Daniela Saban is an Associate Professor of Operations, Information, and Technology at Stanford Graduate School of Business (GSB). She holds the Botha-Chan Faculty Scholar title for 2024–25 and has been recognized with multiple awards, including the MSOM Young Scholar Prize and the 2024 Revenue Management and Pricing Practice Award. Her research focuses on market design, procurement mechanisms, and online marketplace operations, with industry collaborations in government procurement and volunteer-matching platforms. Saban teaches core MBA courses like Optimization and Simulation Modeling and advanced PhD courses such as Engineering Online Markets . She is an associate editor for Management Science , Operations Research , and other top journals. Education: PhD in Operations Management (Columbia University, 2015); M.Sc. and B.Sc. in Computer Science (University of Buenos Aires, 2009 and 2006). Research Interests: Procurement mechanisms, supply chain management, market design, matching markets, auctions, game theory, and combinatorial optimization. Her work bridges operations research, economics, and computer science, with applications in government procurement, dating apps, and volunteer platforms. Awards: Winner of the 2024 Revenue Management & Pricing Practice Award, 2022 INFORMS Revenue Management Prize, and finalist for Stanford GSB’s Distinguished Teaching Award (2020–2022). Recognized for contributions to algorithmic fairness and operational efficiency in marketplaces. Professional Service: Program co-chair of EC ’24; reviewer for journals including Mathematics of Operations Research and Games and Economic Behavior . Prior experience includes a visiting scholar role at UC Berkeley’s Simons Institute.
Daniela Rus is the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science at MIT and Director of the Computer Science and Artificial Intelligence Laboratory (CSAIL). She leads research in robotics, AI/ML, and autonomous systems, emphasizing soft robotics, mobile computing, and data-driven solutions. Her work spans theoretical and applied domains, including self-reconfiguring robots, AI for healthcare, and transportation optimization. Education: Earned her PhD in Computer Science from Cornell University. Recognized as a 2002 MacArthur Fellow, ACM Fellow, IEEE Fellow, and member of the National Academy of Engineering and American Academy of Arts and Sciences. Research focuses on robotics (soft robots, modular systems), AI applications (neural networks, generative models), and societal impacts (autonomous vehicles, data privacy). Projects include the Robot Garden educational platform, M-block modular robots, and AI-driven traffic optimization systems. Her lab, CSAIL, fosters interdisciplinary innovation across computer science, engineering, and biology. Publications highlight breakthroughs in robot design, algorithmic efficiency, and human-robot interaction. Current efforts explore AI for environmental monitoring, surgical robotics, and ethical autonomous systems. CSAIL hosts 100+ research groups, including the Distributed Robotics Lab and Embodied Intelligence Community.
Sam O'Neill is a Lecturer in Computer Science at the College of Science and Engineering. His academic work focuses on applied computational methods across engineering and industrial domains, with strong affiliations to computer science and interdisciplinary research teams. Research interests span: Machine Learning & AI : Development of microservice frameworks for biomedical research and reinforcement learning systems Industrial & Nuclear Engineering : Modular reactor design, off-site construction optimization, and plant layout Operations Research : Network flow modeling, traffic equilibrium, and MILP applications Applied Mathematics : Algorithm design and sequence analysis Publication analysis reveals three primary thematic clusters: Recent focus (2021-2024) on modular nuclear systems and machine learning microservices Sustained work in optimization techniques (MILP formulations, network flows) Foundational research in algorithmic learning theory and mathematical sequences
Sean Nicholson-Crotty serves as O'Neill Professor and Director of the Graduate Mentoring Center at Indiana University's Paul H. O'Neill School of Public and Environmental Affairs, with an adjunct appointment in Political Science (College of Arts and Sciences). His leadership spans public administration education and faculty development. His educational credentials include: Ph.D. in Political Science, Texas A&M University (2003) M.A., Colorado State University (1999) B.A., Western State College (1993) Nicholson-Crotty's research examines public management through federalism and intergovernmental relations lenses, with emphasis on policy diffusion among subnational governments. His work bridges governance theory and practical applications in education policy, fiscal federalism, and bureaucratic representation, frequently analyzing how state/local institutions implement federal mandates while navigating political constraints. Current projects explore equity in public service delivery and organizational performance under varying governance structures. His publication portfolio demonstrates consistent focus on subnational policy dynamics, with recurring themes in education reform (No Child Left Behind impacts), fiscal federalism (grant acceptance/refusal patterns), and representative bureaucracy. Methodologically, he combines large-N statistical analysis with case studies, often leveraging administrative data from school systems and state agencies to examine policy implementation. Award highlights: Provost's Outstanding Junior Faculty Research Award (University of Missouri, 2008) Distinguished Graduate Student Award (Texas A&M, 2004) Brian Jones Graduate Research Paper Award (Texas A&M, 2003) As Director of the Graduate Mentoring Center, he oversees faculty-student mentorship programs while maintaining active NSF-funded research. His pre-IU career included research appointments at Texas A&M's Public Policy Research Institute and National Latino Project, establishing his expertise in state-level policy analysis before transitioning to faculty roles.
