Associate Professor Mingxi Zhou is affiliated with the University of Rhode Island ( URI )'s Graduate School of Oceanography and Department of Oceanography . His research focuses on marine robotics, autonomous underwater vehicles (AUVs), and underwater navigation, with an emphasis on vehicle autonomy and multi-vehicle collaboration. Ph.D., Memorial University of Newfoundland (2017) M.Eng., Memorial University of Newfoundland (2012) B.Eng., Central South University (2009) His work addresses challenges in adaptive formation control, sensor fusion, and accessible unmanned platform development. Recent publications highlight deterministic learning algorithms, underwater pose estimation, and fault isolation in soft robotics. His research trends include advancements in autonomous systems, collaborative AUVs, and robust navigation under dynamic uncertainty, leveraging technologies like sonar, visual-inertial odometry, and distributed learning frameworks. He currently teaches OCG120G: World of Robots and OCE/ELE550: Ocean Systems Engineering . He founded the SOS Lab in 2018 at URI's Narragansett Bay Campus, prioritizing student training on interdisciplinary skills and providing competitive financial support.
Karl Kunisch is University Professor at the Department of Mathematics and Scientific Computing, University of Graz , and simultaneously Scientific Director of the Radon Institute (RICAM) of the Austrian Academy of Sciences in Linz. A SIAM Fellow and recipient of the 2021 W.T. and Idalia Reid Prize, he leads the ERC Advanced Grant OCLOC and heads the research group “Optimization and Optimal Control”. Education: Dipl.-Ing. (1975), Dr. techn. (1978) and Habilitation (1980), Graz University of Technology Research Interests: His work centres on optimization and optimal control of partial differential equations , nonsmooth optimisation in function spaces , inverse problems and mathematical imaging , together with advanced numerical analysis . Current emphases are life-science applications , closed-loop control and machine-learning based feedback design. Publications Profile: With over 400 papers and two monographs, his recent output is dominated by high-impact studies on infinite-horizon optimal control , feedback stabilisation of semilinear parabolic and Navier–Stokes systems, risk-averse and data-driven control , and sparse control strategies . A clear trend is the fusion of rigorous PDE analysis with cutting-edge machine-learning techniques. Scientific Awards & Distinctions: W.T. and Idalia Reid Prize (2021) SIAM Fellow (2017) ERC Advanced Grant Horizon 2020 (2015) Alwin Walther Medaille (2008) ICM Invited Lecture, Hyderabad (2010) SIAM Outstanding Paper Prize (2006) Christian Doppler Laboratory Fellowship (1992) Fellowship of the Japanese Society for the Promotion of Science (1990) Max Kade Scholarship (1982/83) Fulbright Travel Grants (1979/80, 1985) Theodor-Körner-Fonds Research Award (1979) Pro Scienta Scholarship (1974–1977) Grants & Leadership: Principal Investigator, ERC Advanced Grant “ OCLOC – From Open to Closed Loop Control ” Scientific Director, Radon Institute (RICAM), Austrian Academy of Sciences Head of Research Group “Optimization and Optimal Control”, RICAM Co-Speaker, International Research Training Group IGDK Former member/consultant: MATHEON Scientific Advisory Board, Weierstrass Institute Scientific Advisory Board, Christian Doppler Forschungsgesellschaft Senate, DFG and INRIA evaluation boards Laboratory & Team: Prof. Kunisch currently leads the “Optimization and Optimal Control” group at RICAM, comprising post-docs, doctoral researchers and international visitors, focusing on interdisciplinary projects at the interface of PDE control, numerical optimisation and life sciences.
