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Viviana AcquavivaView profile

Professor

• Astrophysics
• Climate Science
• Machine Learning
• +5 more

Viviana Acquaviva is a Professor in the Department of Physics at the City University of New York's New York City College of Technology (City Tech), specializing in the application of artificial intelligence to astrophysics and climate science research. Education Ph.D. from SISSA/ISAS, Trieste, Italy Research Interests Her work bridges machine learning and physical sciences , focusing on climate modeling and galaxy evolution through advanced computational techniques. She develops Bayesian inference frameworks for parameter estimation, model discovery, and causal structure learning while emphasizing ethical AI principles including fairness, responsibility, and explainability in scientific contexts. Her research integrates uncertainty quantification and representation learning to extract knowledge from complex simulations. Scientific Awards American Astronomical Society Award for Sharing the Wonders of the Universe Grants and Advising Awarded a $1.78 million Simons Foundation grant in December 2024 to establish a climate data science research group, supporting graduate student training in AI-driven climate analysis. This initiative funds interdisciplinary research improving climate models and ocean carbon cycle understanding through machine learning applications. Labs and Teams Leads the Climate Data Science Research Group at City Tech, developing data-driven approaches to analyze climate systems and astrophysical phenomena using machine learning pipelines and simulation-based inference techniques.

• Internal Wave Dynamics
• Stratified Turbulence
• Oceanography
• +3 more

Louis Gostiaux is a CNRS Researcher (CR1) at the Laboratory of Fluid Mechanics and Acoustics (LMFA), affiliated with École Centrale de Lyon and the University of Lyon. His research focuses on internal wave dynamics and stratified turbulence in oceanic and laboratory settings, with a particular emphasis on the STRATIMIX project (ANR-funded since 2014) exploring stratified fluid dynamics through experimental approaches. His key research interests include geophysical fluid dynamics, turbulence modeling, and experimental fluid mechanics. Notable contributions involve studying wave-turbulence interactions, stratified flows, and the application of advanced measurement techniques such as particle image velocimetry (PIV). Recent publications highlight work on vorticity-scalar correlations in turbulence, Bayesian models for space weather phenomena, and experimental studies of helium plumes. His work bridges fundamental fluid mechanics with applications in environmental and astrophysical contexts.

Yubo SongView profile

Research Fellow

• Microgrid Engineering
• Power Electronics
• Power Electronic Converters
• +3 more

Yubo Song is a Postdoctoral Researcher at Aalborg University's Faculty of Engineering and Science, specializing in power electronics, microgrid systems, and reliability engineering. His work focuses on stability validation, control strategies, and the application of neuromorphic computing to power grids. His educational background includes: Ph.D. in Energy Technology from Aalborg University (awarded 2023) M.Sc. in Electrical Engineering from Shanghai Jiao Tong University (awarded 2019) B.Eng. in Electrical Engineering and Automation from Shanghai Jiao Tong University (awarded 2016) Dr. Song's research integrates microgrid engineering, power electronics reliability, and neuromorphic computing to address critical challenges in grid stability. His work pioneers Spike Talk technology for energy-efficient grid communication and develops hybrid modeling approaches for real-time power system simulation. Recent publications demonstrate expertise in damping control of grid-forming inverters, sensitivity analysis for reliability evaluation, and Bayesian risk assessment frameworks. His publication trends reveal a strategic shift toward neuromorphic solutions for power grids, with 2024-2025 works focusing on spike-based communication protocols and event-driven semantic systems. Earlier research centered on stability validation and reliability modeling for microgrids, establishing foundational frameworks still cited in current literature. Dr. Song has secured significant research funding including: Power Sentinel (Villum Foundation, 2024-2025): Developing cyber-physical safeguards for future power grids Reliability and Lifetime Analysis (2022-2025): Creating predictive models for power electronic components Stability and Reliability Validation (PhD project, 2020-2023): Establishing comprehensive microgrid assessment methodologies As Principal Investigator on multiple projects, he mentors early-career researchers while collaborating with industry partners on grid security solutions. His work with the Applied Power Electronic Systems group at AAU Energy focuses on translating theoretical advances into practical grid applications, particularly in converter reliability and neuromorphic grid communication.

