Find Academic Supervisors

• Quantum Optics
• Ultracold Atoms
• Condensed Matter Physics
• +4 more

Gediminas Juzeliūnas is a Distinguished Professor at the Institute of Theoretical Physics and Astronomy within the Faculty of Physics at Vilnius University. His research spans quantum optics, ultracold atoms, and condensed matter physics, with particular expertise in quantum simulations and synthetic gauge fields. Professor Juzeliūnas's research program focuses on creating artificial gauge fields for neutral atoms, enabling quantum simulation of complex condensed matter phenomena. His seminal Reviews of Modern Physics article on artificial gauge potentials has become foundational in the field. Current research explores subwavelength optical lattices, topological quantum matter, and spin-orbit coupling phenomena in ultracold atomic systems. His theoretical work often bridges fundamental concepts with experimental feasibility, making it highly influential in both theoretical and experimental communities. Analysis of his recent publications reveals a clear trajectory toward exploring topological phenomena in ultracold atomic systems through subwavelength structures and Raman lattices. Juzeliūnas has pioneered approaches to create and manipulate synthetic gauge fields that simulate condensed matter physics, with applications ranging from quantum simulation to precision measurement technologies. His work frequently addresses the interplay between geometrical frustration, long-range interactions, and topological order in quantum many-body systems. True member of the Lithuanian Academy of Sciences (since 2019) Jucys Prize for Theoretical Physics (2014) Vilnius University Rector's award (2010, 2025) National State Prize for Science of Lithuania (2007) Professor Juzeliūnas has supervised multiple doctoral students including Edvinas Gvozdiovas (working on subwavelength optical lattices), Tomas Andrijauskas (researching artificial magnetic fields), and Hamid Reza Hamedi (studying slow light phenomena). He currently leads four major research projects funded by the Lithuanian Research Council, including "Ultracold atoms in unconventional optical lattices" (2024-2027) and "Quantum dynamics of interacting ultracold atoms in complex sub-wavelength potentials" (2024-2026), demonstrating his leadership in advancing quantum simulation techniques. His research group at the Institute of Theoretical Physics and Astronomy collaborates extensively with international partners, including Japanese, Latvian, and Taiwanese scientists, reflecting the global significance of his work. Professor Juzeliūnas regularly presents at major international conferences and has chaired significant events including the Humboldt Kolleg on Synthetic Quantum Matter (2023) and the 14th European Conference on Atoms Molecules and Photons (2022).

• Cybersecurity
• Critical Infrastructure Security
• Cyber-Physical Systems
• +4 more

Aida Akbarzadeh is a Senior Researcher at the Norwegian University of Science and Technology (NTNU) , specifically within the Department of Information Security and Communication Technology under the Faculty of Information Technology and Electrical Engineering . Her work focuses on cybersecurity, critical infrastructure protection, and cyber-physical systems (CPS). Research Areas: Threat modeling, digital twins for security, dependency-based risk analysis, advanced persistent threats (APT), IT/OT integration, and industrial control system vulnerabilities. Publications: Recent work includes studies on automating threat modeling, digital twin applications, APT attacks on power grids, and protocol-specific vulnerabilities (PTP, IEC 61850, IEC 60870-5-104). Collaborations: Active in interdisciplinary research with colleagues like Laszlo Erdodi, Siv Houmb, Sokratis Katsikas, and Tore Soltvedt. Labs & Groups: Member of the Critical Infrastructure Security and Resilience Group (CISaR) . Contact: aida.akbarzadeh@ntnu.no

