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Antoine LevittView profile

Assistant Professor

• Quantum Chemistry
• Materials Science
• Numerical Analysis
• +3 more

Antoine Levitt is an Assistant Professor (CPJ) at the Laboratoire de Mathématiques d'Orsay, Université Paris-Saclay. Previously, he was a researcher at Inria Paris in the MATHERIALS project-team, affiliated with CERMICS at École des Ponts. His research focuses on numerical methods for quantum chemistry and materials science, including density functional theory (DFT), Wannier functions, and electronic structure calculations. He has contributed to theoretical advances in numerical analysis, such as error bounds for DFT and preconditioning techniques for eigenvalue problems. Levitt’s work spans computational physics and applied mathematics, with applications to quantum dynamics, response properties, and defect systems. He has developed open-source software tools like DFTK.jl and Wannierization algorithms . His teaching includes courses on scientific computing, functional analysis, and quantum mechanics at both undergraduate and graduate levels. Key research themes include numerical stability in DFT, analysis of linear response theory, and efficient implementations of conjugate gradient methods. His recent work explores machine learning applications in canonical sampling and mathematical foundations of Density Matrix Embedding Theory.

Shuang ZhaoView profile

Associate Professor

• Physics-Based Computer Graphics
• Scientific Computing
• Inverse Rendering
• +7 more

Shuang Zhao is an Associate Professor of Computer Science at UC Irvine, co-directing the Interactive Graphics & Visualization Lab (iGravi). He holds a PhD from Cornell University (2014) and a postdoc at MIT. His research focuses on physics-based computer graphics, scientific computing, and inverse rendering, with applications in material science, biomedicine, and robotics. Zhao's NSF CAREER Award (2023) supports his work on Physics-Based Differentiable and Inverse Rendering, enabling automated 3D reconstruction and medical imaging advancements. Education: Ph.D., Computer Science, Cornell University (2014); Postdoc at MIT. Research areas: Inverse rendering, differentiable rendering, Monte Carlo methods, and light transport modeling. Notable projects include Meta's digital twin creation for the Metaverse, non-line-of-sight imaging, and medical imaging applications. His lab develops algorithms for efficient inverse solutions and collaborates with industry (Meta, Nvidia, Adobe). Teaching includes advanced graphics courses like CS 114 and ICS 162. Awards include ACM programming contest championships and Best Paper recognitions. Students supervised include Cheng Zhang (Facebook Fellow), Kai Yan, and Zahra Montazeri. Hobbies include photography and Japanese anime/video games.

Johan Sokrates WindView profile

Research Fellow

• Deep Learning
• Neural Networks
• Overparameterized Machine Learning
• +3 more

Johan Sokrates Wind is a Research Fellow at the University of Oslo's Department of Mathematics, specializing in Differential Equations and Computational Mathematics. His primary affiliation is with the Faculty of Mathematics and Natural Sciences. He holds a Master's in Industrial Mathematics from the Norwegian University of Science and Technology (NTNU) and began his PhD in August 2021. Wind's research focuses on deep learning, neural networks, and overparameterized machine learning systems. He has explored topics such as the Neural Tangent Kernel, Deep Linear Networks, and implicit biases in optimization algorithms. His work bridges theoretical analysis with practical implementations, as evidenced by his blog The Good Minima , where he publishes technical insights on neural network behavior and training dynamics. Notably, he has contributed to projects like real-time visual odometry on smartphones during his part-time role at Arm Ltd. Wind is an active participant in competitive programming and Kaggle competitions, showcasing his problem-solving skills and algorithmic expertise. His research emphasizes analytically tractable models and the mathematical foundations of modern AI systems. Recent investigations include the RWKV language model architecture, efficient CIFAR-10 classification, and the role of initialization and learning rates in SGD's implicit bias. Wind’s publications highlight interdisciplinary approaches, combining elements of optimization theory, computational mathematics, and applied machine learning. He maintains an active blog with detailed technical posts, demonstrating a commitment to open science and knowledge-sharing. His academic journey reflects a balance between theoretical rigor and practical innovation, positioning him as a rising researcher in computational and mathematical aspects of deep learning.

