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Maiken H. Mikkelsen is the James N. and Elizabeth H. Barton Associate Professor in the Department of Electrical and Computer Engineering at Duke University, with a joint appointment in the Department of Physics . Her research focuses on quantum nanophotonics , plasmonics , and light-matter interactions in nanoscale materials, aiming to advance optoelectronics, quantum science, and biomedical diagnostics. Education B.S. in Physics, University of Copenhagen (2004) Ph.D. in Physics, University of California, Santa Barbara (2009) Postdoctoral Fellowship, University of California, Berkeley Her work explores nanophotonic engineering for quantum optics , spintronics , and ultrafast optoelectronics , with recent studies on nonlinear metasurfaces and plasmonic enhancement of immunoassays for point-of-care diagnostics. Publications highlight 2D semiconductor emission control , ultrafast single-photon sources , and metasurface-based photodetectors . Scientific Awards Maria Goeppert Mayer Award (2017) NSF CAREER Award (2015) Moore Inventor Fellow (2021) ONR/Air Force/Army Young Investigator Awards (2015-2017) Cottrell Scholar (2016) Stansell Family Distinguished Research Award (2021) She advises graduate students in Duke’s Electrical & Computer Engineering and Physics programs and leads the Mikkelsen Lab , which emphasizes ultrafast spectroscopy and quantum material development . The lab has graduated PhD students like Eunso Shin and Hengming Li (2025).

• Quantum Nanophotonics
• Plasmonics
• Spintronics
• +7 more

Professor Lydia Bourouiba is a faculty member at the Massachusetts Institute of Technology (MIT), affiliated with the Department of Civil and Environmental Engineering and the Institute for Medical Engineering and Science (IMES). Her research focuses on the intersection of fluid dynamics and disease transmission, with a strong emphasis on biophysics, interfacial flows, turbulence, and multiphase systems. Ph.D. in Applied Mathematics from McGill University (2008) Research on fluid fragmentation, droplet/bubble dynamics, and pathogen transport Co-Founder and Associate Director of the NSF Center for Pandemic Prevention (APPEX) Her work spans fundamental and applied domains, including: Biophysics of pathogen transport in air and water Turbulent multiphase flows in respiratory events Fluid-plant interactions in agricultural disease Nonlinear dynamics in public health contexts Key article trends highlight: Biomedical fluid mechanics Climate and environment-driven disease modeling High-risk/high-reward public health innovations Interdisciplinary approaches combining physics, biology, and engineering Scientific awards include: Fellow of the American Physical Society (2021) American Institute for Medical and Biological Engineering Fellow (2022) Smith Family Foundation Odyssey Award for biomedical research Ole Madsen Mentoring Award She serves as Associate Editor for the journal FLOW and has founded conferences like the Gordon Research Conference on Fluids in Disease Transmission. Her lab (Fluids and Health Network) develops experimental and theoretical frameworks to address contamination, pandemic control, and fluid-health-environment interactions.

• Fluid Dynamics
• Disease Transmission
• Biophysics
• +6 more

Olaf Ronneberger is an associate professor at the Albert-Ludwigs-Universität Freiburg and works at Google DeepMind . His research focuses on deep learning architectures , AI applications to scientific problems , and protein structure prediction . He leads seminars on deep learning and 3D image analysis, emphasizing vision-language integration and generative models. His publications include foundational work on U-Net architectures for biomedical image segmentation, AlphaFold 3 for biomolecular interaction prediction, and Gemini models for multimodal AI systems. Key subfields span medical imaging , protein folding , and vision-language models . Co-developer of U-Net , a widely used biomedical image segmentation framework. Contributor to AlphaFold 3 for structural biology. Research on Gemini 1.5/2.5 models for multimodal reasoning.

