جست‌وجوی استاد و پژوهشگر

• Friction stir welding
• Steel metallurgy
• Materials engineering
• +۴ مورد دیگر

Dr. Athanasios Toumpis is a Senior Lecturer in Mechanical and Aerospace Engineering at the University of Strathclyde, UK. He holds a Master of Science from the University of Glasgow (2012) and a Master of Engineering from the National Technical University of Athens (2003). His research focuses on friction stir welding (FSW), steel metallurgy, and fatigue analysis of structural materials. Key areas include defect analysis in FSW joints, thermal-mechanical behavior of materials, and gigacycle fatigue testing of welded steels. He has led multiple research projects funded by organizations like the Royal Society and Weir Group, including the development of novel FSW technologies for nuclear applications and investigations into steel joint performance under extreme conditions. Dr. Toumpis has authored over 50 publications, with recent work emphasizing very high-cycle fatigue behavior and additive manufacturing processes. He actively participates in international conferences and serves as a principal investigator on various collaborative projects. His teaching includes courses on applied metallurgy, biomaterials, and materials selection. Research Interests: Friction stir welding of low-alloy and stainless steels Fatigue analysis of welded joints, including gigacycle testing Thermal-mechanical analysis of additive manufactured materials Metallurgical characterization of dissimilar material joints Environmental impact assessment of manufacturing processes Professional Activities & Awards: Recipient of the Global Engagement Fund (2024) Organized the First Joint International Conference on Advances in Mechanical and Aerospace Engineering (2023) Participant in the International Institute of Welding Assembly (2024) and Very High Cycle Fatigue Conference (2024) Hosted visiting researchers and collaborated with institutions like Graz University of Technology Grants & Projects (Recent): Development of a novel friction stir additive manufacturing technology (Royal Society, 2025–2026) Investigation of friction stir welded steel joints for giga-cycle applications (Weir Group, 2025–2027) ESCO buckets weld performance investigation (University of Strathclyde, 2024–2027)

Theresa Raimondoمشاهده پروفایل

استادیار

• Biomaterials
• Drug Delivery
• RNA-based Therapeutics
• +۵ مورد دیگر

Theresa Raimondo is the Manning Assistant Professor of Engineering at Brown University, with a secondary appointment in the Division of Biology and Medicine. She joined the Brown Engineering faculty in January 2024 after completing her postdoctoral training at MIT's Koch Institute. Dr. Raimondo leads the Raimondo Research Lab, which focuses on chemically modifying RNA and designing nanoparticles for therapeutic delivery to the body, an immunotherapy concept that holds immense promise in the field of immunoengineering. Her educational background includes: PhD in Engineering Sciences – Bioengineering from Harvard University (2019) MEng from Harvard University (2019) Sc.B. in Chemical and Biochemical Engineering from Brown University (2011) Dr. Raimondo's research is broadly focused on the design of targeted drug-delivery vectors and novel RNA-based therapeutics for applications in cancer, immunotherapy, and tissue regeneration. Her work primarily centers on developing novel lipid nanoparticles (LNPs) for RNA-based therapies, contributing to adjuvanted mRNA-based vaccines and siRNA-based cancer immunotherapies. By optimizing LNP formulation and modulating RNA constructs, she seeks to understand how RNA-LNPs modulate immunity and develop new therapeutic approaches. Her expertise spans biomaterials, drug delivery, biomolecular engineering, nanomedicine, tissue engineering, and regenerative medicine. Analysis of Dr. Raimondo's recent publications reveals a strong focus on RNA delivery systems and lipid nanoparticle technology. Her work spans from fundamental studies on nanoparticle design to applications in cancer immunotherapy, vaccine development, and tissue regeneration. A significant portion of her research involves optimizing lipid formulations for improved mRNA delivery and exploring how these systems interact with the immune system. Her publications demonstrate a trajectory from basic biomaterials research to increasingly translational work with therapeutic applications. Dr. Raimondo has received numerous prestigious awards: 2025 NAE Symposium selection (Grainger Foundation Frontiers of Engineering) 2025 appointment to the inaugural Early Career Board of ACS Applied Bio Materials 2024 selection as MIT Faculty Founder Initiative finalist 2022 Convergence Scholar fellowship from MIT's Marble Center for Cancer Nanomedicine National Science Foundation graduate research fellowship Harvard's Smith family graduate fellowship Dr. Raimondo is actively involved in mentoring students through courses including ENGN 0931L - Biomedical Engineering Design and Innovation II, ENGN 1490 - Biomaterials, and ENGN 1931L - Biomedical Engineering Design and Innovation II. Her research program is supported by various grants, though specific funding sources aren't detailed in the provided text. The Raimondo Research Lab represents a dynamic environment where engineering principles are applied to solve complex biological challenges in drug delivery and regenerative medicine. The Raimondo Research Lab at Brown University serves as a hub for innovation in RNA delivery and biomaterials design. The lab brings together expertise in chemical engineering, molecular biology, and immunology to develop next-generation therapeutic platforms. Current research directions include optimizing lipid nanoparticle formulations, exploring novel RNA modifications, and investigating immune responses to RNA therapeutics across various disease contexts.

