Find Academic Supervisors

David L. DarmofalView profile

Professor

• Computational Fluid Dynamics
• Numerical Analysis
• Uncertainty Quantification
• +3 more

David L. Darmofal is the Vice Chancellor for Undergraduate and Graduate Education and the Jerome C. Hunsaker Professor of Aeronautics and Astronautics at MIT. He leads the Aerospace Computational Science & Engineering (ACSEL) Lab and contributes to the MIT Center for Computational Science & Engineering (CCSE). His research focuses on computational methods for PDEs (especially fluid dynamics) and engineering education innovation. He holds a BS from the University of Michigan and SM/PhD from MIT, with postdoctoral work at the University of Michigan. Notable awards include the MacVicar Faculty Fellow (2004), Earll M. Murman Award (2011), and NSF CAREER Award (1998). Education: B.S.E., University of Michigan, 1989 S.M., MIT, 1991 Ph.D., MIT, 1993 Affiliations: MIT Schwarzman College of Computing Aerospace Computational Design Laboratory (ACSEL) His research emphasizes higher-order adaptive finite element methods, space-time mesh adaptation, and turbulence modeling. He teaches courses in computational methods and fluid dynamics. Recent projects include the Metris open-source meshing software and studies on sonic boom propagation. His work bridges computational science and engineering education, with a focus on evidence-based pedagogy. Awards & Recognition: Michael M. Byram Visiting Professorship (2021) Common Ground Excellence in Teaching Award (2024) AIAA Student Chapter Teaching Awards (2005, 2013) Bisplinghoff Fellow & Alumni Merit Award (2012) Advising & Grants: Over 130 peer-reviewed publications, leadership in MIT’s Common Ground initiative, and mentorship of postdocs/UROPs (e.g., Emily Williams, Lucien Rochery). Active in interdisciplinary collaborations, including DOE projects on physics-informed PDEs and NASA’s CFD Vision 2030 study.

Ram BajpaiView profile

Lecturer

• Epidemiology
• Biostatistics
• Survival Analysis
• +7 more

Dr. Ram Bajpai is a Lecturer in Epidemiology/Applied Statistics at Keele University's School of Medicine. He joined in 2019 as part of the Research Institute for Primary Care and Health Sciences, combining active research and teaching roles. Previously, he worked at the Lee Kong Chian School of Medicine (Nanyang Technological University, Singapore) and the Army College of Medical Sciences (India). Education: BSc in Statistics/Mathematics (University of Lucknow), MSc Health Statistics (Banaras Hindu University), PhD in Medical Statistics (Guru Gobind Singh Indraprastha University). Research focuses on cross-domain applications of statistical/epidemiological methods, including survival analysis, Bayesian methods, risk prediction modelling, and evidence synthesis. Teaching experience includes biostatistics modules for medical students at multiple institutions. Current research interests span prognostic studies, meta-analysis, complex data analysis, and design of epidemiological studies. Key contributions include systematic reviews on gout prophylaxis safety, dementia prognostic factors, and long-term outcomes of pediatric COVID-19. Active in collaborative projects on aging populations, musculoskeletal health, and public health interventions.

Liping LiuView profile

Associate Professor

• Machine Learning
• Data Science
• Deep Learning
• +3 more

Liping Liu is an Associate Professor in the Department of Computer Science at Tufts University's School of Engineering. He holds a Ph.D. from Oregon State University and has held postdoctoral positions at Columbia University and Tufts. His research focuses on machine learning, generative models, graph learning, and their applications in biochemical data analysis and fluid dynamics simulation. His work on graph generative methods earned the NSF CAREER Award. Education: Ph.D. (Oregon State University, 2016), M.Sc. (Nanjing University, 2009), B.S. (Hebei University of Technology, 2006). Research Interests: Machine Learning, Deep Learning, Generative Models, Time Series, Graph Learning. Dr. Liu's research emphasizes probabilistic modeling and neural networks, addressing challenges in graph generation, data-driven physics simulation, and biochemical analysis. His recent work includes advancements in graph-based recommendation systems, turbulence modeling, and enzymatic reaction prediction. His publications span top AI conferences like NeurIPS, ICML, and ICLR. He has secured grants totaling over $9 million, including the NSF CAREER Award and NIH funding for metabolomics and enzymatic promiscuity studies. His teaching includes courses on generative models, deep learning, and machine learning for graph analytics. Awards: NSF CAREER Award (2023), NIH grants, DARPA ACT-NOW project (2019). Service: NSF panelist, program committee member for AAAI, NeurIPS, and IJCAI.

