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Karin SiglochView profile

Visiting Professor

• Seismology
• Geodynamics
• Seismic Tomography
• +5 more

Karin Sigloch is a Visiting Professor at the Department of Earth Sciences , University of Oxford, and a member of Exeter College . Her research focuses on seismology and geodynamics , particularly using seismic tomography to image Earth's interior structure and link it to surface geological processes. She leads the DEEPTIME ERC-funded project, which integrates subduction zone studies with paleogeography. Teaching: Geophysical Methods (2nd year), Seismology (3rd year), Solid Earth seminars (4th year), and NERC Doctoral Training modules. Key Projects: RHUM-RUM ocean-bottom seismic experiment (2011-2014), DEEPTIME mantle tomography initiative. Research Interests: Seismic tomography advancements, mantle plumes dynamics, paleo-trench reconstructions, and vertical slab sinking hypotheses. Her work bridges seismic imaging with geological field data and geodynamic modeling. Awards: Philip Leverhulme Prize (2015) for outstanding contributions in Earth Sciences. Collaborations include the NERC Doctoral Training Programme and Leverhulme Trust-funded paleogeography studies. Her research group emphasizes cutting-edge waveform inversion techniques and interdisciplinary integration with plate reconstructions.

Pasquale FerrentinoView profile

Researcher

• Robotics
• Soft Robotics
• Materials Science
• +4 more

Pasquale Ferrentino is a Researcher at the Department of Applied Mechanics, Vrije Universiteit Brussel. His work focuses on soft robotics, self-healing materials, and finite element analysis (FEA) modeling. He has contributed to developing sustainable soft robots using self-healing polymers and recyclable materials, as well as advancing simulation techniques in the SOFA framework for soft actuator design. Ferrentino has published extensively on topics like material-based modeling of continuum robots, human-robot interaction interfaces, and biomedical robotics applications. He received the SOFA Award - Technical Committee Prize in 2024 for his contributions to robotics modeling. His research spans collaboration with international teams, addressing challenges in wearable robotics, soft tissue interaction, and multi-material actuator optimization. Ferrentino has presented his work at major conferences and co-created datasets for self-healing soft finger tutorials and FEA-based modeling.

Brian ZiebartView profile

Professor

• Machine Learning
• Robotics
• Assistive Technologies
• +3 more

Brian Ziebart is a Professor in the Department of Computer Science at the University of Illinois at Chicago. He earned his Ph.D. in Machine Learning from Carnegie Mellon University in 2010. Research Interests: Machine Learning, Robotics, Assistive Technologies, Human-Computer Interaction, Adversarial Prediction, Inverse Optimal Control, Structured Prediction. Key Grants: NSF CAREER (RI)-1652530, NSF EAGER (SCH)-1650900, NSF IIS-1526379, NSF III-1514126, Future of Life Institute grant, NSF NRI-1227495. Notable Awards: Best Paper Runner-Up (ECCV, 2012), Best Paper Award (ICML, 2011), CMU School of Computer Science Dissertation Honorable Mention (2011). Teaching & Leadership: Senior Lecturer at CMU, actively involved in mentoring students and leading research teams.

Linda A. ReinenView profile

Professor

• Structural Geology
• Rock Mechanics
• Earthquake Mechanics
• +8 more

Linda A. Reinen is Professor of Geology at Pomona College, where she has been a faculty member since 1995. She is currently on leave for Fall 2025. Her work bridges structural geology, rock mechanics, and earthquake science, with a focus on fault system behavior and earthquake initiation processes. Education: Ph.D., Brown University M.S., University of Massachusetts B.S., University of Massachusetts Her research centers on understanding how tectonic slip is accommodated on faults—either through earthquakes or stable creep—with a particular focus on serpentinite, a key rock in fault zones. She combines numerical modeling , laboratory friction experiments , and field studies to investigate the physical processes behind earthquake generation. Her recent work, supported by an NSF grant, explores serpentinite’s role in deep-focus earthquakes within subducting plates. The trends in her publications reflect a long-standing commitment to fault mechanics and rock friction , particularly in serpentinite-rich environments. Her work spans seismology , tectonics , and geohazards , with applications to major faults like the San Andreas. She employs both theoretical models (e.g., spring-slider systems) and empirical data to understand slip styles, precursory signals, and aseismic behavior. Scientific Awards and Honors: Geological Society of America, Biggs Award for Excellence in Earth Science Teaching (2003) Invited Speaker, International Symposium on Slip and Flow Processes, Sendai, Japan (2001) Counselor, Council on Undergraduate Research, Geology Division (1999–present) Steering Committee Member, Physical Properties of Earth Materials (AGU) (1998–2001) Linda Reinen is deeply committed to undergraduate research education , regularly supervising independent research and senior projects. She has taught courses such as Structural Geology, Tectonics, and Geohazards. Her grant funding, including from the NSF, supports experimental and modeling work on fault processes. She plays an active role in professional service and mentoring within the geoscience education and research communities. She leads a research program integrating computational, experimental, and field-based approaches, often involving undergraduate collaborators. Her lab and research team focus on rock friction testing and numerical simulation of fault behavior, contributing to broader understanding of earthquake hazards and fault zone evolution.

