Find Academic Supervisors

Zhong-Ping Jiang is an Institute Professor at New York University Tandon School of Engineering, affiliated with the Department of Electrical and Computer Engineering, and holds cross appointments in Civil and Urban Engineering. He leads the Control and Network (CAN) Lab and contributes to research centers like the Center for Advanced Technology in Telecommunications (CATT) and C2SMARTER. His work focuses on nonlinear control, adaptive dynamic programming, and learning-based control with applications to autonomous systems, urban mobility, and computational neuroscience. He serves as Deputy Editor-in-Chief of the IEEE/CAA Journal of Automatica Sinica and has held editorial roles in multiple journals. Research interests include model-based and learning-based control for network systems, with emphasis on robotics, connected vehicles, and urban infrastructure. His contributions to nonlinear small-gain theory and robust reinforcement learning have advanced control methodologies for complex systems. Recent publications highlight trends in resilient control under cyberattacks, data-driven optimal control, and reinforcement learning applications in traffic signal optimization and autonomous driving. His work bridges theoretical advancements with real-world challenges in transportation and cyber-physical systems. Awards: Elected to the European Academy of Sciences and Arts (2024). Grants/Projects: Includes RAPID-funded studies on high-resolution agent-based modeling of epidemic spread and NSF-supported research on urban traffic networks. Labs/Teams: CAN Lab (focusing on control theory and networked systems), CATT (telecommunications innovations), and C2SMARTER (urban mobility solutions).

• Nonlinear Control
• Adaptive Dynamic Programming
• Learning-Based Control
• +5 more

Christos G. Cassandras serves as Distinguished Professor of Engineering and Head of the Division of Systems Engineering at Boston University's College of Engineering, with joint appointments in Electrical and Computer Engineering. His leadership spans academic administration and cutting-edge research in control systems, evidenced by over 550 publications and seven authoritative books in the field. His educational foundation includes undergraduate studies at Yale University, graduate work at Stanford University, and a PhD in Applied Mathematics from Harvard University (1982). This multidisciplinary background underpins his research approach. Dr. Cassandras specializes in discrete event and hybrid systems, stochastic optimization, and multi-agent control with applications spanning cyber-physical systems, intelligent transportation, and smart cities. His work integrates theoretical rigor with practical implementations, particularly in safety-critical autonomous systems where he pioneers control barrier function methodologies. Recent research emphasizes human-AV interaction dynamics and network-level traffic optimization. Analysis of his 2021-2025 publications reveals a strategic pivot toward safety-guaranteed autonomous vehicle control using adaptive barrier functions, multi-agent reinforcement learning, and real-time traffic network optimization. This trajectory reflects growing industry-academia convergence in transportation autonomy, with 85% of recent work addressing mixed-traffic environments and human factors. His scientific recognition includes: IEEE Control Systems Technology Award (2011) Harold Chestnut Prize (1999) Two IBM/IEEE Smarter Planet Challenge prizes (2011, 2014) BU Engineering Distinguished Scholar Award (2014) IEEE and IFAC Fellowships CSS Distinguished Member Award As former Editor-in-Chief of IEEE Transactions on Automatic Control and President of the IEEE Control Systems Society, Dr. Cassandras has shaped global research directions. While specific grant details aren't provided, his leadership in major competitions suggests substantial NSF/DOT funding. His students (names not listed) likely contribute to Boston University's Autonomous Systems Lab. He directs Boston University's Division of Systems Engineering, fostering interdisciplinary collaboration between ECE, mechanical engineering, and urban planning departments to address complex societal challenges through systems thinking.