Sinan YıldırımView profile

Researcher

• Bayesian Statistics
• Monte Carlo Methods
• Data Privacy
• +3 more

Sinan Yıldırım is a Researcher in the Faculty of Engineering and Natural Sciences at Sabancı University, Turkey. His primary research focuses on Bayesian Statistics, Monte Carlo methods, and data privacy, with interdisciplinary applications in machine learning and signal processing. He holds a BSc and MSc in Electrical and Electronics Engineering from Boğaziçi University, followed by a PhD in Mathematical Statistics from the University of Cambridge. Postdoctoral research (2013-2015) at the University of Bristol’s School of Mathematics involved the EPSRC-funded project 'Intractable Likelihood: New Challenges from Modern Applications (i-like).' His work bridges theoretical statistics with practical problems in privacy, control systems, and energy optimization. Research interests emphasize Bayesian methodologies for privacy-preserving data analysis, dynamic modeling of complex systems, and stochastic optimization algorithms. Recent publications explore differential privacy in machine learning, Monte Carlo techniques for high-dimensional inference, and applications of Bayesian methods in robotics and energy systems. Advising and grants include contributions to multi-party resource sharing frameworks and privacy-aware algorithms. His work integrates computational methods with real-world challenges in engineering and policy modeling.

Chong LiuView profile

Assistant Professor

• Machine Learning
• Bayesian Optimization
• Bandit Algorithms
• +3 more

Dr. Chong Liu is an Assistant Professor of Computer Science at the State University of New York at Albany (SUNY Albany) in the College of Nanotechnology, Science, and Engineering. He received his PhD in Computer Science from UC Santa Barbara in 2023 and completed a postdoctoral fellowship at the University of Chicago's Data Science Institute (2023-2024). His research focuses on Machine Learning and AI for Science, particularly Bayesian optimization, bandit algorithms, generative models, and AI applications in drug discovery. He has received the SUNY IITG/OER Impact Grant and serves as Associate Editor for IEEE-TNNLS, Area Chair for ICML/AISTATS, and editorial board reviewer for JMLR. PhD: UC Santa Barbara (2023), advised by Yu-Xiang Wang Postdoc: University of Chicago Data Science Institute (2023) Research Interests : Broad: Machine Learning, Optimization, AI for Science Specific: Bayesian optimization, Bandit algorithms, Active learning, Experimental design, Generative models, AI for drug discovery Applications: Binding affinity prediction, Drug screening, Policy optimization Recent Article Trends : His 2024-2025 publications focus on extending Bayesian optimization theory under practical constraints, quantum-accelerated bandit methods, and multi-objective optimization for drug discovery. Earlier works include private learning frameworks and human-in-the-loop systems. Scientific Awards : 2025: SUNY IITG/OER Impact Grant Professional Activities : Organized NeurIPS workshops on AI for Drug Discovery (2023, 2025), co-organizing INFORMS sessions, and serving on program committees for ICML, NeurIPS, ICLR, and AAAI. He has given invited talks at institutions including University of Chicago, UC Santa Barbara, and Genentech. Teaching : Teaching courses like Numerical Methods (CSI 401) and Machine Learning (CSI 436/536) with syllabi spanning 2024-2025 semesters.

William OatesView profile

Professor

• Solid mechanics of active materials
• Smart material device design
• Nonlinear control
• +6 more

William S. Oates is the Cummins, Inc. Professor of Engineering in the Department of Mechanical Engineering at Florida A&M / Florida State University. He holds affiliations with the Mechatronics and Energy Center and the Florida Energy Systems Consortium (FESC). His research focuses on solid mechanics of multifunctional materials, quantum-informed continuum modeling, and applications in robotics, aerospace, and energy systems. He has advised over 20 graduate students and holds awards including ASME Fellow (2018) and NSF CAREER Award (2011). Education: Ph.D. from Georgia Institute of Technology. Research spans smart materials, fractal media mechanics, and quantum computing for material modeling. Key projects include high-temperature sapphire pressure sensors, photomechanical polymers, and Bayesian uncertainty quantification in materials science. Notable awards include DARPA Young Faculty Award (2009) and FSU Guardian of the Flame Teaching Award (2010). His lab collaborates with the National High Magnetic Field Lab and Challenger Learning Center for K-12 outreach. Current research includes quantum algorithm implementation for engineering applications and fractal-based viscoelastic models.