Valentina KRACHMALNICOFFView profile

Researcher

• Nanophotonics
• Plasmonics
• Near-field Optics
• +5 more

Valentina KRACHMALNICOFF is a CNRS Research Scientist at Institut Langevin, affiliated with ESPCI Paris and PSL University. She joined the institute in 2012 after completing her postdoctoral fellowship there in 2010. Her research focuses on experimental nanophotonics, particularly studying near-field interactions between fluorescent nano-emitters and nanostructured plasmonic or dielectric materials. Dr. KRACHMALNICOFF obtained her PhD from University Paris-Sud in 2009 with a thesis on quantum atom optics experiments supervised by Alain Aspect and Charles Westbrook. Her research interests span experimental nanophotonics with plasmonic and dielectric media, near-field optical microscopy with fluorescent nanoprobes, quantum optics applications, and the study of electromagnetic local density of states. She has developed expertise in fluorescence intensity and decay rate measurements of nano-objects grafted on scanning probe microscope tips, as well as nano-manipulation techniques. Her work bridges fundamental physics with potential applications in quantum technologies, biosensing, and thermal management at the nanoscale. She frequently employs super-resolution imaging techniques to overcome diffraction limits in optical measurements. Analysis of Dr. KRACHMALNICOFF's recent publications reveals a strong methodological evolution toward increasingly sophisticated combinations of experimental techniques with theoretical modeling. Her work consistently focuses on probing light-matter interactions at the nanoscale, with growing integration of biophysical approaches. The publications demonstrate expertise in thermal radiation at nanoscale distances, plasmonic and dielectric nanostructures, and super-resolution fluorescence lifetime imaging. Her research shows a trajectory from fundamental near-field optics toward applications in quantum information and biosensing. Dr. KRACHMALNICOFF has received notable scientific recognition: 2017: CNRS Bronze Medal for her pioneering work in nanophotonics 2007: L'Oréal France - UNESCO "For Women in Science" Prize Dr. KRACHMALNICOFF actively mentors doctoral students and postdoctoral researchers. Current advisees include Guillaume Blanquer and Dorian Bouchet (PhD candidates) and Vivien Loo (postdoctoral researcher). Former students include Da Cao and Etienne Castanié. Her research is supported by CNRS funding and collaborative grants with other institutions, evident from her extensive co-authorship network spanning theoretical physicists, materials scientists, and optical engineers. Dr. KRACHMALNICOFF leads an experimental research team at Institut Langevin specializing in nanophotonics. Her laboratory features advanced near-field optical microscopy capabilities, fluorescence lifetime imaging systems, and nano-manipulation setups. The team collaborates closely with other researchers at Institut Langevin, including Yannick De Wilde (CNRS Research Director) and Ignacio Izeddin (Associate Professor at ESPCI), forming a cohesive research group focused on light-matter interactions at the nanoscale.

• Computational Intelligence
• Agent-Based Simulation Systems
• Neuroscience
• +4 more

Darius Plikynas is a Senior Researcher at the Smart Technologies Research Group within the Institute of Data Science and Digital Technologies at Vilnius University . His research integrates computational intelligence methods with agent-based simulation to model cognitive and social processes. Position: Senior Researcher, Chief Researcher in the Project Address: Akademijos St. 4, room 224, Vilnius Contact: +370 5 210 9333, +370 620 95101 Email: darius.plikynas@mif.vu.lt Personal page: http://www.dariusplikynas.eu Dr. Plikynas' research spans interdisciplinary domains including neuroscience, physics methods, complexity theory, and distributed cognitive systems. He led the 2017–2019 project "Development of a metric, conceptual and simulation model of the social impact of cultural processes" under the LMT Research Group Funding Program. His recent publications (2016–2025) reflect trends in combining machine learning with social science questions (fake news analysis, propaganda detection) and agent-based modeling of cultural/social capital dynamics. Key collaborations include Leonidas Sakalauskas, Rimvydas Laužikas, and Arunas Miliauskas. Scientific supervision includes doctoral students: Andrius Budrionis (University of Tromsø, Norway) Ieva Rizgelienė (PhD topic: "Propaganda detection and classification in social media using hybrid deep learning") He also serves as an expert at Vilnius University and has contributed to projects involving: 2D financial market visualization Neural oscillation-based cognitive modeling Indoor navigation for blind individuals Cultural participation impact on social capital

