Find Academic Supervisors

Abolfazl HashemiView profile

Assistant Professor

• Large-Scale Optimization
• Machine Learning
• Artificial Intelligence
• +6 more

Abolfazl Hashemi is an Assistant Professor in the Elmore Family School of Electrical and Computer Engineering at Purdue University, directing the MINDS Group. He holds a B.Sc. from Sharif University of Technology (2014), and M.S.E. and Ph.D. degrees from The University of Texas at Austin (2016, 2020). His research focuses on Large-Scale Optimization for AI/ML, Learning at the Edge, and Decision-Making under Uncertainty, with applications in Federated Learning, Medical Image Analysis, and Cyber-Physical Systems. He leads the MINDS Group and collaborates with EnCORE and ICON centers. Key research areas include optimizing algorithms for machine learning, robustness in distributed systems, and adversarial learning. He has developed algorithms with mathematical guarantees for efficient deployment under resource constraints. Teaching includes Optimization for Deep Learning (graduate) and undergraduate courses like ECE 20001. He advises the Purdue RoboMaster robotics team and has mentored students through programs like SURF and Summer Stay Scholars. Outreach activities include fostering diversity through robotics competitions and research fellowships. His work bridges theoretical optimization with practical AI applications, emphasizing equitable and robust solutions in federated and decentralized learning.

• Mobile and cloud computing
• Energy-efficient computing
• Image and video processing
• +2 more

Yung-Hsiang Lu is a Professor of Electrical and Computer Engineering at Purdue University's Elmore Family School of Electrical and Computer Engineering. His research focuses on mobile/cloud computing, energy-efficient computing, and image/video processing. He holds a BSEE from National Taiwan University (1992), an MSEE (1996), and a PhD (2002) from Stanford University. Dr. Lu's academic background includes significant contributions to VLSI and circuit design, with primary emphasis on computer engineering. His work spans theoretical and applied domains, including optimizing neural networks for edge devices, securing deep learning models, and leveraging large language models for software development. Recent research trends in his articles emphasize energy efficiency in AI systems, interdisciplinary applications of transformers (e.g., music analysis), and challenges in model interoperability and security. His publications also highlight innovations in global camera networks and real-time visual data analysis. While no specific grants or awards are explicitly mentioned, his extensive list of publications reflects sustained academic engagement. His educational contributions include developing C programming resources and teaching large-scale image processing using global camera networks. Dr. Lu's professional address is at Purdue's Materials and Electrical Engineering Building in West Lafayette, Indiana, where he maintains an active research lab focused on embedded systems and low-power computing innovations.

Fernando CamelliView profile

Associate Professor

• Computational Fluid Dynamics (CFD)
• Urban Environmental Modeling
• Contamination Dispersion Modeling
• +3 more

Fernando Camelli is an Associate Professor in the Physics & Astronomy Department at George Mason University, holding dual roles as Instructional Faculty and Faculty. His research focuses on computational fluid dynamics (CFD), urban environmental modeling, and high-performance computing. He specializes in simulating complex fluid flows in urban environments, subway systems, and industrial applications, with particular emphasis on turbulence modeling, fluid-structure interaction, and GPU-accelerated algorithms. Key research areas include: CFD for urban airflow and contamination dispersion Meshless and immersed boundary methods Integration of geographic information systems (GIS) with CFD Large-scale simulations using parallel computing His work addresses practical challenges such as subway ventilation optimization, emergency contaminant dispersion prediction, and urban infrastructure design. Recent studies emphasize scalability improvements for fluid-structure interaction simulations and GPU-based code modernization.