• Causal Machine Learning
• Shrinkage Methods
• Regularization Methods
• +3 more

Jana Mareckova is an Assistant Professor of Econometrics at the Swiss Institute for Empirical Economic Research (SIEW), part of the School of Economics and Political Science (SEPS) at the University of St. Gallen. She joined the university in 2020 after completing a postdoc at SEW-HSG following her PhD from the University of Konstanz (2019). Her research focuses on causal machine learning, shrinkage methods, regularization techniques, and labor economics. She explores applications in labor market outcomes and fairness, leveraging econometric tools to address real-world economic questions. Education: PhD in Econometrics, University of Konstanz (2019); Postdoc at SEW-HSG (pre-2020). Research interests include shrinkage estimation for categorical regressors, causal inference via machine learning, and predicting economic outcomes using noncognitive skills. Her work bridges statistical theory with practical policy analysis, as seen in her 2021 Journal of Econometrics publication on shrinkage methods. Recent projects emphasize causal forests and comprehensive frameworks for policy evaluation. No scientific awards are listed, though her contributions to causal ML and econometric methods are notable. She has no documented advising or grant information. Her research is affiliated with SIEW, focusing on empirical economic research.

Douglas NychkaView profile

Professor

• Spatial Statistics
• Nonparametric Regression
• Computational Statistics
• +5 more

Douglas Nychka is a Professor in the Department of Applied Mathematics and Statistics at Colorado School of Mines since 2018. He holds an emeritus position at the National Center for Atmospheric Research (NCAR), where he previously directed the Institute for Mathematics Applied to Geosciences (IMAGe) from 2004 to 2017. Nychka earned his PhD in Statistics from the University of Wisconsin-Madison and a BA in Mathematics (with Physics emphasis) from Duke University. His research focuses on spatial statistics, nonparametric regression, and computational methods for large datasets, particularly applied to environmental and geophysical problems. He has developed influential R packages like fields and LatticeKrig , which are widely used for spatial data analysis. Nychka received prestigious awards including the Jerry Sacks Award for Multidisciplinary Research (2004) and recognition as a Fellow of both the American Statistical Association and the Institute of Mathematical Statistics. His work bridges statistical theory, computational innovation, and real-world applications in climate science and renewable energy. His academic career includes 14 years as a faculty member at North Carolina State University and roles at the National Institute of Statistical Sciences. Nychka's research emphasizes spatial statistics for climate data, statistical downscaling, and uncertainty quantification, with contributions to solar radiation modeling and extreme event analysis. He actively engages in interdisciplinary projects, collaborating with experts in climatology, environmental science, and data science. Professional service includes roles on committees for the National Research Council and leadership in statistical societies. His teaching focuses on modernizing curricula to integrate data science with applied statistics. Nychka’s work is characterized by a commitment to open-source software and reproducible research, exemplified by his R package contributions.

Jungbin HwangView profile

Associate Professor

• Econometrics Theory
• Applied Econometrics
• Financial Econometrics
• +5 more

Jungbin Hwang is an Associate Professor in the Department of Economics at the University of Connecticut. He specializes in econometrics theory, with a focus on improving the accuracy and robustness of Generalized Method of Moments (GMM) methods in handling time series and panel data with dependence and heterogeneity. His research also extends to financial econometrics, Bayesian methods, and cointegration analysis. Education: Ph.D., Economics, University of California, San Diego (2016) M.A., Economics, Seoul National University (2010) B.A., Economics, Seoul National University (2008) Research Interests: Efficiency and approximation in GMM estimation Cluster-robust inference and bootstrap methods Cointegration in non-stationary systems Applications to financial markets and policy analysis Teaching: Courses include Empirical Methods in Economics, Econometrics I, and advanced topics in panel data analysis. Key Contributions: His work addresses challenges in GMM inference for time series and panel data, including finite-sample corrections and robust variance estimation. Recent studies explore low-frequency cointegration and quantile regression in dynamic settings. Grants & Collaborations: Collaborations with scholars like Yixiao Sun and Gonzalo Valdés have produced influential methods for accurate econometric testing and inference. Contact: Located in 333 Herbst Hall, Storrs, CT. Office hours: Wednesdays 3:00-4:00 PM or by appointment.

