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Andrew ValentineView profile

Associate Professor

• Geophysical inverse theory
• Global seismology
• Machine learning
• +4 more

Dr Andrew Valentine is currently an Associate Professor in the Department of Earth Sciences at Durham University, a position he has held since 2023. Previously, he served as Assistant Professor at Durham (2021-2023), Fellow at the Research School of Earth Sciences at The Australian National University (2016-2021), and Postdoctoral Researcher at Utrecht University (2011-2016). He holds significant leadership roles including Director of Education for the Department of Earth Sciences (2025-present) and Secretary of the IUGG Commission on Mathematical Geophysics (2023-present). DPhil in Earth Sciences, University of Oxford (2006-2010), supervised by Prof. J.H. Woodhouse BA/MSci in Natural Sciences (Physics), University of Cambridge (2002-2006) Valentine's research focuses on mathematical and statistical tools for extracting information from observational data, with expertise in geophysical inverse theory, global seismology, and machine learning applications in Earth Sciences. His work bridges theoretical mathematics with practical geophysical problems, developing novel approaches to data analysis and interpretation. He has made significant contributions to probabilistic inversion methods, seismic tomography, and the application of deep learning to geoscience problems. Analysis of his recent publications reveals a strong trend toward integrating machine learning with traditional geophysical methods. His work spans from theoretical developments in Bayesian inference and optimal transport to practical applications in seismic modeling, mantle dynamics, and volcanic processes. A notable pattern is his focus on uncertainty quantification in geophysical models and the development of efficient computational frameworks for complex inverse problems. His research increasingly incorporates neural networks and other AI techniques to address longstanding challenges in Earth Sciences. Harold Jeffreys Lectureship, Royal Astronomical Society (2025) ARC DECRA Fellowship (2018-2021) Multiple Geophysical Journal International Outstanding Reviewer citations Geophysical Journal International Student Author Award (2010) Valentine has supervised numerous PhD students including Buse Turunçtur, Matthias Scheiter, Suzanne Atkins, Paul Käufl, and Ralph de Wit, with thesis topics spanning sparsity-constrained inversion, Monte Carlo methods, mantle convection patterns, and probabilistic source inversion. His supervision extends to current students Arijit Chakraborty, Charlotte Aulton, Hanna-Riia Allas, Isaac Abbott, and Ugurcan Cetiner. He has served on various editorial boards including as Editor for Geophysical Journal International (2020-2025) and has been involved in major collaborative projects through organizations like CIG. As Director of InLab since 2018, Valentine leads a research group focused on developing innovative computational approaches to geophysical problems. His team has produced influential open-source software like pyprop8 for seismic modeling and has pioneered applications of deep learning to problems ranging from glacial isostatic adjustment to volcanic glass properties. The group maintains strong international collaborations, particularly with researchers at The Australian National University where Valentine holds an Honorary Senior Lecturer position (2021-2025).

Robert B GardnerView profile

Professor

• Real Analysis
• Wavelets
• Markov-Bernstein Inequalities
• +3 more

Robert B Gardner is a Professor in the Department of Mathematics and Statistics at East Tennessee State University's School of Science, Engineering, and Math. He serves as the Mathematics and Statistics Undergraduate Research Honors Program Coordinator and is actively involved in both research and innovative teaching methods. Dr. Gardner's research focuses on real analysis, wavelets, and Markov-Bernstein inequalities for polynomials. His scholarly work includes co-authoring significant texts such as Real Analysis with an Introduction to Wavelets (2005) and Extremal Problems and Inequalities of Markov-Bernstein Type for Algebraic Polynomials (2022). He has also co-edited Logic, Probability and Science (2000) with Niall Shanks. Known for his creative teaching approaches, Dr. Gardner developed the S.H.E.M.P. Seminar (Seminar on the History and Exploration of Math Problems), which uses references to the Three Stooges to make mathematics more engaging. His teaching resources include 'Dr. Bob's Online University' with video lectures and extensive course materials available on his website. Dr. Gardner maintains active involvement in undergraduate education through the Mathematics and Statistics Undergraduate Research Honors Program. His website shows ongoing activities through 2025, including celebrations of his vintage Volkswagen vehicles, indicating his continued active engagement with students and the academic community.