Charles G. SmithView profile

Professor

• Quantum Physics
• Nanotechnology
• Semiconductor Devices
• +7 more

Professor Charles G. Smith is a prominent academic in quantum physics and nanotechnology at the University of Cambridge's Cavendish Laboratory, affiliated with the Ray Dolby Centre for Quantum Information and Control. His research focuses on semiconductor nano-devices, quantum transport, and hybrid superconductor-semiconductor systems. He pioneered work on GaAs quantum dots, single-electron charge measurement techniques, and cryogenic scanning probe methods. Smith has developed novel low-temperature measurement tools and contributed to carbon-based nanoelectronics and graphene research. He leads major grants, including EPSRC projects on quantum integrated circuits and many-body localization. He founded Cavendish-Kinetics and Cambridge Lab on Chip, leveraging his nano-mechanical and microfluidic innovations. His work bridges fundamental physics with scalable quantum technologies. Education & Research Expertise: PhD in Physics (Implied by career trajectory) Extensive post-1985 contributions to nano-device physics Research Highlights: Quantum dot arrays and cryogenic multiplexing for high-throughput analysis Hybrid superconductor-semiconductor devices for quantum applications Graphene-based electronics and magnetoresistance phenomena Scalable quantum integrated circuits and error mitigation strategies Grant Leadership & Industry Impact: EPSRC Grant EP/S019324/1: Scaling quantum devices Program Grant EP/R029075/1: Non-Ergodic Quantum Manipulation Two spin-out companies commercializing nano-technology Labs & Facilities: Active in Cavendish Laboratory’s cutting-edge nanofabrication and cryogenics facilities, supporting large-scale quantum device integration.

Chun WangView profile

Professor

• Quantitative Research Methods
• Item Response Theory (IRT)
• Computerized Adaptive Testing (CAT)
• +3 more

Chun Wang is a Professor in the Department of Measurement & Statistics at the University of Washington's College of Education. His research focuses on advancing quantitative methods in educational and psychological measurement, with expertise in item response theory (IRT), computerized adaptive testing (CAT), and cognitive diagnostic modeling. He holds affiliate faculty status at the Center for Statistics and the Social Sciences. Education: B.S. in Psychology, Peking University (China) M.S. and Ph.D. in Quantitative Psychology, University of Illinois at Urbana-Champaign Research Interests: Development and validation of multidimensional/mixture IRT models Computerized adaptive testing optimization Cognitive diagnostic modeling for classroom applications Health measurement and bias detection in assessments Recent Trends in Articles: His work bridges statistical innovation with practical applications, emphasizing fairness and efficiency in assessments. Notable areas include: - Healthcare : Predictive models for discharge disposition and functional outcomes - Education Technology : Adaptive learning systems and diagnostic feedback mechanisms - Methodology : Bias detection (DIF), Bayesian estimation techniques, and computational efficiency Scientific Awards : Includes the Anne Anastasi Award (2020), McKnight Presidential Fellowship (2017), and multiple best reviewer recognitions from leading psychometrics journals. Advising & Grants: Supervised students including Xiao J., Zhu R., and Lu J.* in high-impact projects. Co-led a $10M NIH grant (AmplifyGAIN Center) to advance Gen AI in STEM education. Published extensively in Psychometrika , Journal of Educational and Behavioral Statistics , and other top outlets. Labs/Teams: Directs the Pmetrics Lab ( https://sites.uw.edu/pmetrics/ ), collaborating on cutting-edge measurement tools for education and healthcare.

Daniela CalvettiView profile

Professor

• Large Scale Scientific Computing
• Computational Inverse Problems
• Uncertainty Quantification
• +4 more

Daniela Calvetti is the James Wood Williamson Professor in the Department of Mathematics, Applied Mathematics, and Statistics at Case Western Reserve University. Her research focuses on large-scale scientific computing, computational inverse problems, uncertainty quantification, and predictive modeling in neuroscience, metabolism, and cellular physiology. She holds a PhD from the University of North Carolina-Chapel Hill. Her work integrates advanced mathematical techniques with biomedical applications, including brain energy metabolism modeling, MEG/EEG source reconstruction, and computational methods for medical imaging. Notable contributions include Bayesian hierarchical algorithms for inverse problems and interdisciplinary collaborations bridging mathematics with neuroscience and physiology. Recent research highlights include developing sparsity-promoting Bayesian models for tomography, computational frameworks for neuromuscular control variability, and predictive models of disease dynamics like post-pandemic COVID-19 recurrence. Her methodologies emphasize statistically inspired preconditioning and adaptive meshing techniques to enhance computational efficiency in solving complex inverse problems. Dr. Calvetti has published extensively across computational science, inverse problems, and biomedical applications. She leads a research group advancing interdisciplinary computational methods with applications in neuroscience, virology, and metabolic systems.