Roles & Affiliations Associate Professor at HEC Montréal (Department of Decision Sciences) with adjunct ties in Computer Science. Holder of a Canada CIFAR AI Chair. Member of MILA, the Chair Data Science for Real-Time Decision Making, and the Centre de recherche en mathématiques. Education Ph.D. in Computer Science (University of Toronto), MMath (Computer Science, University of Waterloo). Research Interests Focuses on machine learning applications in decision-making, including reinforcement learning for traffic control, energy systems, and recommender systems. Specializes in topics like continual learning, graph-based methods, and operations research integration. Develops foundational models such as collaborative topic Poisson factorization and neural combinatorial optimization approaches. Publications Overview Recent work includes reinforcement learning for sustainable energy use (2025), traffic signal control innovations (2024-2022), and foundational contributions like CTPF (2014). His research bridges theory and application, emphasizing scalable solutions for complex systems. Awards & Recognition Canada CIFAR AI Chair in Artificial Intelligence Advising & Grants Supervised over 30 students across PhD and Master's programs, with notable projects in traffic optimization, electricity pricing, and toxic content analysis. Active in grant-funded research through collaborations with industry partners like Beneva and academic networks like MILA. Labs & Collaborations Core member of MILA and affiliated with HEC's decision science research groups. Leads projects in real-time decision-making and data science applications across domains.
Dr. Haoning Xi is a Lecturer in Business Analytics at the Newcastle Business School, University of Newcastle (UON), Australia. She previously served as a Research Fellow at the Institute of Transport and Logistics Studies (ITLS), The University of Sydney Business School. Dr. Xi received her Ph.D. in Transportation & Operations Research from the University of New South Wales (UNSW) Sydney, where she was also a co-cultured Ph.D. student at CSIRO Data 61. She holds a Master's degree from Tsinghua University and a Bachelor's from Central South University, China, and has research experience at the University of California, Berkeley and Hong Kong University of Science and Technology. Ph.D. in Transportation & Operations Research, University of New South Wales Master of Engineering, Tsinghua University, China Bachelor of Engineering, Central South University, China Research Assistant, University of California, Berkeley Visiting Researcher, Hong Kong University of Science and Technology Dr. Xi's research focuses on applying business analytics, machine learning, and operations research to mobility services and transportation systems. Her work centers on Mobility-as-a-Service (MaaS), travel behavior analysis, data-driven optimization, and sustainable transportation. She investigates how to leverage millions of smart card data from various transport modes to uncover user travel patterns and preferences, enabling intelligent decision-making for transport authorities. Her research also explores predictive analysis using AI and ML algorithms to forecast travel patterns, service disruptions, and resource allocation strategies. A significant portion of her work examines personalized mobility services and how to integrate transportation with non-mobility offerings to create comprehensive subscription models. Dr. Xi has published over 23 SCI/SSCI indexed papers, including 9 ABDC A* journal articles (8 as first/corresponding author) in top journals like European Journal of Operational Research and Transportation Research series. Her publications reveal a strong emphasis on mathematical modeling of transportation systems, with increasing focus on AI/ML applications in recent years. The articles demonstrate progression from traditional transportation modeling toward more sophisticated data-driven approaches integrating machine learning with operational research techniques, particularly in the context of Mobility-as-a-Service ecosystems and pandemic-related travel behavior changes. Rising Stars Women in Engineering, Asian Deans' Forum (2024) Best Research Silver Award, International Symposium on Sustainable Development of Urban Transport Systems (2024) Best Paper Award, International Workshop on Computational Transportation Science (2024) Global Talent Independent Scheme, Australian Government (2021) University Postgraduate Award, UNSW (2021) CSIRO Data 61 Top-up Ph.D. Scholarship (2020) Dr. Xi currently supervises 5 PhD students across various topics including digital transformation's impact on ESG, digital sustainability measurement, data analytics for hospitality management, social media sentiment analysis, and organizational capabilities in regulated environments. She has secured over $463,500 in research funding from multiple sources including National Natural Science Foundation of China ($80,000), iMOVE Australia Limited ($300,000), and various internal university grants. Her current projects focus on AI-driven bus network optimization, parking management models, and enhancing user mobility experience through business analytics. Dr. Xi serves as CHSF College Research Committee Member and NBS Equity Diversity and Inclusion (EDI) Committee Member at the University of Newcastle. She is Co-chair of the Multimodal Urban Transportation Systems Analysis Committee in the World Transport Congress (2024-2026) and serves on editorial boards for International Journal of Transportation Science & Technology and Transportation Safety and Environment. She also acts as a peer reviewer for top transportation journals including Transportation Science and Transportation Research series.
