Yasaman Amannejad is an Associate Professor in the Department of Mathematics and Computing at Mount Royal University (MRU), Faculty of Science. She holds a PhD in Software Engineering from the University of Calgary (2017) and an MSc/BSc in Computer Information Technology from Amirkabir University of Technology (2011/2008). Her research focuses on applying machine learning to healthcare diagnostics, performance analysis of cloud and edge computing systems, and addressing social challenges like domestic violence and homelessness. Her work has been funded by NSERC, Petro-Canada, and the New Frontiers in Research Fund. Education: PhD in Software Engineering, University of Calgary (2017) MSc in Computer Information Technology, Amirkabir University of Technology (2011) BSc in Computer Information Technology, Amirkabir University of Technology (2008) Research Interests: Machine Learning for Healthcare (e.g., tropical disease diagnosis, Multiple Myeloma cancer) Performance Optimization in Cloud/Edge Systems Resource-Constrained Device Learning (wearables, IoT) Social Impact Technologies (domestic violence detection, homelessness solutions) Awards & Grants: NSERC Discovery Grant (2020) New Frontiers in Research Fund-Exploration (2020) Petro-Canada Young Innovator Award (2019) Multiple Teaching Awards (2015-2016) Teaching & Industry: Incorporates industry experience into courses, emphasizing real-world applications Outstanding Teaching Performance Award (2016) Over 5 years of industry experience in software engineering Labs/Teams: Part of interdisciplinary teams at MRU's Faculty of Science Collaborates on NSERC-funded projects
Tapan Mukerji is a Professor (Research) at Stanford University with joint appointments in the Department of Energy Science & Engineering, the Department of Earth & Planetary Sciences, and the Department of Geophysics within the School of Earth Sciences. He co-directs the Stanford Center for Earth Resources Forecasting (SCERF), the Basin Processes and Subsurface Modeling (BPSM) consortium, and the Stanford Rocks and Geomaterials Project (SRGP), and previously co-directed the Stanford Rock Physics and Borehole Geophysics Project (SRB). His educational background includes: Ph.D. in Geophysics from Stanford University (1995) M.Sc.(Tech) in Geophysics from Banaras Hindu University, India (1989) B.Sc. in Physics from Banaras Hindu University, India (1986) Tapan Mukerji's research focuses on integrating rock physics, wave propagation physics, spatial data science, and machine learning to address challenges in remote sensing of subsurface systems, stochastic geomodeling, uncertainty quantification, and value of information analysis in Earth sciences. His work uses theoretical, computational, and statistical methods to discover fundamental relations between geophysical data and rock properties, quantify uncertainty in subsurface models, and address decision making under uncertainty. He is particularly interested in forging links between geosciences, engineering, and decision sciences, believing these interdisciplinary connections are critical for the future of energy resources research. His research has broad applications in hydrocarbon exploration, geothermal energy, carbon sequestration, and critical mineral exploration. His recent publications demonstrate a strong trend toward integrating advanced machine learning techniques with traditional geophysical methods. There's increasing focus on physics-informed neural networks, generative models for geological facies simulation, and uncertainty quantification in subsurface characterization. His work bridges the gap between theoretical rock physics and practical applications in energy resource development, with particular emphasis on making robust decisions under uncertainty. Professor Mukerji has received numerous scientific awards and recognitions: Karcher Award for Outstanding Young Geophysicist, Society of Exploration Geophysicists (2000) ENI Award 2014: New frontiers of Hydrocarbons - upstream, ENI - Italy (2014) Best paper, honorable mention, Society of Exploration Geophysicists (2020) Best paper, International Association of Mathematical Geosciences (2010) Multiple best paper awards from various geophysical societies Invited keynote speaker at numerous international conferences Haider Fellowship and Green Fellowship from Stanford University Professor Mukerji actively advises and mentors graduate students, serving as Doctoral Dissertation Advisor for Jaehong Chung and Jiayuan Huang, Doctoral Dissertation Reader for several students, and Postdoctoral Faculty Sponsor for Qi Hu and Suihong Song. His research has been supported by multiple industrial consortia including the Stanford Rock Physics and Borehole Geophysics Project (SRB), Stanford Center for Earth Resources Forecasting (SCERF), Basin Processes and Subsurface Modeling (BPSM), Stanford Rocks and Geomaterials Project (SRGP), and Smart Fields Consortium (SFC). He has also received funding from the Department of Energy and various fellowship programs throughout his career. Professor Mukerji co-directs several major research groups at Stanford including the Stanford Center for Earth Resources Forecasting (SCERF), the Basin Processes and Subsurface Modeling (BPSM) consortium, and the Stanford Rocks and Geomaterials Project (SRGP). These groups bring together faculty, researchers, and industry partners to tackle complex problems in subsurface characterization, reservoir modeling, and energy resource development. His labs focus on developing computational methods for integrating geophysical data with rock physics models, creating advanced uncertainty quantification frameworks, and building decision support tools for subsurface resource management.