• Deep Learning
• Computer Vision
• Biomedical Image Segmentation
• +4 more

Zhi-Pei Liang is the Franklin W. Woeltge Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign, with joint appointments in the Department of Bioengineering, Beckman Institute for Advanced Science and Technology, and Coordinated Science Laboratory. His research spans biomedical engineering, medical imaging, and signal processing with a focus on advancing magnetic resonance imaging and spectroscopy technologies. His educational background includes a Ph.D. in Biomedical Engineering from Case Western Reserve University (1989) and a B.S. in Electrical Engineering from South-China University of Technology (1982), followed by postdoctoral training at UIUC (1989-1991). Professor Liang's research interests center on magnetic resonance imaging and spectroscopy , with particular emphasis on ultrafast imaging techniques , model-based reconstruction methods , and the integration of physics-based modeling with machine learning . His pioneering work on SPICE (SPectroscopic Imaging by exploiting spatiospectral CorrElation) has revolutionized high-resolution metabolic brain imaging by enabling label-free molecular imaging through the marriage of spin physics and machine learning. His research spans pattern recognition, parameter estimation, image formation theory, and algorithms for medical imaging applications. Analysis of his recent publications reveals a strong focus on high-resolution metabolic imaging , particularly using SPICE methodology to map brain metabolism with unprecedented detail. His work bridges fundamental physics of magnetic resonance with advanced computational methods to overcome traditional limitations in imaging speed and resolution. Current research directions include J-resolved spectroscopic imaging, deuterium-based metabolic mapping, and multimodal integration of PET and MRSI for studying neurological disorders. Elected to International Academy of Medical and Biological Engineering (2012) Gold Medal, International Society for Magnetic Resonance in Medicine (2022) Technical Achievement Award, IEEE Engineering in Medicine and Biology Society (2014) Fellow, National Academy of Inventors (2021) Author of influential book 'Principles of Magnetic Resonance Imaging' (1999) President of IEEE Engineering in Medicine and Biology Society (2011-2012) Professor Liang has advised numerous students and postdocs in biomedical imaging research and has received multiple teaching honors including the Ronald W. Pratt Outstanding Teaching Award (2005) and multiple listings among UIUC's Excellent Teachers. His research has been supported by various grants from NIH, NSF, and other funding agencies. He leads the SPICE (Spectroscopic Imaging by exploiting spatiospectral Correlation) research group which focuses on developing novel imaging techniques that combine physics-based modeling with machine learning for ultrafast metabolic imaging. His laboratory, part of the Beckman Institute's Integrative Imaging Theme, collaborates extensively with clinical researchers at Carle Illinois College of Medicine and other institutions to translate advanced imaging techniques into clinical applications for neurological disorders, cancer, and metabolic diseases. Current projects focus on high-resolution mapping of brain metabolism in Alzheimer's disease, stroke, and brain tumors using novel MR spectroscopic imaging techniques.

• Magnetic Resonance Imaging
• Medical Imaging
• Biomedical Engineering
• +5 more

David Juncker is a Professor and Department Chair of the Department of Biomedical Engineering at McGill University. He serves as a Principal Investigator at the McGill University & Genome Quebec Innovation Centre and holds associate memberships in the Department of Neurology and Neurosurgery, Department of Electrical and Computer Engineering, Division of Experimental Medicine, Department of Surgery, and Goodman Cancer Research Centre. His research focuses on micro- and nano-bioengineering technologies for bioanalysis, precision medicine, and organs-on-chips. Key areas include microfluidics, lab-on-a-chip devices, biomedical sensors, medical diagnostics, biomaterials, tissue engineering, and cancer biomarker discovery. His lab develops scalable antibody microarrays, self-powered diagnostic platforms, microfluidic probes for brain tissue perfusion, and nanogradients for neuronal navigation, with applications in cancer diagnostics, global health, and neuroscience. Recent publications (2023-2025) reveal strong emphasis on extracellular vesicle analysis, single-cell proteomics, 3D-printed microfluidic/organ-on-a-chip systems, and capillary-driven circuits. Key trends include low-cost point-of-care diagnostics, advanced circulating tumor cell isolation methods, and biomimetic synthetic vesicles for drug delivery, demonstrating translational potential in early disease detection. Dr. Juncker leads a highly interdisciplinary team comprising undergraduate and graduate students, post-doctoral fellows, and staff from diverse scientific, engineering, and cultural backgrounds. His lab actively recruits Canadian/permanent resident graduate students for projects on single extracellular vesicle and protein detection in cancer and infectious diseases, leveraging microfluidics and wearables for biomarker discovery. The Juncker Lab operates from the McGill University & Genome Quebec Innovation Centre (740 Dr. Penfield Avenue, Room 6206). It maintains a collaborative, multicultural environment focused on developing transformative micro- and nano-bioengineering technologies with significant potential impact on human health diagnostics and treatment.