Iqbal Husainمشاهده پروفایل

عضو هیئت علمی

• Power Electronics
• Electric Vehicles
• Control Systems
• +۳ مورد دیگر

Dr. Iqbal Husain is the Director of the FREEDM Center and an ABB Distinguished Professor in the Department of Electrical and Computer Engineering at North Carolina State University. Previously, he served at the University of Akron for 17 years before joining NC State. He holds a Ph.D. (1993), M.S. (1989), and B.S. (1987) in Electrical Engineering from Texas A&M University and Bangladesh University of Engineering and Technology, respectively. His research focuses on power electronics, electric drives, and renewable energy systems, with applications in transportation, automotive, and aerospace. Notable contributions include advancements in electric machine design, inverter controls, and grid synchronization. He authored the textbook *Electric and Hybrid Vehicles: Design Fundamentals*, now in its third edition. Dr. Husain’s awards include the NSF CAREER Award (1997), SAE Vincent Bendix Award (2006), and IEEE Fellow (2009). His recent work includes developing AI-enabled tools for power grid cybersecurity and medium-voltage solid-state transformers for EV fast charging. He leads interdisciplinary projects at the FREEDM Systems Center, addressing challenges in clean energy and smart grid technologies.

Jan Akmalمشاهده پروفایل

استادیار

• Additive Manufacturing
• 3D Printing Technology
• Defect Detection
• +۵ مورد دیگر

Jan Akmal is an Assistant Professor at Aalto University, holding dual affiliations in the Department of Energy and Mechanical Engineering and the Materials to Products group. His research specializes in additive manufacturing (AM), focusing on defect detection, smart materials, and 4D printing applications. He leads the AIM-Zero project (2023–2026), exploring AI-driven zero-defect AM processes. Akmal has received the Aalto Doctoral Incentive Scholarship (2023) and an Honorary Award (2023). He serves on editorial boards for Frontiers in Manufacturing Technology and Frontiers in Mechanical Engineering , and chairs the Finnish Rapid Prototyping Association (FIRPA). Key research areas include AI-based defect detection in metal AM, self-sensing components, and hybrid materials for dynamic displays. He collaborates globally on topics like optical tomography in powder bed fusion and medical AM applications. His work addresses sustainability, industrial adoption of AM, and legal frameworks for military logistics. Akmal has authored 24 publications and contributed to datasets on AM inaccuracies and defect classification, emphasizing practical applications and industry integration.

Ivan Coleمشاهده پروفایل

استاد مدعو

• Corrosion Science
• Multiscale Modelling
• Additive Manufacturing
• +۷ مورد دیگر