Professor Martin Gallagher is a leading atmospheric scientist at The University of Manchester's Earth and Environmental Sciences department. He holds a BSc in Physics (University of Edinburgh, 1982) and a PhD in Modelling & Observations of Airflow over Hills (UMIST, 1986). His research focuses on interdisciplinary atmospheric science, including surface-atmosphere trace gas exchange, cloud microphysics, bioaerosol detection, and airborne instrumentation. He leads projects like the BEACHON Programme and contributes to global initiatives such as IAGOS-ERI and the Facility for Airborne Atmospheric Measurements. Key research areas include cloud-aerosol interactions, ice nucleation in convective systems, and real-time bioaerosol monitoring using UV-LIF spectrometers. His work addresses UN SDGs related to climate action and sustainable cities. Professor Gallagher has coordinated major field campaigns like DCMEX and Fatima-GB, advancing understanding of cloud dynamics in tropical and marine environments. He collaborates internationally, contributing to over 335 research outputs and serving on editorial and advisory boards. Recent studies explore nocturnal pollen fragmentation in urban environments, marine fog microphysics, and ice production mechanisms in deep convective clouds. His projects integrate machine learning for bioaerosol analysis and address societal impacts like aviation safety during ash crises. Grants include the £2.8M Climate and Weather Impacts on Society initiative, focusing on extreme weather and health outcomes.
Francesca Zaffora Blando serves as an Assistant Professor in the Department of Philosophy at Carnegie Mellon University's Dietrich College of Humanities and Social Sciences. Her academic profile bridges rigorous formal methods with foundational questions in epistemology and scientific methodology. Her educational trajectory includes: Ph.D. in Philosophy and Symbolic Systems, Stanford University (2020) M.Sc. in Logic, Institute for Logic, Language and Computation, University of Amsterdam M.A. in Philosophy, University of Edinburgh Zaffora Blando's research centers on algorithmic randomness —a computability-theoretic framework for patternless sequences—and its implications for inductive learning and Bayesian inference . She investigates how algorithmically random data streams constrain the learning performance of computationally bounded agents, revealing deep connections between randomness, convergence to truth, and probabilistic reasoning. Her work spans modal logic applications in dynamic epistemic scenarios and historical analyses of probability theory from von Mises to contemporary formalizations. Her publication record (2015-2025) demonstrates sustained innovation at the intersection of computability and epistemology, with increasing focus on Schnorr randomness, Bayesian consistency, and learning-theoretic characterizations. Key themes include the role of randomness in merging opinions, disintegration of measures, and historical evolution of randomness concepts. She actively contributes to the Center for Formal Epistemology through event organization including the Pittsburgh Formal Epistemology Workshop (PFEW), the September 2024 Workshop on Chance, Credence, Computation, and Progic 2025—the Twelfth Workshop on Combining Probability and Logic with special focus on theoretical learning approaches.
Dr. Chloé Arson is a Professor in the Department of Earth and Atmospheric Sciences at Cornell University and an adjunct faculty member at Georgia Tech’s School of Civil and Environmental Engineering. She holds a Ph.D. in geomechanics from École Nationale des Ponts et Chaussées (2009) and has held academic roles at Texas A&M (2009–2012) and Georgia Tech (2012–2023) before joining Cornell in 2023. Research: Her work focuses on damage and healing in rock mechanics, AI-driven subsurface exploration, and bio-inspired geotechnical systems. Key areas include computational modeling of porous media, geothermal energy systems, and climate change mitigation through poromechanics. Her lab develops tools like the Burrowing Robot with Integrated Sensor System (BRISS) and investigates slime mold network dynamics for infrastructure adaptation. Teaching: Teaches mechanics-focused courses at Cornell and Georgia Tech, including 'Modern Structures,' 'Theoretical Geomechanics,' and 'Finite Element Method for Porous Media.' Awards: 2023 Susan G. and Christopher D. Pappas Professorship 2021 NSF BRITE Award 2016 NSF CAREER Award Service: Editorial roles in Scientific Reports and Open Geomechanics , leadership in ASCE committees, and director of the CEE Gateways to France program fostering Franco-American collaborations. Labs/Teams: Leads the Arson Lab at Cornell, focusing on computational geomechanics, AI integration, and bio-inspired engineering solutions.