• Biostatistics
• Statistical Analysis
• Applied Statistics
• +5 more

Professor Ufuk Karadavut serves at Karabük University Faculty of Medicine, Department of Basic Medical Sciences, where he holds the position of Professor and currently serves as Vice Dean. Previously, he was Dean of the Faculty of Medicine from 2021-2022 and has held multiple leadership roles including Head of Department since 2020. With an extensive publication record of 493 works and significant contributions to both medical and agricultural statistics, his career spans multiple Turkish universities including Ahi Evran University and Bingöl University. PhD, Selçuk University, Institute of Science (2001-2005) Bachelor's Degree, Ankara University, Faculty of Agriculture, Department of Animal Science (1987-1991) Professor Karadavut's research spans biostatistics, medical informatics, and agricultural statistics, with particular expertise in statistical experimental design. His work bridges healthcare applications (medical informatics, clinical data analysis) with agricultural science (crop breeding, animal husbandry). He has developed statistical methodologies applicable across diverse fields, from analyzing palliative care outcomes to optimizing chickpea breeding programs for drought tolerance. His research demonstrates a consistent pattern of applying advanced statistical techniques to solve practical problems in both medical and agricultural contexts. Analysis of his recent publications reveals a strong focus on applying statistical methodologies to address contemporary challenges. In medical informatics, he examines healthcare quality, medical education, and pandemic impacts. In agricultural science, his work centers on crop improvement (particularly chickpeas and other legumes) and animal breeding. His statistical approaches include multivariate analysis, QTL mapping, growth modeling, and machine learning techniques, demonstrating methodological versatility across disciplinary boundaries. While specific awards aren't detailed in the available information, his h-index of 11 (UNIS) and 8 (WoS), along with 583 total citations, indicate significant scholarly impact. His research has attracted funding from major Turkish research bodies including TÜBİTAK and university-supported projects. Professor Karadavut has supervised 13 theses across multiple institutions and taught diverse courses ranging from medical bioinformatics to agricultural statistics. His research portfolio includes 31 projects, with recent work focusing on climate change adaptation in livestock, medical informatics applications, and advanced statistical methodologies for agricultural research. He has collaborated extensively with researchers across multiple Turkish institutions, demonstrating strong interdisciplinary networking capabilities.

Li HuaView profile

Professor

• Multiphysics Modeling
• Soft Matter
• Machine Learning
• +5 more

Professor Li Hua is a faculty member at the School of Mechanical & Aerospace Engineering, Nanyang Technological University (NTU), Singapore. He holds a Ph.D. in Mechanical Engineering from the National University of Singapore (1999) and has been recognized as a Fellow of the American Society of Mechanical Engineers (ASME) since 2019. His academic journey includes postdoctoral research at the University of Illinois at Urbana-Champaign (2000-2001), a visiting scientist role at Johns Hopkins University (2005), and research scientist positions at A*STAR’s Institute of High Performance Computing (2001-2006). Research Interests: Professor Li specializes in multiphysics modeling of soft matters (e.g., smart hydrogels in BioMEMS and biological cell microscale dynamics), machine learning-based prediction for 3D printing process-microstructure-property correlation, numerical computational methodologies (meshless and multiscale algorithms), sustainable energy simulations (building efficiency and fuel cells), and structural dynamics of high-speed rotating shells and composites. His work bridges computational mechanics, materials science, and biomedical applications. Publications & Collaborations: With over 200 peer-reviewed journal articles, he has authored/co-authored monographs such as Smart Hydrogel Modelling (Springer) and Rotating Shell Dynamics (Elsevier). His research is funded by agencies like NRF, EDB, SMI, and industry partners including Rolls-Royce, Emerson, and ABB. Scientific Awards: Fellow of ASME (2019) ICCM Investigator Award (2018) SMI Top Project Winner (2015) IBM & IHPC Silver Award (2003) Advising & Grants: He has advised Ph.D. students like Meng Zhang (awarded Best Student Paper at ICMFM XIX, 2018) and secured grants for computational BioMEMS, sustainable buildings, and advanced manufacturing projects. His industrial collaborations span aerospace, maritime, and energy sectors. Labs & Teams: Professor Li leads a research group at NTU, focusing on interdisciplinary projects with institutions like Imperial College, Technische Universität Dresden, and Johns Hopkins University. He contributes to academic governance as an NTU Senator and active member of professional societies.

