Find Academic Supervisors

• Control Theory
• Applied Mathematics
• Computational Mechanics
• +7 more

Goong Chen is a Professor at Texas A&M University (TAMU) within the College of Arts & Sciences. His research focuses on Control Theory, Applied Mathematics, and interdisciplinary applications in computational mechanics, fluid dynamics, and quantum systems. He holds a Ph.D. from the University of Wisconsin (1977) and a B.S. from National Tsing-Hua University (1972). His research interests span computational biomechanics, forensic modeling, nanofluidics, and mathematical physics. Recent work includes modal analysis of animal motion, crash mechanics of aircraft, and forensic reconstruction of disasters. He has contributed to theoretical advancements in PDEs, nonlinear dynamics, and quantum computing. Chen’s articles address cutting-edge topics like thermoelastic plates, Volterra equations, and DFIM systems. He has pioneered numerical methods for complex systems using OpenFOAM and finite element techniques. His computational models have been applied to real-world scenarios such as submarine implosions and chemical warfare forensics. No academic awards are explicitly listed, but his extensive publication record reflects significant contributions to applied mathematics and engineering. He leads research teams focused on interdisciplinary challenges in computational science and engineering.

Quan ZhouView profile

Assistant Professor

• Markov chain Monte Carlo sampling
• Bayesian methodology
• Variable selection
• +7 more

Quan Zhou is an Assistant Professor in the Department of Statistics at Texas A&M University, part of the College of Arts & Sciences. His research focuses on developing advanced sampling methods, particularly Markov chain Monte Carlo (MCMC) algorithms, with applications in Bayesian methodology, variable selection, stochastic optimization, and statistical genetics. He holds a BS from Fudan University and a PhD from Baylor College of Medicine, followed by a postdoctoral fellowship at Rice University. He teaches courses such as Mathematical Probability, Multivariate Analysis, and Advanced Stochastic Processes. Notable contributions include work on informed MCMC samplers, Schrödinger bridge theory, and high-dimensional structure learning. He advises PhD students like Hyunwoong Chang (now at UT Dallas) and Guanxun Li (Beijing Normal University). Active in academic service, he served as President of the Southeastern Texas Chapter of the American Statistical Association (SETCASA).

Yubai YuanView profile

Assistant Professor

• Complex Network Analysis
• Causal Inference
• Optimal Transport
• +7 more

Yubai Yuan is an Assistant Professor of Statistics at the Pennsylvania State University, affiliated with the Department of Statistics within the Eberly College of Science. He holds a PhD from the University of Illinois Urbana-Champaign (2020) and completed postdoctoral research at UC Irvine. His research focuses on network science, causal inference, and statistical machine learning, with applications to neuroscience and social systems. Notable awards include the 2022 NSF-Simons Center Fellow Award and the 2019 ASA Student Paper Award. Education: PhD in Statistics (UIUC, 2020), MS in Statistics (Sun Yat-sen University, 2016), BS in Mathematics (Shandong University, 2012). Research interests span complex network analysis, optimal transport, active learning, and mediation analysis. Current projects include de-confounding causal inference and hypergraph modeling. Teaching includes courses on probability theory and statistical modeling at Penn State. Advises PhD students Yuanchen Wu (active learning on graphs) and Siyu Huang (latent network structures). Collaborates with the Center for Social Data Analytics and organizes workshops in statistical network science. Publications emphasize methodological advancements in network analysis, causal pathways, and data integration. Recent work addresses disaster response via social media data and neuronal activity analysis using optimal transport frameworks.

Simon SpencerView profile

Professor

• Bayesian Inference
• Epidemiology
• Statistical Modeling
• +3 more

Simon Spencer is a Professor of Statistics at the University of Warwick, affiliated with the Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER) and the Warwick Analytical Sciences Centre (WASC). His research focuses on Bayesian inference applied to epidemiology, stochastic epidemic models, and statistical methods for analytical science. He has held previous positions at the University of Nottingham and Massey University in New Zealand. His teaching includes advanced courses such as CH923: Statistics for Data Analysis , ST925: Graduate Topics in Statistics , and MA4M1/MA6M1: Epidemiology by Example . His research group currently includes PhD students Matthew Adeoye and Richard Haughey, and MSc students Olli Smith and Sangavi Pirabakaran. He collaborates extensively with global health institutions on projects addressing infectious disease modeling and public health policy. Spencer’s work bridges statistical methodology and real-world applications, with a focus on outbreak detection, model comparison, and the integration of geostatistical data with transmission models. His recent contributions include frameworks for lymphatic filariasis elimination projections and analyses of HIV transmission dynamics in Uganda. He actively contributes to interdisciplinary research in systems biology and analytical chemistry, leveraging advanced statistical techniques to address complex health challenges.