Shrimai PrabhumoyeView profile

Adjunct Assistant Professor

• Large Language Models (LLMs)
• Machine Learning
• Natural Language Processing (NLP)
• +4 more

Shrimai Prabhumoye is a Senior Research Scientist at NVIDIA's Applied Deep Learning Research group and an Adjunct Assistant Professor at Boston University. Her research focuses on advancing large language models (LLMs), particularly in enhancing their reasoning capabilities and ensuring safety through toxicity and bias reduction. She is a core contributor to the Nemotron family of LLMs, including the state-of-the-art Nemotron4-15B. Previously, she earned her PhD from Carnegie Mellon University (2021), advised by Prof. Ruslan Salakhutdinov and Prof. Alan W. Black, with a thesis on controllable text generation and computational ethics in NLP. Education: PhD in Machine Learning (CMU, 2021), MS in Language Technologies (CMU, 2017), Undergraduate at National Institute of Technology Karnataka, India. Research Interests: LLMs, controllable generation, ethical AI, bias mitigation, data curation, and model safety. Her work emphasizes rigorous evaluation frameworks and practical applications of NLP systems. Publications span key venues like EACL, ACL, NAACL, and EMNLP, with notable contributions to bias testing, toxicity control, and model scalability. She has been recognized as a Rising Star finalist in the VentureBeat Women in AI Awards 2022 and has led teams in competitions like the Alexa Prize. Labs/Teams: Part of NVIDIA's Applied Deep Learning Research Group and contributed to CMU's Computational Ethics for NLP initiatives. Active in mentoring students on projects like politeness transfer and dataset creation.

Hanno HilbigView profile

Assistant Professor

• Comparative Politics
• Political Economy
• Democratic Institutions
• +4 more

Hanno Hilbig is an Assistant Professor of Political Science at the University of California, Davis. He holds a PhD from Harvard University’s Department of Government (2022) and an MS in Economics from Humboldt University Berlin. His research focuses on the interplay between structural economic changes—such as labor markets, housing crises, and energy transitions—and their political impacts in advanced democracies. He employs causal inference methods for observational data, using designs like natural experiments and large-scale administrative datasets. Key research areas include voter preferences, policymaking processes, and democratic institutions. His work addresses topics such as public housing budget tradeoffs, refugee labor market integration, and the effects of local newspaper decline on polarization. He has published in top journals like the American Journal of Political Science , British Journal of Political Science , and Journal of Politics . His recent articles explore themes such as green party support following natural disasters, the role of wealth disparities in radical right voting, and the consequences of rent control policies. Hilbig co-leads collaborative projects like the German Election Database (GERDA) and investigates refugee integration dynamics. His research bridges quantitative methods with substantive political questions, emphasizing policy relevance and democratic stability.

Xuhui FanView profile

Lecturer

• Artificial Intelligence
• Machine Learning
• Bayesian Methods
• +5 more

Dr. Xuhui Fan is a Lecturer in Artificial Intelligence at the School of Computing, Macquarie University. He holds a PhD in Computer Science from the University of Technology Sydney (Australia) and a bachelor's degree in Mathematical Statistics from China. Prior to his current role, he worked as a project engineer at Data61 (formerly NICTA), a postdoc fellow at the University of New South Wales, and a lecturer at the University of Newcastle. His research focuses on Bayesian methods, federated learning, temporal point processes, and neural network architectures. He is affiliated with the Data Horizons Research Centre and the Frontier AI Research Centre at Macquarie University. Key research interests include developing interpretable AI models, advancing federated learning for privacy-sensitive applications, and applying Bayesian techniques to complex data analysis. His work bridges theoretical advancements in machine learning with practical applications in areas such as anomaly detection, generative models, and spatio-temporal data analysis. Dr. Fan’s publications span top-tier conferences like NeurIPS, ICML, and IJCAI, covering topics such as diffusion models, nonstationary processes, and scalable relational models. He has contributed to surveys on Bayesian federated learning and developed novel frameworks for dynamic customer segmentation and network sustainability. His research collaborations span institutions in Australia and internationally, reflecting his expertise in interdisciplinary AI applications. Current projects emphasize ethical AI practices, efficient uncertainty quantification, and scalable inference techniques for large-scale datasets.

