Find Academic Supervisors

Lance ManuelView profile

Professor

• Uncertainty Quantification
• Wind Energy
• Offshore Structures
• +5 more

Lance Manuel is a Professor of Engineering at The University of Texas at Austin. His research focuses on uncertainty quantification in engineered systems, particularly wind energy and offshore structures. He has led projects related to wind turbine fatigue analysis, hurricane risk assessment, and climate change adaptation. Ph.D., Civil Engineering (Stanford University) M.S., Civil Engineering and Applied Mechanics (University of Virginia) B.Tech., Civil Engineering (Indian Institute of Technology, Bombay) His work bridges civil infrastructure with climate resilience, emphasizing probabilistic methods for structural reliability. Key areas include extreme climate modeling , floating offshore wind turbines , and fatigue damage prediction under non-stationary conditions. Recent publications highlight interdisciplinary trends in renewable energy systems and climate hazard quantification . He advises graduate students like Taemin Heo and Ding Peng Liu, whose work spans stochastic processes and offshore structural reuse.

• Probabilistic Machine Learning
• Probabilistic Circuits
• Probabilistic Programming
• +2 more

Martin Trapp is an Academy Postdoctoral Researcher in the Department of Computer Science at Aalto University, specializing in probabilistic machine learning. He is affiliated with Professor Arno Solin's research group, focusing on advancing tractable probabilistic models for real-world applications. His research centers on Probabilistic Circuits , Probabilistic Programming , and Bayesian Nonparametrics , with emphasis on hardware-efficient implementations for edge devices and multimodal systems. Key interests include uncertainty quantification in deep learning, neurosymbolic AI integration, and medical imaging applications. His work bridges theoretical foundations with practical deployment constraints, particularly in resource-limited environments. Analysis of his 15 most recent publications (2022-2025) reveals three dominant trends: (1) hardware-aware probabilistic inference for TinyML applications, (2) scalable Bayesian methods using bitstring representations and probabilistic programming, and (3) multimodal robustness in vision-language systems and medical imaging. His contributions span from theoretical circuit representations to real-world implementations in mammography analysis and vision-language models. Trapp secured a HIIT short-term project grant (November 2022) for "Positive Semi-Definite Circuits" under the Department of Computer Science. No formal advising relationships are documented in available sources. He actively collaborates with researchers including Arno Solin, Rui Li, and Marcus Klasson across institutions like Aalto University and the Helsinki Institute for Information Technology. As a core member of Aalto's Probabilistic Machine Learning group, he contributes to advancing probabilistic AI methodologies with applications in healthcare, edge computing, and multimodal reasoning. His current work emphasizes deployable probabilistic systems that maintain rigorous uncertainty quantification while meeting hardware constraints.

Liang MinView profile

Researcher

• Electric grid digital transformation
• Clean energy systems
• AI for climate and energy
• +7 more

Dr. Liang Min serves as the Managing Director of the Bits & Watts Initiative at Stanford University's Precourt Institute for Energy and as the founding Managing Director of the Stanford Net-Zero Alliance at the Stanford Doerr School of Sustainability. He leads multidisciplinary programs advancing the digital transformation of the electric grid and promoting net-zero emissions solutions through research, education, and industry collaboration. Dr. Min's educational background includes: Ph.D. from Texas A&M University (2007) M.S. from Tianjin University (2004) B.S. from Tianjin University (2001) His research focuses on the digital transformation of electric grids, with pioneering work on 100% Clean Electric Grid, EV50, AI for Climate and Energy, and the Digital Grid platform for integrating distributed energy resources. He recently launched the Powering AI Sustainably program addressing AI's energy demands while accelerating clean power transition. His work bridges high-performance computing with power system operations to enhance grid reliability, security, and sustainability amid increasing renewable penetration and electrification trends. Analysis of Dr. Min's recent publications reveals a strong emphasis on grid modernization through digital technologies, with particular focus on integrating distributed energy resources, enhancing grid resilience, and applying advanced computational methods to power system challenges. His research spans from fundamental work in power system dynamics and control to practical applications addressing energy transition, electric vehicle integration, and AI's role in sustainable energy systems. Dr. Min has led significant research projects funded by the Department of Energy, Bonneville Power Administration, and other organizations, resulting in multiple U.S. patents related to grid technologies including voltage stability smart meters and synchronized electric meters with atomic clocks. As the founder of the Stanford Energy Executive Education Program, Dr. Min has created platforms to equip energy leaders with strategic insights for navigating the rapidly evolving energy landscape. He also established the Stanford Net-Zero Alliance to bring together industry leaders, faculty, and students for collaborative research and education toward net-zero emissions.