Dong NgoduyView profile

Associate Professor

• Connected and Autonomous Vehicles
• Traffic Flow Theory and Simulation
• Data Fusion
• +4 more

A/Professor Dong Ngoduy is the Head of Transport Engineering and an Associate Professor in Transport Engineering at Monash University's Department of Civil and Environmental Engineering. He holds a PhD in Traffic Flow Theory from TU Delft (Netherlands) and an MSc from Linköping University (Sweden). His research focuses on Connected and Autonomous Vehicles (CAVs), Traffic Flow Theory, Data Fusion, and Urban Network Optimization. He received the EPSRC Advanced Fellow Award (2011-2016) and chairs the Connected Traffic Systems Lab. His current work aims to develop smart city platforms for multi-modal transport infrastructure management. Education: MSc in Traffic Engineering, Linköping University PhD in Traffic Flow Theory, Delft University of Technology Research Expertise: Traffic Flow Theory Data Fusion Techniques Urban Network Optimization CAV Dynamics Grants & Awards: EPSRC Advanced Fellow Award (2011-2016) EU Horizon 2020 Project (~$1.2M) Australian Research Council (ARC) Assessor Teaching: Courses include Transport and Traffic Engineering (CIV2282) and Autonomous Vehicle Systems (CIV4100). Labs & Teams: Leads the Connected Traffic Systems Lab and collaborates on projects like Digital Twins for urban networks.

• Health Care Management
• Operations Research
• Patient Scheduling
• +5 more

Professor Patrick Jonathan is a Full Professor at the Telfer School of Management, University of Ottawa. His research focuses on applying operations research methods to healthcare management challenges, particularly in patient scheduling, capacity planning, and disaster response logistics. He holds a B.A.Sc. from McMaster University, M.Sc. and Ph.D. from the University of British Columbia (UBC). Key research areas include healthcare system efficiency, stochastic optimization, approximate dynamic programming, and resource allocation in emergency and long-term care settings. He has contributed to projects like optimizing inpatient unit staffing during pandemics and developing robust logistics models for disaster relief operations. Education: B.A.Sc. (McMaster), M.Sc. (UBC), Ph.D. (UBC) Affiliations: INFORMS, CORS, Centre for Health Care Management (UBC) Recent work emphasizes pandemic-related healthcare operations, including staff scheduling under crisis conditions and elective patient admission strategies during the COVID-19 pandemic. His research also addresses dynamic home care routing and multi-priority patient scheduling policies. He has secured over $2.3 million in research funding through grants from NSERC, MITACS, and the National Research Council. Notable projects include the 'Resilience and Adaptation to Climatic Extreme (RACE) Wildfires' initiative and 'Stochasticity in Approximate Dynamic Programming.' Labs/Teams: Collaborates with interdisciplinary teams on healthcare logistics and emergency department optimization projects.

Hao WangView profile

Senior Lecturer

• Applied Machine Learning
• Smart Grid Optimization
• Data Analytics for Energy Systems
• +4 more

Hao Wang is a Senior Lecturer in the Department of Data Science and Artificial Intelligence at Monash University, affiliated with the Monash Energy Institute and Monash Data Futures Institute. He holds an ARC DECRA Fellowship and leads interdisciplinary research on applied machine learning and optimization in smart grids, energy systems, and smart cities. His work focuses on leveraging AI for energy data analytics, EV integration, and distributed energy resource management. Education: PhD from The Chinese University of Hong Kong, supervised by Prof. Jianwei Huang. Postdoctoral research at Stanford University (Prof. Ram Rajagopal) and University of Washington (Prof. Baosen Zhang). Research interests include reinforcement learning for energy systems, decentralized control, and incentive mechanisms for prosumer participation. He serves on editorial boards of journals like Energy Conversion and Economics and IET Renewable Power Generation , and organizes conferences like ACM e-Energy and IEEE SmartGridComm. Awards: Best Paper Awards at IEEE PECON 2016, IEEE ICC 2017, and IEEE SmartGridComm 2020. Current projects include the Human-in-the-loop Microgrid Project and AI for Clean Energy and Sustainability initiative.

