Find Academic Supervisors

• Autonomous Systems
• Bayesian Statistics
• Big Data
• +7 more

Professor Simon Maskell is a Professor of Autonomous Systems at the University of Liverpool, affiliated with the School of Electrical Engineering, Electronics and Computer Science and the Faculty of Science and Engineering. He holds degrees including MEng, MA, PhD, and is a Fellow of the Institution of Engineering and Technology (FIET) and Chartered Engineer (CEng). His research focuses on algorithmic solutions at the intersection of Computer Science, Engineering, and Statistics. Key areas include Bayesian statistics for decision-making under uncertainty, particle filters for high-dimensional parameter estimation, and applications in autonomous systems, big data analytics, and data fusion for sectors like defense, healthcare, and maritime surveillance. He leads the Liverpool Big Data Network and contributes to the Stone Soup open-source tracking framework. Maskell has secured grants from the Home Office, European Commission, and EPSRC, addressing challenges like combating cyber threats, improving drug interaction detection, and enhancing sensor technologies. His notable award includes an Honorary Research Fellow title from Imperial College (2010). In teaching, he oversees modules such as Instrumentation & Control and supervises research on topics like Bayesian methods in proteomics, traffic signal control, and antimicrobial resistance. He serves as an Associate Editor for IEEE Transactions on Aerospace and Electronic Systems and IEEE Signal Processing Letters. Professional activities include editorial roles and administrative leadership in interdisciplinary initiatives, emphasizing ethical AI deployment and data-driven healthcare solutions.

Aubrey PoonView profile

Senior Lecturer

• Applied Macroeconometrics
• Bayesian Estimation
• Mixed-Frequency Methods
• +3 more

Dr. Aubrey Poon is a Senior Lecturer in Econometrics at the School of Economics, University of Kent, with affiliated researcher positions at Örebro University (Sweden), Centre for Applied Macroeconomic Analysis (Australian National University), and UK Economic Statistics Centre of Excellence. He completed his PhD in Economics from the Australian National University in 2017. His primary research focuses on Applied Macroeconometrics , specializing in Bayesian estimation methodologies including: Mixed-frequency analysis techniques Non-linear state-space modeling Quantile regression frameworks Macroeconomic forecasting systems His work has been featured in prominent outlets including The Economist and New York Times. Publication analysis reveals strong thematic consistency in econometric innovation, with recent works emphasizing: Advanced Bayesian VAR methodologies International financial market interconnections Macroeconomic tail risk quantification Regional economic nowcasting techniques The research demonstrates growing sophistication in handling high-dimensional data structures and missing data problems across global economies. Dr. Poon maintains active research collaborations across multiple international institutions focusing on macroeconomic measurement and policy analysis.

• Probabilistic Machine Learning
• Bayesian Experimental Design
• Causal Inference
• +2 more

Desi R. Ivanova is a Florence Nightingale Bicentennial Fellow in the Department of Statistics at University of Oxford. Her research focuses on robust evaluation of large language models and Bayesian experimental design. Ivanova holds a DPhil in Statistics (StatML CDT) from Oxford and previously worked in quantitative finance at Goldman Sachs. Research Interests: Ivanova develops information-theoretic methods for contextual optimization and causal dataset acquisition. Current work addresses statistical rigor in AI evaluation, semantic calibration, and privacy-preserving data integration. Publications: Her contributions introduce novel frameworks for adaptive experimental design (DAD, Step-DAD) and critically examine evaluation practices for foundation models. Additional: Organizes ELLIS RobustML workshops and contributes to uncertainty quantification methods. Research integrates Bayesian principles with modern AI challenges.

Xiaodong ChengView profile

Assistant Professor

• Control Systems
• Optimization
• Machine Learning
• +5 more

Xiaodong Cheng is an Assistant Professor at the Mathematical and Statistical Methods (Biometris) group in the Department of Plant Science at Wageningen University & Research. His research focuses on control systems, optimization, and machine learning, with applications in agricultural and energy systems. He holds a Ph.D. (cum laude) from the University of Groningen, under Prof. Jacquelien Scherpen, and prior roles include Research Associate at the University of Cambridge and Postdoctoral Researcher at Eindhoven University of Technology. Education: B.S. and M.E. from Northwestern Polytechnical University, China (2011, 2014) Ph.D. (cum laude) in Engineering from the University of Groningen, Netherlands (2018) Research Interests: Data-driven modeling, dimensionality reduction, learning-based control, system identification, and applications in agriculture and energy. He emphasizes practical implementations through tools like SYSDYNET and Bayesian neural ODEs for greenhouse systems. Key Contributions: His work spans model reduction for network systems, fault-tolerant control, and stochastic MPC for greenhouse production. Recent trends in his publications highlight advancements in resilient microgrid control, precision agriculture via drone-based sensing, and Bayesian methods for dynamic systems. Awards: Automatica Paper Prize Award (2017–2019) IEEE Transactions on Control Systems Technology Outstanding Paper Award (2020) Labs/Teams: Leads the Biometris group's efforts in integrating control theory and machine learning for sustainable systems. His work often collaborates with agricultural and energy sector stakeholders for real-world impact.