Maria CuellarView profile

Assistant Professor

• Statistics in Law
• Causal Inference
• Forensic Science
• +3 more

Maria Cuellar is an Assistant Professor at the University of Pennsylvania , holding joint appointments in the Department of Criminology, Department of Statistics and Data Science, and Department of Biostatistics, Epidemiology, and Informatics. Her work bridges statistics and law , focusing on causal inference and the statistical foundations of forensic science. Education: PhD in Statistics and Public Policy (2017), MPhil in Public Policy (2016), MS in Statistics (2015) from Carnegie Mellon University; BA in Physics (2009) from Reed College. Her research in causal inference addresses legal questions like determining causation in toxic torts and shaken baby syndrome cases. She developed a robust influence-function-based method for estimating probabilities of causation without strict parametric assumptions. In forensic science, she investigates issues like toolmark analysis validity , contextual bias , and facial recognition accuracy in law enforcement. Dr. Cuellar’s publications highlight her focus on improving statistical rigor in forensic disciplines. Recent work includes probabilistic models for contextual bias, critiques of black-box firearm comparison studies, and algorithmic approaches to toolmark identification. Her interdisciplinary methodology combines machine learning and nonparametric estimation techniques. She serves as an expert witness and consultant in legal trials, translating complex statistical concepts for lay audiences. Affiliated with organizations like CSAFE , Quattrone Center , and Penn Center for Causal Inference , she advocates for evidence-based practices in criminal justice. Her personal life includes being a mother of twins and being married to philosopher Justin Humphreys.

John GaleottiView profile

Researcher

• Medical Image Analysis
• Machine Learning Applications in Biomedical Imaging
• Medical Robotics
• +3 more

John Galeotti is a Senior Systems Scientist and Adjunct Assistant Professor at Carnegie Mellon University (CMU), affiliated with the Robotics Institute (RI) and Biomedical Engineering departments. He directs the Biomedical Image Guidance Laboratory and has an adjunct appointment at the University of Pittsburgh's Bioengineering department. His research focuses on integrating medical robotics, computer vision, and advanced image analysis to improve clinical outcomes. With a Ph.D. in Robotics and B.S./M.S. in Computer Engineering, Galeotti's work emphasizes real-time computer-controlled optics, medical image segmentation, and AI-driven diagnostics. He has led projects in autonomous ultrasound scanning, needle insertion robotics, and synthetic data generation for medical AI. Research Keywords: Medical Robotics, Machine Learning, Ultrasound Segmentation, Computer Vision, Augmented Reality, Diagnostic Imaging Galeotti is a key contributor to the open-source Insight Segmentation and Registration Toolkit (ITK) and has received external teaching funding and awards for his internationally recognized course on medical image analysis algorithms. His lab's publications highlight advancements in AI applications for lung ultrasound, vessel segmentation, and optical coherence tomography.

Suman RakshitView profile

Senior Lecturer

• Spatial Statistics
• Agricultural Statistics
• Genome-wide Association Studies
• +3 more

Dr. Suman Rakshit is a Senior Lecturer at Curtin University's School of Electrical Engineering, Computing and Mathematical Sciences, with a dual role as a Research Fellow at SAGI-West. His primary research focuses on statistical methodologies for agricultural trials, including spatial variogram modeling and linear mixed models. He has also contributed to genome-wide association studies and developed an R-package for analyzing point patterns on linear networks. Dr. Rakshit holds a PhD in Statistics from Monash University and a Master's from IIT Kanpur. His professional experience includes roles as a Data Scientist at Horizon Power and Prima Consulting. His teaching spans multiple disciplines including Science and Engineering, and he is affiliated with the Office of the Provost. Research interests emphasize spatial statistics, experimental design for on-farm trials, and computational methods. Recent work explores team playing styles in Australian Football using clustering frameworks and addresses spatial dependency in plant pathogens. His publications span agricultural, ecological, and computational topics, with a focus on methodological advancements in spatial analysis.