Nicolas ZuffereyView profile

Professor

• Operations Management
• Supply Chain Management
• Optimization
• +4 more

Nicolas Zufferey is a Full Professor of Operations Management at the University of Geneva, Switzerland, where he has served since 2008. He leads research in optimization methods for complex systems, focusing on applications in supply chain management, production planning, inventory control, and transportation logistics. His affiliations include the Research Institute of Management and collaborations with CIRRELT (Transportation & Logistics) and GERAD (Decision Analysis). Education: PhD in Operations Research (EPFL, 2002), MSc/BSc in Mathematics (EPFL) Prior Experience: Postdoc at University of Calgary (2003–2004), Assistant Professor at Université Laval (2004–2007) Research Interests: His work emphasizes developing advanced metaheuristics (e.g., VNS, Tabu Search, PSO) for challenging optimization problems. Key domains include: Multi-objective scheduling with resource constraints Inventory deployment under uncertainty Network design for supply chains and transportation systems Publications: Over 150 peer-reviewed articles across journals like European Journal of Operational Research , Transportation Research , and INFORMS Journal on Computing . Recent work addresses electric vehicle routing, drone integration in delivery systems, and robust decision-making under uncertainty. Collaborations: Engaged with 35+ universities and 27 private companies globally. Active in applying operations research to industrial problems (e.g., Swiss railways, luxury watch production, pharmaceutical networks).

Dorsa SadighView profile

Associate Professor

• Robotics
• Artificial Intelligence
• Human-Robot Interaction
• +5 more

Dorsa Sadigh is an Associate Professor of Computer Science and Electrical Engineering at Stanford University, and a Senior Fellow at the Stanford Institute for Human-Centered AI. Her work focuses on advancing robotics , particularly in areas such as human-robot collaboration , reinforcement learning , and vision-language models . She explores how robots can learn from human demonstrations, adapt to dynamic environments, and safely interact with humans in caregiving and assistive tasks. Her research interests span autonomous systems , improving robot generalization , and foundation models for robotics . Key projects include developing policies for dexterous manipulation, proactive human-robot teamwork, and scalable data collection methods. She emphasizes ethical considerations in robotics, including perceived safety and human trust. Recent work highlights include the ProVox framework for personalized collaboration, HoMeR for mobile manipulation, and Octo —an open-source generalist robot policy. Her contributions bridge theoretical advances in AI with real-world robotic applications, leveraging large language models and vision-language integration. Dr. Sadigh’s research is funded by grants from NSF, DARPA, and industry partnerships. She collaborates with interdisciplinary teams to address challenges in assistive robotics, autonomous driving, and socially intelligent AI systems.

Ahmad IbrahimView profile

Associate Professor

• Internet of Things
• Fog Computing
• Cloud Computing
• +7 more

Dr. Ahmad Ibrahim is an Associate Professor in Computer Science at the University of Birmingham Dubai, where he serves as the Undergraduate Programme Director and Year 2 Coordinator for BSc students. He is also the School Outreach Coordinator for the Computer Science program at the Dubai campus. Educational Background: Postgraduate Certificate in Higher Education (PGCHE), University of Birmingham, 2021 Fellow of the Higher Education Academy (FHEA), University of Birmingham, 2021 PhD in Computer Science, University of Pisa, Italy, 2016 MSc in Computer Science, Korea Advanced Institute of Science and Technology (KAIST), South Korea, 2009 BSc in Computer System Engineering, G.I.K Institute of Engineering Sciences and Technology, Pakistan, 2005 Dr. Ibrahim's research focuses on the Internet of Things, Quality of Service (QoS) prediction, Web services, and Cloud and Fog computing. His teaching interests include Object-oriented programming, Full Stack application development, Security and Networks, Operating Systems and Systems Programming, and Advanced Networking. His recent publications demonstrate a strong trend in fog and edge computing, with particular emphasis on QoS optimization, cost-effective deployment, and resource management in distributed environments. His work bridges theoretical modeling with practical application deployment strategies across cloud-edge continua. Scientific Awards and Recognition: Fellow of the Higher Education Academy (FHEA), 2021 Postgraduate Certificate in Higher Education (PGCHE), University of Birmingham, 2021 Dr. Ibrahim has supervised and contributed to numerous research projects in distributed systems and cloud computing, particularly through collaborations on European initiatives like SeaClouds. He has secured recognition for his teaching excellence and is actively involved in curriculum development and academic outreach.

Jorg LiebeherrView profile

Professor

• Computer Networks
• Network Calculus
• Internet of Things (IoT)
• +6 more

Jorg Liebeherr is a Professor in the Department of Electrical and Computer Engineering at the University of Toronto, holding the Nortel Chair of Network Architecture and Services. His research focuses on computer networks , particularly network calculus , self-organizing networks , protocol design , and traffic scheduling . Education: Diplom-Informatiker (with distinction), University of Erlangen (Germany), 1988 PhD, Computer Science, Georgia Institute of Technology, 1991 His recent work includes low-cost LoRa mesh networks for environmental sensing and mathematical frameworks for traffic control in 5G and IoT systems. Publications span journals like IEEE Internet of Things Journal and conferences such as IEEE Infocom and ACM Sigmetrics . Scientific Awards: IEEE Fellow (2008) Outstanding Service Award, IEEE ComSoc TC on Computer Communications (2006) ACM Sigmetrics Best Student Paper Award (2005) NSF CAREER Award (1996) Advising and Grants: Supervised 15+ theses (MASc/PhD) and secured grants from NSF, Virginia Engineering Foundation, and industry partners. Labs: Leads the Network Research Lab and HyperCast projects, an open-source platform for application-layer internetworking.