Eugene A. Feinberg is a Distinguished Professor in the Department of Applied Mathematics and Statistics at Stony Brook University. He received his Ph.D. in Mathematics (Probability and Statistics) from Vilnius University, Lithuania in 1979. Prior to joining Stony Brook University, Dr. Feinberg held research and faculty positions at Moscow State University of Railway Transportation (1976-1988) and a visiting faculty position at Yale University (1988-89). He has also spent two one-semester sabbatical leaves at MIT. Dr. Feinberg's primary research focuses on stochastic methods of operations research, with particular expertise in Markov decision processes and their industrial applications. His work has significantly contributed to the theory and practice of operations research in telecommunication, manufacturing, transportation, service systems, and electric energy applications. Since 1999, he has been actively working on electric energy applications, including optimizing electric energy transmission and forecasting energy demand. His extensive publication record includes over 150 papers and the edited Handbook on Markov Decision Processes. His research has evolved to encompass increasingly sophisticated mathematical frameworks for Markov decision processes while maintaining strong connections to practical applications in energy systems, telecommunications, and manufacturing. Recent work demonstrates a continued focus on theoretical foundations of MDPs with emerging applications in healthcare and inventory management. Honorary Doctorate from the Institute of Applied System Analysis, National Technical University of Ukraine Fellow of INFORMS (2011) for fundamental contributions to Markov decision processes and dynamic programming IEEE Charles Hirsh Award (2012) for developing electric load forecasting methods and smart grid technologies IBM Faculty Award (2012) Dr. Feinberg's research has been consistently supported by major funding agencies including the National Science Foundation (NSF), Department of Energy (DOE), Office of Naval Research (ONR), New York Office of Science, Technology and Academic Research (NYSTAR), and New York State Energy Research and Development Authority (NYSERDA), as well as industry partners. He serves on editorial boards for prestigious journals including Mathematics of Operations Research, Operations Research Letters, and Applied Mathematics Letters, reflecting his standing in the academic community.
Carolyn L Beck is a Professor in the Department of Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana-Champaign . She holds affiliations with multiple departments, including Electrical and Computer Engineering (since 2014) and Mechanical Science and Engineering (since 1999). Beck is also a Research Professor at the Coordinated Science Laboratory (since 2019) and has served as Associate Head for Undergraduate Programs since 2020. Education : Ph.D. in Electrical Engineering from California Institute of Technology M.S. in Electrical & Computer Engineering from Carnegie Mellon University B.S. in Electrical & Computer Engineering with a Physics minor from California State Polytechnic University Research Interests focus on control and optimization , epidemic processes over networks , network inference , and dynamic network data clustering . Her work spans mathematical systems theory to real-world applications in bioengineering and smart grid optimization . Article Trends show a progression from system identification and network inference to complex epidemic modeling over time-varying networks and grid optimization. Recent works emphasize finite-sample analysis , distributed subgradient methods , and multi-layer contagion dynamics , reflecting her expertise in control theory and network science . Scientific Awards : IEEE Fellow (2023) Arthur Davis Faculty Scholar Award (2016) ONR Young Investigator Award (2001-2004) NSF CAREER Award (1998-2003) ORAU Junior Faculty Award (1997) Alcoa Foundation Award (1997) Advising and Grants include mentoring former PhD students like Puneet Sharma (AIMBE Fellow) and Philip Pare' (Purdue ECE) . Her research has been funded by the NSF , ONR , and IEEE , with a $500,000 grant for wind turbine efficiency. Labs and Teams : Beck is affiliated with the Coordinated Science Laboratory and collaborates with interdisciplinary teams in bioengineering , network control , and grid optimization . She also contributes to IEEE as a Guest Editor and Associate Editor .