Qianqian Zhang is an Assistant Professor in the Department of Electrical and Computer Engineering at Rowan University, affiliated with the Henry M. Rowan College of Engineering. She holds a Ph.D. from Virginia Tech (2021) and worked as a full-time R&D Engineer in satellite communications (2021–2024). Her expertise lies in machine learning for wireless networks, integrated space-air-ground systems, semantic communications, and cybersecurity. Education: Ph.D., Electrical and Computer Engineering, Virginia Tech (2021) M.S., Electrical and Computer Engineering, Virginia Tech (2019) B.S., Telecommunication Engineering, Beijing University of Posts and Telecommunications (2015) Research Interests: Dr. Zhang focuses on AI-driven wireless systems, UAV network optimization, satellite communication integration, and secure semantic protocols. Her work emphasizes practical applications of reinforcement learning and generative models in millimeter-wave and 6G technologies. Recent Research Trends: Her publications (2016–2025) highlight advancements in machine learning for UAV networks, millimeter-wave channel modeling, and intelligent reflector systems. Recent work explores LEO satellite latency optimization and privacy-preserving multi-hop learning frameworks. Advising & Grants: Currently advising 5 Ph.D./undergraduate students and actively recruiting motivated candidates. Her group collaborates on predictive UAV deployment and semantic communication frameworks. Teaching includes machine learning (ECE 455/555) and electrical communication systems courses. Labs & Teams: Leads a research group at Rowan focused on AI-driven wireless innovation, with industry partnerships in satellite communication sectors.
Janos Simon is a Professor of Computer Science at the University of Chicago. His research focuses on computational complexity, algorithms, and distributed systems, with special interests in lower bound techniques and fault-tolerant models. He serves as Editor in Chief of the Chicago Journal of Theoretical Computer Science. His research explores diverse areas including combinatorial algorithms for optical networks, distributed computing in mobile networks, and biologically-inspired computational models. Work spans theoretical foundations to applied problems in vehicular networks and sensor systems. Professor Simon has supervised numerous PhD students in theoretical computer science and maintains active collaborations in fault-tolerant distributed computations and complexity theory.
Dr. Jinli Cao is a full-time Associate Professor in the Department of Computer Science and Information Technology at La Trobe University. She holds a BSc from Hebei University, China, and a PhD from the University of Southern Queensland, Australia (1997). Her research focuses on evolutionary computing, data privacy, deep learning for vulnerability assessment, and decision support systems. She has published over 150 papers in top venues such as VLDB and IEEE Transactions series. Dr. Cao leads an ARC-funded project on software vulnerability risk discovery and has secured three ARC grants. She has supervised 11 PhD, 2 Master’s, and 57 Honours students, many of whom work in academia and industries like Oracle and Commonwealth Bank. Teaching contributions include developing courses in databases, data warehouses, and artificial intelligence. Research Interests: Privacy-preserving data publishing and optimization Evolutionary algorithms for dynamic data partitioning Deep learning applications in cybersecurity and healthcare Graph-based machine learning for access control and anomaly detection Decision support systems and top-k query processing Her recent articles explore cutting-edge topics like privacy-preserving spatial crowdsourcing tasks, graph neural networks for traffic prediction, and cybersecurity frameworks for vulnerability prioritization. Awards include competitive ARC grants totaling $450,000 (2023-2025). She actively serves as an Associate Editor for Health Information Science and Systems and has examined over 100 PhD theses across Australian universities. Teaching highlights: Developed 20+ courses including Database Management Systems, Data Warehousing, and Artificial Intelligence. Coordinates units like Decision Support Systems and Intermediate Programming in Java.