• Microfluidics
• Nanotechnology
• Lab-on-a-Chip
• +6 more

Philipp Eichmeir is a Researcher at the Research Center Wels within the Upper Austria University of Applied Sciences . His work focuses on optimal control , multibody dynamics , and adjoint methods applied to robotics and automotive systems. Expertise in adjoint gradient computation for extremal value optimization Active in automotive/mobility and smart production domains Philipp's research spans computational mathematics , robotics , and mechanical engineering , utilizing advanced numerical methods and simulation modeling for complex dynamic systems. His recent publications focus on multibody dynamics , adjoint optimization , and inequality constraint handling in control systems. Collaborative projects include IOMMS (Innovative Optimization Methods for Multibody Systems) and JR-Centre for Thermal NDE of Composites . Scientific Awards Best Paper Award (2020) Automatisierte Körperschallauswertung (2015)

• Mechanical Engineering
• Robotics
• Optimal Control
• +4 more

Weiyu Xu is a Professor at the University of Iowa, affiliated with both the Department of Electrical and Computer Engineering and the Department of Applied Mathematical and Computational Sciences. He joined the College of Engineering in 2012 and leads the Intelligent Information Processing Lab (IIPL). Ph.D., Electrical Engineering, California Institute of Technology, 2009 M.S., Electrical Engineering, California Institute of Technology, 2006 M.S., Electronic Engineering, Tsinghua University, 2005 B.E., Information Engineering, Beijing University of Posts and Telecommunications, 2002 His research focuses on compressive sensing, information theory, signal processing, network optimization, and deep learning applications in medical imaging and cybersecurity. He has made significant contributions to adversarial attack robustness, distributed optimization algorithms, and medical imaging techniques for OCT segmentation and brachytherapy. Recent publications highlight trends in Adversarial machine learning Medical imaging algorithms Compressed sensing for diagnostics Optimization in wireless communication He has also contributed to federated learning over tree networks and theoretical guarantees for sparse signal recovery. Weiyu Xu's lab, IIPL, integrates deep learning with classical optimization and signal processing. His work bridges foundational theories (e.g., information-theoretic robustness) with real-world applications in healthcare (e.g., cancer treatment planning) and communication systems (e.g., MIMO channel estimation).

• Compressive sensing
• Information and coding theory
• Signal processing
• +12 more

Professor Faye Rogers serves as Professor of Therapeutic Radiology at Yale University School of Medicine, holding multiple leadership positions including Associate Cancer Center Director for YCC Collaborative Excellence, Vice Chair for Collaborative Excellence in Therapeutic Radiology, Associate Director of the Yale MD-PhD Program, and Director of the Yale BioMed Amgen Scholars Program. Her work bridges radiation oncology, DNA repair mechanisms, and cancer therapeutics within Yale's comprehensive cancer research ecosystem. PhD from University of Maryland at Baltimore (1998) Postdoctoral Fellow at Yale School of Medicine Dr. Rogers' research focuses on the intersection of DNA repair mechanisms and cancer therapeutics, with particular expertise in triplex DNA structures, genomic instability, and targeted cancer therapies. Her work explores how DNA damage responses can be leveraged for therapeutic benefit, especially in breast neoplasms and other malignancies. Through her leadership in the DNA Damage and Genome Integrity program, she investigates novel approaches to disrupt cancer cell survival mechanisms while sparing healthy tissue. Her research has significant implications for radiation oncology, particularly in developing more precise and effective radiation-based treatments. Analysis of Dr. Rogers' publication record reveals a consistent trajectory in DNA repair mechanisms and cancer therapeutics, with increasing focus on translational applications. Her work demonstrates expertise in triplex DNA structures, RAD51 inhibition, and synthetic lethality approaches. Recent publications show a shift toward more clinically applicable research, particularly in targeting DNA repair pathways in specific cancer subtypes like IDH1-mutant cancers and PTEN-deficient glioblastomas. Her collaborative work with Peter Glazer and others demonstrates strong interdisciplinary connections between radiation oncology, molecular biology, and drug development. Leadership in Diversity, Equity & Inclusion Award (Yale School of Medicine, 2022) Translational Science Research Prize (Yale Cancer Center, 2022) Kingsley Fellowship in Medical Research Carl Storm Underrepresented Minority Fellowship NCI Research Supplement to Promote Diversity in Health-Related Research As Associate Director of the Yale MD-PhD Program and Director of the Yale BioMed Amgen Scholars Program, Dr. Rogers plays a pivotal role in mentoring the next generation of physician-scientists and supporting underrepresented minorities in biomedical research. Her leadership in collaborative excellence initiatives demonstrates commitment to fostering interdisciplinary research teams across Yale's scientific ecosystem. Through the YCC Collaborative Excellence program, she has secured significant funding for innovative cancer research that bridges basic science and clinical applications, particularly in the areas of DNA repair targeting and radiation oncology. Dr. Rogers leads research within Yale's Therapeutic Radiology department, with strong connections to the Radiobiology program and the Yale Cancer Center. Her work is integrated with the Program in Translational Biomedicine and the Yale Combined Program in the Biological and Biomedical Sciences. Through the BioMed Amgen Scholars Program, she provides critical research opportunities for undergraduate students, particularly those from underrepresented backgrounds, fostering talent in DNA repair research and cancer therapeutics.