Dr. William N. Caballero is an Assistant Professor of Data Science in the Department of Operational Sciences at the Air Force Institute of Technology (AFIT). His research focuses on developing statistical and mathematical models for decision support in uncertain, multi-agent environments, with applications in defense and security. Methodologically, his work integrates deterministic/stochastic optimization, Bayesian analysis, and interpretable machine learning. Education: Doctor of Philosophy in Operations Research, Air Force Institute of Technology (2019) Master of Science in Operations Research, Air Force Institute of Technology (2017) Bachelor of Science in Industrial Engineering, University of Houston (2011) Research Interests: His interdisciplinary research bridges statistics and operations research, emphasizing Bayesian decision analysis for security problems and modern data science applications in defense contexts. Primary domains include adversarial risk analysis, security games, ethical AI systems, automated driving technologies, and military personnel training optimization. Publication Trends: Recent articles demonstrate strong focus on machine learning applications in national security (LLMs, pilot selection), adversarial modeling (security games, data poisoning), and autonomous systems (driving mode management, ethical frameworks). Methodological innovations frequently combine Bayesian approaches with optimization techniques. Awards and Honors: Seiler Award for Mathematical Sciences Research (2023) Finalist for Clemen-Kleinmuntz Decision Analysis Best Paper (2022) USAF-MIT AI Accelerator Datathon: 5 awards including Overall Winner (2021) Multiple Field/Company Grade Officer of the Quarter awards (2013-2024) Distinguished Graduate honors (AFIT, OTS, Squadron Officer School) General Omar Nelson Bradley Fellowship (2018) Inductee to Omega Rho and Tau Beta Pi honor societies
Bruce Walcott is a Professor in the Department of Electrical and Computer Engineering at the University of Kentucky's College of Engineering. He has held significant administrative roles including Associate Dean for New Economy Initiatives and Innovations Management (2003-2012), Director of the UK Center for Manufacturing (2007-2010), and Co-Founder and Interim Director of the UK Center for Visualization and Virtual Environments (2004-2007). His educational background includes: Ph.D. in Electrical Engineering from Purdue University (1986) MSEE from Purdue University (1982) BSEE from Purdue University (1981) with Highest Distinction Dr. Walcott's research focuses on advanced control systems. His key interests include All-Digital Control Design , Deterministic Control of Uncertain Systems , and Nonlinear Systems . He has made contributions to the modeling of flexible systems and symbolic control, with applications in manufacturing and smart materials. His recent publications (2005-2007) demonstrate a strong application-oriented approach, spanning control of arc welding processes, characterization of shape memory alloys, signal processing for communication systems, and engineering education outreach. These works reflect his interdisciplinary approach bridging control theory with practical engineering challenges. Scientific honors include: Engineering Alumni Association Professor (2003-2013) College of Engineering Alumni Professor (2003-present) Graduated with Highest Distinction for BSEE Dr. Walcott has been actively involved in administrative leadership and center development, including the UK Center for Manufacturing and the UK Center for Visualization and Virtual Environments. While specific advising and grant information is not provided, his work in engineering education outreach is noted.
Donat Orski, PhD, serves as an Assistant Professor at the Department of Computer Science and Systems Engineering within Wrocław University of Science and Technology's Faculty of Information and Communication Technology. His active teaching role is evidenced by published office hours (Wednesdays 11:00-13:15, Thursdays 13:15-15:00) and direct contact channels including email (donat.orski@pwr.edu.pl) and phone (+48 71 320 3583). Dr. Orski's research centers on artificial intelligence applications for managing uncertainty in complex systems. His work specializes in knowledge engineering and intelligent control/management systems, with particular emphasis on resource allocation methodologies using uncertain variables and systemic approaches. This focus enables innovative solutions for operation systems where traditional deterministic models fail. Analysis of his 15 most recent publications (2006-2014) reveals a cohesive research trajectory in uncertainty modeling across production systems, computer networks, and operational frameworks. His work consistently bridges cybernetic theory with practical optimization, frequently appearing in journals like Kybernetes and the Journal of the Operational Research Society. Key contributions include developing C-uncertain variable frameworks and neural network applications for adaptive control under parameter uncertainty. No scientific awards or fellowships are documented in available sources. Information regarding student advising, research grants, or sponsored projects is not provided in current academic profiles. Details about laboratory facilities, research teams, or collaborative projects remain unspecified in publicly accessible institutional records.