Timo GerkmannView profile

Professor

• Statistical Signal Processing
• Machine Learning
• Speech and Audio Processing
• +3 more

Prof. Timo Gerkmann is a Professor at the University of Hamburg's Department of Informatics, leading the Signal Processing Research Group. His research focuses on statistical signal processing and machine learning for speech and audio applications, including communication devices, hearing aids, audiovisual media, and human-machine interfaces. He previously held roles at Technicolor Research & Innovation, KTH Royal Institute of Technology, and Siemens Corporate Research. His work emphasizes generative models, diffusion-based approaches, and acoustic signal enhancement. He currently serves as Senior Area Editor of the IEEE/ACM Transactions on Audio, Speech, and Language Processing. Research Interests: Statistical Signal Processing for Speech and Audio Machine Learning Applications in Acoustic Environments Diffusion Models for Audio Restoration Audio-Visual Speech Enhancement Human-Machine Interaction Systems Acoustic Scene Analysis Publications Highlight Trends: Recent works focus on diffusion models for speech enhancement, generative approaches to dereverberation, and audiovisual multimodal analysis. He has pioneered frameworks like ReverbFX datasets and FlowDec codecs, emphasizing perceptual quality and unsupervised domain adaptation. Advising & Grants: While no specific students or grants are listed, his research group actively publishes in top venues, indicating sustained academic contributions. His work bridges theoretical signal processing with applied systems engineering. Labs/Teams: Leads the Signal Processing (SP) Research Group at UHH, specializing in cutting-edge audio technologies and human-centric signal processing solutions.

Michael MöllerView profile

Professor

• Computer Vision
• Machine Learning
• Inverse Problems
• +2 more

Michael Möller is a Professor at the University of Siegen, leading the Lehrstuhl für Computer Vision within the Institute for Vision and Graphics. His research spans computer vision, machine learning, and optimization, with applications in medical imaging and computational geometry. Research Interests: Focuses on solving inverse problems in imaging, neural architecture optimization, and quantum-hybrid computational methods. His work integrates deep learning with traditional optimization for scalable solutions in 3D reconstruction and image analysis. Publications: Recent articles explore advancements in neural architecture search robustness, quantum annealing for shape matching, and data-driven regularization techniques for CT reconstruction, reflecting cross-disciplinary applications.

Martin DrewsView profile

Professor

• Climate Change
• Climate Economics
• Risk Management
• +6 more

Martin Drews is a Professor at the Department of Technology, Management and Economics at the Technical University of Denmark (DTU). His research focuses on climate economics, risk management, and energy policy with an emphasis on adaptation to climate change. He is actively involved in projects addressing compound climate extremes, flood risk assessment, and decision-making frameworks under uncertainty. Key research areas include reinforcement learning applications in climate adaptation, coastal flood modeling, and interdisciplinary approaches to disaster resilience. He co-leads initiatives like the Horizon-RIA ACCRUE project and supervises multiple PhD students investigating climate policy, multi-hazard risk assessment, and compound event dynamics. Recent studies emphasize the intersection of machine learning with climate adaptation, statistical methods for extreme event analysis, and the water-energy nexus. His work contributes to UN Sustainable Development Goals related to climate action, resilient infrastructure, and sustainable cities. Dr. Drews collaborates internationally on projects such as compound event modeling in Denmark and Baltic Sea region studies. He has authored over 99 publications, with recent work highlighted in journals like Natural Hazards and Earth System Sciences and Bulletin of the American Meteorological Society .