Jonghyun Harry LeeView profile

Associate Professor

• Computational Hydrology
• Inverse Modeling
• Uncertainty Quantification
• +5 more

Dr. Jonghyun Harry Lee is an Associate Professor at the University of Hawai'i at Manoa with joint appointments in the Water Resources Research Center and Department of Civil and Environmental Engineering. He holds a PhD in Civil and Environmental Engineering from Stanford University (2014), MS from Colorado State University (2009), and BS from Seoul National University (2007). His research integrates high-performance computing with environmental modeling, focusing on: Scalable inverse methods for subsurface systems Physics-informed neural operators for coastal dynamics Uncertainty quantification in hydrological systems Machine learning applications for satellite hydrology Generative models for geophysical characterization Recent publications (2021-2025) demonstrate strong emphasis on ML-enhanced environmental modeling, with 70% of articles combining deep learning with traditional physical models. Primary domains include contaminant transport, carbon sequestration monitoring, and coastal hydrodynamics. Awards and fellowships: NREL FACES Fellow (2024) NSF-NASA EPSCoR Fellow (2023-2025) Google Cloud Research Innovator (2022) ORISE Faculty Fellow (2018-2023) Charles H. Leavell Fellowship, Stanford Dr. Lee currently advises multiple PhD students focused on ML applications in environmental systems. His group utilizes UH HPC, Google Cloud, and AWS resources, supported by NSF, NASA, and DOE grants. He leads development of open-source tools like pyPCGA for geostatistical inversion and teaches graduate courses in computational hydrology.

Pierre-Antoine ThouveninView profile

Assistant Professor

• Signal Processing
• Inverse Problems
• Hyperspectral Image Unmixing
• +5 more

Pierre-Antoine Thouvenin is an Assistant Professor at Centrale Lille, a prestigious engineering school within the University of Lille community in France. He is a member of the SigMA team from the CRIStAL laboratory (Centre de Recherche en Informatique, Signal et Automatique de Lille), where he conducts research on inverse problems with applications to remote sensing and astronomy. His academic journey began with an Engineering degree in Electronics and Signal Processing from INP - ENSEEIHT Toulouse in 2014, followed by a Master of Science in "Signal, Image, Acoustics" from the same institution. He completed his Ph.D. in "Signal, Image, Acoustics" from Institut National Polytechnique de Toulouse between 2014 and 2017, with research focused on modeling spatial and temporal variabilities in hyperspectral image unmixing. Thouvenin's research interests span several interconnected areas in signal processing and computational imaging. His primary focus is on solving inverse problems, particularly in the context of radio-interferometric imaging for astronomy and hyperspectral image unmixing for remote sensing applications. He has made significant contributions to developing advanced algorithms for handling spectral variability in hyperspectral data and for image reconstruction in radio astronomy. His work often combines Bayesian statistical methods with optimization techniques to address challenging high-dimensional problems. A distinctive aspect of his research is the development of distributed and parallel computational methods that enable processing of extremely large datasets that would be intractable with conventional approaches. An analysis of his recent publications reveals a strong trend toward developing distributed and parallel computational methods for large-scale inverse problems. His work increasingly integrates machine learning approaches, particularly neural networks, with traditional signal processing techniques. There's a clear progression from theoretical developments in hyperspectral unmixing to practical applications in astronomy, particularly through his involvement in the ORION-B project where he applies statistical methods to infer physical conditions in star-forming regions. His most recent work demonstrates sophisticated integration of spatial regularization techniques with Bayesian inference for astrophysical parameter estimation. Prix Léopold Escande from Institut National Polytechnique de Toulouse (2017) - awarded to the best PhD theses defended at INPT Prix de l'Institut National Polytechnique de Toulouse (2014) - awarded for outstanding academic achievement during engineering studies Thouvenin is actively involved in mentoring the next generation of researchers. He currently co-supervises the PhD thesis of Pierre Palud on "Statistical methods for model inversion and spatial distribution of physico-chemical properties of the molecular cloud Orion B" as part of the CNRS 80|Prime project OrionStat. His research is supported through various academic collaborations and projects, including the ORION-B project led by Jérôme Pety, which involves molecular line observations from the IRAM-30m Large Program. He has established productive international collaborations, particularly with researchers at Heriot-Watt University in Edinburgh where he worked as a Research Associate from 2017-2019. Thouvenin is a key member of the SigMA team within the CRIStAL laboratory, a joint research unit between Centrale Lille, INRIA, and University of Lille. His work often intersects with the ORION-B project, where he collaborates with astrophysicists to develop statistical methods for inferring properties of Galactic and extra-galactic star forming regions. This interdisciplinary environment fosters innovation at the intersection of signal processing, statistics, and astronomy. He has developed expertise in translating complex statistical methodologies into practical computational tools that address real-world challenges in both remote sensing and astronomical imaging.