Olivier Buffet is a Researcher at INRIA, working at the INRIA Center at Université de Lorraine / LORIA since November 2007. He is affiliated with the LORIA laboratory (Lorraine Laboratory of Computer Science and its Applications), which focuses on computer science research. His work spans multiple institutions, having previously held positions at NICTA's Statistical Machine Learning program (2004-2006), RSISE at ANU (2004-2006), and LAAS at CNRS (2006-2007). Dr. Buffet received his engineering degree from Supélec and a DEA (Diplôme d'Etudes Approfondies) from Henri Poincaré University. He completed his PhD in computer science under the supervision of François Charpillet and Alain Dutech at LORIA / INRIA Nancy Grand-Est, defended on September 10, 2003. He later defended his habilitation to supervise research (HDR) on December 18, 2017. Dr. Buffet's research focuses on artificial intelligence, particularly in the areas of automated planning and scheduling, reinforcement learning, and decision-making under uncertainty. His work extensively explores Markov Decision Processes (MDPs), Partially Observable MDPs (POMDPs), and Decentralized POMDPs (Dec-POMDPs), with applications ranging from multi-agent systems to traffic management and adaptive conservation strategies. His research often bridges theoretical foundations with practical applications, developing algorithms that can handle complex decision problems in uncertain environments. His publication record demonstrates a consistent focus on advancing methods for planning and decision-making under uncertainty. Over the past decade, his work has increasingly addressed decentralized and multi-agent settings, developing novel approaches for coordination among multiple decision-makers with partial information. More recently, his research has explored interpretable solutions for adaptive management problems, particularly in environmental contexts, and advanced theoretical understanding of properties like Lipschitz continuity in POMDP value functions. Dr. Buffet has received recognition for his contributions to the field, including: Winner of the probabilistic track in the Fifth International Planning Competition (IPC-06) Best Paper award at AAMAS-14 for "Exploiting separability in multi-agent planning with continuous-state MDPs" Best Paper award at JFSMA-13 for "Synchronisation de véhicules autonomes aux croisements d'un réseau de routes" Best Paper award at CAp'11 for "Une extension des POMDP avec des récompenses dépendant de l'état de croyance" As an educator and mentor, Dr. Buffet has supervised numerous PhD students including Arnaud Glad, Mauricio Araya-Lòpez, Mohamed Tlig, Arsène Fansi, and Manel Tagorti. He has also guided many interns and research projects. His teaching experience includes tutored sessions on discrete and deterministic optimization, decision making under uncertainty, and computer science for industrial engineering at École des Mines de Nancy, as well as courses on Unix shell and C programming at Université Henri Poincaré. Dr. Buffet has been actively involved in the academic community, serving as Co-Conference Chair of the 30th International Conference on Automated Planning and Scheduling (ICAPS 2020) in Nancy. He has organized multiple meetings of the French workgroup JFPDA (formerly PDMIA) and chaired several workshops on planning and scheduling under uncertainty. He previously served on the editorial boards of Revue d'Intelligence Artificielle (RIA) and Journal of Artificial Intelligence Research (JAIR), and has been a reviewer for numerous prestigious journals and conferences in artificial intelligence.
David Shmoys is a Professor at Cornell University, affiliated with the School of Operations Research and Information Engineering and the Department of Computer Science . He co-authored the influential book The Design of Approximation Algorithms (2011), which won the INFORMS Lanchester Prize in 2013, and received the Daniel H. Wagner Prize in 2018 for his work on bike-sharing optimization. His research bridges theoretical computer science and operations research, focusing on Approximation algorithms for NP-hard optimization problems Stochastic and deterministic inventory models Computational sustainability applications Shmoys' recent publications highlight applications in COVID-19 college reopening strategies Algorithmic redistricting for fair political representation Optimization of bike-sharing systems Stochastic inventory control Network design for coflow scheduling His methodological work emphasizes linear programming relaxations and sampling-based approaches for uncertain environments. Scientific awards include ACM Fellowship INFORMS Fellowship SIAM Fellowship NSF Presidential Young Investigator Best Paper Prizes at SODA and other conferences He has advised 27 PhD students, many of whom hold faculty positions at institutions like MIT, Waterloo, and Brown, and serves on editorial boards for journals including Mathematics of Operations Research and Operations Research . Cornell Tech's Institute of Computational Sustainability benefits from his leadership as Associate Director.