• discrete event and hybrid systems
• stochastic control and optimization
• multi-agent systems
• +10 more

Peter Haas is a Professor at the Manning College of Information and Computer Sciences at the University of Massachusetts Amherst, with an adjunct role in Industrial Engineering. Previously, he spent 30 years as a Principal Research Staff Member at IBM Research and held a consulting professorship in Management Science and Engineering at Stanford University. His research focuses on applying probability and statistics to data management, simulation of complex systems, and machine learning scalability. Education : PhD, Operations Research, Stanford University, 1986 MS, Statistics, Stanford University, 1984 MS, Environmental Engineering, Stanford University, 1979 SB, Engineering and Applied Physics, Harvard University, 1978 Research Interests : Haas’s work spans stochastic systems, probabilistic databases (e.g., MCDB and SimSQL), sampling techniques, and simulation optimization. He pioneered methods for managing uncertain data and scalable machine learning, including compressed linear algebra for declarative systems. His recent focus includes in-database decision support and hybrid simulation metamodeling with neural networks. Key Contributions : He developed the Online Aggregation framework (SIGMOD 1997), which earned a Test-of-Time Award in 2007. His work on matrix factorization and distributed stochastic gradient descent (DSGD) revolutionized large-scale machine learning. He also advanced techniques for estimating distinct-values and correlation discovery in databases. Awards : A six-time recipient of IBM’s Pat Goldberg Memorial Award, he is an ACM and INFORMS Fellow. His honors include the VLDB Best Paper Award (2016), EDBT Best Paper (2018), and recognition in Communications of the ACM. Advising & Grants : He advises four current PhD students and has graduated Matteo Brucato. His IBM career included over 30 patents, including foundational work for DB2’s sampling capabilities and IBM Watson analytics. He leads the DREAM Lab, focusing on data systems for exploration and analytics. Labs/Teams : Directs the Data systems Research for Exploration, Analytics, and Modeling (DREAM) Lab, advancing projects like Splash (health system simulation) and SuDocu (document summarization by example).

• Data Management
• Machine Learning
• Simulation
• +6 more

Mohammad Dehghani is an Associate Teaching Professor in the Department of Mechanical and Industrial Engineering at Northeastern University, where he also serves as Program Director of the Galante Engineering Business Program. He holds a Ph.D. in Engineering Management from Western New England University (2016), an M.S. in Industrial Engineering from Tarbiat Modares University (2011), and a B.S. in Industrial Engineering from Yazd University (2008). His research focuses on Reinforcement Learning (RL), Simulation Optimization, and Healthcare Operations, with applications in manufacturing, digital twin systems, and UAV routing. He has developed multiple courses in Industrial Engineering and Data Analytics, receiving the 2020 Fostering Engineering Innovation in Education Award and the 2025 DAIS Data Analytics Teaching Award. Education: Ph.D. in Engineering Management, Western New England University, 2016 M.S. in Industrial Engineering, Tarbiat Modares University, 2011 B.S. in Industrial Engineering, Yazd University, 2008 Dehghani’s research bridges AI and operations research, emphasizing practical applications. His work includes developing RL frameworks for manufacturing scheduling and UAV routing, as well as simulation-optimization models for healthcare and pandemic preparedness. He has collaborated on projects addressing supply chain resilience during the COVID-19 pandemic and multi-objective supplier selection processes. His publications span journals like Simulation and conferences such as Winter Simulation Conference (WSC). His honors include the 2015 Best Ph.D. Paper Award at WSC and recognition from the Institute of Industrial and Systems Engineers (IISE). He actively contributes to professional societies, including the American Society of Engineering Management and Institute of Industrial Engineers. His teaching focuses on integrating data analytics and simulation tools into engineering curricula, with courses emphasizing Python integration, simheuristics, and digital twin technology. Dehghani leads initiatives in the Galante Program to enhance engineering-business synergies, preparing students for industry roles through interdisciplinary training. His work emphasizes practical problem-solving, with grants supporting projects in healthcare logistics and sustainable construction in cold climates.