Dr. Charith Abhayaratne is a Senior Lecturer and EEE Foundation Year Tutor at the School of Electrical and Electronic Engineering, University of Sheffield. He leads the Communications Research Group and serves as the accreditation team lead for the school. With qualifications including a PhD from the University of Bath and a B.E. from the University of Adelaide, his research focuses on signal processing, machine learning, multimedia security, and video coding. His work explores blockchain for content protection, visual salience in robotics, human activity recognition, and advanced video coding techniques (HDR, UHD, 360° video). His research has been funded by Innovate UK, EPSRC, and industry partners. Education: B.E. (Electrical and Electronic Engineering), The University of Adelaide, Australia (1998) PhD (Electronic and Electrical Engineering), University of Bath, UK (2002) PGCertHE (Higher Education), University of Sheffield (2008) Fellow of the Higher Education Academy (FHEA), Member of the Institution of Engineering and Technology (MIET), Member of IEEE (MIEEE) Research Interests: His work spans multimedia security (data hiding, blockchain), computer vision (visual salience, object recognition), and video coding (HDR/UHD). Current projects include robotic vision applications, assisted living through activity recognition, and international standards development (JPEG/MPEG). He has contributed to scalable video standards and serves on technical committees for IEEE, EURASIP, and APSIPA. Awards & Service: Recipient of the Alain Bensoussan Fellowship (ERCIM, 2002) Associate Editor for IEEE Transactions on Image Processing, IEEE Access, and Elsevier JISA Member of EPSRC Peer Review College and British Standards Institution (BSI) Grants & Labs: Active grants from Innovate UK and EPSRC support projects in multimedia security and video coding. His lab leads interdisciplinary work in AI-driven visual analytics and secure media distribution frameworks.
Ingrid Ullmann is a Researcher and Research Group Leader (Wave-Based Sensing Techniques) at the Institute of Microwaves and Photonics (LHFT) within the Department of Electrical-Electronic-Communication Engineering at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). She holds a Dr.-Ing. (PhD) from FAU, awarded in 2021 for her thesis on 'Novel Concepts for Radar Imaging in Heterogeneous Media'. Her academic journey includes a 2016 M.Sc. in Electrical Engineering from the same institution. Her research focuses on radar imaging (millimeter-waves, terahertz), ultrasound imaging, non-destructive testing, and biomedical engineering applications. She leads two laboratories: the Radar Imaging Laboratory and the Medical Imaging Laboratory, advancing innovations in security screening, medical diagnostics, and automotive radar systems. Her work integrates machine learning with radar techniques to enhance imaging resolution and analysis efficiency. Ullmann’s publications (over 50 entries from 2017–2025) emphasize radar-based human motion tracking, material reconstruction, and UAV applications for environmental monitoring. Key trends include: 1) fusion of radar and ultrasound for enhanced visibility, 2) machine learning-driven signal processing in biomedical contexts, and 3) development of compact radar systems for space debris tracking and historical document analysis. She actively supervises student theses in radar imaging, medical engineering, and security systems. While no explicit awards are listed, her contributions to IEEE journals and conference proceedings highlight her technical leadership. Collaborations with Erlangen University Hospital and industry partners (e.g., Siemens) underscore her interdisciplinary reach.
Benjamin Berkels is an apl. Professor (equivalent to Associate Professor) at the Institute for Geometry and Practical Mathematics (IGPM) within the Faculty of Mathematics, Computer Science and Natural Sciences at RWTH Aachen University, Germany. His office is located at Rogowski, Raum 124, Schinkelstraße 2, 52062 Aachen. He has held his current position since May 2025 and also serves as Akademischer Rat at IGPM since October 2024. Previously, he was a Juniorprofessor for Mathematical Image and Signal Processing and Junior Research Group Leader at AICES, RWTH Aachen from 2013 to 2024, with several interim professorships at RWTH Aachen and the University of Lübeck. Dr. Berkels received his educational foundation with a Dipl.-Math. from the University of Duisburg-Essen in 2005, followed by a Dr. rer. nat in Mathematics from the University of Bonn in 2010, and completed his Habilitation-equivalent with a positive intermediate evaluation as Juniorprofessor from RWTH Aachen in 2016. His professional journey includes postdoctoral positions at the University of Bonn and the University of South Carolina, establishing his expertise in mathematical image analysis before returning to Germany for his faculty positions. His research focuses on the intersection of mathematical theory and practical image analysis applications, with core interests in Image Processing, Computer Vision, Variational Methods, Joint Methods, Registration, and Segmentation. Berkels' work demonstrates exceptional interdisciplinary reach, applying advanced mathematical techniques to solve complex problems in materials science, microscopy, medical imaging, and environmental monitoring. His recent publications reveal a strategic expansion into machine learning applications while maintaining strong foundations in variational methods and mathematical image analysis. Analyzing his 15 most recent publications reveals a clear research trajectory emphasizing atomic-scale image analysis for materials characterization. Approximately 70% of his recent work focuses on applying sophisticated image processing techniques to electron microscopy data for materials science applications, particularly in analyzing grain boundaries, phase transformations, and defect structures. The remaining publications show increasing integration of machine learning approaches, especially deep learning and GANs, for industrial and scientific image analysis problems. This demonstrates his ability to bridge fundamental mathematical research with practical applications across multiple scientific domains. Dr. Berkels maintains an exceptionally active research profile with consistent publication output across high-impact journals in both mathematics and materials science. His extensive collaboration network spans multiple continents and disciplines, with frequent co-authorship with materials scientists, microscopists, and computer vision researchers. While specific grant information isn't provided in the text, his sustained research output and leadership of a junior research group suggest successful grant acquisition throughout his career. His work at IGPM positions him at the forefront of mathematical approaches to image analysis with significant impact on materials characterization techniques.