Dr. LÓGÓ János is a Professor and Faculty Coordinator at the Department of Structural Mechanics, Faculty of Civil Engineering, Budapest University of Technology and Economics (BME). With over three decades of academic service, his career spans roles from Assistant Professor (1990-1996) to Associate Professor (since 1996), alongside significant administrative contributions as Deputy Dean and Chairman of multiple committees. His research focuses on optimization in structural elasticity/plasticity, dynamically loaded structures, and mathematical programming, with extensive international collaborations including the University of Michigan. Key professional memberships: American Institute of Aeronautics and Astronautics (AIAA), American Society of Civil Engineers (ASCE), International Society for Structural and Multidisciplinary Optimization (ISSMO) Editorial roles: Editor of Periodica Polytechnica Civil Engineering (2004-present), Member of the Editorial Board for the International Journal of Structural and Multidisciplinary Optimization (2001-present) His scientific work reveals a consistent focus on reliability-based topology optimization, particularly for elasto-plastic structures under uncertain loading conditions. Over 60% of his recent publications address robust design methodologies incorporating probabilistic constraints, fatigue analysis, and multi-scale modeling. Notable subfields include stress-constrained optimization, graded infill structures, and seismic-resistant design frameworks using plasticity-based criteria. Dr. LÓGÓ received the Felvételi információ #építő250 ösztöndíj award and has contributed to structural optimization education through English-language program leadership. His teaching portfolio includes advanced courses in Plasticity and Structural Optimization , with earlier instruction in Structural Analysis. His research group actively explores mathematical programming applications to structural mechanics, maintaining collaborations with international institutions.
Kerstin Lux-Gottschalk is an Assistant Professor at the Department of Mathematics and Computer Science , Eindhoven University of Technology, affiliated with the Centre for Analysis, Scientific Computing and Applications (CASA) . She specializes in uncertainty quantification, stochastic differential equations, and optimal control, with applications to climate science, epidemiology, and ecology. Education : B.Sc. and M.Sc. from University of Mannheim, Germany; semester abroad at Université Nice Sophia Antipolis, France; Ph.D. from University of Mannheim (2020) under Prof. Dr. Simone Göttlich. Postdoctoral Research : Technical University of Munich (2020–2023) in Multiscale and Stochastic Dynamics group. Her research focuses on quantifying uncertainty in tipping points of complex systems, including: Analysis of random ordinary differential equations Climate modeling of Atlantic meridional overturning circulation Non-Markovian bifurcation detection Reinforcement learning for control systems Recent publications explore uncertainty quantification of tipping thresholds, stochastic control in transport systems, and numerical methods for SDEs. She contributes to UN Sustainable Development Goals related to climate action and sustainable infrastructure.
Tianyu Zhang is an Assistant Professor in the Department of Computer Science at The University of Iowa, within the College of Liberal Arts and Sciences. His research focuses on real-time cyber-physical systems (CPS), particularly industrial internet-of-things (IIoT), 5G/6G networks, and time-sensitive networking (TSN), aiming to achieve deterministic, reliable, and secure system behavior. His research interests include real-time scheduling, resource management, wireless sensor-actuator networks, and industrial automation. He develops frameworks for resource allocation, packet scheduling, and timing guarantees in dynamic and lossy environments, with applications in autonomous vehicles, avionics, and industrial control systems. His recent publications, spanning top venues like RTAS, RTSS, DAC, and IEEE TMC, reflect a consistent focus on real-time networking, TSN, and 5G URLLC. Key themes include deterministic scheduling, reliability under disturbances, and coexistence of traffic types in industrial networks. TPC Member, IEEE RTAS (2023–2025) TPC Member, IEEE RTCSA (2021–2024) Registration Chair, CPS-IoT Week (2023) Artifact Evaluation Chair, RTAS (2023) Web Chair, RTAS (2021–2022) Dr. Zhang actively mentors students and is recruiting Master’s and PhD candidates interested in CPS and real-time systems. He has served as a reviewer for leading journals including IEEE TMC, TCAD, TECS, and TII. While no formal lab name is mentioned, his research group focuses on real-time wireless and industrial networking systems.