• Physical Cosmology
• Theoretical Cosmology
• Cosmic Microwave Background (CMB) Anisotropies
• +3 more

Professor Anthony David Challinor is a cosmologist and astrophysicist at the University of Cambridge , affiliated with the Department of Applied Mathematics and Theoretical Physics (DAMTP) under the Faculty of Mathematics . He serves as Professor of Cosmology and Astrophysics (2017-present) and Deputy Director of both the Institute of Astronomy and the Kavli Institute for Cosmology, Cambridge . His research focuses on testing cosmological models using observational data, particularly through the study of temperature anisotropies and polarization in the Cosmic Microwave Background (CMB). Key Research Areas : CMB anisotropies, gravitational lensing, B-mode polarization, cosmological parameter estimation, and gravitational waves from the early universe. Scientific Affiliations : Core Team member of the Planck High-Frequency Instrument , member of the Simons Observatory , and Fellow of Queens' College since 1998. His recent publications emphasize advancements in CMB data analysis, cosmological simulations, and instrumental sensitivity for space missions like LiteBIRD. He has contributed to major collaborations including the Atacama Cosmology Telescope and Planck 2018 results. Awards include Royal Society University Research Fellowship (2002-2007) and PPARC Postdoctoral Research Fellowship (2000-2002).

Mark NewmanView profile

Professor

• Network Science
• Statistical Physics
• Complex Systems
• +3 more

Mark Newman is the Anatol Rapoport Distinguished University Professor of Physics at the University of Michigan, affiliated with the Department of Physics within the College of Literature, Science, and the Arts. His research focuses on network science, statistical physics, and complex systems, with notable contributions to community detection algorithms, network topology analysis, and applications in epidemiology, social dynamics, and computational modeling. Newman's work has been widely recognized, including his election to the Royal Society (2022) and the 2024 Leo P. Kadanoff Prize. His research spans theoretical frameworks and practical applications, such as developing methods for inferring network structures from noisy data, analyzing core-periphery hierarchies in social systems, and exploring the interplay between luck, skill, and competition in games. He is also renowned for his cartogram mapping techniques, featured in the Washington Post for visualizing election data through network-based spatial distortions. Newman's academic contributions include foundational textbooks like *Networks: An Introduction* (2010), which remain pivotal in teaching and research. His recent work addresses challenges in drug-disease network analysis, Bayesian mixture models, and improving mutual information measures for classification tasks. Awards and honors include the Royal Society Fellowship and the Kadanoff Prize, underscoring his impact on both theoretical and applied physics.

Camille AvestruzView profile

Assistant Professor

• Astrophysics
• Cosmology
• Galaxy Clusters
• +3 more

Camille Avestruz is an Assistant Professor of Physics at the University of Michigan, affiliated with the Leinweber Center for Theoretical Physics. She holds a Ph.D. from Yale University (2015) and previously worked as a postdoctoral fellow at the Kavli Institute for Cosmological Physics and the Enrico Fermi Institute at the University of Chicago. Her research focuses on computational cosmology and astrophysics, particularly on simulating galaxy cluster evolution and applying machine learning to analyze gravitational lensing data from large-scale surveys like LSST and DESI. She is also passionate about STEM outreach, teaching coding workshops for underrepresented groups through The Carpentries, and advocating for inclusive academic environments. A 2025 Cottrell Scholar, her work bridges theoretical models with observational data to advance understanding of cosmic structure formation and dark matter distribution. Her academic home is the Department of Physics within the College of Literature, Science, and the Arts. She leads the Avestruz Lab for Computational Cosmology and Astrophysics (ALCCA), collaborating with researchers in astronomy and statistics. Current projects include developing software tools for cluster mass modeling and exploring observable tracers of dark matter halo shapes. She emphasizes interdisciplinary approaches and prepares for future data volumes from next-generation telescopes.