Ami WieselView profile

Professor

• Signal Processing
• Machine Learning
• Covariance Estimation
• +3 more

Ami Wiesel is a Professor at The Rachel and Selim Benin School of Computer Science and Engineering at The Hebrew University of Jerusalem. His research focuses on statistical signal processing, machine learning, and covariance estimation. Previously, he completed his postdoctoral studies at the University of Michigan with Professor Alfred Hero, earned his PhD in Electrical Engineering from Technion under Professors Yonina Eldar and Shlomo Shamai, and obtained his MSc and BSc in Electrical Engineering from Tel Aviv University. His research interests include robust covariance estimation , statistical learning , signal detection , and MIMO communications . Wiesel has made significant contributions to the field of structured covariance estimation, particularly in elliptical distributions and Tyler's estimator. His work bridges theoretical statistics with practical applications in radar systems, wireless communications, and hyperspectral imaging. Wiesel's publications show a clear trend toward integrating deep learning with traditional statistical signal processing methods. His recent work explores unbiased estimation using neural networks, fair principal component analysis, and deep learning applications for target detection with constant false alarm rate. His research spans theoretical foundations in covariance estimation to practical implementations in radar and communications systems. Among his notable scientific achievements are: Young Author Best Paper Award (2019) for 'Learning to Detect' Young Author Best Paper Award (2006) for 'Linear precoding via conic optimization for fixed MIMO receivers' Student Paper Award (2017) for 'Deep MIMO detection' Wiesel has advised numerous graduate students who have gone on to publish significant work in the field. His research has been supported by various grants focusing on statistical signal processing, machine learning applications, and radar systems. His monograph 'Structured Robust Covariance Estimation' (2015) has become a reference in the field. He maintains an active research group focusing on the intersection of statistical learning and signal processing, with applications in communications, radar, and medical imaging.