Josie CarberryView profile

Lecturer

• Biomedical Engineering
• Cardiovascular Engineering
• Fluid Dynamics
• +7 more

Josie Carberry is a Lecturer in the Department of Mechanical & Aerospace Engineering at Monash University's Faculty of Engineering. Her research focuses on fluid dynamics in cardiovascular systems, particularly how hemodynamic forces influence thrombosis, coagulation, and medical device performance. She leads projects involving lab-on-a-chip technologies, computational modeling, and patient-specific flow simulations to improve cardiovascular implant design and disease understanding. Key areas of contribution include development of microfluidic tools for thrombus investigation, optimization of ventricular assist devices (VADs), and ECMO cannula design. She has collaborated on sustainable development goals (SDGs) related to health innovation and personalized medicine. Notable grants include an NHMRC Project Grant (APP1126536, 2017-2019) supporting thrombosis research. Her work bridges engineering and medicine, addressing challenges in hemodynamic modeling, biomaterials, and clinical translation. Recent studies focus on shear-responsive drug delivery systems and in silico prediction of thrombosis risks in mechanical circulatory support devices. Research Projects: Chief Investigator (2021-2022): 'Development of a 'COVID recovery' project' Associate Investigator (2024-2025): 'Bringing personalized aneurysm rupture predictions to the patient' Awards: NHMRC Project Grant APP1126536 (2017-2019) Labs/Teams: Active in cardiovascular bioengineering and medical device innovation groups at Monash

• Soil Science
• Environmental Toxicology
• Biogeochemistry
• +4 more

Tom Sizmur is a Professor at the University of Reading, specializing in soil science, environmental toxicology, and biogeochemical processes. His research focuses on soil microbial dynamics, biochar applications for contaminant remediation, mercury cycling, and the ecological impacts of earthworms on heavy metal mobility. Key Research Areas: Biochar for soil recovery, nitrogen and carbon cycling, earthworm-plant-soil interactions, and floodplain/marine ecosystem health. Recent Publications (2022–2025): Sizmur's work examines how biochar and organic amendments immobilize heavy metals in contaminated soils, the role of soil microbial biomass in nitrogen retention from anaerobic digestate, and the influence of climate factors like temperature oscillations on soil respiration and microbial activity. He investigates microplastic fibers as mercury vectors and evaluates sustainable farming practices such as Zero Budget Natural Farming in India. Collaborative Efforts: Sizmur collaborates with international researchers on topics spanning cocoa agroecosystems, heathland restoration, and pollutant source diagnostics in marine sediments. His studies often integrate multidisciplinary approaches to address soil fertility, contaminant management, and ecosystem sustainability.

Robert Lewis-SwanView profile

Professor

• Atomic, Molecular, and Optical Physics
• Quantum Many-Body Systems
• Quantum Information Science
• +3 more

Robert Lewis-Swan is the Tedd S. Webb Presidential Professor in the Homer L. Dodge Department of Physics and Astronomy at the University of Oklahoma. His research focuses on non-equilibrium many-body physics in atomic, molecular, and optical (AMO) systems, with applications to quantum technologies. He explores quantum phenomena like entanglement and coherence in diverse systems including neutral atoms, polar molecules, and trapped-ion crystals. His work combines analytical and numerical methods to study complex interactions and quantum control. Education: B.Sc., University of Queensland, 2011 Ph.D., University of Queensland, 2015 Research interests include quantum simulation, cavity QED, spinor Bose-Einstein condensates, and quantum-enhanced sensing. Recent work highlights include studies on dynamical phase transitions, quantum chaos, and precision metrology using trapped ions and Rydberg systems. Selected Awards: Tedd S. Webb Presidential Professorship (2025) His lab develops tools for quantum state preparation, noise mitigation, and quantum control, with applications in next-generation quantum devices. Collaborative efforts span theoretical modeling and experimental implementations in AMO systems.