Dinh PhungView profile

Professor

• Machine Learning
• Deep Learning
• Generative AI
• +8 more

Professor Dinh Phung is the Head of the Department of Data Science & AI at Monash University. His research focuses on machine learning, deep learning, generative AI, and robust AI systems. He has authored over 250 publications, with applications in NLP, computer vision, digital health, and cybersecurity. Phung holds a PhD and BSc(Hons) in Computer Science from Curtin University. He leads major projects like 'Can Machines Unlearn?' and 'Trustworthy Generative AI', funded by the Australian Research Council and the Department of Defence. Education: Doctor of Philosophy, Computer Science, Curtin University (2005) Bachelor of Science (Honours), Computer Science, Curtin University (2001) Research Interests: Machine learning, deep learning, and generative models Optimal transport and Bayesian methods Robust and trustworthy AI Applications in digital health, cybersecurity, and autism research Key Projects (2023–2029): Can Machines Unlearn? (2025–2029): Safety in AI Trustworthy Generative AI (2024–2026): Foundation models Robust Machine Learning via Optimal Transport (2023–2025) Awards and Grants: Australian Research Council grants for AI safety and robustness Department of Defence funding for robust learning systems Collaborations: Global partnerships in AI ethics, cybersecurity, and healthcare. Active advisory roles, including with the Victorian Parliamentary Library.

Keke LaiView profile

Associate Professor

• Quantitative Psychology
• Structural Equation Modeling
• Missing Data Analysis
• +5 more

Keke Lai is an Associate Professor of Quantitative Psychology at the University of California, Merced. Previously, she held positions at the University of Houston and Arizona State University. She earned her Ph.D. in Quantitative Psychology from the University of Notre Dame (2012), and a B.A. in English from China (2005). Her research focuses on advanced statistical methodologies in psychology and education, including structural equation modeling (SEM), robust statistical methods, model evaluation, longitudinal/multilevel data analysis, missing data techniques, and Bayesian statistics. She addresses critical gaps in SEM methodology, such as accurate estimation under missing data, nonnormal distributions, and model misspecification. Her recent work emphasizes improving confidence intervals for fit indices (e.g., RMSEA, CFI), comparing nonnested models, and developing methods for standardized parameters in misspecified models. These contributions enhance the reliability of statistical analyses in psychological research. Lai’s publications appear in top journals like Structural Equation Modeling and Psychometrika . She advises graduate students, including X. Zhang. Her website is https://sites.google.com/view/laikeke .

Jonathan ScarlettView profile

Associate Professor

• Machine Learning
• Information Theory
• High-Dimensional Statistics
• +3 more

Jonathan Scarlett is an Associate Professor jointly appointed in the Department of Computer Science, Department of Mathematics, and Institute of Data Science at the National University of Singapore (NUS). He also serves as Assistant Dean (Graduate Studies) in the School of Computing. His research focuses on information theory, machine learning, and high-dimensional statistics, with applications to optimization, group testing, and statistical inference. Education: Ph.D. (Information Engineering), University of Cambridge (2014) B.Eng. (Electrical Engineering) and B.Sci. (Computer Science), University of Melbourne (2010) Research Interests: Algorithmic foundations of machine learning and statistical estimation Information-theoretic limits and adaptive algorithms Applications in group testing, compressed sensing, and DNA storage Robust optimization under uncertainty and adversarial settings His work bridges theoretical guarantees with practical algorithm design, emphasizing scalable solutions for high-dimensional problems. Key Achievements: Recipient of Singapore NRF Fellowship (2018) and NUS Presidential Young Professorship Listed in MIT Technology Review's 'Innovators Under 35' Asia Pacific (2021) Over 100 publications in top venues like ICML, NeurIPS, and IEEE Transactions Advising & Grants: Supervised multiple PhD students in areas like Bayesian optimization, group testing, and compressed sensing Lead researcher on projects supported by NRF and NUS grants Developed novel frameworks for safe Bayesian optimization and robust bandit algorithms Labs & Collaborations: Active in the Information Theory and Statistical Learning Group at NUS, collaborating with global institutions like EPFL and MIT on data science challenges.

