, advised by Prof. Rajesh Sundaresan, and was previously a Research Fellow at the National University of Singapore (NUS) working with Prof. Vincent Y. F. Tan. Research Interests: His primary research areas include multi-armed bandits, federated learning, differential privacy, reinforcement learning, information theory, and statistics. He focuses on sequential decision-making under uncertainty, privacy-aware learning systems, and optimization in distributed environments. Publication Trends: His recent work centers on best arm identification in various bandit settings, including restless, federated, and differentially private frameworks. These studies, published in top venues like IEEE Transactions on Information Theory, AISTATS, ISIT, ICLR, and AAAI, reflect a strong theoretical foundation with practical implications in AI and data science. Scientific Awards: Faculty Teaching Excellence Award 2025, IIT Hyderabad First place, Electronics and Communication Engineering category, 100 seconds competition, INAE Kanpur Chapter Teaching and Mentoring: He teaches courses such as Stochastic Processes and Programming for AI, receiving consistently high instructor ratings (up to 4.95/5.0). He mentors TAs and emphasizes conceptual clarity, active learning, and student engagement. He has collaborated with industry and government bodies like BMTC and Netradyne on transportation research. Academic Service: He serves on technical program committees for ISIT and APWDSIT and has organized events like the 2024 JTG/IEEE ITSoc Summer School. His collaborators include prominent researchers such as Vincent Y. F. Tan, Rajesh Sundaresan, Krishna Jagannathan, and Yeow Meng Chee.

• Multi-armed bandits
• Federated learning
• Differential privacy
• +7 more

Alessandro Di Nuovo is a Professor in the Department of Computing and Mathematics at Sheffield Hallam University, within the College of Business, Technology and Engineering. He is a leading researcher in cognitive and developmental robotics, with a focus on human-robot interaction, assistive technologies, and neuromorphic computing. His work bridges AI, robotics, and healthcare, particularly in applications for autism therapy and active ageing. His research spans cognitive robotics , neuromorphic computing , assistive robotics , and human-robot interaction , with a strong emphasis on embodied cognition, numerical cognition in robots, and privacy-aware systems. He has pioneered the use of spiking neural networks and event-based vision for real-time robotic perception and learning. His work often integrates cloud computing and multimodal interfaces to enhance robotic services for elderly care and children with developmental disorders. The recent publications highlight a consistent trend in personalized, interactive learning systems , where robots learn from human feedback using reinforcement learning, natural language, and sleep-inspired consolidation. There is also a growing focus on privacy and cybersecurity in social robotics, reflecting ethical considerations in assistive technologies. Applications are strongly oriented toward healthcare , including stroke rehabilitation, ASD screening, and in-home monitoring for older adults. He has been involved in numerous collaborative projects, often as a principal investigator or senior researcher, working with institutions across Europe. His contributions to major conferences and journals underscore his leadership in the field of cognitive and developmental systems. Di Nuovo has supervised and collaborated with a wide range of students and researchers, including Simone Varrasi, Roberto Vagnetti, Muhammad Aitsam, and Daniela Conti. His work has been supported by grants from EPSRC and other UK research councils, particularly in the domain of robotics for healthcare and social good. He is actively involved in research groups focusing on assistive robotics , cognitive systems , and cybersecurity in healthcare robotics , contributing to both theoretical models and real-world implementations of socially intelligent robots.

• Cognitive Robotics
• Human-Robot Interaction
• Developmental Robotics
• +14 more

Mathias Lecuyer is an Assistant Professor at the University of British Columbia (UBC), Department of Computer Science, where he leads the Systopia research group. His research focuses on trustworthy AI systems, including differential privacy, adversarial robustness, and causal machine learning. He holds a PhD from Columbia University and was a postdoctoral researcher at Microsoft Research. Education: PhD in Computer Science, Columbia University Postdoctoral Researcher, Microsoft Research (New York) Research Interests: Privacy-preserving machine learning (Differential Privacy) Certified adversarial robustness via randomized smoothing Causal inference for model generalization Systems for privacy and security in AI Awards: SOSP Distinguished Artifact Honourable Mention (2024) Google Research Award Supervised Students: PhD: Qiaoyue Tang, Saiyue Lyu, Frederick Shpilevskiy MSc: Mishaal Kazmi, Shadab Shaikh, Shiqi He Undergrad: Alain Zhiyanov, Jessica Bator, Ryan Shar, Eric Xiong, Joel Hempel Labs/Teams: Member of UBC S&P, TrustML, and CAIDA research groups.