Courtney KlepacView profile

Researcher

• Marine Biology
• Coral Resilience
• Climate Change Adaptation
• +3 more

Courtney Klepac is a Researcher at Stanford University's Hopkins Marine Station, focusing on coral resilience under climate change. She holds a B.S. in Marine Biology from Texas A&M University Galveston, an M.S. in Biology from Florida Atlantic University (Voss Lab), and a Ph.D. in Ecology and Evolution from Old Dominion University (Barshis Lab). Her postdoctoral work at More Marine Laboratory examined coral nursery resilience under stressors like temperature and acidification. At Stanford, she collaborates on the NSF-funded 'Super Reefs' project, mapping coral heat resistance across Pacific reefs and training local researchers. Her work integrates environmental factors, physiological plasticity, and genetic adaptation to predict coral responses to climate change and identify resilient strains for conservation. Research interests emphasize symbiotic associations, coral stress tolerance, and restoration strategies. She employs tools like the Coral Bleaching Automated Stress System (CBASS) and digital twin technology. Courtney actively engages in community science, particularly mentoring students in coral experiments. Beyond academia, she enjoys outdoor activities with her family. Education: B.S., Marine Biology, Texas A&M University Galveston M.S., Biology, Florida Atlantic University Ph.D., Ecology and Evolution, Old Dominion University Her NSF-funded projects underscore her commitment to bridging science and conservation, aiming to provide actionable insights for coral reef management.

Alexander TereninView profile

Research Professor

• Machine Learning
• Artificial Intelligence
• Decision Theory
• +7 more

Alexander Terenin is an Assistant Research Professor at Cornell University , specializing in machine learning and artificial intelligence. His work focuses on decision-making under uncertainty, Bayesian optimization, and Gaussian processes, particularly in non-Euclidean spaces. He has contributed to geometric learning, scalable Gaussian process methods, and applications in robotics, plasma science, and legal AI. His research integrates theoretical foundations with practical algorithms, emphasizing principles like the Gittins Index for optimal decision-making. Notable projects include the GeometricKernels software package for manifold learning and the Cambridge Law Corpus for legal AI. His work bridges statistics, geometry, and computer science to address challenges in autonomous systems, energy optimization, and data-driven decision-making. Recent Talks and Contributions: An Adversarial Analysis of Thompson Sampling (INFORMS Applied Probability Society 2025) Cost-aware Bayesian Optimization (NeurIPS 2024) Stochastic Poisson Surface Reconstruction (ICML 2025) Key Research Themes: Bayesian Optimization for multi-objective problems (e.g., plasma-driven energy systems) Geometric Gaussian Processes for robotics and 3D modeling Statistical guarantees for Gaussian processes on manifolds Grants and Collaborations: His work involves interdisciplinary projects with institutions like Carnegie Mellon University, ETH Zürich, and the University of Cambridge, reflecting a global network in AI and statistical learning.

Ali GolbafView profile

Lecturer

• Artificial Intelligence
• Clinical Management of Cancers
• Medical Imaging
• +6 more

Ali Golbaf is an Associate Lecturer at the School of Engineering, Computing and Mathematics within the Faculty of Science and Engineering at the University of Plymouth. He is currently pursuing a PhD in Computer Science, funded by UK Research and Innovation (UKRI). Prior to this, he earned a BSc in Mechanical Engineering from Shahrood University of Technology (2014) and an MSc in Mechanical Engineering from the University of Guilan (2017), both in Iran. His research focuses on leveraging Artificial Intelligence to develop non-invasive, objective biomarkers for clinical management of cancers, particularly brain tumors. His interdisciplinary work bridges mechanical engineering principles—such as finite element analysis, fluid-structure interaction, and scaling laws—with AI-driven healthcare solutions. Notable contributions include studies on turbulent boundary layers, dynamic systems scaling, and material compaction processes. Ali’s publications span topics ranging from AI applications in oncology to mechanical engineering fundamentals, reflecting his dual expertise. He has actively collaborated on experimental and numerical investigations across diverse fields, including impact mechanics, powder metallurgy, and thermal dynamics. His PhD research aims to integrate machine learning with clinical diagnostics, addressing unmet needs in precision oncology. While no scientific awards are explicitly listed, his work contributes to the UN Sustainable Development Goals (SDGs) related to health and innovation. Ali is funded by UKRI for his doctoral studies and currently has no listed advisees or grants.