NG Hui KhoonView profile

Associate Professor

• Quantum Information Theory
• Quantum Computation
• Quantum Error Correction
• +3 more

NG Hui Khoon is an Associate Professor at the National University of Singapore , affiliated with Yale-NUS College and the Centre for Quantum Technologies . She holds a PhD in Physics from the California Institute of Technology (Caltech), USA (2009). Research Interests: Her work focuses on theoretical aspects of quantum information and computation, particularly quantum error correction and fault tolerance , quantum noise modeling , and quantum tomography . She investigates how resource constraints limit quantum computing and develops adaptive methods for quantum state estimation using neural networks. Publication Trends: Her recent articles (2021–2013) emphasize quantum error correction frameworks, tomography techniques, and statistical methods for quantum systems. Key themes include fault tolerance under amplitude-damping noise, randomized benchmarking for time-correlated dephasing, and Bayesian approaches for prior-data conflict checking. Scientific Awards: Early Career Teaching Award (2019, Inaugural recipient) CQT Fellowship (2019 – current) Advising & Grants: No explicit advising or grant details are provided. She collaborates with institutions like the Centre for Quantum Technologies and Yale-NUS College. Labs & Teams: She is associated with the Centre for Quantum Technologies, a leading research center in quantum information science.

Ronojoy AdhikariView profile

Lecturer

• Statistical Physics
• Soft Matter
• Stochastic Processes
• +2 more

Dr Ronojoy Adhikari is a Lecturer in the Department of Applied Mathematics and Theoretical Physics (DAMTP) at the University of Cambridge, affiliated with the Faculty of Mathematics. His research focuses on statistical physics, soft matter, stochastic processes, Bayesian inference, and machine learning. He has taught Mathematical Biology (2018–2021) and Electrodynamics (2021–2023). His work bridges theoretical frameworks with experimental insights, addressing phenomena such as active matter dynamics, non-equilibrium thermodynamics, and stochastic modeling of biological systems. Key contributions include studies on autophoretic particles, path probabilities in stochastic systems, and Bayesian approaches to epidemiological modeling. His research group, part of the Soft Matter program at DAMTP, explores interdisciplinary topics like colloidal crystallization and enzymatic network kinetics. Notable publications highlight investigations into fluctuating hydrodynamics, entropy production measurements, and the mechanics of rigid inclusions on curved surfaces. His interdisciplinary approach integrates computational methods (e.g., lattice Boltzmann simulations) with mathematical rigor to understand complex systems. While no awards are explicitly listed, his extensive publication record underscores sustained academic impact. Ongoing research includes projects on path probabilities, active particle dynamics, and the interplay between geometry and material behavior in Cosserat solids. Advising and grants are not explicitly detailed in the provided texts, but his role as a faculty member suggests involvement in student supervision and collaborative projects. His work frequently appears in top journals like Physical Review Letters , Journal of Fluid Mechanics , and Science Advances , reflecting high-quality contributions to theoretical and applied physics.

• Artificial Intelligence in Education
• Educational Technology
• Online Learning Systems
• +3 more

David Lefevre is a Professor of Practice at the Department of Management and Entrepreneurship, Imperial College Business School. His research focuses on AI applications in education, digital innovation in higher education, and tech-transfer. He co-founded the Edtech Lab in 2004, which pioneered Imperial’s online courses and Global Online MBA. The Lab has won awards including a Gold at IMS Learning Impact (2010) and Silver at QS Reimagine (2018). He also co-founded educational tech companies Epigeum (now part of Oxford University Press) and Insendi (now Study Group). He is Special Advisor on Digital Innovation at Study Group and a British Council Trustee, focusing on digital transformation. Lefevre’s work emphasizes strategic adoption of AI, online education quality, and bridging cultural barriers in learning. Education & Background: While formal education details are not provided, his career reflects deep expertise in education technology and entrepreneurship. His roles include: Professor of Practice, Imperial College Business School Co-founder & former leader of the Edtech Lab Co-founder of Epigeum and Insendi Expert in Residence, Imperial Enterprise Lab Research Interests: Lefevre’s work spans AI in education, online learning infrastructure, and cross-cultural e-learning. He advocates for data-driven strategies to improve online course design and learner engagement. His recent focus includes AI-driven feedback systems and holographic teaching methods. Key Achievements: Launched Imperial’s first online MBA program in 2015 Developed an LMS alternative using middleware Promoted adaptive learning environments for diverse learners Awards & Recognition: Gold award at IMS Learning Impact awards 2010 Silver award for Business Education at QS Reimagine Education 2018 Advisory & Grants: Advises Study Group and the British Council on digital innovation. His ventures have secured significant investments, though specific grant details are not disclosed. Labs & Collaborations: Leads the Edtech Lab and collaborates with Imperial Enterprise Lab. Active in global education networks such as QS Reimagine and the British Council.