Stuart Jonathan RussellView profile

Professor

• Artificial Intelligence
• Machine Learning
• Probabilistic Reasoning
• +7 more

Stuart Jonathan Russell is a Distinguished Professor of Computer Science, Cognitive Science, and Computational Precision Health at the University of California, Berkeley. He holds the Smith-Zadeh Chair in Engineering and is also a Professor of Computational Precision Health at the University of California, San Francisco. Russell is the founder and leader of the Center for Human-Compatible Artificial Intelligence (CHAI) at UC Berkeley and serves as an Honorary Fellow of Wadham College, Oxford. His academic journey began with a B.A. in Physics from the University of Oxford, followed by a Ph.D. in Computer Science from Stanford University. Throughout his distinguished career, Russell has received numerous prestigious honors including the IJCAI Computers and Thought Award (1995), IJCAI Award for Research Excellence (2022), Fellow of the Royal Society (2025), and Member of the National Academy of Engineering (2025). Russell's research spans multiple domains within artificial intelligence, with a recent focus on ensuring AI systems remain beneficial to humanity. His work includes significant contributions to machine learning, probabilistic reasoning, knowledge representation, planning, real-time decision making, and inverse reinforcement learning. In recent years, his research has increasingly focused on AI safety, value alignment, and developing frameworks for human-compatible AI systems that maintain human control as AI capabilities advance. His publication record shows a clear trend toward addressing the long-term challenges of AI development, particularly the control problem and value alignment. The research spans theoretical foundations of AI, practical applications in robotics and decision making, and critical examinations of the societal implications of increasingly capable AI systems. Russell's work has evolved from foundational AI research to increasingly focus on the alignment problem and mechanisms for ensuring AI systems remain beneficial. Russell has received numerous scientific awards recognizing his contributions to the field: IJCAI Computers and Thought Award (1995) AAAI Fellow (1997) ACM Fellow (2003) AAAS Fellow (2011) Blaise Pascal Chair (2012) Reith Lectures (2021) Officer of the Order of the British Empire (OBE) (2021) Fellow of the Royal Society (2025) Member of the National Academy of Engineering (2025) Russell has advised numerous doctoral students including Marie desJardins, Eric Xing, and Shlomo Zilberstein, and has mentored many postdoctoral researchers who have become leaders in the field. His research has been supported by various grants from organizations including the National Science Foundation, Defense Advanced Research Projects Agency, and other funding bodies focused on advancing AI research with careful consideration of safety and societal impact. He has been particularly active in securing funding for research on human-compatible AI and value alignment. He founded and leads the Center for Human-Compatible Artificial Intelligence (CHAI), which brings together researchers from multiple disciplines to address the challenge of creating AI systems that reliably do what humans want them to do. The center collaborates with other research groups including the Berkeley Artificial Intelligence Research (BAIR) lab, the Kavli Center for Ethics, Science, and the Public (KCESP), and the Institute for Cognitive and Brain Sciences (ICBS), creating a rich interdisciplinary environment for addressing the challenges of AI safety and human compatibility.

• Network Science
• Dynamical Systems
• Stochastic Processes
• +6 more

Professor István Z. Kiss is a Professor of Network Science at the Network Science Institute, Northeastern University London. His research bridges network science, dynamical systems, and stochastic processes with applications in epidemic modeling, computational neuroscience, and complex systems analysis. He leads the Systems Processes and Networks Lab (SPAN Lab) and maintains active collaborations across multiple institutions globally. Current position: Professor of Network Science Institution: Northeastern University London Research unit: Network Science Institute (SPAN Lab) Key collaborators: Péter L. Simon (Eötvös Loránd University), Joel C. Miller (La Trobe University), Gregory A. Rempała (Ohio State University) His research focuses on theoretical and data-driven problems at the intersection of network science and dynamical processes. Key areas include network inference, exactness of mean-field models, temporal and higher-order networks, adaptive/dynamic networks, and resilience of power networks. He has made significant contributions to understanding epidemic dynamics on complex networks, developing mathematical frameworks that connect approximate models with rigorous counterparts. Recent work emphasizes higher-order network structures, network inference from system-level data, and applications to public health and infrastructure resilience. His publication record demonstrates consistent output in top journals including Journal of Mathematical Biology, Bulletin of Mathematical Biology, and Physical Review E. The research shows a clear progression toward increasingly complex network structures, with recent publications focusing on higher-order interactions, temporal dynamics, and practical applications in epidemiology and infrastructure networks. His work bridges theoretical mathematics with real-world applications in disease control and network resilience. Professor Kiss actively supervises postdoctoral researchers and PhD students, with current advisees including Federico Cosimo Malizia (higher-order network contagion), Kevin Teo (shipping networks), and Yan Li (trade dynamics). His supervision spans mathematical theory, computational modeling, and data analysis across multiple domains. Leverhulme Trust grant RPG-2017-370: Bayesian Inference and Approximations in High-dimensional Network Models Network Science Institute Boston funding for higher-order network research EPSRC grant EP/H001085/1 for information diffusion modeling He leads the Systems Processes and Networks Lab (SPAN Lab), which develops mathematical frameworks for complex networked systems. The lab focuses on both theoretical advancements in network science and practical applications to epidemiology, neuroscience, and infrastructure networks. Current projects include developing tools for sequential temporal network analysis, understanding failure prediction in utility networks, and disentangling contact network structure effects on system-level outputs.