Sonia FlissView profile

Professor

• Wave Propagation
• Mathematical Analysis
• Numerical Analysis
• +5 more

Sonia Fliss is a Full Professor at ENSTA Paris , affiliated with the Department of Applied Mathematics and the POEMS laboratory (UMR CNRS-INRIA-ENSTA) . She teaches applied mathematics courses on partial differential equations (PDEs) , finite element methods , and periodic homogenization to undergraduate and graduate students. Doctor in Applied Mathematics (2009) Authorized to supervise research (2019) Research Interests : Sonia Fliss specializes in the modeling and numerical analysis of wave propagation in periodic, quasi-periodic, and random media . Her work includes transparent boundary conditions , guided waves , and asymptotic methods for acoustic, electromagnetic, and elastic wave phenomena . Recent Publications highlight her contributions to the Half-Space Matching Method , edge states in honeycomb structures , and scattering problems in unbounded domains . Her numerical techniques address multi-scale waveguides and time-harmonic propagation . Laboratory : As a member of the POEMS team, she collaborates on interdisciplinary projects involving mathematical analysis , computational physics , and engineering applications in domains like defence, energy, and transport .

Christopher J. EarlsView profile

Professor

• Scientific Artificial Intelligence (SciAI)
• Inverse Problems
• Applied Mathematics
• +5 more

Christopher J. Earls is a Professor in the Department of Civil and Environmental Engineering at Cornell University's College of Engineering. His work bridges applied mathematics, artificial intelligence, and scientific computing to address challenges in understanding natural and engineered systems, particularly focusing on uncertainty quantification, sparse sensing, and complexity. B.S. (Civil Engineering), Virginia Tech 1990 M.S. (Civil Engineering), Virginia Tech 1992 Ph.D. (Civil Engineering), University of Minnesota 1995 Earls' research explores Scientific Artificial Intelligence (SciAI) and Inverse Problems, with applications to computer-aided diagnosis and complex systems. His recent publications highlight intersections with Large Language Models (LLMs), geometric analysis, and neural scaling laws in dynamical systems. Outstanding Young Alumni Award (Virginia Tech) 2004 Outstanding Professor of the Year Award (ASCE) 2001 James and Mary Tien Teaching Award (Cornell) 2016 Ralph E. Powe Junior Faculty Enhancement Award (ORAU) 2000 Peter S. Michie Outstanding Teacher Award (West Point) 1998

Simon de SzoekeView profile

Professor

• Atmosphere-ocean interaction
• Stratiform clouds
• Tropical meteorology
• +3 more

Simon de Szoeke is a Professor in the College of Earth, Ocean, and Atmospheric Sciences at Oregon State University. His research focuses on atmosphere-ocean interaction and its influence on climate, with particular emphasis on tropical regions. He conducts observational studies and modeling work to understand air-sea interactions, cloud processes, and their representation in climate models. Dr. de Szoeke received his Ph.D. in Atmospheric Sciences from the University of Washington in spring 2004, with a dissertation on "Evolution of the cross-equatorial atmospheric boundary layer in the east Pacific: observations and models." He earned his B.A. summa cum laude in Physics with departmental honors and Mathematics from the University of Oregon Robert D. Clark Honors College in 1997. His research interests center on atmosphere-ocean interaction, stratiform clouds, and tropical meteorology . He investigates how clouds influence the Earth's radiative heating, the processes responsible for the transition from stratiform to cumuliform clouds, and the role of inversion strength in cloud maintenance. His work on the Madden-Julian oscillation (MJO) involves analyzing data from the DYNAMO international field campaign to study air-sea flux feedbacks and the role of sea surface temperature in tropical weather phenomena. Dr. de Szoeke is particularly known for his groundbreaking research on cold pools in the tropical ocean, which he describes as "footprints" of convection. His research shows that these cold, invisible phantoms play an important role in the atmospheric heat budget and can organize towering clouds at their intersection points. Contrary to previous assumptions, he found that cold pools are drier than their surroundings, challenging existing theories about their role in convection. His scientific contributions include numerous publications on air-sea interaction, tropical meteorology, and cloud processes. His research has been supported by major field campaigns including DYNAMO in the Indian Ocean and VOCALS in the southeastern Pacific, with findings published in leading journals such as Journal of Climate, Bulletin of the American Meteorological Society, and Monthly Weather Review. Dr. de Szoeke teaches courses in atmospheric sciences including The Changing Climate (AS 320), Atmospheric Thermodynamics and Cloud Physics (AS 411/511), and Large-Scale Interactions of the Atmosphere and Oceans (AS 615). He has advised several graduate students, including June Marion who graduated in summer 2014 with a thesis on turbulent heat flux estimates, as well as Michael Makiyama and Kathryn Verlinden.