• Applied Informatics in Engineering
• Biomedical Engineering
• Machine Learning
• +3 more

Tijana Geroski is an Assistant Professor at the Faculty of Engineering, University of Kragujevac, Serbia, affiliated with the Department of Applied Mechanics and Automatic Control. Her work integrates applied informatics in engineering with biomedical applications, focusing on machine learning, medical imaging, and computational modeling. She contributes to interdisciplinary research through platforms like SGABU, designed to bridge education and research in multiscale modeling. Her research spans topics such as predictive modeling for cardiovascular diseases, AI-driven diagnostic systems (e.g., lung and spine imaging analysis), and simulation tools for personalized medicine. Notable projects include developing FPGA-based expert systems for disc hernia diagnosis and optimizing drug delivery via computational fluid dynamics. She also explores the intersection of big data, materials science, and epidemiological modeling, particularly in the context of the SARS-CoV-2 pandemic. Her articles emphasize interdisciplinary collaboration, coupling experimental data with numerical simulations. Recent work highlights advancements in transfer learning for medical imaging, physics-driven deep learning for thermal modeling, and FPGA implementations of Bayesian diagnostic systems. These contributions underscore her commitment to advancing healthcare through computational and AI-based solutions. She collaborates actively with institutes like SARTIK (Steinbeis Advanced Risk Technologies Institute) to enhance biomaterial risk assessment and in-silico clinical trials. Her teaching and research roles reflect a dedication to both academic innovation and practical biomedical engineering applications.

Chanda Prescod-WeinsteinView profile

Associate Professor

• Astrophysics
• Theoretical Physics
• Dark Matter
• +3 more

Chanda Prescod-Weinstein is an Associate Professor in the Department of Physics & Astronomy at the University of New Hampshire. Her research focuses on theoretical physics, astrophysics, and dark matter, with particular expertise in neutron star physics, ultralight dark matter dynamics, and multifield inflationary cosmology. She holds a Ph.D. from the University of Waterloo, an M.S. from the University of California-Santa Cruz, and an A.B. from Harvard University. Her work bridges particle physics and astrophysics, addressing questions about dense matter equations of state via neutron star observations, axion-like dark matter simulations, and cosmological structure formation. Recent contributions include developing the NEoST Python package for neutron star studies and analyzing NICER mission data to constrain dense matter properties. Prescod-Weinstein teaches advanced courses such as Astrophysics I, Quantum Mechanics I, and doctoral research seminars. Her research incorporates numerical simulations, Bayesian statistical methods, and observational data from space-based telescopes like NICER and XMM-Newton.

• Computer Vision
• Machine Learning
• Signal Processing
• +6 more

Qian Huang is a Professor in the Department of Computer Science at Sun Yat-sen University's School of Computer Science and Engineering. With over 350 publications spanning from 1992 to 2025, Dr. Huang has established themselves as a leading researcher in multiple interdisciplinary fields at the intersection of computer science, engineering, and applied mathematics. Dr. Huang's research spans several critical domains in modern computing. Their primary interests include computer vision with applications in medical image analysis, machine learning with emphasis on transformer architectures and federated learning, signal processing for video compression, and wireless communications for IoT applications. Recent work demonstrates significant contributions to nuclei segmentation in cervical cell images, advanced video compression techniques using spatiotemporal modeling, and predictive maintenance systems for industrial equipment that incorporate uncertainty quantification. An analysis of Dr. Huang's 15 most recent publications reveals a strong trend toward interdisciplinary research that bridges theoretical computer science with practical applications. Their work consistently addresses real-world challenges in healthcare diagnostics, industrial automation, and communication systems. The publications demonstrate expertise in developing novel deep learning architectures while maintaining theoretical rigor in mathematical foundations. Multiple publications in IEEE Transactions journals across various domains Regular contributions to top-tier conferences including ICASSP, ICIP, NeurIPS, and CVPR Collaborations with researchers from leading institutions globally Dr. Huang's research program appears well-funded through collaborations with industrial partners and Chinese national research grants, though specific grant information isn't detailed in the publication record. Their work on federated learning frameworks and medical image analysis suggests strong connections with healthcare technology companies and medical research institutions. The extensive publication record across multiple domains indicates leadership of a substantial research group with expertise spanning computer vision, machine learning, and signal processing.