Chao Li is a Lecturer of Chinese at the Georgia Institute of Technology's School of Modern Languages, part of the Ivan Allen College of Liberal Arts. He has been with Georgia Tech since 2001, focusing on teaching Chinese language at all proficiency levels and co-directing the Chinese Language for Business and Technology (LBAT) Program since 2006. His expertise includes developing online Chinese language courses, creating multimedia content, and compiling grammatical notes for effective language learning materials. Chao Li holds a Master of Arts in International Relations from Beijing Institute of Foreign Affairs and a Bachelor of Arts in Economics and Chinese from Yunnan University, China. He is a key figure in advancing online Chinese language education at Georgia Tech, emphasizing the integration of technology into language pedagogy. His work bridges language instruction with business and technology contexts, reflecting his commitment to practical, industry-relevant language training. His professional contributions include pioneering efforts in online course development, particularly in designing interactive and adaptive learning environments. While no formal awards are listed, his role in shaping Georgia Tech's Chinese language program highlights his significant impact on language education innovation. Chao Li’s teaching philosophy centers on student engagement through culturally immersive and technologically enhanced methodologies.
Scott Findlay is an Associate Professor in the School of Physics and Astronomy at Monash University. He holds an ARC Future Fellowship (2020–present) and has expertise in theoretical physics and advanced electron microscopy techniques. His research focuses on atomic resolution imaging via scanning transmission electron microscopy (STEM), including novel detector geometries, compositional analysis of nanostructures, and quantitative structure determination. He has led multiple ARC-funded projects, including 'UltraTEM' and 'Nanoscale field mapping in functional materials.' Education: PhD in Physics (Theoretical Aspects of Scanning Transmission Electron Microscopy), The University of Melbourne (2005) BSc (Hons) in Physics, The University of Melbourne (2001) Research Interests: Developing theoretical models and numerical simulations to enhance STEM capabilities, particularly in dynamic scattering analysis. Current projects include optimizing segmented/pixel detectors for imaging, quantifying material composition at the nanoscale, and analyzing thick nanostructures. Articles Trends: Recent work emphasizes 4D-STEM advancements, including denoising algorithms, scattering matrix reconstruction, and atom-counting techniques. His research bridges theory and experiment, addressing challenges in dynamical scattering and phase retrieval. Awards: AMMS Microscopy and Microanalysis Award (2018) Exceptional Educational Service Award (2021) Outstanding Reviewer for Microscopy and Microanalysis (2017) Advising & Grants: Supervises PhD students and has secured over AUD 10 million in ARC grants. Coordinates Monash's Level 2 Physics curriculum and chairs the School's Education Committee. Labs/Teams: Collaborates with institutions like the University of Tokyo and the University of Melbourne on projects such as magnetic field mapping and electron ptychography.
Dr. Jan Dettmer is an Associate Professor in the Department of Earth, Energy, and Environment at the University of Calgary's Faculty of Science. His research focuses on quantitative analysis of Earth structures through geophysical data inversion, specializing in Bayesian methods for uncertainty quantification. His work spans seismology, acoustical oceanography, and tsunami hazard prediction, with applications ranging from shallow seabed characterization to deep mantle structures. Research interests include: Probabilistic inversion methods for earthquake source parameters and earth structure Wave propagation modeling in complex media Computational algorithm development for large-scale inverse problems Integration of supercomputing (CPU/GPU clusters) in geophysical analysis Recent publications demonstrate strong focus on geophysical inversion techniques, computational methods, and applications to energy and environmental challenges. Awarded the Faculty of Science Research Award for early career excellence (2019).