Marko Porjazoski is a Professor at the Faculty of Electrical Engineering and Information Technologies, University Ss. Cyril and Methodius, Macedonia. His academic roles include Professor (2021–present), Associate Professor (2017–2021), and Assistant Professor (2012–2017). He holds a Ph.D. in Telecommunications (2012), M.Sc. (2006), and Dipl.Ing. (2000) from the same institution. His research focuses on telecommunications, wireless networks, Quality of Service (QoS), LTE/LTE-Advanced, and network security. He has authored over 20 peer-reviewed publications, including works on network forensics, OTT billing systems, and interference coordination in LTE. His teaching spans undergraduate and postgraduate courses in network forensics, telecommunications services, and smart society ICT solutions. He leads projects on network performance analysis and security, with recent work emphasizing cloud-based video streaming and deep learning for cybersecurity. Education: Ph.D. in Telecommunications, 2012 M.Sc. in Telecommunications, 2006 Dipl.Ing. in Electronics and Telecommunications, 2000 Research Interests: Wireless network optimization, LTE/LTE-Advanced QoS management for video/OTT services Network forensics and cybersecurity Radio access technology selection Key Contributions: Developed a Service Quality Testing System for mobile networks Proposed architectures for OTT billing systems Analyzed fractional frequency reuse in LTE Designed algorithms for heterogeneous network performance Labs/Teams: Telecommunications Institute at FEIT.
Michał Pióro is a Professor at the Institute of Telecommunications and Cybersecurity within the Faculty of Electronics and Information Technology at the Warsaw University of Technology. His research focuses on telecommunications, networking optimization, and resilient network design with a particular emphasis on SDN security, wireless sensor networks, and adverse weather resilience. He has published extensively in top journals and conferences including Networks and IEEE Transactions . Key research areas include: Optimization of network controller placements against targeted attacks Resilience strategies for SDN architectures MIMO-based industrial network scheduling Free-space optics (FSO) network reliability in adverse conditions Game-theoretic approaches to network security Recent work highlights joint optimization of primary/backup controllers for SDN resilience and novel scheduling techniques for industrial control traffic using Massive MIMO systems. His research frequently intersects with practical applications in critical infrastructure and 5G deployments. He has collaborated extensively with researchers from institutions like INRIA (France), Telecom ParisTech, and multiple industry partners. His work has been supported by grants focusing on resilient communication networks and cybersecurity innovations.
Halit Özen is a Professor in the Department of Civil Engineering at Istanbul Technical University, with a focus on Transportation Engineering and Intelligent Transportation Systems. He holds a Doctorate in Transportation from Yildiz Technical University and has served as a faculty member at both Yildiz Technical University (1995–2024) and Florida International University (FIU, 2008–2013) as a visiting scholar. His expertise spans transportation planning, traffic control systems, and infrastructure materials research. Education: PhD in Transportation Engineering, Yildiz Technical University (1993–1999) MSc in Transportation Engineering, Yildiz Technical University (1990–1993) BSc in Civil Engineering, Yildiz University (1986–1990) Post-Doctoral Research, Florida International University (2006–2010) Research Interests: Dr. Özen’s work focuses on optimizing transportation networks, intelligent transportation systems (ITS), pavement materials, and traffic simulation. He has pioneered studies on charging station infrastructure, asphalt mixture performance, and incident management systems. His research emphasizes sustainability, safety, and data-driven decision-making in urban mobility. Grants & Leadership: He has led numerous projects on transportation modeling, including roles as Director of Research and Application Center (2003–2006) and Member of the Faculty Board of Directors at Yildiz Technical University. His contributions include developing tools for dynamic traffic assignment and evaluating the socioeconomic impacts of smart transportation systems. Labs & Teams: His research group collaborates on infrastructure resilience, ITS integration, and sustainable materials. Key collaborations include work on roadside safety systems and pavement durability at Istanbul Technical University’s Civil Engineering facilities.