• Apoptosis
• Biochemistry
• Breast Neoplasms
• +5 more

Dan Nguyen, Ph.D., is a faculty member in the Department of Radiation Oncology at UT Southwestern Medical Center, where he is part of the Division of Medical Physics and Engineering. He is a founding member of the Medical Artificial Intelligence and Automation (MAIA) Laboratory, collaborating closely with Dr. Steve Jiang to advance AI applications in radiotherapy. His work focuses on deep learning for treatment planning, dose prediction, auto-segmentation, and adaptive radiotherapy. Ph.D. in Biomedical Physics, University of California, Los Angeles (UCLA), 2017 Mentor: Dr. Ke Sheng Faculty appointment at UT Southwestern since 2017 Dr. Nguyen’s research is centered on applying artificial intelligence to solve critical challenges in radiation oncology. His primary interests include deep learning-based dose prediction, auto-segmentation of anatomical structures, optimization of treatment plans, and real-time adaptive radiotherapy. He has pioneered work in direct aperture optimization, 4π radiotherapy, and uncertainty quantification in AI models. His research bridges the gap between AI innovation and clinical implementation, with a focus on improving plan quality, reducing planning time, and enhancing accessibility for less experienced clinicians. The most recent publications (2023–2025) demonstrate a consistent trend in developing fast, accurate, and robust deep learning models for radiotherapy. Key themes include dose prediction with transfer and meta-learning, adaptive segmentation using test-time optimization, uncertainty assessment in AI predictions, and mathematical modeling of radiotherapy-immunotherapy synergy. These works span high-impact journals in medical physics, AI, and oncology, reflecting interdisciplinary innovation. While no specific scientific awards are listed, Dr. Nguyen’s leadership in the MAIA Lab and extensive publication record in top-tier journals indicate significant recognition in the field of medical physics and AI in medicine. Dr. Nguyen has co-authored numerous studies involving mentoring and collaborative research, particularly with trainees and junior faculty in the MAIA Lab. His work is supported by institutional and likely federal funding, given the scale and scope of AI deployment studies. He has contributed to large-scale collaborative efforts such as OpenKBP-Opt, involving international teams evaluating knowledge-based planning pipelines. The MAIA Laboratory is a multi-investigator research group focused on innovating, developing, and applying artificial intelligence technologies to empower clinicians—especially those with less experience or limited resources—for improved patient care. The lab’s work spans machine learning, deep learning, reinforcement learning, and mathematical modeling in radiation oncology.

• Medical Physics
• Artificial Intelligence in Medicine
• Radiation Therapy Treatment Planning
• +7 more