Peter M. HoffmannView profile

Adjunct Professor

• Experimental soft condensed matter physics
• Biophysics
• Interatomic and intermolecular forces
• +7 more

Peter M. Hoffmann is an Adjunct Professor in the Department of Physics and Astronomy at Wayne State University's College of Liberal Arts and Sciences, with secondary appointments in the College of Engineering and Biomedical Physics program. His research focuses on molecular machines, nanoconfined liquids, and biophysics, employing advanced atomic force microscopy (AFM) techniques. PhD in Materials Science, Johns Hopkins University M.S. in Physics, Southern Illinois University Vordiplom (BS equivalent) in Physics & Mathematics, Technische Universität Clausthal Hoffmann's experimental work investigates how molecular motors extract order from chaos through Brownian ratcheting mechanisms. His team has developed cutting-edge AFM methodologies to study nanomechanical properties of confined water films, ionic effects on liquid ordering, and biomolecular interactions at the single-molecule level. Recent publications focus on graphene oxide fluid dynamics, mechanochemical solvation effects, and advanced AFM data analysis techniques. His research bridges soft matter physics, biophysics, and materials science through interdisciplinary approaches. Scientific recognition includes: National Science Foundation CAREER Award Wayne State Presidential Excellence in Teaching Award Richard Barber Faculty Award Sultana N Nahar Excellence in Teaching Award Abel Wolman Fellowship Studienstiftung des deutschen Volkes Fellowship Hoffmann's educational initiatives include co-authoring the textbook Quantitative Understanding of Biosystems and leading the $3M NSF IUSE grant to transform STEM education through evidence-based pedagogy. His lab actively mentors graduate students like Zachary Auner, Gobin Acharya, and Ramesh Tripathi, working at the intersection of physics and biological systems.

Jason KnightView profile

Senior Lecturer

• Aerodynamics
• Fluid-Structure Interaction
• Computational Fluid Dynamics (CFD)
• +7 more

Dr. Jason Knight is a Senior Lecturer at the University of Portsmouth, affiliated with the School of Electrical and Mechanical Engineering and the Department of Mechanical Engineering. He holds a PhD from the University of Warwick and has extensive industrial experience, including roles at the Motor Industry Research Association and Porsche. His research focuses on aerodynamics, fluid-structure interaction (FSI), and computational fluid dynamics (CFD), with applications in automotive, aerospace, and marine engineering. Dr. Knight currently supervises three PhD students investigating flow simulation, filtration systems, and CFD modeling of airships. Education: BEng (Hons) in Aerospace Engineering, University of Hertfordshire, 1995 PhD in Mechanical Engineering (CFD modeling of convertible car hoods), University of Warwick, 2002 Research Interests: Dr. Knight specializes in experimental and numerical aerodynamics, with a focus on fluid-structure interactions involving membranes and porous media. His work extends to wind tunnel testing techniques, bio-inspired aerodynamics, and applications in road vehicles and motorsport. Recent projects include spring-mounted wing systems for drag reduction and ionic propulsion systems. Recent Research Trends: His publications from 2020 to 2025 emphasize advanced CFD techniques (e.g., detached-eddy simulation) and FSI modeling in automotive and aerospace contexts. He explores dynamic systems like spring-mounted wings and investigates turbulence models for curved surfaces. Advising & Grants: Supervises 3 PhD students and collaborates on projects funded by EPSRC and industry partners like Jaguar. His work integrates experimental and computational approaches, often involving industry partnerships. Labs & Affiliations: Part of the Portsmouth Centre for Advanced Materials and Manufacturing, focusing on interdisciplinary engineering solutions.

Amine MezghaniView profile

Assistant Professor

• Wireless Communication
• Digital Signal Processing
• Communication Systems
• +12 more

Dr. Amine Mezghani is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Manitoba's Price Faculty of Engineering. His research focuses on wireless communication systems, signal processing, and antenna engineering, particularly under hardware constraints like 1-bit quantization and phase-only DACs. Education: PhD (2015) in Electrical Engineering, Technical University of Munich; Dipl.-Ing. (Electrical Engineering), Technical University of Munich; Diplôme d'Ingénieur, École Centrale Paris His work explores sustainable wireless infrastructure, with emphasis on reconfigurable intelligent surfaces (RIS), millimeter-wave Massive MIMO, V2X communications, and physically consistent modeling combining Shannon and Maxwell theories. Publications include over 100 papers on hardware-constrained systems. Scientific contributions have earned him the 2021 IEEE Signal Processing Society Best Paper Award and the 2016 Rohde & Schwarz Outstanding Dissertation Award . He serves as an Associate Editor for IEEE Transactions on Wireless Communications . Current research includes electromagnetic consistency in multi-band MIMO, unlabeled compressed sensing, and VAMP-based algorithms. He advises graduate students in wireless communications and signal processing at the Intelligent Communication and Sensing Lab .