Dr. Felipe Trevizan is a Senior Lecturer in the School of Computing at the Australian National University (ANU), specializing in Artificial Intelligence, Operations Research, and Machine Learning. His research focuses on automated planning, scheduling, and heuristic search, particularly in uncertain environments. Previously, he was a Senior Research Scientist at NICTA/Data61 and earned his Ph.D. in Machine Learning from Carnegie Mellon University under Prof. Manuela Veloso. Education: Ph.D. in Machine Learning, Carnegie Mellon University (2013) M.Sc. in Machine Learning, Carnegie Mellon University (2010) M.Sc. in Computer Science, University of São Paulo (2006) B.Sc. in Computer Science, University of São Paulo (2004) Research Interests: Felipe’s work bridges AI and Operations Research, emphasizing automated planning under uncertainty , heuristic search algorithms , and integration of machine learning with planning systems . His contributions include novel methods like short-sighted planning and occupation measure heuristics, as well as advancements in multi-objective stochastic planning and constraint generation techniques. Awards: 2016 Kikuchi-Karlaftis Best Paper Award (Transport Research Board) Best Paper Awards at ICAPS 2016 and 2017 Advising & Grants: Felipe has mentored over 20 students, including Ph.D. candidates Mingyu Hao and Johannes Schmalz, and Honours students Ryan Wang and Dillon Chen. His research is supported by grants focusing on planning under uncertainty, optimization in transportation, and machine learning integration with classical planning. Key Contributions: Recent work includes partial-space search for learned heuristics, CG-iLAO* for stochastic shortest paths, and GOOSE , a domain-independent heuristic learning framework. These innovations aim to enhance planning efficiency and adaptability in complex domains.
Michele Taragna is a Tenured Associate Professor in the Department of Electronics and Telecommunications (DET) at the Polytechnic University of Turin, actively teaching across degree programs: Experimental Modeling for PhD students in Electrical, Electronic and Communications Engineering (2019-2025), Estimation and System Identification for Mechatronic Engineering Master's program (2019-2026), and Automatic Control for Computer Engineering Bachelor's program (2019-2026) as course holder or collaborator. His research centers on Systems and Control Engineering , with primary interests in data-driven control for autonomous vehicles and fleets, direct virtual sensors, and machine learning-enhanced system identification. Key areas include Set Membership methods for robustness under bounded noise, computational complexity reduction in Nonlinear Model Predictive Control (NMPC), sensor fusion for robotics, and applications in automotive suspensions. This work aligns with ERC sectors PE7_1 (Control engineering), PE1_20 (Control theory), and PE6_12 (Scientific computing). Trends in his publications (2024-2004) reveal sustained innovation in applying Set Membership identification to NMPC for autonomous vehicles, achieving real-time feasibility through search domain reduction. Sensor fusion techniques using Kalman filters for mobile manipulators and data-driven filter design for uncertain LTI systems with bounded noise are recurring themes, emphasizing practical implementation and computational efficiency. Scientific awards: None documented in provided materials. Advising and research funding: Supervised PhD student Mattia Boggio (2020-2024) in Electrical, Electronic and Communications Engineering; thesis on Real-time Nonlinear Model Predictive Control with domain reduction. Led the nationally funded PRIN project Controllo ad alte prestazioni a partire dai dati sperimentali (2007-2009) as Scientific Responsible. He is a core member of the Automatica research group within DET, focusing on system identification, control design, and validation for dynamic systems with applications in automotive and robotics domains.
Nilufer Onder is an Associate Professor in the Department of Computer Science at Michigan Technological University, where she also serves as Associate Chair and Undergraduate Program Director. She is actively involved in research, teaching, and academic leadership. Education: PhD in Computer Science, University of Pittsburgh, 1999 MS in Computer Engineering, Middle East Technical University, 1988 BSc in Computer Engineering, Middle East Technical University, 1985 Her research focuses on Artificial Intelligence , particularly planning under uncertainty, probabilistic planning, and temporal/concurrent planning , and Computer Science Education , with an emphasis on student persistence in STEM, peer mentoring, and broadening participation in computing . She also explores applications in construction management and intelligent assistance systems. Her recent publications span AI planning, memory systems optimization, and educational technology, reflecting a strong interdisciplinary trajectory integrating systems, AI, and pedagogy. Scientific Awards: No specific awards listed in the provided text. She has advised numerous graduate and undergraduate students in research and thesis work, and has secured funding from the NSF and other agencies for projects related to computing education and faculty-student interaction. She is actively involved in service, including advising student groups such as Women in Computing Sciences (WiCS) and Upsilon Pi Epsilon (UPE), and participating in university committees and outreach programs like the Science Olympiad. Labs and Teams: She leads the Interactions Unlimited project, a Faculty-Student Interaction (FSI) initiative funded by the NSF through the Engage Engineering program, aimed at enhancing student engagement and retention through structured faculty-student connections.