Ghassan AlRegib is the John and Marilu McCarty Chair Professor in the School of Electrical and Computer Engineering at Georgia Institute of Technology. He directs the Omni Lab for Intelligent Visual Engineering and Science (OLIVES), the Center for Energy and Geo Processing (CeGP), and previously led Georgia Tech's MENA initiatives (2015-2018). His research spans machine learning, image processing, and seismic interpretation with real-world applications in autonomous vehicles, medical imaging, and subsurface analysis. His research focuses on trustworthy AI systems through three pillars: enhancing interpretability, improving robustness/generalizability, and tackling domain-specific challenges. Key interests include human-in-the-loop frameworks, uncertainty quantification, explainable AI, and physics-driven learning. The OLIVES lab pioneered modern machine learning applications in seismic interpretation and developed open-source datasets for geological fault analysis. Dr. AlRegib's scientific contributions include over 270 publications, multiple U.S. patents, and leadership roles as Technical Program co-Chair for ICIP 2020/2024. His work demonstrates significant impact through awards like the IEEE Fellow designation (2022) and multiple best paper awards at premier conferences. IEEE Fellow (2022) 2023 EURASIP Best Paper Award 2019 ICIP Best Paper Award 2017 Denning Faculty Award for Global Engagement CSIP Research & Service Awards (2003) He has advised numerous PhD students including Dr. Ashraf Alattar (now Auburn professor) and Dr. Zhiling Long (Kennesaw State faculty). His lab structure emphasizes collaborative teams comprising postdocs, senior/junior PhD students, and undergraduates working on high-impact problems from autonomous systems to medical diagnostics. Current research thrusts include trustworthy neural networks, human-in-the-loop frameworks, and deployment of machine learning in seismic interpretation and ophthalmology.

• Machine Learning
• Image and Video Processing
• Computer Vision
• +7 more

Jon Weissman is a Professor of Computer Science at the University of Minnesota, Twin Cities. His research focuses on distributed systems, edge and cloud computing, and high-performance computing (HPC), aiming to enhance performance, reliability, and energy efficiency. Education: Ph.D. in Computer Science, University of Virginia (1995) M.S. in Computer Science, University of Virginia (1989) B.S. in Applied Mathematics and Computer Science, Carnegie-Mellon University (1984) His research explores edge and cloud computing, IoT, and HPC, including subtopics like storage systems, resource management, and security. Publications highlight trends in adaptive prefetching, compressed sensing for medical devices, and IoT-informed autoscaling. Scientific Awards: NSF CAREER Award (1995) Senior Member, IEEE He has advised Ph.D. students like Albert Jonathan, Kwangsung Oh, and Francis Liu. His lab is located in 4-204A Keller Hall, and he serves on steering committees for conferences like HPDC.

• Distributed Systems
• Cloud Computing
• Edge Computing
• +7 more

Kwantae Kim is an Assistant Professor at the Department of Electronics and Nanoengineering within Aalto University's School of Electrical Engineering . He leads the Tiny Systems and Circuits (TSirc) Group , focusing on power-efficient analog/mixed-signal ICs for biomedical and neuromorphic sensor systems. IEEE Senior Member (2025) Collaborates with institutions across Europe, Asia, and America Specializes in ultra-low-power AI-embedded IoT platforms His research emphasizes Tiny, Sensory, Intelligent, and Wireless IoT systems through: Development of energy-efficient IC architectures Democratizing access to advanced chip design Hardware-software co-design for edge computing Recent publications highlight innovations in: Spoken-language-understanding SoCs Temporal-sparsity-aware keyword spotting Open-source silicon frameworks Awards include: 2025 IEEE Senior Member 2023 Best Poster Award (AICAS) 2019 Samsung HumanTech Silver Award Research partnerships span: Prof. Tobi Delbruck (UZH/ETH Zurich) Prof. Hoi-Jun Yoo (KAIST) Prof. Shih-Chii Liu (UZH) Prof. Sohmyung Ha (NYU Abu Dhabi)

• Power-Efficient Analog/Mixed-Signal Integrated Circuits
• Biomedical Sensor Systems
• Neuromorphic Sensor ICs
• +3 more

Jodi Schneider is an Associate Professor at the University of Illinois Urbana-Champaign , with affiliate appointments at the Beckman Institute , Health Care Engineering Systems Center , European Union Center , and Center for Health Informatics . She directs the Information Quality Lab and focuses on the science of science through argumentation and evidence analysis. PhD in Informatics (National University of Ireland, Galway) M.S. in Library and Information Science (UIUC) M.A. in Mathematics (UT-Austin) B.A. in Liberal Arts (St. John's College) Her research examines how scientific controversies persist through citation patterns, the role of knowledge brokers in public policy, and information quality in biomedical contexts. She has developed semantic frameworks for micropublications and knowledge maintenance in digital libraries. Recent publications include citation integrity studies in Scientometrics , retraction indexing in STI Conference , and argumentation mining in Human Language Technologies . Collaborative projects span institutions like Harvard Radcliffe Institute and RWTH Aachen . NSF CAREER Award IMLS Early Career Award Senior Member, Association of Computing Machinery Marie Curie Fellow She advises graduate students in information quality and knowledge representation , with funding from the Alfred P. Sloan Foundation , NIH , and European Commission . Her lab develops tools to combat scientific misinformation and improve public health informatics .