Zihe GaoView profile

Assistant Professor

• Optics
• Integrated Photonics
• Quantum Optics
• +5 more

Zihe Gao serves as a tenure-track Assistant Professor in the Department of Electrical and Computer Engineering at Auburn University's College of Engineering since August 2023, following postdoctoral research at the University of Pennsylvania and industry experience at Meta (Facebook Reality Labs). His academic foundation includes: PhD in Electrical and Computer Engineering, University of Illinois Urbana-Champaign (2018) MS in Physics, University of Illinois Urbana-Champaign (2012) BS in Physics, Nanjing University (2011) Dr. Gao's research integrates optics, microelectronics, and physics to develop programmable photonic systems. His work focuses on controlling collective behaviors in multi-element photonic systems for scalable integrated chips, with applications spanning dynamically steerable laser sources and reconfigurable quantum optical platforms . Key methodologies include non-Hermitian physics, topological photonics, and spin-orbit coupling engineering. Analysis of his 2023-2025 publications reveals dominant trends in non-Hermitian photonic switching , high-dimensional quantum state manipulation , and topological semiconductor laser arrays . His team pioneers lithography-free reconfigurable photonics and spin-orbit microlasers for quantum key distribution, demonstrating strong industry-academia translation from prior Meta work on AR/VR structured-light systems. His scholarly trajectory shows continuous progression from VCSEL array fundamentals (PhD under Prof. Kent Choquette) to quantum-topological photonics (postdoc with Prof. Liang Feng), now establishing independent research at Auburn with emphasis on integrated quantum-classical hybrid systems.

Carrie WeidnerView profile

Senior Lecturer

• Quantum Physics
• Quantum Engineering
• Atom Interferometry
• +3 more

Dr. Carrie Weidner is a Senior Lecturer at the University of Bristol, affiliated with both the School of Physics and the School of Electrical, Electronic and Mechanical Engineering. Her research spans quantum control, atom interferometry, and quantum technology education, with a focus on robust control techniques in optical lattices and spin networks. Principal Investigator for Quantum Positioning, Navigation, and Timing Hub (2024-2029) Lead on EPSRC-funded project EP/Y004728/1 for trapped ultracold atom interferometry (2023-2025) Her recent work includes energy landscape shaping for quantum systems, deterministic generation of squeezed states, and innovative educational tools like the Quantum Composer. Publications analyze robustness metrics, control algorithms, and quantum-classical system comparisons. Collaborations span international institutions in quantum physics and engineering domains. She contributes to quantum outreach through gamification and interactive platforms, targeting improved education and community inclusivity. Current research trends emphasize precision measurement, error mitigation, and AI integration in quantum control systems.

William SchonbergView profile

Professor

• Micrometeoroid and Orbital Debris (MMOD)
• Spacecraft Shielding
• Lunar In-Situ Resource Utilization
• +3 more

William Schonberg is a Professor in the Department of Civil, Architectural and Environmental Engineering at Missouri University of Science and Technology, specializing in aerospace engineering applications within civil infrastructure frameworks. His research bridges terrestrial engineering disciplines with space exploration challenges, particularly in spacecraft protection systems and lunar resource utilization. His primary research domains include: Micrometeoroid and Orbital Debris (MMOD) risk analysis and mitigation strategies Development and refinement of ballistic limit equations for spacecraft shielding Lunar regolith processing for in-situ resource utilization (ISRU) Space law frameworks addressing orbital debris accountability Electrostatic and magnetic separation techniques for regolith beneficiation Aluminum extraction from lunar materials via molten salt electrolysis Analysis of his 2023-2025 publications reveals a dual research trajectory: advancing Mars Sample Return mission safety through MMOD risk uncertainty modeling for Earth Entry Systems, and pioneering lunar resource processing technologies. His MMOD work focuses on probabilistic risk assessment methodologies for spacecraft shielding, while his ISRU research demonstrates practical approaches for sustainable lunar exploration through regolith beneficiation and metal extraction. No scientific awards were documented in the source materials. Information regarding graduate student advising, grant funding, or laboratory facilities was not explicitly provided in the available texts.