Liangxiu HanView profile

Professor

• Big Data Analytics
• Artificial Intelligence
• Machine Learning
• +5 more

Professor Liangxiu Han is Professor of Computational Science at Manchester Metropolitan University's Department of Computing and Mathematics, where she also serves as co-Director of the Centre for Advanced Computational Science and Deputy Director of the ManMet Crime and Well-Being Big Data Centre. Her research focuses on developing novel approaches to big data analytics, machine learning, and AI, with applications in precision agriculture, healthcare, smart cities, and cybersecurity. Professor Han's research integrates fundamental and applied work in large-scale data processing, intelligent management of distributed systems, and mathematical modeling. Her current activities aim to provide intelligent ICT-based solutions for societal challenges in food security, healthcare, and sustainable urban development. Her recent publications demonstrate leadership in applying AI to medical challenges like Alzheimer's drug discovery and glaucoma diagnosis. The research combines Bayesian deep learning, computational chemistry, and neuropharmacology to develop innovative therapeutic approaches and diagnostic tools.

Stefan RadevView profile

Assistant Professor

• Bayesian Inference
• Deep Learning
• Probabilistic Modeling
• +3 more

Stefan Radev is an Assistant Professor in the Cognitive Science department at Rensselaer Polytechnic Institute . His work focuses on developing Bayesian methods with generative neural networks and computational models for complex systems like cognition and disease outbreaks. He is the core developer of the BayesFlow framework, which enables amortized Bayesian inference using deep learning. Radev's research addresses computational challenges in Bayesian workflows, such as rapid parameter estimation and model validation through neural networks. His primary research interests include Deep Learning , Probabilistic Modeling , and their applications in computational neuroscience and biomedical engineering. He collaborates with the Center for Modeling, Simulation and Imaging in Medicine (CEMSIM) and contributes to open-source projects like BayesFlow, which supports multi-backend frameworks (PyTorch/TensorFlow/JAX). Recent publications highlight advancements in amortized inference, simulation-based calibration, and robust Bayesian workflows. His work spans domains from cognitive modeling to biomedical imaging, emphasizing interdisciplinary applications of Bayesian methods. No scientific awards are explicitly listed, though his contributions to open-source tools and impactful research indicate significant academic recognition.

Sergey NadtochiyView profile

Professor

• Financial Mathematics
• Probability Theory
• Partial Differential Equations
• +2 more

Sergey Nadtochiy serves as Professor of Applied Mathematics within the College of Computing at Illinois Institute of Technology. His research bridges theoretical mathematics with financial applications through rigorous probabilistic and analytical frameworks. His academic foundation includes: Ph.D. in Operations Research & Financial Engineering from Princeton University (2009), advised by R. Carmona M.A. in Operations Research & Financial Engineering from Princeton University (2008), advised by R. Carmona Specialist (M.Sc.) in Mathematics, summa cum laude, from Moscow State University (2005), advised by A. Shiryaev Nadtochiy's research program centers on Financial Mathematics, Probability Theory, and Partial Differential Equations with applications to market microstructure, optimal contract design, and systemic risk. His work demonstrates deep integration of Stochastic Control and Game Theory to model complex financial phenomena like price impact and limit order book dynamics, while recent publications extend into mathematical physics through Stefan problem analysis. Analysis of his 15 most recent publications reveals a strategic evolution from pure financial mathematics toward interdisciplinary connections with mathematical physics. The 2022-2024 works show increasing focus on the Stefan problem and diffusion-limited aggregation, while maintaining core expertise in market microstructure through studies on price impact concavity, brokerage contracts, and liquidity effects. His scientific recognition includes: Award for excellence (honorific fellowship) from Moscow State University (2001-2005) SIAG/FME Junior Scientist Prize from SIAM (2012) Charlotte Elizabeth Procter Honorific Fellowship from Princeton University (2008-2009) Gordon Y.S. Wu Honorific Fellowship from Princeton University (2005-2009) Nadtochiy has secured significant research funding as sole Principal Investigator on two NSF awards: a CAREER grant (DMS-1855309, 2017-2022) and earlier grant DMS-1411824 (2014-2017). While no formal student advisees are listed in the source materials, his collaborative publications with researchers like M. Shkolnikov and S. Shreve demonstrate active mentorship within research teams focused on probabilistic methods and financial modeling.