Puyan MojabiView profile

Professor

• Applied Electromagnetics
• Antennas and Propagation
• Electromagnetic Inverse Problems
• +5 more

Puyan Mojabi is a Professor and a Canada Research Chair (Tier 2) in the Department of Electrical and Computer Engineering at the University of Manitoba within the Price Faculty of Engineering. His research integrates electromagnetic inverse problems with metasurface design for applications in wireless communication and non-destructive testing. Education BSc Electrical Engineering, University of Tehran (2002) MSc Electrical Engineering, Iran University of Science and Technology (2004) PhD Electrical Engineering, University of Manitoba (2010) His work focuses on two key themes: systematic metasurface design for next-generation wireless systems and metasurface-enhanced electromagnetic imaging for cost-effective non-destructive testing. Articles demonstrate expertise in deep learning integration , implicit solvers , and phaseless inversion techniques . Scientific Awards University of Manitoba Falconer Emerging Researcher Award University of Manitoba Excellence in Teaching Award (x2) University of Manitoba Graduate Students Association Teaching Award International Union of Radio Science (URSI) Young Scientist Award (x3) Mojabi actively seeks graduate students in applied electromagnetics and contributes to professional communities as an Early Career Representative of URSI and Chair of the IEEE Winnipeg Waves Chapter.

• Epigenetics
• Sociology of Health
• Developmental Psychology
• +6 more

Laurel Raffington is a Research Fellow and leader of the Max Planck Research Group Biosocial – Biology, Social Disparities, and Development at the Max Planck Institute for Human Development in Berlin, Germany. Her work bridges genetics, epigenetics, and social inequality to understand how early-life conditions influence long-term health and cognitive outcomes. She has secured major grants, including a NIH R01 ($2.5M, 2024–2029) and the 2024 Jacobs Foundation Fellowship. Her research employs epigenetic biomarkers to track the biological impacts of socioeconomic disparities, with a focus on children and adolescents. Key research areas include epigenetic pathways of health disparities , social determinants of aging , and genetic-environment interactions . Her lab uses multi-omics approaches and randomized controlled trials to evaluate interventions targeting child well-being. Notable projects include analyzing epigenetic data from longitudinal cohorts to trace lifelong health trajectories. Education: PhD in Psychology (Humboldt-Universität Berlin, 2018), MSc in Social Neuroscience (Freie Universität Berlin, 2013), BSc in Experimental Psychology (University of Bristol, 2010). Recent awards also include recognition for innovative epigenetic methodologies and contributions to biosocial science. Her work emphasizes translating molecular findings into actionable public health strategies.

Yuexiao DongView profile

Associate Professor

• Sufficient Dimension Reduction
• High-dimensional Data Analysis
• Machine Learning
• +3 more

Yuexiao Dong is an Associate Professor in the Department of Statistics, Operations, and Data Science at Temple University's Fox School of Business and Management. He holds a PhD from Pennsylvania State University (2009) and a Bachelor's in Mathematics from Tsinghua University. His research focuses on sufficient dimension reduction, high-dimensional data analysis, and machine learning. Dr. Dong has been funded by the NSF for his work on 'New Developments in Sufficient Dimension Reduction' and serves as a Gilliland Research Fellow. He is an Associate Editor for the Journal of Systems Science and Complexity since 2015. His research is published in top journals such as the Annals of Statistics and Biometrika . Key interests include developing methodologies for efficient data analysis, particularly in handling high-dimensional and non-Euclidean data. He teaches Quantitative Foundations for Data Science at the undergraduate level. Education: Bachelor’s in Mathematics, Tsinghua University PhD in Statistics, Pennsylvania State University Awards: Gilliland Research Fellow NSF Grant: New Developments in Sufficient Dimension Reduction Dr. Dong’s work bridges statistical theory and applications, emphasizing computational efficiency and real-world relevance. Recent projects include Bayesian model averaging, real-time dimension reduction, and expectile-based regression techniques.