Lasse Løvstakken is a Professor at the Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU). His research focuses on medical ultrasound imaging, particularly in developing advanced techniques for blood flow analysis and cardiac imaging. Key projects include 3D ultrasound imaging of blood flow in pediatric and adult hearts, leveraging artificial intelligence and deep learning for automated measurements and diagnostic improvements. His work emphasizes the integration of AI into echocardiography, such as real-time guidance systems and automated strain analysis, to enhance reproducibility and reduce variability. Collaborative efforts span clinical validation of new imaging modalities and interdisciplinary applications, such as seabed classification using deep learning. He leads projects funded by institutions like NTNU and collaborates with international teams on innovations in cardiac mechanics, valve timing, and hemodynamic modeling. Løvstakken’s research also addresses translational challenges, including telemedicine applications of handheld ultrasound devices and automated quantification tools for clinical use. His contributions bridge biomedical engineering and clinical cardiology, aiming to improve diagnostic accuracy and patient care through cutting-edge imaging technologies.
Mikhail Gilman is an Associate Research Professor in the Department of Mathematics at North Carolina State University (NCSU), based in SAS Hall 3210. He serves as a core faculty member in NCSU's Radar Imaging research group, focusing on advanced radar technologies and their mathematical foundations. Dr. Gilman earned his PhD from the State Institute of Physical and Technical Problems in Russia in 1998. His academic journey spans interdisciplinary research connecting applied mathematics, electromagnetics, and remote sensing. His research centers on radar imaging, particularly synthetic aperture radar (SAR) systems. Key interests include mitigating phase distortions from turbulent media propagation (especially ionospheric effects), developing novel radar target models, and innovating change detection paradigms in SAR imagery. He integrates analytical methods, computational simulations, and machine learning to solve complex problems in transionospheric SAR imaging, with significant contributions to autofocus algorithms and ionospheric distortion correction. His work bridges theoretical mathematics and practical remote sensing applications. Recent publications reveal a concentrated focus on transionospheric SAR challenges, with 10 of the last 15 papers addressing ionospheric effects, autofocus techniques, and machine learning applications. His 2017 book Transionospheric Synthetic Aperture Imaging (Birkhäuser) established foundational frameworks, receiving positive reviews in SIAM Review and AMS MathSciNet. Dr. Gilman actively leads the Radar Imaging group at NCSU, which develops analytical tools and machine learning approaches for radar technology advancement. The group's work, detailed in their research flyer, emphasizes practical solutions for real-world remote sensing challenges.
Dr Graeme MacGilchrist is a UKRI Future Leaders Fellow at the University of St Andrews' School of Earth & Environmental Sciences. His research focuses on oceanography and climate science, particularly the role of ocean circulation in transporting climate-relevant tracers like heat and carbon dioxide. He employs numerical simulations, Lagrangian trajectory analysis, and observational data to study processes affecting marine environments and global climate. Education: PhD in Oceanography (University of Oxford), MSc in Oceanography (University of Southampton), MMath (University of Newcastle) Research interests include biogeochemical cycles, atmosphere-ocean interactions, and the impact of ocean dynamics on climate. Recent work highlights Southern Ocean ventilation patterns, North Atlantic deep-water variability, and Antarctic ice shelf dynamics. He leads projects funded by UK Research & Innovation and collaborates on global ocean modeling initiatives like the NEMO framework. Publications span topics such as ocean-atmosphere feedbacks, meltwater impacts on sea level, and predictive modeling of nutrient dynamics. His tools include advanced numerical models and trajectory analysis, with contributions to open-source software for oceanographic research.
Stefano Discetti is a Full Professor in the Department of Aerospace Engineering at Universidad Carlos III de Madrid, leading the Aerospace Engineering Research Group. His work bridges experimental fluid dynamics, machine learning, and flow control. Key research themes include turbulence characterization, heat transfer enhancement, and advanced measurement techniques like PIV/PTV. He has developed data-driven methods for flow estimation, sensor placement optimization, and physics-informed neural networks. Research Trends from his publications show a focus on: Machine learning for turbulent flow reconstruction (GANs, CNNs, KNN) Non-intrusive sensing from wall measurements Manifold learning and reduced-order modeling Heat transfer control via plasma actuators and passive structures Time-resolved diagnostics using hybrid experimental/numerical approaches Projects include principal roles in EU and national grants like NEXTFLOW (2021-2026) and EXCALIBUR (2023-2026), with industry collaborations at Airbus and TU Delft.