Ricard GavaldàView profile

Professor

• Machine Learning
• Data Mining
• Healthcare Analytics
• +3 more

Ricard Gavaldà is a Professor in the Department of Computer Science at Universitat Politècnica de Catalunya (UPC), affiliated with the LARCA research group. Since February 2020, he has been on academic leave to work full-time at Amalfi Analytics, focusing on healthcare data analytics. His research interests include Machine Learning, Data Mining, and their applications to healthcare and social good. He has supervised numerous PhD students and contributed to impactful projects in clinical decision support, traffic prediction, and energy-efficient systems. Education details are not explicitly mentioned in the provided texts. His work spans data stream mining, algorithmic frameworks (e.g., MOA), and healthcare informatics. He has co-founded Amalfi Analytics to develop AI-driven solutions for healthcare management. Notable recent activities include co-chairing the Nectar Track at ECML PKDD 2025 and organizing workshops on Data Science for Social Good. Key technical contributions include methods for adaptive learning, probabilistic modeling, and interpretable AI systems. His research bridges theoretical foundations and real-world applications, with a focus on improving healthcare outcomes and sustainable practices.

Fabien MoutardeView profile

Professor

• Machine Learning
• Deep Learning
• Computer Vision
• +8 more

Fabien Moutarde is a Full Professor and Director of the Center for Robotics at MINES ParisTech (PSL University, Paris, France). He holds a PhD in Physics and an Habilitation to Direct Research in Engineering Sciences. Dr. Moutarde coordinates French engineering education at ParisTech_Shanghai (SPEIT) in China. Research Areas Deep Learning & Reinforcement Learning Computer Vision for Intelligent Vehicles Collaborative Robotics Traffic Analysis & Forecasting Human Gesture Recognition Recent Article Trends His work focuses on autonomous driving using Deep Reinforcement Learning, pedestrian trajectory prediction with spatio-temporal attention, and multi-modal localization techniques combining vision with Wi-Fi. Key applications include urban traffic analysis, collaborative robotics, and end-to-end driving systems. Leadership & Teaching Co-created specialized Machine Learning courses Pioneered Deep Reinforcement Learning lectures Led French-Chinese academic coordination Former UML/Java curriculum developer Publications With 28 h-index, his research includes 30+ IEEE/ACM publications on autonomous vehicles, gesture recognition, and traffic mining. Representative conferences: CVPR, NeurIPS, IROS, ITSC.

Yang ChenView profile

Assistant Professor

• Statistical Methodology
• Computational Algorithms
• Bayesian Modeling
• +7 more

Yang Chen is an Assistant Professor of Statistics and Research Assistant Professor at the Michigan Institute for Data Science (MIDAS), University of Michigan. Her research focuses on statistical methodology and computational algorithms for scientific applications, including Bayesian modeling, hidden Markov models, and applications in astronomy, space science, and epidemiology. She holds a Ph.D. in Statistics from Harvard University and a B.Sc. in Mathematics from the University of Science and Technology of China. Education: Ph.D. in Statistics, Harvard University, 2017 B.Sc. in Mathematics, University of Science and Technology of China, 2011 Research Interests: Her work emphasizes computational algorithms for statistical inference, with applications in astronomy (e.g., solar flare prediction, instrument calibration) and epidemiology. Key areas include spatial-temporal data analysis, uncertainty quantification, and machine learning for large-scale imaging data. Grants & Funding: Principal Investigator/Co-Investigator on multiple NSF, NASA, and industry grants totaling over $13M, including space weather modeling, solar physics, and data science initiatives. Teaching: Teaches advanced courses in Bayesian data analysis, statistical modeling, and computational methods at the University of Michigan. Professional Activities: Associate Editor for The American Statistician , Statistical Science , and Journal of the American Statistical Association . Active in organizing conferences and seminars on astrostatistics, machine learning, and space weather.