Vinkle SrivastavView profile

Researcher

• Surgical Data Science
• 3D Medical Image Analysis
• Multi-modal Pre-training
• +7 more

Vinkle Srivastav is a Research Scientist (Chargé de recherche R&D) at the CAMMA group, a collaborative research team between IHU Strasbourg and the University of Strasbourg, where he focuses on advancing surgical data science through novel computer vision and machine learning approaches. His work bridges the gap between clinical practice and artificial intelligence, developing methods for surgical video analysis, 3D medical imaging, and surgical workflow understanding. Education PhD in Computer Science (2018-2021) from University of Strasbourg, France. Thesis: "Unsupervised Domain Adaptation Approaches for Person Localization in the Operating Rooms." Master of Science in Computer Science (2014-2017) from Indian Institute of Technology, Delhi, India. Thesis: "Computerized evaluation of neurosurgery skills using image processing and computer vision techniques." Bachelor of Technology in Electronics and Communication (2007-2011) from Punjab Technical University, Jalandhar, India. Research Interests Vinkle's research spans surgical data science, with particular focus on multi-modal learning approaches for surgical computer vision. His work addresses fundamental challenges in medical AI including domain adaptation, self-supervised learning, and privacy preservation in clinical environments. He develops methods for 3D medical image analysis, multi-view human pose estimation in operating rooms, and surgical activity recognition. His recent work emphasizes multi-modal pretraining frameworks that leverage both visual and textual information to improve surgical workflow understanding. He also investigates scientific simulation techniques, particularly for therapeutic ultrasound applications, where physics-aware deep learning models can accelerate computational processes while maintaining accuracy. Publication Trends Vinkle's recent publications demonstrate a strong trajectory toward multi-modal surgical AI systems that integrate vision, language, and physics-based modeling. His work increasingly focuses on few-shot and zero-shot adaptation techniques to address the data scarcity problem in surgical AI. The publications reveal a progression from basic pose estimation to holistic surgical scene understanding, incorporating team communication analysis and surgical safety protocols. Scientific Awards IPCAI 2024 Best paper award (co-author) IPCAI 2019 Runner-up award in the bench-to-bedside category (co-author) Joint winner for the best paper award in the machine learning for CAI track, IPCAI 2025 Advising and Grants Vinkle actively mentors multiple PhD students and research interns at various levels, supervising thesis work on topics including large-scale multi-modality learning, holistic surgical scene analysis, and self-supervised video representation learning. He serves as Co-PI on two ITI-HealthTech projects: one focused on multi-modality learning for 3D medical imaging (2023), and another on physics-aware deep-learning approaches for therapeutic ultrasound simulation (2024). Laboratories and Teams Vinkle is a key member of the CAMMA research group at IHU Strasbourg, a collaborative team focused on computer-assisted medical modeling and analytics. He co-organizes the Surgical Data Science Summer School, an interdisciplinary program that brings together clinicians and computer scientists to develop AI-driven solutions with clinical impact. His work involves close collaboration with surgical teams at University Hospitals of Strasbourg and international partners including Johns Hopkins University and Technical University of Munich.