Colin WilsonView profile

Professor

• Theoretical Phonology
• Experimental Phonology
• Phonetics
• +3 more

Colin Wilson is a Professor in the Department of Cognitive Science at Johns Hopkins University . He is on leave during Fall 2025. His research spans theoretical and experimental phonology, phonetics, cross-language perception/production, and computational modeling. Specialties : Theoretical phonology, phonotactics, constraint learning Techniques : Artificial grammar, probabilistic models, acoustic analysis Research Trends Recent publications focus on interpretable neural networks for morphological learning, phonetic covariation in American English stops, and cross-language speech processing. Key areas include constraint-based grammar, Bayesian inference, and phonotactic probability. Teaching History Colin has taught courses at JHU (2008-2017) and UCLA (2001-2007) including Phonology I/II, Bayesian Inference, and language processing seminars. He co-taught with Donca Steriade, Bruce Hayes, and others.

Pauline BarrieuView profile

Professor

• Model Uncertainty
• Insurance-Linked Securitization
• Contract Designing
• +5 more

Pauline Barrieu is a Professor in Statistics at the London School of Economics and Political Science (LSE) and Head of the Statistics Department. She also serves as Vice Chair of the Academic Board. Her research focuses on decision-making under model uncertainty , with applications in insurance-linked securitization , contract design , and environmental economics . Co-Director, Centre of the Analysis of Time Series (CATS) Member, Probability in Finance and Insurance Group Associate Editor, Annals of Applied Probability , SIAM Journal on Control and Optimization , Insurance Mathematics and Economics , and Stochastic Processes and their Applications Her recent publications analyze climate risk, longevity risk, and model uncertainty, with applications in insurance markets and financial risk management. She has written extensively on weather derivatives , microinsurance , and environmental policy under uncertainty. Scientific Awards: Bachelier Prize (2018) She has collaborated with researchers across disciplines on topics like contaminated land cleanup , optimal environmental policies , and robust asset allocation .

• Renewable Energy Integration
• Power Systems Optimization
• Energy Storage
• +7 more

Daniel Fernández-Muñoz is an Associate Professor at the Universidad Politécnica de Madrid (UPM) , affiliated with the Department of Physical Electronics, Electrical Engineering and Applied Physics. He earned his PhD in 2021 with a thesis on "Generation scheduling in isolated power systems with high variable renewable generation and pump-storage," receiving both the Extraordinary Doctoral Award and Carlos González Cruz Award. He has held academic roles since 2006, including Assistant Professor positions from 2016-2021 and a current permanent Associate Professor appointment. Education : PhD in Electrical Engineering (UPM), DEA in Electrical Engineering (2010), Civil Engineering degree (2006) Research Focus : Renewable energy integration, power system optimization, battery degradation modeling, and frequency control in isolated grids Collaborations : Instituto de Sistemas Eléctricos de Potencia (Austria), EERA Joint Programme on Energy Storage, H2020 project eNeuron His work emphasizes hybrid wind-battery systems , pumped-storage hydropower , and virtual power plants , with notable publications in JCR Q1 journals. He has contributed to the Energy2Win project on sustainability education and served as a peer reviewer for multiple scientific journals. Scientific Awards Premio Extraordinario de Doctorado (UPM) Premio Carlos González Cruz Teaching activities include Physics for Biomedical Engineering and Energy Systems for Telecommunications. He has participated in international conferences as an invited speaker and contributed to projects funded by the Spanish government and private entities.

Sina AnsariView profile

Assistant Professor

• Operations Research
• Healthcare Operations Management
• Service Systems Optimization
• +4 more

Sina Ansari is an Assistant Professor at DePaul University's Driehaus College of Business , specializing in Management & Entrepreneurship . He co-directs the MS Business Analytics program and has held academic roles at Northwestern University and Tuck School of Business . Northwestern University (PhD, Industrial Engineering & Management Sciences, 2018) Tuck School of Business (Postdoctoral Research Fellow) His research focuses on optimizing service systems through mathematical models, with applications in healthcare operations, opioid crisis mitigation, and logistics. Key areas include: Dynamic resource allocation Patient scheduling optimization Emergency department operations Humanitarian logistics Recent publications highlight his work in humanitarian operations , healthcare scheduling , and medical decision-making . He has received recognition through editorial roles in journals like Naval Research Logistics and Healthcare Management Science . Professional memberships include: INFORMS (Vice President, Chicago Chapter) Production and Operations Management Society Decision Sciences Institute