Jon CockayneView profile

Lecturer

• Statistical Computing
• Bayesian Computation
• Probabilistic Numerical Methods
• +3 more

Dr Jon Cockayne is a Lecturer in Statistics at the University of Southampton's Mathematical Sciences Department. His research focuses on probabilistic numerical methods, Bayesian computation, and statistical computing, particularly in numerical analysis and uncertainty quantification. He co-leads the second-year Statistical Modelling module (MATH2010). Education: Bachelor's in Mathematics from Imperial College London. PhD in Statistics at the University of Warwick under Prof. Mark Girolami, titled Bayesian Probabilistic Numerical Methods . Research Interests: Jon develops probabilistic numerical methods that integrate statistical principles into computational algorithms for uncertainty quantification. His work spans Bayesian linear solvers, radiative transfer modeling, and calibration of machine learning procedures. He collaborates on EPSRC-funded projects like Unifying Probabilistic Computation for PDEs and Linear Systems . Grants & Projects: EPSRC grant for Unifying Probabilistic Computation for PDEs and Linear Systems . Postdoctoral research at the Alan Turing Institute (2017–2019). Advising: Supervises PhD students Zoe Abbott (iPhD AI for Sustainability) and Disha Hegde (Mathematical Sciences). Labs & Teams: Member of the Statistics group and the Statistical Sciences Research Institute (S3RI) at Southampton.

H. Dharma KwonView profile

Associate Professor

• Game Theory
• Strategic Decision Making under Uncertainty
• Stochastic Dynamic Games
• +5 more

H. Dharma Kwon is an Associate Professor of Business Administration and Robert and Karen May Faculty Fellow at the Gies College of Business, University of Illinois at Urbana-Champaign. He holds a Ph.D. in Management and Operations Research from UCLA Anderson School of Management (2008) and a B.S. in Physics from KAIST (1991). His research focuses on strategic decision making under uncertainty, stochastic dynamic games, and technology management. He explores topics such as free-rider problems, Bayesian sequential decisions, and real options, with applications in supply chain management, project contracts, and employee retention strategies. His work bridges theoretical rigor and practical business challenges, addressing issues like optimal pricing strategies, investment decisions under spillovers, and cooperative game theory. He has published extensively in journals like Operations Research , Mathematics of Operations Research , and Management Science . His research also extends to early-career physics, with studies on superconductors and quantum phase transitions. Awarded the Robert and Karen May Faculty Fellowship (2018–present), Kwon is recognized for his teaching excellence, receiving the Excellent Teacher award multiple times (2014–2018, 2019–2020, 2023–2023). He serves as an Associate Editor for Management Science and Decision Analysis . His courses include Operations Management (BADM 275) and Decision Analytics (BADM 573), emphasizing data-driven decision frameworks and probabilistic methods.

Kevin CarlbergView profile

Associate Professor

• Contextual AI
• Computational Physics
• Computational Mathematics
• +3 more

Kevin Carlberg is an Affiliate Associate Professor of Applied Mathematics and Mechanical Engineering at the University of Washington. He currently holds leadership roles in AI Research Science at Meta Reality Labs and has prior experience at Sandia National Laboratories. His research focuses on contextual AI, computational physics, and model reduction techniques. He holds a PhD in Aeronautics and Astronautics from Stanford University (2011), an MS from Stanford (2006), and a BS from Washington University in St. Louis (2005). Carlberg leads interdisciplinary teams to develop AI-driven methodologies for extreme-scale simulations and wearable technologies. His work emphasizes reducing computational complexity for high-consequence applications, such as national security and VR/MR devices. Key areas include nonlinear model reduction, uncertainty quantification, and data-driven time-parallelism. He has pioneered techniques like the network uncertainty quantification (NetUQ) method and deep neural network-based error modeling. His research has been published in top journals like Computer Methods in Applied Mechanics and Engineering and Journal of Computational Physics , focusing on ROMs, machine learning integration, and high-performance computing. His contributions address challenges in Kolmogorov widths and real-time simulation requirements for large-scale systems.