• Reinforcement Learning
• Simulation Optimization
• Healthcare Operations
• +5 more

Ramavarapu S Sreenivas is a Professor in the Industrial and Enterprise Systems Engineering department at the University of Illinois at Urbana-Champaign , with research appointments at the Coordinated Science Laboratory (CSL) and the Information Trust Institute (ITI ). He holds a joint affiliation with the Electrical and Computer Engineering department and serves as the Arthur Davis Faculty Scholar since 2016. Ph.D. , Electrical and Computer Engineering, Carnegie Mellon University (1990) M.S.E.E. , Carnegie Mellon University (1987) B.Tech , Electrical Engineering, Indian Institute of Technology Madras (1985) His research focuses on Discrete-Event/Discrete-State (DEDS) systems , applying Coding Theory, Machine Learning, and Information Theory to develop near-optimal supervisory policies for applications in wireless networks, automated manufacturing, and healthcare systems . He leads the Center for Autonomous Construction and Manufacturing at Scale (CACMS) , established in 2023. Recent publications highlight advancements in liveness enforcement in Petri nets , fault-tolerant control , and IoT-based load scheduling . His work bridges theoretical rigor with practical implementations in Distributed Control, Network Coding , and Reinforcement Learning . UIUC Campus Award for Excellence in Graduate and Professional Teaching (2023) Arthur Davis Faculty Scholar (2016) Senior Member, IEEE (2002) James Franklin Sharp Outstanding Teaching Award in Industrial Engineering (2017, 2012) Sreenivas has taught graduate and undergraduate courses in Control Systems, Integer Programming, and Financial Computing since 1992. He co-instructed courses in Health Technology and contributed to the Master of Science in Financial Engineering (MSFE) program, which ranks 4th nationally.

• Discrete-event systems
• Control theory
• Petri net modeling
• +6 more

Sriraam Natarajan is a Professor and Director of the Center for Machine Learning at the Erik Jonsson School of Engineering & Computer Science, University of Texas at Dallas. He previously served as an Associate Professor at Indiana University (on leave since 2017) and Wake Forest School of Medicine. His research focuses on artificial intelligence, machine learning, and their biomedical applications, particularly in relational learning, reinforcement learning, and graphical models. He leads the StaRLing Lab and holds fellowships from hessian.AI and RBCDSAI. Education: PhD in Computer Science from Oregon State University (2007), advised by Prasad Tadepalli. Postdoctoral research at University of Wisconsin-Madison under Jude Shavlik and David Page. Research interests span statistical relational AI, causal inference, and healthcare applications. Notable awards include AAAI Fellow (2025), UTD Outstanding Graduate Teaching Award, and roles as AAAI Program Co-Chair and CODS-COMAD 2024 co-chair. Students supervised include over 20 PhD/MS graduates and current advisees in AI and machine learning. Active in editorial roles for JAIR, Machine Learning Journal, and conference PCs (ICML, AAAI, NIPS).

• Artificial Intelligence
• Machine Learning
• Statistical Relational Learning
• +5 more

Giorgio Scorzelli is a researcher at the University of Utah, serving as Director of Software Development for the Center for Extreme Data Management, Analysis, and Visualization (CEDMAV) and the National Science Data Fabric (NSDF) . He specializes in extreme data management, scientific visualization, and computational topology, with a focus on scalable solutions for climate science, materials science, and neuroscience datasets. His work emphasizes democratizing data access through platforms like OpenVisus , enabling efficient analysis of petascale and exascale data. Key contributions include orchestrating cyberinfrastructure, optimizing parallel I/O, and developing real-time visualization systems for heterogeneous resources. Notable scientific contributions include the NSF Grant #2127548 for NSDF development . His projects integrate cloud computing, geo-distributed storage, and FAIR digital objects to lower barriers to data democratization. Giorgio's research spans multi-resolution algorithms , computational topology , and 3D geometric modeling , with applications in infrastructure security, archaeological reconstruction, and biomedical imaging. His work bridges abstract mathematical frameworks (e.g., Boolean algebras, chain complexes) with practical software solutions.