Lei TianView profile

Associate Professor

• Computational imaging and sensing
• Computational microscopy
• Imaging in scattering media
• +5 more

Lei Tian is an Associate Professor in the Department of Electrical and Computer Engineering and the Department of Biomedical Engineering at Boston University's College of Engineering. He leads the Computational Imaging Systems Lab and maintains affiliations with the Neurophotonics Center, Photonics Center, Center for Information & System Engineering, Rafik B. Hariri Institute for Computing, and Nanotechnology Innovation Center. His educational background includes: PhD, Massachusetts Institute of Technology, 2013 MS, Massachusetts Institute of Technology, 2010 Professor Tian's research integrates optics and computation to overcome physical limitations in imaging systems. His work spans computational imaging and sensing, computational microscopy, imaging in scattering media, phase retrieval, and neurophotonics. He develops next-generation imaging systems with applications in biomedical microscopy, neuroscience, semiconductor metrology, and advanced vision applications, emphasizing the joint design of optical components and computational algorithms. His publication record shows a strong progression from fundamental computational imaging techniques to practical applications, with increasing integration of deep learning approaches to solve challenging imaging problems in scattering media and neural environments. His work consistently bridges theoretical advances with real-world applications. Professor Tian has received numerous prestigious awards: Boston University Provost's Scholar-Teacher of the Year Award (2025) Optica Fellow (2025) Early Career Excellence in Research, BU College of Engineering (2021) NSF CAREER Award (2019) Dean's Catalyst Award (2018) The Fumio Okano Best 3D Paper Prize (2018) As an advisor, he has successfully mentored at least 10 PhD students to completion as of mid-2025, with recent graduates including Jeffrey Alido, Jiabei Zhu, Chang Liu, Hao Wang, and Joseph Greene. His research is supported by substantial funding including a $2 million NIH grant for the Computational Miniature Mesoscope (CM2), a $1.75M grant from NIBIB for cancer cell metabolism research, and funding from the Chan Zuckerberg Initiative. His Computational Imaging Systems Lab pioneers innovative imaging techniques that synergistically combine optical hardware with computational algorithms, making significant contributions to computational microscopy, intensity diffraction tomography, neural imaging systems, and deep learning applications in optical imaging for both biomedical and industrial applications.

• Glaciology
• Climate Change
• Sea-Level Rise
• +3 more

Bert Wouters is an Associate Professor at Utrecht University's Faculty of Science, affiliated with the Department of Dynamics Meteorology. He specializes in satellite-based observations of polar ice sheets and climate dynamics, with a part-time appointment since joining the Institute for Marine and Atmospheric Research (IMAU) in 2015. His work focuses on global glacier mass loss, ice-sheet interactions with climate change, and sea-level rise contributions. Research interests span glaciology, satellite geodesy, and climate modeling. Key themes include: Quantifying ice-sheet mass balance using GRACE/GRACE-FO and ICESat-2 data Analyzing surface melt processes on Antarctic ice shelves Assessing impacts of Arctic warming on glacier dynamics Developing machine learning methods for remote sensing of cryospheric changes Publications emphasize Antarctic and Greenland ice-sheet vulnerabilities, with recurring themes of satellite validation, meltwater hydrology, and decadal climate variability. Research consistently integrates field data, climate models, and novel remote sensing techniques. Wouters leads projects like Future Deltas (water/climate interactions) and Eratosthenes (glacier topography via shadow motion analysis). He collaborates with international teams on IPCC-relevant assessments and edits for The Cryosphere .