Mrinal K SenView profile

Professor

• Seismic Wave Propagation
• Geophysical Inverse Problems
• Seismic Anisotropy
• +7 more

Mrinal K Sen is a Professor and Morgan J. Davis Centennial Chair in Geosciences at the University of Texas at Austin's Jackson School of Geosciences. He leads the Sen Research Group and holds affiliations with the Department of Earth and Planetary Sciences and the Institute for Geophysics. His work focuses on seismic wave propagation, anisotropy, and geophysical inverse problems, with applications in exploration seismology, reservoir characterization, and whole-Earth seismology. Sen pioneered methods combining Bayesian statistics, machine learning, and advanced numerical techniques to address uncertainty quantification and improve seismic imaging. Education: Ph.D., University of Hawaii at Manoa M.S., Indian School of Mines (now IIT) B.S., Indian School of Mines (now IIT) Research Interests: Seismic Inversion and FWI Bayesian Methods and Uncertainty Analysis Machine Learning in Geophysics Anisotropy and Wave Propagation Reservoir and CO2 Storage Modeling Recent work emphasizes hybrid quantum-neural networks for image generation, trans-dimensional MCMC for FWI, and physics-guided deep learning. His articles highlight advancements in stochastic inversion, fluid saturation modeling, and ambient noise monitoring for groundwater studies. Labs/Teams: Director of the Sen Research Group and former Interim Director (2017-2018) and Associate Director (2016-2020) of the UT Institute for Geophysics.

• Evaluation of probability forecasts
• Statistical information theory in Accounting
• Asset pricing and the cost of capital
• +3 more

Professor David Johnstone is a Senior Professor at the School of Business within the Faculty of Business and Law at the University of Wollongong since 2017. He holds a PhD from the University of Sydney. His research focuses on evaluating probability forecasts, statistical information theory in accounting, asset pricing, cost of capital, and energy regulation. He has supervised doctoral research on corporate governance and partnership structures. Key research interests include: Probability forecasting methodologies Accounting theory and statistical rigor Energy market regulation and infrastructure valuation Asset pricing under CAPM and ESG frameworks Recent funding includes an internal grant exploring Natural Language Processing techniques to measure economic sentiment (2023). His work bridges finance theory with practical applications in regulatory frameworks and market analysis. Teaching focuses on advanced topics in finance including capital budgeting, risk assessment, and regulatory economics. He is available for PhD supervision in his core research areas.

Kalil ErazoView profile

Teaching Professor

• Structural Health Monitoring
• Civil Infrastructure Resilience
• Stochastic Methods
• +4 more

Kalil Erazo is an Assistant Teaching Professor in the Department of Civil and Environmental Engineering at Rice University. His research focuses on structural health monitoring (SHM) for resilient civil infrastructure, particularly in regions prone to natural hazards. Erazo emphasizes integrating stochastic methods, Bayesian estimation, and advanced sensor technologies to assess structural integrity and predict performance under extreme events. Education: Postdoctoral Scholar, Rice University (2015-2016) Ph.D. in Civil and Environmental Engineering, University of Vermont (2015) M.S. in Civil and Environmental Engineering (Fulbright Fellow), Georgia Tech (2012) B.S. in Civil Engineering, Instituto Tecnológico de Santo Domingo (2009) Research Interests: Erazo’s work bridges theory and practice in resilient infrastructure design. Key areas include SHM for historic structures (e.g., UNESCO’s Colonial City of Santo Domingo), stochastic modeling for uncertainty quantification, Bayesian methods for nonlinear systems, and post-disaster decision-making frameworks. He advocates for integrating computational tools with physical infrastructure to enhance safety and sustainability. Research Group: The Structural Monitoring for Resilient and Sustainable Infrastructure group addresses National Academy of Engineering Grand Challenges by developing cyber-physical systems that monitor infrastructure health and predict performance under hazards like hurricanes and earthquakes. Outputs include sensor-based frameworks, digital twin technologies, and risk-assessment protocols.

• Mechanical Engineering Asset Management
• System Reliability Engineering
• Condition-Based Maintenance
• +8 more

Tieling Zhang is an Associate Professor at the University of Wollongong's School of Mechanical, Materials, Mechatronic and Biomedical Engineering. He holds a PhD from Tokyo University of Marine Science and Technology. His research focuses on system reliability engineering, machine learning applications, energy storage systems, and pipeline integrity management. He has published over 100 peer-reviewed articles and secured over AUD 7 million in research funding. Research Interests: Statistical Data Modelling, Big Data Analytics, Bayesian Inference, System Reliability Modelling, Degradation Modelling, Wind Turbine Maintenance, Battery Management, Smart Grids. Awards: Recipient of three Best Paper Awards from international conferences. He leads the Engineering Asset Management and Systems Engineering Research Group and serves on editorial boards of journals like Information and Machines . Teaching: Contributes to Master's programs in Engineering Asset Management and Engineering Management. Supervises PhD/Master’s students on topics like battery diagnostics, UAV wildfire management, and human-robot collaboration safety. Funding Highlights: Includes grants for battery health estimation, pipeline integrity, and railway asset management. Collaborates with industry partners like the Australasian Centre for Rail Innovation and Energy Pipeline Cooperative Research Centre.