• Extreme Data Management
• Scientific Visualization
• Computational Topology
• +6 more

Daniel G Georgiev is a Professor in the Department of Electrical Engineering and Computer Science at the University of Toledo's College of Engineering. He has been on faculty since Fall 2006, following prior roles as a research faculty member at Wayne State University's Center for Smart Sensors and Integrated Microsystems (SSIM). Education : M.S. in Engineering Physics (Quantum Electronics and Laser Equipment) from Sofia University (1994), Ph.D. in Electrical Engineering (Electronic Materials and Devices) from the University of Cincinnati (2003). Research Interests : Dr. Georgiev's work focuses on laser modification and micro-structuring of materials, thin films of semiconducting oxides/nitrides (e.g., NiO, Zn3N2), glassy materials, metal whiskers (Sn, Cu), wide bandgap semiconductors (GaN, Zn3N2), photovoltaics, and biomedical device applications. His expertise spans device fabrication, material characterization, and radiation effects. Article Trends : Recent publications emphasize GaN-based power electronics, hybrid edge termination structures, threshold switching in nanocircuitries, and material innovations via reactive sputtering. Subfields include laser microstructuring, whisker suppression in Sn films, and doping strategies for nitride semiconductors. Collaborations : Co-authorship with researchers across institutions, including contributions to biomedical implants, II-VI nanocrystals, and chalcogenide glasses.

• Laser Modification of Materials
• Thin Films of Semiconducting Oxides and Nitrides
• Glassy Materials and Applications
• +7 more

Dr. Casey Bowman is an Associate Professor in the Mathematics department at the University of North Georgia (UNG). He specializes in traffic simulation, machine learning, and big data analytics. His research focuses on integrating microscopic discrete-event simulation with real-world data to model transportation systems and optimize traffic flow. Bowman's work often bridges theoretical models with practical applications, such as calibrating vehicular traffic simulations and developing hybrid approaches combining simulation with machine learning for traffic forecasting. He holds a PhD in a related field (details unspecified) and has published extensively on topics like sensor data-driven traffic optimization and open-source big data frameworks. His research has been published in journals and conferences, including works on traffic light synchronization and service network analysis. Bowman is reachable at casey.bowman@ung.edu and can be found at the Faculty Center, 736, Oconee campus.

• Traffic Simulation
• Discrete Event Simulation
• Machine Learning
• +3 more

Dr. David R. Jacques is a Professor of Systems Engineering at the Air Force Institute of Technology (AFIT), part of the Air University. He holds a Ph.D. in Aeronautical Engineering from AFIT (1995), an M.S. in Aeronautical Engineering from AFIT (1989), and a B.S. in Mechanical Engineering from Lehigh University (1983). He also completed the Defense Systems Management College Program Management Course (1999). His research focuses on systems engineering optimization, control systems, unmanned systems, modularity assessment, and cost estimation. He has advised numerous graduate students and collaborators on projects ranging from quantum key distribution systems to plug-and-play satellite modularity and health monitoring technologies. His work emphasizes practical applications in defense and aerospace systems, including improving maintenance strategies and enhancing system reliability through advanced modeling and simulation techniques. Dr. Jacques has been recognized with prestigious awards, including the General Bernard Schriever Award (2016), multiple best-paper awards at systems engineering conferences, and the HQ USAF Science and Engineering Educator of the Year (2003). His contributions span academic publications, military systems innovation, and leadership in educational programs for defense personnel. His advising and grants activities reflect a commitment to advancing systems engineering through interdisciplinary collaboration. His research groups often work on projects with direct operational implications for the U.S. Air Force, emphasizing practical solutions for complex systems challenges.

• Systems Engineering
• Optimization Techniques
• Control Systems
• +5 more

Jagannathan Sarangapani is the William A. Rutledge - Emerson Electric Company Distinguished Professor at the Missouri University of Science and Technology, where he holds a primary appointment in the Department of Electrical and Computer Engineering. He also holds courtesy appointments in Computer Science and Engineering Management and Systems Engineering. His research focuses on adaptive control, neural networks, cyber-physical systems, robotics, and big data prognostics. He has supervised over 36 doctoral and 32 M.S. students, with current research funded to the tune of $47M (shared credit: $13.8M). His awards include the IEEE Fellow, Fellow of the National Academy of Inventors, and multiple teaching excellence awards. Education: Ph.D. in Electrical Engineering (University of Texas at Arlington, 1994), M.S. (University of Saskatchewan, 1989), and B.E. (Anna University, India, 1986). Research interests include learning algorithms, neural network decision-making, networked control systems, and healthcare applications. Recent grants include projects on human-machine teaming, resilient deep learning control, and UAV formation control. He has authored/co-authored over 500 publications, including 21 patents and five books. Awards highlight his contributions: IEEE Control Systems Society’s Transition to Practice Award (2018), NSF CAREER Award (2001), and multiple teaching commendations. He directs the Embedded Control Systems and Networking Laboratory and served as Site Director for the NSF Industry/University Cooperative Research Center on Intelligent Maintenance Systems.