Brian StreetView profile

Professor

• Mathematics
• Partial Differential Equations
• Singular Integrals
• +7 more

Brian Street is a Professor in the Department of Mathematics at the University of Wisconsin-Madison . He specializes in partial differential equations, harmonic analysis, and geometric analysis, with a focus on subelliptic and multi-parameter singular integral theories. His work extends classical results like the Newlander-Nirenberg theorem and Frobenius theorem to quantitative and boundary settings. Research Highlights: Development of maximal subellipticity theory (2023), generalization of Calderón-Zygmund singular integrals to multi-parameter settings (2011–2013), and novel approaches to hypoelliptic and mixing flow problems. Publications: Authored a monograph on Multi-parameter Singular Integrals (Princeton University Press, 2012) and co-authored influential papers with leading mathematicians in functional analysis and PDEs. Methodologies: Employed techniques from ODEs, PDEs, and harmonic analysis, including wave equation propagation, non-isotropic Sobolev spaces, and diffeomorphism-invariant frameworks.

Thomas WöhlingView profile

Researcher

• Integrated environmental systems modeling
• Surface water-groundwater interactions
• Braided river systems
• +9 more

PD Dr. habil. Thomas Wöhling serves as a Senior Research Scientist and Team Leader for Stochastic Modelling of Hydrosystems at the Chair of Hydrology, Dresden University of Technology's Faculty of Environmental Sciences. His research spans integrated environmental systems modeling with particular expertise in surface water-groundwater interactions, braided river systems, and vadose zone processes. Previously, he held research positions at Water and Earth System Sciences Competence Cluster in Tübingen (2010-2015) and Lincoln Environmental Research in New Zealand (2006-2010). Dr. Wöhling completed his Dipl.-Hydrol. (1999) and PhD in Hydrology (2005) at Dresden University of Technology, followed by habilitation in Stochastic Hydrology (2021). His educational background includes extensive research at the Institute of Hydrology and Meteorology at TU Dresden (1999-2005) where he developed foundational expertise in hydrological modeling. Wöhling's research focuses on integrated modeling of coupled environmental systems , particularly flow and contaminant transport in surface water-groundwater systems, nutrient and energy fluxes in soil-plant-atmosphere systems, and distributed hydrological modeling. His work emphasizes stochastic modeling and uncertainty analysis , with significant contributions to inverse modeling, model calibration, multiobjective optimization, and Bayesian model averaging techniques. He has pioneered methods for evaluating monitoring network worth and data utility for environmental models. His publication record demonstrates consistent contributions to hydrological science, with recent work (2023-2025) focusing on machine learning applications in hydrology, advanced statistical inversion techniques, and complex karst system modeling. Key trends include integration of physics-based and data-driven approaches, improved uncertainty quantification methods, and applications to climate change impacts on water resources. His work bridges theoretical advances with practical applications in New Zealand's braided rivers and European hydrological systems. STAHY Best Paper Award (2018) ASCE Journal of Irrigation and Drainage Engineering Best Reviewer Awards (2008, 2010, 2011, 2015, 2018) ASCE Journal of Irrigation and Drainage Engineering Best Paper Awards (2008, 2009) Dr. Wöhling leads the Stochastic Modelling of Hydrosystems team and has secured funding for numerous projects including Klimakonform, ISOSIM, VAMOS II, and the International Research Training Group 'Integrated Hydrosystem Modelling.' His work combines novel monitoring techniques with modeling and optimal sensor placement to improve prediction reliability for river-groundwater exchange fluxes. He collaborates extensively with international partners, particularly in New Zealand through the Lincoln Agritech's Braided Rivers program. His laboratory work focuses on combining traditional hydrological measurements with advanced computational techniques, including deep learning applications for soil surface hydrology and time-windowed Bayesian analysis for predictive modeling. The team maintains strong connections with field sites in Germany's Saxon region and New Zealand's Canterbury Plains, facilitating integrated theoretical and empirical research approaches.

• Materials Science
• Computational Chemistry
• Artificial Intelligence
• +9 more

Dr. Jed Pitera is an Adjunct Assistant Professor at the University of California, San Francisco (UCSF) Department of Pharmaceutical Chemistry and currently serves as the strategy co-lead for Accelerated Discovery in Sustainable Materials at IBM Research - Almaden. He has spent over two decades at IBM Research, applying computational tools and machine learning to materials R&D challenges. Caltech (Biology, Chemistry) University of California, San Francisco (Ph.D. in Biophysics) ETH Zurich (Postdoctoral work in computational physical chemistry) His research focuses on leveraging AI, machine learning, high-performance computing, and quantum computing for advanced materials discovery, particularly in sustainability applications such as carbon capture, energy storage, and PFAS replacement. He also works on improving the sustainability of existing materials in semiconductor manufacturing and directed self-assembly techniques. His work spans computational physical chemistry, polymer science, and AI-driven approaches to material design. His publications demonstrate a focus on AI-driven materials discovery (6 papers), directed self-assembly applications (4 papers), semiconductor manufacturing (4 papers), computational modeling (4 papers), and sustainability-focused research (5 papers). Notable trends include integrating robotics with AI for materials discovery and developing lifecycle assessment tools for sustainable design. Dr. Pitera leads the Accelerator Technologies project at IBM and contributes to the IBM Safer Materials Advisor initiative. He has collaborated with researchers across multiple institutions, including Dan Sanders, Brandi Ransom, Seiji Takeda, and Teodoro Laino.