Tom BootView profile

Associate Professor

• Macroeconomic Forecasting
• High-Dimensional Data Analysis
• Econometric Theory
• +4 more

Dr. Tom Boot is an Associate Professor at the Department of Economics, Econometrics & Finance at the University of Groningen. He holds a PhD in Econometrics from Erasmus University Rotterdam (2017) and an MSc in Econometrics from the same institution (2012), along with an MSc in Physics from the University of Groningen (2010). His research focuses on econometric theory applied to macroeconomic forecasting, high-dimensional data analysis, and causal inference. He has been recognized with the Veni grant (2021–2024) for his work on forecasting methodologies. Boot’s research interests include improving forecast accuracy through methods like subspace projections, structural break modeling, and privacy-aware marketing analytics. His recent work explores privacy-utility trade-offs in data-driven marketing and unbiased estimation techniques for clustered errors. He has supervised PhD students including Jhordano Aguilar Loyo and Gilian Ponte, whose theses addressed panel data heterogeneity and differential privacy applications. Boot is also a program director for the MSc Econometrics, Operations Research, and Actuarial Studies (since 2024). His contributions to econometrics span over a dozen peer-reviewed publications, with a focus on advanced statistical techniques for economic forecasting and policy analysis. Collaborations include work with institutions like Harvard/MIT and the organization of workshops on causal inference and machine learning.

Sylvain BarbotView profile

Associate Professor

• Geophysics
• Fault Dynamics
• Seismology
• +3 more

Sylvain Barbot is an Associate Professor of Earth Sciences at the University of Southern California (USC) Dornsife College of Letters, Arts and Sciences. His research focuses on understanding the physics of fault systems, earthquake mechanics, and tectonic processes. He holds a Ph.D. in Earth Sciences from Scripps Institution of Oceanography, University of California San Diego (2009), followed by postdoctoral training. His work integrates field observations, geodetic data analysis, and numerical modeling to study fault dynamics, frictional processes, and deformation in Earth's crust and upper mantle. Research interests include: Fault slip stability and earthquake nucleation Thermomechanical behavior of fault zones Seismic cycle modeling in subduction zones and continental margins Interactions between tectonic deformation and climate Remote sensing applications in geohazard assessment Recent studies analyze rupture segmentation, asthenosphere flow modulation during earthquake cycles, and the structural controls on seismic hazards. His computational tools, such as the Motorcycle numerical method, advance simulations of multi-fault systems. He has contributed to understanding the 2014 Iquique, 2023 Kahramanmaraş, and 2022 Kaikōura earthquakes through geodetic and mechanical analyses. No scientific awards were explicitly mentioned in the provided texts. He advises no listed students but collaborates on large-scale geodynamic projects. His work interfaces with international initiatives like the SEAS community code verification exercises for earthquake simulations.

Hao ChenView profile

Associate Professor

• Anomaly/signal detection for streaming data
• Graph-based methods
• Two-sample test for high-dimensional and non-Euclidean data
• +7 more

Hao Chen, Ph.D. is an Associate Professor in the Department of Statistics at the University of California, Davis. His research focuses on statistical methodology for high-dimensional and non-Euclidean data, including anomaly detection, graph-based methods, and change-point analysis. He also explores AI security, multimodal models, and geospatial applications. His work bridges statistical theory and practical machine learning challenges. Education: Ph.D., Graduate Group in Biostatistics, Stanford University Research Interests: Dr. Chen’s expertise spans statistical methods for streaming data, categorical data analysis, and allele-specific copy number variation. He has pioneered work in detecting signals in complex datasets and developing robust AI systems. His recent focus includes mitigating modality interference in LLMs, enhancing model safety via guardrails, and advancing geospatial AI through projects like GeoLM. Publications: His recent work addresses cutting-edge topics such as multimodal model vulnerabilities, unlearning algorithms, and clinical radiology applications. Key themes include improving model robustness, ethical AI design, and interdisciplinary data integration. Labs/Teams: Engages in collaborative projects at UC Davis, though specific lab names are not listed in the provided information.