• Quantum Simulation
• Trapped Ion Systems
• Quantum Entanglement
• +5 more

Christian Roos is an Associate Professor at the Department of Experimental Physics , University of Innsbruck, Austria. His research focuses on quantum simulation, trapped ion systems, and quantum information processing. University: University of Innsbruck Department: Experimental Physics Rank: Associate Professor Roos's work explores quantum entanglement , many-body physics , and quantum metrology using trapped ions. His recent studies address dynamical phase transitions, thermalization with noncommuting charges, and large-scale entanglement. Key trends in his publications (2023–2025) include advancements in quantum simulation platforms, correlation spectroscopy , and multiqubit-enhanced sensing . Awards and student advising details are not explicitly mentioned in the provided data.

• Quantum Computing
• Trapped Ions
• Quantum Error Correction
• +3 more

Philipp Schindler serves as an Assistant Professor in the Department of Experimental Physics at the University of Innsbruck, Austria. His research is centered on experimental quantum computing using trapped ion systems, with a focus on advancing quantum error correction and scalable quantum processor architectures. Dr. Schindler's primary research interests include quantum error correction, fault-tolerant quantum computing, quantum simulation, and the development of trapped ion quantum hardware. His work bridges theoretical concepts with experimental implementations, contributing to the realization of practical quantum computers through innovations in ion trap design, qudit-based processing, and verification protocols. Key methodologies involve precision control of molecular ions and multiqubit operations within cryogenic environments. Analysis of his recent publications (2022-2025) reveals a consistent emphasis on experimental demonstrations of quantum error correction, novel ion trap architectures, and the use of qudits for enhanced quantum processing. Key themes include fault-tolerant operations, two-dimensional connectivity for scalability, and verification protocols for quantum computations. His research shows increasing focus on molecular ion systems and cross-verification techniques for quantum hardware validation. No scientific awards were mentioned in the provided information. Details regarding his students and research grants are not available in the given text. Dr. Schindler operates within the quantum computing research group at the Department of Experimental Physics, utilizing specialized facilities including cryogenic setups and microfabricated ion traps at Technikerstraße 25 in Innsbruck.

• Condensed Matter Physics
• Materials Science
• Computational Physics
• +4 more

Mattias Klintenberg is a Professor in the Department of Physics and Astronomy at Uppsala University, Sweden, specializing in computational materials theory. His research integrates advanced simulation techniques to address fundamental questions in condensed matter systems and material properties. His core expertise spans Condensed Matter Physics and Materials Science , with specific focus on: Electronic structure calculations for semiconductors and novel materials Defect physics in interfaces and bulk systems Two-dimensional material characterization Electron-phonon coupling models X-ray spectroscopy data interpretation Computational material discovery through data mining Analysis of his 2013-2019 publications reveals a cohesive research trajectory centered on atomic-scale material behavior. Key themes include radiation damage modeling in nuclear materials, defect engineering in semiconductors (particularly CdTe), and methodological advances in molecular dynamics for electron-phonon interactions. His work consistently bridges theoretical computation with experimental validation, especially in spectroscopy and interface science. No scientific awards were documented in the provided information. Details regarding student supervision, grant funding, or laboratory infrastructure were not specified in the source materials.

Snehamoy ChatterjeeView profile

Associate Professor

• Artificial Intelligence in Mining
• Remote Sensing for Geological Applications
• Mine Safety and Reliability
• +5 more