Paromita NathView profile

Assistant Professor

• Uncertainty Quantification
• Additive Manufacturing
• Bayesian Inference
• +4 more

Dr. Paromita Nath is an Assistant Professor in the Department of Mechanical Engineering at Rowan University's Henry M. Rowan College of Engineering. She holds a Ph.D. in Civil Engineering from Vanderbilt University and specializes in uncertainty quantification with applications in additive manufacturing and healthcare systems. Her research employs computational methods to develop probabilistic models for manufacturing process optimization. Her primary research integrates Bayesian inference and computational modeling to study process-structure-property relationships in additive manufacturing. Current projects focus on developing probabilistic digital twins for manufacturing systems and multi-fidelity modeling approaches. Research spans health diagnostics, sustainable materials development, and digital engineering solutions. Dr. Nath's publications demonstrate consistent focus on uncertainty management in manufacturing systems, with recent emphasis on surrogate modeling, probabilistic control systems, and multi-objective optimization. Her work frequently combines computational methods with practical manufacturing challenges.

Markus HeglandView profile

Professor

• Computational Mathematics
• High-Dimensional Problems
• Ill-Posed Problems
• +4 more

Markus Hegland is a Professor and Head of the Centre for Mathematics and its Applications (CMA) at the Australian National University (ANU). He holds a PhD from ETH Zurich (1988) and has been affiliated with ANU since 1992, focusing on High-Performance Computing (HPC) and numerical analysis. As a Hans Fischer Senior Fellow at TUM-IAS, his research emphasizes high-dimensional problems, ill-posed systems, and data mining applications. His work bridges computational mathematics with practical domains like systems biology and spectral enhancement. Research interests include sparse grid techniques, regularization methods, and algorithm development for HPC. Notable contributions include the OPTICOM method for stable sparse grid solutions and convergence theory for variable Hilbert scales regularization. He has led projects on fault-tolerant HPC algorithms and collaborated with Fujitsu on HPC applications. Publications span numerical analysis, bioinformatics, and computational physics. His work on the chemical master equation and gyrokinetics showcases interdisciplinary impact. Currently, he explores resilient grid-based solvers and machine learning integration with HPC frameworks. No awards are explicitly listed, but his senior fellowship underscores recognition in his field. Grants and collaborations include ARC-funded research in bioinformatics and HPC resilience. His work on digital twins and algorithm optimization reflects broader interests in advanced computational modeling. He is actively involved in teaching and supervising in computational mathematics and data science at ANU.

• Observational Cosmology
• Cosmic Expansion Rate
• Dark Energy and Dark Matter
• +7 more

Dr. Sherry H. Suyu is an Associate Professor at the Technical University of Munich (TUM) and a Max Planck Fellow at the Max Planck Institute for Astrophysics. Her research focuses on gravitational lensing applications for cosmology, particularly in measuring the cosmic expansion rate and studying dark energy/dark matter. She leads the HOLISMOKES program funded by an ERC Consolidator Grant. Her research group at TUM School of Natural Sciences Department of Physics investigates: Dark cosmos through gravitational lensing Supernova observations and Tidal Disruption Events Bayesian inference methods for astrophysical modeling Machine learning applications in lensed system analysis Scientific achievements include: 2021 Berkeley Prize from American Astronomical Society ERC Consolidator Grant for HOLISMOKES program Discovery of 330 high-quality lens candidates in Pan-STARRS survey She teaches courses including: Experimental Physics 1 (Winter 2024/5) Gravitational Lensing (Winter 2024/5) Introduction to Nuclear/Particle/Astrophysics (Summer 2025) The group includes 13 active members and 14 alumni across 7 institutions. Current work integrates HST and LSST imaging for next-generation lens discovery.

Suryakanta BiswalView profile

Research Fellow

• Structural Health Monitoring
• Uncertainty Modelling
• Finite Element Model Updating
• +4 more

Dr. Suryakanta Biswal is a Research Fellow at the University of Surrey's School of Sustainability, Civil and Environmental Engineering. Previously, he held roles including Assistant Professor at SSN College of Engineering, Chennai, and Research Fellow in India's BRNS/DAE project. He earned a PhD and MSc from the Indian Institute of Science, Bangalore, with a thesis focusing on uncertainty-based damage identification in concrete structures. His research emphasizes structural health monitoring (SHM), uncertainty quantification, finite element model updating, and long-term deformation prediction in concrete. Key work includes developing algorithms for bolted connection damage detection using virtual viscous dampers, optimal sensor placement strategies, and deep learning frameworks like SHMnet achieving human-level performance in condition assessment. Recent publications address offshore wind turbine foundations, seismic risks, and digitally fabricated timber columns. His projects include EPSRC-funded SHM initiatives and collaborative work on Tension Leg Platforms. Biswal's expertise spans MATLAB, ANSYS, and ABAQUS, aiding colleagues in computational modeling. Grants include Engineering & Physical Sciences Research Council (EPSRC) funding for SHM projects. He collaborates with organizations like the Association of Consulting Civil Engineers (India). Current research focuses on advancing AI-driven structural monitoring and sustainable infrastructure solutions.