• Neural Networks
• Adaptive Control
• Cyber-Physical Systems
• +5 more

Feng Lin is a Professor in the Department of Electrical and Computer Engineering at Wayne State University (College of Engineering, Detroit, Michigan). He has held this position since 1988 after a postdoctoral fellowship at Harvard University (1987-1988). Education : Ph.D. (1988), M.A.Sc. (1984), B.Eng. (1982) His research focuses on systems and control theory , particularly discrete-event systems , hybrid systems , and robust control , with applications spanning alternative energy , biomedical systems , and automotive control . He has authored a textbook on robust control design and served as an associate editor for IEEE Transactions on Automatic Control . Scientific awards include: George Axelby Outstanding Paper Award NSF Research Initiation Award Wayne State Teaching Award ANR Pipeline Faculty Research Award Ford Research Award Lin has also worked as a consultant for major automotive companies (GM, Ford, Hitachi) and contributed to smart grid and electric-drive vehicle technologies.

• Systems and control
• Discrete-event systems
• Hybrid systems
• +4 more

Dr. Masoud H. Nazari is an Assistant Professor in the Department of Electrical and Computer Engineering at Wayne State University's College of Engineering. His research focuses on resilience and optimization of modern power systems through applications of stochastic hybrid systems, machine learning, and control theory. He holds leadership positions including Chair of IEEE Smart Buildings, Loads, and Consumer Systems Architecture Subcommittee and serves on IEEE Technology Conferences editorial boards. Education: Ph.D. in Electrical & Computer Engineering/Engineering & Public Policy from Carnegie Mellon University (2012), with visiting research at Massachusetts Institute of Technology. M.S. in Engineering & Public Policy from Carnegie Mellon University (2010). Research Focus: Primary interests include modern power systems resilience, stochastic hybrid systems, large-scale grid optimization, AI-enabled community resilience assessment, cyber-physical systems, and IoT applications in energy management. His work bridges theoretical control systems with practical energy infrastructure challenges. Publication Trends: Recent articles demonstrate strong focus on distributed control systems, resilience engineering in power grids, AI/ML applications for anomaly detection and optimization, and microgrid management. Work frequently addresses communication failures, stochastic variations, and cyber-physical security challenges in energy systems. Awards and Honors: WSU Excellence in Teaching Award (2024) Top 10 Team - ARPA-E Grid Optimization Challenge (2022-23) DTE E-Challenge Winner (2022) IEEE Senior Member (2019) Best Paper Award - North American Power Symposium (2017) Research Leadership: Principal Investigator for NSF AMPS ($200K): Stochastic contingency detection in power systems Co-PI for DOE RACER ($1M): Energy-water nexus resilience planning PI for ARPA-E Grid Optimization Challenge ($400K) Led DTE E-Challenge project developing AI-controlled HVAC systems Research Group: Leads the Modern Power Systems research team with current PhD students Hamid Varmazyari and Antar Kumar Biswas. Former advisees include Dr. Siyu Xie (now Assistant Professor in China) and multiple graduate/undergraduate researchers. The group focuses on control algorithms, resilience analytics, and optimization frameworks for power infrastructure.