Tianxin Li, Ph.D., is an Assistant Research Professor at the Connecticut Transportation Safety Research Center (CTSRC) at the University of Connecticut since September 2023. His work focuses on software development, research, and proposal writing in transportation safety and emerging technologies. Dr. Li holds a Master’s and Ph.D. in Civil Engineering with a concentration in Transportation Engineering from the University of Texas at Austin. Education: Ph.D. in Civil Engineering (Transportation Engineering), University of Texas at Austin Master of Science in Civil Engineering (Transportation Engineering), University of Texas at Austin Research Interests: Dr. Li specializes in traffic safety analysis, traffic demand modeling, traffic signal control systems, and the integration of connected/autonomous vehicles with reinforcement learning. He also explores precision agriculture through terrain analytics and sensor fusion. His work bridges theoretical models with practical applications, such as simulating traffic incidents using SUMO extensions and optimizing urban traffic policies. Publications & Trends: His recent studies highlight data-driven methods for traffic volume calibration, incident management, and policy evaluation. Earlier work includes foundational research on autonomous vehicles’ safety implications and their societal benefits. His articles span traffic simulation tools, emergency response systems, and interdisciplinary applications in agriculture. Grants & Advising: While no student advisees are listed, his research at CTSRC actively contributes to grant-funded projects on transportation safety and smart infrastructure. Grants and collaborations are central to his work but not explicitly detailed here. Labs & Teams: Dr. Li is affiliated with the Connecticut Transportation Safety Research Center (CTSRC), a hub for interdisciplinary transportation research at UConn.
Dr Giovanni Piccioli is a Postdoctoral Researcher in the Department of Mathematics at King's College London, within the Faculty of Natural, Mathematical & Engineering Sciences. His research applies quantitative techniques from machine learning, physics, and statistics to study complex legal systems and other interdisciplinary applications. His educational background includes: Bachelor and Master's degrees from Sapienza Università di Roma, graduating in 2020 with a thesis on high dimensional inference in the angular synchronization problem. PhD from EPFL in 2024, conducted in the Statistical Physics of Computation laboratory. Dr Piccioli's research focuses on disordered systems, complexity in legal systems, Bayesian learning in neural networks, and traffic assignment. His work spans multiple domains including Monte Carlo methods for Bayesian learning, algorithms for traffic optimization on graphs, and message-passing algorithms for graph alignment and low-rank matrix denoising. He is a member of the Disordered Systems group at King's, which is at the forefront of research in statistical mechanics of disordered and complex systems. He is based at the Strand Building, Strand Campus, London, WC2R 2LS.
Dr. Feng Wang is an Assistant Professor in the School of Engineering at Liberty University, specializing in network reliability, interdomain routing, and software-defined networking. His research addresses challenges in next-generation internet architectures, network performance measurement, and secure IoT device management. Education: B.E. in Electrical and Computer Engineering from Zhejiang University (China) M.S. in Electrical and Computer Engineering from Yanshan University (China) Ph.D. in Electrical and Computer Engineering from the University of Massachusetts, Amherst Research focuses on: - Detecting transient routing failures and improving network performance through real-time diagnostic systems (e.g., collaboration with AT&T). - Designing scalable addressing schemes for IoT and wireless sensor networks. - Developing intrusion detection systems (e.g., MOCA framework) and network anomaly mitigation techniques. His publications (2015-2023) emphasize network security, routing protocol optimization, and scalable internet architectures. Notable work includes BGP rerouting solutions, real-time routing failure diagnosis, and variable-length addressing for 6LoWPAN. No scientific awards were explicitly listed in the provided texts. Collaborations include Agilent, AT&T, and Intel, focusing on practical network reliability and security solutions. Dr. Wang has advised no listed students or managed grants in the provided texts. His research extends to lab implementations of lightweight routers (SoC-based) and stability-aware protocols for RPL networks.