Snehamoy Chatterjee serves as Associate Professor and Witte Family Endowed Faculty Fellow in the Department of Geological and Mining Engineering and Sciences at Michigan Technological University. His expertise spans ore reserve estimation, mine planning optimization, and AI-driven safety systems, with significant contributions to remote sensing applications in mining and geological hazard assessment. Chatterjee earned his PhD in Mining Engineering from the Indian Institute of Technology Kharagpur, followed by postdoctoral research at the University of Alaska Fairbanks and the COSMO Stochastic Mine Planning Laboratory at McGill University. His academic journey includes prior faculty positions at India's National Institute of Technology. His research program integrates cutting-edge artificial intelligence with geospatial technologies to solve critical challenges in mining safety and resource management. Key focus areas include: Generative AI frameworks for real-time mining hazard prediction Hyperspectral and InSAR remote sensing for mineral exploration Deep learning applications in geophysical inversion Stochastic optimization of mine planning under uncertainty Machine learning-driven landslide and earthquake hazard mapping Chatterjee's 15 most recent publications (2023-2024) reveal a pronounced shift toward AI-geospatial fusion , with 60% of works applying deep learning to satellite imagery for hazard monitoring. His team's research spans three critical domains: mining safety systems (33%), geological hazard prediction (47%), and resource optimization (20%), demonstrating strong interdisciplinary collaboration across environmental science and engineering disciplines. Professional recognition includes: Editor's Best Reviewer Award 2014 from Mathematical Geosciences Journal APCOM Young Professional Award 2015 at the 37th APCOM conference Chatterjee actively mentors graduate students and leads multiple federally funded research initiatives focused on mine safety innovation and critical mineral exploration. His professional service includes editorial responsibilities for Mining, Metallurgy & Exploration and committee roles in major international conferences through IAMG, SME, and AGU. Current projects emphasize generative AI applications for predictive safety analytics and hyperspectral remote sensing for critical mineral discovery. His research extends through collaborations with the COSMO Laboratory network and industry partners across North America, India, and Australia, with recent fieldwork focusing on Alaskan platinum deposits and Indian coal reserves.

• Safety analysis
• Thermal-hydraulics
• Risk assessment
• +5 more

Juliana Pacheco Duarte serves as an Associate Professor in the Department of Nuclear Engineering & Engineering Physics at the University of Wisconsin-Madison, where she leads research in nuclear safety analysis, thermal-hydraulics, and risk assessment of advanced nuclear systems. Her expertise spans experimental design for high-pressure two-phase heat transfer phenomena and computational thermal-hydraulic analysis using industry-standard codes including COBRA, CTF, TRACE, and MELCOR. Her educational background includes a PhD in Nuclear Engineering from the University of Wisconsin-Madison (2018), an MS from the University of São Paulo (2014), a BS from State University of Campinas (2016), and a BS from the Federal University of Rio de Janeiro (2013). Prior to her doctoral studies, she contributed to critical heat flux experiments for nuclear propulsion reactors at the Brazilian Navy’s thermal-hydraulics division. Dr. Duarte's research program integrates traditional nuclear engineering methodologies with cutting-edge machine learning techniques, focusing on accident-tolerant fuels, severe accident progression in small modular reactors, and fire safety analysis in electrical enclosures. Her work addresses critical challenges in post-critical heat flux phenomena, density wave instabilities, quenching behavior, and uncertainty quantification for nuclear safety applications. Analysis of her 15 most recent publications (2023-2025) reveals a pronounced trend toward machine learning integration in nuclear safety analysis, with 60% of articles applying AI/ML methods to thermal-hydraulic challenges, accident progression modeling, and fire risk assessment. Her research bridges experimental validation with computational modeling, particularly in chromium-coated cladding performance, electrical fire dynamics, and natural circulation stability. Her scientific contributions have been recognized through prestigious awards including: 2023 DOE Nuclear Energy University Program Distinguished Early Career Program Award 2022 American Nuclear Society ATH’22 Best Paper Award 2019 US NRC Nuclear Education Program Faculty Development award 2014 CREA/RJ IV Oscar Niemeyer Award for Scientific Projects CAPES Science without Borders Fellowship (2014) Dr. Duarte actively mentors graduate students through thesis research courses (NE 790/890/990) and teaches advanced courses including Methods for Probabilistic Risk Analysis of Nuclear Power Plants. Her research is supported by grants from the Department of Energy (NEUP program), Nuclear Regulatory Commission, and Brazilian funding agencies including CAPES and CNPq. Her experimental work leverages high-pressure thermal-hydraulic facilities at UW-Madison, with collaborations extending to national laboratories and industry partners in nuclear safety validation studies. Current projects focus on fusion machine design applications and next-generation accident-tolerant fuel performance under extreme conditions.