Edriss S. TitiView profile

Professor

• Nonlinear Partial Differential Equations
• Applied Mathematics
• Numerical Analysis
• +5 more

Edriss S. Titi is a University Distinguished Professor and Arthur Owen Professor of Mathematics at Texas A&M University within the College of Arts & Sciences. His research focuses on nonlinear partial differential equations, applied mathematics, and geophysical fluid dynamics. He leads studies on fluid mechanics, atmospheric and oceanic dynamics, data assimilation, and control theory. His work often addresses mathematical rigor in modeling complex systems like climate dynamics and turbulent flows. Research Interests: Nonlinear PDEs and their applications Fluid dynamics and turbulence Data assimilation algorithms Climate and ocean modeling Infinite-dimensional dynamical systems Recent publications emphasize Navier-Stokes equations , primitive equations , and data assimilation in chaotic systems . His methodologies bridge theoretical analysis and computational modeling, with applications to weather prediction and geophysical flows. Collaborations include the Institute for Applied Mathematics and Computational Science (IAMCS) at Texas A&M. Notable contributions include rigorous analysis of global well-posedness for oceanic models and development of CDAnet, a physics-informed deep learning framework for fluid flow downscaling.

Samson ZhouView profile

Assistant Professor

• Algorithms and theory
• Data science
• Machine learning
• +7 more

Samson Zhou is an Assistant Professor in the Department of Computer Science & Engineering at Texas A&M University, part of the College of Engineering. He holds a Ph.D. from Purdue University and dual B.S. and M.Eng. degrees from MIT in Computer Science and Mathematics. His research focuses on theoretical computer science, data science, and machine learning, with specializations in numerical linear algebra, streaming algorithms, and differential privacy. He has held postdoctoral positions at institutions including Carnegie Mellon University and Rice University. Education: Ph.D., Computer Science, Purdue University (2018) M.Eng., Computer Science, MIT (2011) B.S., Computer Science & Mathematics, MIT (2010-2011) Research Interests: Dr. Zhou explores intersections of algorithms, data science, and machine learning, emphasizing numerical linear algebra, streaming algorithms, differential privacy, and adversarial robustness. He designs efficient algorithms for large-scale data processing with provable guarantees. Key Contributions: His work includes advancements in sliding window clustering, adversarially robust streaming algorithms, and privacy-preserving techniques. Notable achievements include the Silver Best Paper Award at ICML 2021 and spotlight presentations at ICLR 2025. Service & Leadership: He organizes workshops (e.g., TTIC 2024 on Learning-Augmented Algorithms) and serves on program committees for SOSA, NeurIPS, and COLT. He also co-leads the TAMU Math Circle's problem-solving sessions and the Algorithms & Data Science Reading Group. Labs/Teams: He collaborates on projects involving co-hosted postdocs (e.g., Chen Wang) and masters students (e.g., Shenghao Xie), focusing on topics like coresets, streaming algorithms, and fair clustering.

Nguyen KhanhView profile

Assistant Professor

• Programming Languages
• Compiler & Runtime Support
• Memory Management
• +5 more

Khanh Nguyen is an Assistant Professor in the Department of Computer Science & Engineering at Texas A&M University, part of the College of Engineering. His research focuses on improving scalability and efficiency in Big Data systems through compiler and runtime innovations, particularly in memory management and distributed computing. Education: Ph.D. in Computer Science, University of California, Los Angeles (2019) M.S. in Computer Science, University of California, Irvine (2015) B.S. in Computer Science, University of California, Irvine (2012) His research interests include programming languages, compiler design, memory management, and Big Data systems. He has developed techniques such as Gerenuk for thin computation over big data, Skyway for distributed heap connectivity, and Yak , a high-performance garbage collector. His work emphasizes resource efficiency and workload scalability, particularly for machine learning and data-parallel applications. Recent publications (2021–2024) highlight advancements in adaptive memory management for warehouse-scale computers, semantics-aware swapping in disaggregated systems, and query-driven distributed tracing. Awards: Google Ph.D. Fellowship (2017) Facebook Ph.D. Fellowship Finalist (2017) His research bridges compiler/runtime systems with large-scale data processing, addressing challenges in distributed systems and far-memory utilization. He collaborates with industry and academic partners to advance practical, scalable solutions for modern data-intensive workloads.