François Raymond J CornetView profile

Researcher

• Organometallic Chemistry
• Computational Chemistry
• Catalyst Design
• +3 more

François Raymond J Cornet is a Postdoctoral Researcher in the Department of Energy Conversion and Storage and a PhD Student in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU). His dual affiliation bridges energy conversion research and computational science, focusing on AI-driven molecular design. His research spans Organometallic Chemistry , Computational Chemistry , and Machine Learning , with specialization in catalyst design through diffusion models and inverse design methodologies. Key areas include metallocene chemistry, density functional theory applications, and generative modeling for chemical space exploration, targeting organometallic complexes like Vaska's complex. Cornet's publication trajectory reveals a concentrated effort in advancing equivariant diffusion models for molecular generation, particularly addressing small-data challenges in catalyst design. His work consistently integrates quantum chemistry with deep generative architectures, establishing new paradigms for inverse-design pipelines in computational chemistry. No scientific awards were documented in the source material. He recently completed the PhD project Machine learning for electronic scale inverse design of enzymatic catalysts (2021-2025) under supervisors M. N. Schmidt (primary), A. Bhowmik, and O. Winther, with examiners W. K. Boomsma and S. Olsson. Collaborators include P. Deshmukh, B. Benediktsson, and C. A. Naesseth across multiple publications. Research operations occur within DTU's interdisciplinary framework connecting the Department of Energy Conversion and Storage and Department of Applied Mathematics and Computer Science, leveraging computational infrastructure for molecular simulations and AI model training.

Sirojan TharmakulasingamView profile

Lecturer

• Machine Learning
• Artificial Intelligence
• Data Science
• +6 more

Dr. Sirojan Tharmakulasingam serves as a Lecturer and Research and Development Coordinator at the Signals, Information & Machine Intelligence lab within the Faculty of Engineering at the University of New South Wales (UNSW) Sydney. His work bridges theoretical machine learning with practical applications in edge computing and high-performance systems. His research spans multiple cutting-edge domains including machine learning, artificial intelligence, data science, edge computing, and high-performance computing. Dr. Tharmakulasingam specializes in developing next-generation inference models by integrating machine learning, signal processing, mathematical modeling, and computing across diverse data types including images, video, audio, and quantum molecular data. His work has significant implications for scientific computing, telecommunications, and healthcare applications. Analysis of his publication trends reveals a strong focus on practical AI implementations, with increasing emphasis on edge computing solutions, quantum applications, and energy-efficient models. His recent work demonstrates progression from foundational machine learning techniques toward specialized applications in scientific computing and real-time systems. Dr. Tharmakulasingam holds a Doctor of Philosophy from UNSW Sydney and a Bachelor of Science of Engineering from the University of Moratuwa in Sri Lanka. His academic journey reflects a strong foundation in both theoretical and applied engineering principles. As Research and Development Coordinator for the Signals, Information & Machine Intelligence lab, he oversees critical research infrastructure and collaborations. His work location in Room 447 of the EE&T Building (G17) places him at the heart of UNSW's engineering research ecosystem, with access to the Mark Wainwright Analytical Centre's extensive facilities.

Matthew John M. KraneView profile

Professor

• Materials Process Modeling
• Solidification of Metal Alloys
• Thermal Processing
• +3 more

Dr. Matthew John M. Krane is a Professor in the Department of Materials Engineering at Purdue University and a member of the Purdue Center for Metal Casting Research. His work focuses on the design, development, and modeling of materials processes , particularly solidification and thermal processing of metal alloys, with strong emphasis on defect prevention and uncertainty quantification in numerical models. Current projects include grain-refined particle transport in DC casting , exergy optimization in copper smelting , and boron segregation in continuous casting . Past research includes through-process modeling of Al alloys , microsegregation studies , and laser hardening techniques . Scientific awards include an invited keynote lecture at the 2015 International Symposium on Liquid Metal Processing and Casting and multiple invited papers in high-impact journals.