Guillaume CrevecoeurView profile

Associate Professor

• Dynamical Systems
• Nonlinear Optimal Control
• Model-Based Optimization Algorithms
• +4 more

Guillaume Crevecoeur (born 1981) serves as Associate Professor at Ghent University's Faculty of Engineering and Architecture within the Department of Electrical Energy, Metals, Mechanical Constructions, and Systems. He leads research at the Electrical Energy Laboratory (EELAB) and is an affiliate member of Flanders Make, the strategic research center for manufacturing industry. His international collaborations include membership in scientific steering committees and prior visiting researcher positions at Technical University Ilmenau and Physikalische Technische Bundesanstalt in Berlin. Crevecoeur earned his MSc (2004) and PhD (2009) in Engineering Physics from Ghent University, followed by a postdoctoral fellowship with Research Foundation Flanders (FWO-Flanders). His research spans dynamical systems modeling, optimization, and control with foundational work in model-based algorithms, inverse problems, and nonlinear optimal control. Current strategic focus areas include machines and motion systems, particularly airborne wind energy applications and robotics. Analysis of his 2022-2025 publications reveals dominant trends in physics-informed machine learning for control systems , with significant contributions to reinforcement learning for underactuated systems, tactile sensing in robotics, and computational fluid dynamics for renewable energy. His work consistently integrates model-based optimization with data-driven approaches, especially in wind turbine control and aerial robotics co-design. Best Paper Award at 22nd International Conference on System Theory, Control and Computing (2018) As Principal Investigator, Crevecoeur directs research funded through FWO-Flanders and industry partnerships with Flanders Make. His lab focuses on mechatronic systems development, particularly in robotic manipulation and renewable energy conversion. Current projects include airborne wind energy system optimization, collision-tolerant robotics, and digital twin implementations for industrial machinery. He actively contributes to international scientific steering committees and serves as academic advisor for graduate students in electrical energy systems. Crevecoeur's research group operates within EELAB's infrastructure, utilizing high-performance computing resources for computational fluid dynamics simulations and experimental facilities for mechatronic system validation. The team collaborates with industry partners through Flanders Make on industrial automation challenges, with recent work focusing on real-time control for high-speed manufacturing processes and adaptive energy management systems.

Yasser RoudiView profile

Professor

• Theoretical Neuroscience
• Neural Networks
• Information Processing
• +3 more

Professor Yasser Roudi holds the position of Professor of Disordered Systems at King’s College London’s Department of Mathematics, within the Faculty of Natural, Mathematical & Engineering Sciences. He earned his PhD from SISSA (International School for Advanced Study) in 2005 and a physics degree from Sharif University of Technology in Tehran. Prior to joining King’s, he worked at prestigious institutions like the Kavli Institute for Systems Neuroscience in Norway and NORDITA. His research focuses on theoretical neuroscience, neural networks, and the physics of disordered systems, employing mathematical tools to model information processing in brains and machines. He has been recognized with awards including the Eric Kandel Young Neuroscientist Prize (2015) and membership in the Royal Norwegian Society of Sciences and Letters. His work explores topics such as grid cells’ toroidal topology, restricted Boltzmann machines, and maximum entropy models for cortical populations. Recent publications highlight advancements in understanding neural oscillations, stochastic processes, and reinforcement learning algorithms. Roudi leads the Disordered Systems research group at King’s, contributing to the study of complex systems and statistical mechanics. Education: PhD, SISSA (2005); BSc, Sharif University of Technology (Physics). Affiliations: King’s College London, Kavli Institute for Systems Neuroscience, NORDITA, UCL, and the Institute for Advanced Study. Roudi’s research bridges theoretical physics and neuroscience, addressing fundamental questions about neural information processing and system dynamics. His awards underscore his impactful contributions to the field, while his interdisciplinary approach continues to push boundaries in understanding complex biological and artificial systems.