• Machine Learning Systems
• Differential Privacy
• Adversarial Robustness
• +3 more

Francesca Parise is an Assistant Professor in the School of Electrical and Computer Engineering at Cornell University. Her research focuses on multi-agent systems, particularly analyzing large networks of autonomous decision-makers in transportation, finance, and social systems, employing tools from control theory, game theory, and optimization. She holds a PhD from ETH Zurich and was a Postdoctoral Research Fellow at MIT's Laboratory for Information and Decision Systems under Prof. Asuman Ozdaglar. Her work bridges theoretical foundations with practical applications in networked systems and interventions. Education: Bachelor's and Master's from University of Padua, Italy PhD in Control Engineering from ETH Zurich (2016) Her research interests span game-theoretic models for network analysis, learning dynamics in time-varying systems, and optimal intervention strategies. Key themes include synchronization in power grids, epidemic control via testing, and dynamic resource allocation. Recent work emphasizes graphon-based frameworks for large-scale network games and computational methods for equilibrium analysis. She explores both theoretical guarantees and real-world applications in transportation and energy systems. Publications highlight contributions to networked game theory, stochastic systems, and control applications, with a focus on scalable solutions for complex systems. Her work often intersects with interdisciplinary challenges in systems biology and socio-technical networks. She currently holds an office at Frank H. T. Rhodes Hall, Cornell University.

• Multi-Agent Systems
• Network Theory
• Control Systems
• +6 more

Qinglong Han is the Pro-Vice Chancellor (Research Quality) and a Distinguished Professor at Swinburne University of Technology in Melbourne, Australia. He previously held academic and leadership roles at Griffith University and Central Queensland University. His research focuses on networked control systems, multi-agent systems, time-delay systems, smart grids, and unmanned vehicles. He is a Fellow of IEEE, IFAC, and multiple other institutions, and has received prestigious awards including the IEEE Dr.-Ing. Eugene Mittelmann Achievement Award (2024) and Norbert Wiener Award (2021). His research interests span control engineering, applied mathematics, and artificial intelligence. Notable contributions include secure platooning control for autonomous vehicles, resilient control under cyber-physical threats, and optimization of industrial systems. He leads editorial roles in journals like IEEE Transactions on Industrial Informatics and IEEE/CAA Journal of Automatica Sinica. His work emphasizes interdisciplinary applications in smart grids, robotics, and industrial automation. Dr. Han has supervised numerous PhD students in areas like networked control and vehicle dynamics. He has secured grants from ARC and NSFC for projects on networked control systems and renewable energy integration. His achievements include multiple best paper awards and recognition as a Clarivate Highly Cited Researcher in Engineering and Computer Science.

• Control Engineering
• Networked Control Systems
• Multi-Agent Systems
• +6 more

Professor Tony Jebara is a faculty member in the Department of Computer Science at Columbia University, where he chairs the Center on Foundations of Data Science and directs the Columbia Machine Learning Laboratory. His research focuses on machine learning with applications in vision, graphs, and spatio-temporal data. He holds a PhD from MIT (2002) and has advised startups including Sense Networks, Evidation Health, and Agolo. Notable awards include the NSF Career Award (2004), Best Paper at ICML 2009, and recognition as one of Esquire's Best and Brightest (2008). His work has been featured in major media outlets. Jebara's academic contributions span over 100 peer-reviewed papers and a textbook on machine learning. He served as General Chair for ICML 2017 and Program Chair for ICML 2014. His research explores generative and discriminative models, Bayesian inference, and optimization techniques. Current projects include neural ensemble analysis in neuroscience and robust learning algorithms for environmental modeling. Recent publications emphasize scalable methods for collaborative filtering, survival analysis in online experiments, and graphical model applications in neuroscience. His work bridges theoretical advancements with practical systems, including contributions to privacy-preserving algorithms and recommendation systems.

• Machine Learning
• Computer Vision
• Graph Theory
• +3 more

Kostas Vlachos is an Assistant Professor in the Department of Computer Science and Engineering at the University of Ioannina, Greece. He has been in this position since 2014, following prior teaching roles at the University of Thessaly (2007–2013). He is a member of the Information Processing and Analysis (I.P.AN.) research group and actively supervises PhD, MSc, and diploma students in robotics and control systems. PhD, School of Mechanical Engineering, National Technical University of Athens, 2004 MSc, Interdepartmental Postgraduate Program in Automation Systems, National Technical University of Athens, 2000 Diploma in Electrical Engineering, Technical University of Dresden, Germany, 1993 His research focuses on robotics and control, with emphasis on microrobotics , haptic mechanisms , medical simulators , and autonomous navigation . He has made significant contributions to over-actuated marine platforms, reinforcement learning for navigation, and tactile robotic systems. His work bridges mechanical engineering and computer science, particularly in intelligent robotic control. The 15 most recent publications highlight a strong trend in autonomous marine robotics , multi-agent reinforcement learning , and intelligent control systems . Key themes include energy-efficient control, obstacle avoidance, sensor fusion, and learning-based navigation. The research spans from theoretical control design to real-world implementation in unmanned surface vehicles and microrobots. Best Student Paper Award, 9th Hellenic Conference on AI (SETN 2016) Vlachos has supervised over 30 students at various levels and has participated in multiple national and European research projects in robotics and automatic control. His teaching includes courses such as Computational Mathematics, Robotics, and Robotic Systems. He collaborates extensively with researchers like E. Papadopoulos and K. Blekas. He leads research within the Information Processing and Analysis (I.P.AN.) group, focusing on intelligent perception and control of robotic systems. His lab works on mobile manipulators, haptic devices, mini-robots, and marine platforms, integrating simulation (ROS/Gazebo) with real-world experimentation.

• Robotics
• Control of Robotic Mechanisms
• Microrobotics
• +7 more