• Controversies in Science
• Science of Science
• Argumentation and Evidence
• +12 more

Aniello Murano is a Professor of Computer Science at the Department of Electrical Engineering and Information Technologies , University of Naples Federico II. He serves as Scientific Director of the ASTREA (Automated Strategic Reasoning) Laboratory and leads cutting-edge research in Artificial Intelligence, Strategic Reasoning, Multi-Agent Systems , and Formal Verification . Research Interests : Strategic reasoning under perfect/imperfect information, specification/verification/synthesis of reactive systems, temporal/modal logics, automata theory, parity games, game theory, mechanism design, and formal languages. Notable Projects : PNRR Research Unit Coordinator (2023-2025) on Resilient AI, PRIN 2020 Unit Coordinator (RIPER: Resilient AI-Based Self-Programming and Strategic Reasoning), H2020-MSCA SEAL (Principal Coordinator). Awards & Honors : JPMorgan Faculty Research Award (2022), Royal Society Award (2016), Best Paper PRIMA (2015), INDAM Project Leader (2023), Italian Scientific Habilitation (2017-2018). Students & Postdocs : Supervised 6 PhD students (e.g., Silvia Stranieri, Vadim Malvone) and mentored postdocs such as Munyque Mittelmann and Bastien Maubert. Laboratory : Leads ASTREA Lab, focusing on automated strategic reasoning and resilient AI systems.

• Artificial Intelligence
• Strategic Reasoning in Multi-Agent Systems
• Formal Verification
• +6 more

Dr. Lucy Gloag is a Lecturer at the Research School of Chemistry at the Australian National University (ANU), where she joined in 2024 after previously serving as a Lecturer at the University of Technology Sydney in 2023. Her research focuses on the development of advanced nanomaterials for energy applications, particularly in electrocatalysis and energy storage. Education: BSc/BCA and BSc(Hons) from Victoria University of Wellington, New Zealand PhD from the University of New South Wales (2018) on synthesis and characterization of Ru-based nanocatalysts Dr. Gloag is a nanomaterials chemist and electron microscopist specializing in the synthesis and characterization of nanomaterials for electrocatalytic applications. Her research addresses the fundamental question of how nanostructure can be used to enhance the performance of electrocatalysts . She employs solution-phase synthesis techniques to create nanoparticles with precise control over crystal structure, dimensions, and surface faceting, then correlates these structural features with electrocatalytic properties using transmission electron microscopy and electrochemistry. Her work spans energy conversion technologies, biomedical applications of nanoparticles, and advanced materials characterization. Analysis of her recent publications reveals a strong focus on single-atom catalysts, hierarchical nanostructures, and the relationship between nanomaterial structure and function. Her research spans both fundamental materials science and practical applications in energy conversion, with significant work on oxygen evolution reaction, hydrogen evolution reaction, and methanol oxidation electrocatalysts. She has also made notable contributions to biomedical applications of nanoparticles, particularly in magnetic particle imaging and Alzheimer's disease diagnostics. Scientific Awards: ARC Discovery Project Grant (2023) ARC Linkage Project Grant (2023) UNSW Science COVID19 Strategic Support Grant (October 2021) Dementia Australia Research Foundation – Yulgilbar Innovation Grant (2019-2022) Australian Postgraduate Research Scholarship (2015) AMN-7 Image Competition Finalist (2015) Dr. Gloag currently leads the ANU Futures Scheme 2.0 project (2024-2028) and has secured multiple competitive research grants, demonstrating strong research leadership. Her work involves extensive collaboration with researchers at UNSW and other institutions, particularly with Professors Richard Tilley and Justin Gooding. She has published 28 research outputs since 2015, with significant citation impact (h-index of 17). Her laboratory at ANU (Building 137, room 2.49) focuses on developing single atom and nanomaterials for energy storage and conversion technologies, continuing her trajectory as an emerging leader in advanced materials synthesis and electron microscopy characterization.

• Nanomaterials Chemistry
• Electron Microscopy
• Electrocatalysis
• +3 more