Ron SmithView profile

Lecturer

• Business Education
• Engineering Education
• Mechanical Engineering
• +3 more

Ron Smith is a Lecturer and Counselor in the B2B Revenue Readiness Program at Mississippi State University’s College of Business. His work bridges academic instruction with professional development, focusing on business-to-business strategies and student engagement in technical coursework. He holds a strong background in mechanical engineering pedagogy, evidenced by publications on curriculum design and innovative teaching methods such as gamified learning using platforms like Kahoot!. His research spans multiple disciplines including biomedical research (e.g., NGS applications in oncology), thermal systems engineering, and statistical methodologies. Notable contributions include work on heat recovery systems, Markov chain Monte Carlo analysis, and uncertainty quantification in energy systems. His instructional innovations aim to enhance conceptual understanding through historical context and active learning strategies. Ron’s articles reflect a blend of practical engineering solutions and pedagogical advancements, emphasizing real-world applications such as energy efficiency improvements and student motivation techniques. His interdisciplinary approach integrates technical expertise with educational theory to address challenges in both academic and industrial contexts.

• Resilience of Infrastructure Systems
• Disaster Management
• Transportation and Supply Network Analysis
• +6 more

Dr. Jinzhu Yu is an Assistant Professor at the University of Texas at Arlington, holding primary affiliation in Civil Engineering and a secondary appointment in Industrial, Manufacturing, and Systems Engineering. He earned his Ph.D. in Interdisciplinary Systems Engineering from Vanderbilt University and conducted postdoctoral research at Rensselaer Polytechnic Institute. His work focuses on resilient urban systems through network science, operations research, and AI/ML. Research interests include infrastructure resilience, disaster management, transportation networks, and decision-making under uncertainty. Education: PhD, Interdisciplinary Systems Engineering, Vanderbilt University (2020) MS, Civil Engineering, Tongji University (2016) BS, Civil Engineering, Tongji University (2013) Research Highlights: Dr. Yu develops models for infrastructure resilience, climate adaptation, and equity. Recent projects include TxDOT-funded work on transportation asset management and crowd-sourced bicycle/pedestrian safety data. His lab integrates data science and network analytics to enhance urban systems. Awards: Urban Resilience Fellow (2024) STARs Award (2022) Student Merit Award (2017) Grants & Advising: Active projects include $319K TxDOT grant (2024) and leadership in NSF reviews. Supervises interdisciplinary students in civil engineering, systems engineering, and computer science.

Aaron SmithView profile

Associate Clinical Professor

• Engineering Education
• Uncertainty Analysis
• Energy System Modeling and Optimization
• +3 more

Aaron Smith is an Associate Clinical Professor at the Michael W. Hall School of Mechanical Engineering, Mississippi State University (MSU), part of the Bagley College of Engineering. He holds a Ph.D. (2012) and B.S. (2008) in Mechanical Engineering from MSU. Before academia, he worked as a Senior Engineer at Eaton Aerospace (2013-2015) and as a Graduate Research Assistant at MSU (2009-2012). His research interests focus on engineering education, uncertainty analysis, energy systems, and control systems design. He has received multiple teaching awards, including the Mechanical Engineering Outstanding Faculty Teaching Award (2022, 2017) and the Schillig Special Teaching Projects Award (2020). Dr. Smith teaches courses such as Thermodynamics, Engineering Analysis, and Mechanical System Design. His work emphasizes student engagement through innovative pedagogical methods, including gamification and project-based learning. He has authored patents related to hydraulic systems and control logic, demonstrating his industry-academia bridge. His publications span thermal system modeling, educational strategies, and uncertainty analysis in engineering systems. Notable achievements include the Eaton Aerospace Innovation Award (2015) and Green Belt in Design for Six Sigma Certification (2015). His research and teaching contributions reflect a commitment to advancing both mechanical engineering practice and education. He currently leads efforts in curriculum development and student motivation strategies within the mechanical engineering program.

Zhiqian ChenView profile

Assistant Professor

• Graph Machine Learning
• Network Science
• AI for Science
• +2 more

Zhiqian Chen is an Assistant Professor in Computer Science and Engineering at Mississippi State University. He holds a Ph.D. from Virginia Tech and researches graph machine learning, network science, and AI applications in scientific domains. His work develops unified frameworks for graph dynamics and graph neural networks. Research interests include graph Bayesian optimization, uncertainty quantification, and applications in spatial epidemiology and circuit analysis. Recent NSF CAREER award supports unification across graph dynamics. He leads the XGraph.Team research group and has secured multiple NSF grants. Awards include teaching excellence recognition and outstanding reviewer honors. Mentors 14 graduate students with research spanning traffic forecasting, vulnerability prediction, and microbiome analysis.