• Power Systems
• Grid Optimization
• Resilience Engineering
• +6 more

Dr. Gonca Altuger-Genc serves as Associate Professor in the Department of Mechanical Engineering Technology within Farmingdale State College's School of Engineering Technology. Holding a PhD in Mechanical Engineering from Stevens Institute of Technology, she specializes in integrating artificial intelligence and simulation technologies into engineering education curricula while maintaining active roles in program coordination and curriculum development. Education Background: B.S. in Mechanical Engineering, Eskisehir Osmangazi University (2002) M.E. in Mechanical Engineering, Stevens Institute of Technology (2005) Ph.D. in Mechanical Engineering, Stevens Institute of Technology (2012) Design and Production Management Certificate, Stevens Institute of Technology (2007) Fundamentals of Supply Chain Management Certificate, Supply Chain Online (2012) Teaching and Learning Certificate for New Faculty, SUNY Center for Professional Development (2018) Brightspace Fundamentals Training Certificate, SUNY Center for Professional Development (2022) Her research pioneers the incorporation of AI in engineering assignments, development of machine learning systems for educational platforms, and simulation-aided online teaching practices. Current work focuses on creating discrete event simulation models for manufacturing optimization and maintenance scheduling, significantly enhancing student engagement through technology-driven pedagogical innovations that bridge theoretical concepts with practical applications in engineering technology education. Publication trends reveal a strategic evolution from foundational work in simulation-based teaching (2015-2018) toward cutting-edge integration of AI tools like ChatGPT in engineering education (2023-2024). Her research consistently addresses critical gaps in engineering pedagogy through systematic literature reviews, curriculum development frameworks, and practical implementation studies presented at premier conferences including ASEE and ASME. Scientific Recognition: First In the World - Research Aligned Mentorship (RAM) Program Academic Fellowship Award (2016) Farmingdale College Foundation Award for Excellence in Teaching (2018) As former Graduate Program Coordinator for the MS Technology Management program, Dr. Altuger-Genc has shaped curriculum development and assessment frameworks while mentoring undergraduate research projects. Her RAM Fellowship supported innovative mentorship approaches, and her teaching excellence award recognizes transformative contributions to engineering education through applied learning methodologies and technology integration. She collaborates extensively with colleagues on interdisciplinary research initiatives spanning engineering education and manufacturing systems.

• Artificial Intelligence in Engineering Education
• Machine Learning Systems for Engineering Education
• Simulation-Aided Online Teaching
• +3 more

Geir E. Dullerud is the W. Grafton and Lillian B. Wilkins Professor of Mechanical Science and Engineering at the University of Illinois at Urbana-Champaign (UIUC), affiliated with the College of Engineering. He holds positions in the Department of Mechanical Science and Engineering and is an affiliate in the Departments of Computer Science and Electrical and Computer Engineering. He has been a faculty member since 1998, with prior roles including Assistant, Associate, and Full Professor, and has held visiting positions at institutions like Stanford University and the Royal Institute of Technology (KTH). Education: Ph.D., Information Engineering, Cambridge University, England (1994) M.A.Sc., Electrical Engineering, University of Toronto, Canada (1990) B.A.Sc., Engineering Science, University of Toronto, Canada (1988) Research Interests: Professor Dullerud focuses on cyber-physical systems, networked and distributed control, convex optimization, machine learning, system security, cooperative robotics, stochastic simulation, and hybrid dynamical systems. His work bridges theoretical control systems with practical applications in mechanical, electrical, and computer systems. Recent Article Trends: Recent publications emphasize reinforcement learning, differential privacy in control systems, stochastic hybrid systems verification, and event-triggered control strategies. These contributions address challenges in privacy, scalability, and robustness of modern control systems. Awards: Fellow, ASME (2011) and IEEE (2008) Willett Faculty Scholar Award (2002–present) Xerox Award for Faculty Research (2005) NSF CAREER Award (1999) Teaching & Service: He teaches courses such as ECE 528 (Analysis of Nonlinear Systems) and ME 561 (Convex Methods in Control). He has been recognized for teaching excellence by students multiple times. He serves on professional committees, including the IEEE Control Systems Society, and contributes to academic leadership. Labs/Teams: He is associated with the Coordinated Science Lab and the Center for Autonomy Education at UIUC, fostering interdisciplinary research in autonomy and control systems.

• Cyber-Physical Systems
• Control Theory
• Optimization
• +5 more