Prof. Ivan Cole is an Adjunct Professor at RMIT University's School of Engineering, specializing in rapid materials discovery for corrosion protection, nanostructures, and additive manufacturing. His work integrates computational modeling with high-throughput experimentation, focusing on corrosion inhibitors, biocompatible surfaces, and additive manufacturing process optimization. With over 30 years of experience across academia and industry (including leadership roles at CSIRO and Centro-Svilluppo Materiali), he leads the Rapid Discovery & Fabrication Team (RDF) to advance these research areas. Research Interests: Corrosion science, microbially induced corrosion (MIC), additive manufacturing surfaces, nanostructure sensing, multiscale modeling, and green materials discovery. His team addresses challenges in corrosion protection, biomedical implants, and environmental remediation through innovative methodologies. Awards: 2019 Australian Corrosion Medal 2016 CSIRO Lifetime Achievement Award 2013 Best Paper in NACE Corrosion Supervision & Projects: Active in mentoring PhD/Master’s students across corrosion inhibition, additive manufacturing, and nanostructure design. Notable projects include developing quorum sensing inhibitors for biofilm control, in-situ monitoring for metal AM, and eco-friendly corrosion inhibitors. Labs & Collaborations: Leads the Rapid Discovery & Fabrication Team and collaborates with industry partners to translate research into practical solutions for materials durability and sustainability.

Jun Zhuangمشاهده پروفایل

استادیار

• Artificial Intelligence
• Robust Machine Learning
• Trustworthy AI Systems
• +۵ مورد دیگر

Jun Zhuang is an Assistant Professor in the Department of Computer Science at Boise State University. He holds a Ph.D. from Indiana University-Purdue University Indianapolis (IUPUI), M.S. degrees in Computer Science (University at Buffalo) and Finance (Rochester Institute of Technology), and a B.E. in Safety Engineering (South China University of Technology). His research focuses on trustworthy and robust AI systems, Bayesian inference, generative models, quantum computing, and medical imaging. Education: Ph.D., Computer Science, IUPUI (2023) M.S., Computer Science, University at Buffalo (2018) M.S., Finance, Rochester Institute of Technology (2013) B.E., Safety Engineering, South China University of Technology (2011) Research Interests: Jun investigates robust machine learning algorithms, particularly in quantum information, medical imaging, and graph-based systems. He emphasizes mitigating adversarial attacks, enhancing model interpretability, and integrating blockchain for AI security. His work spans theoretical foundations and practical applications, including generative adversarial networks (GANs) and trustworthy AI frameworks. Recent Articles: His recent work addresses jailbreaking vulnerabilities in large language models (LLMs), quantum computing optimization challenges, and robust graph structure learning. These studies highlight interdisciplinary approaches to advancing AI reliability and security. Awards & Grants: Recipient of the SIGIR Student Travel Grant for CIKM 2022. Active in grant activities through research collaborations and institutional funding. Advising & Labs: Advisor to Ph.D. student Maqsudur Rahman and M.S. students Chia-Ying Wu and Shipra Kumari. Leads the T rustworthy and R obust AI L ab (TRAIL), focusing on developing resilient AI systems.

Esther Dufloمشاهده پروفایل

استاد

• Development Economics
• Global Poverty
• Health Economics
• +۴ مورد دیگر

Esther Duflo is the Abdul Latif Jameel Professor of Poverty Alleviation and Development Economics at MIT's Department of Economics. She co-founded and co-directs the Abdul Latif Jameel Poverty Action Lab (J-PAL) and holds the Chaire Pauvreté et politiques publiques at the Collège de France. A graduate of École Normale Supérieure (Paris) and MIT (PhD, 1999), her work focuses on experimental approaches to combat poverty through health, education, financial inclusion, environment, and governance research. She received the 2019 Nobel Prize in Economics for her experimental methods in development economics, alongside Abhijit Banerjee and Michael Kremer. Other honors include the John Bates Clark Medal (2010), MacArthur Fellowship (2009), and Princess of Asturias Award (2015). Duflo has advised governments and international bodies, including the U.S. President’s Global Development Council (2013–2021). Her books Poor Economics and Good Economics for Hard Times popularized her research. She also authored children’s books explaining poverty, aiming to demystify socioeconomic challenges for younger audiences.

Jennifer L. Westمشاهده پروفایل

استاد

• Biomaterials
• Nanotechnology
• Tissue Engineering
• +۳ مورد دیگر

Jennifer L. West is the Dean of the University of Virginia School of Engineering and Applied Science and holds the Saunders Family Professorship in Engineering. She is a dual professor in Biomedical Engineering and Mechanical and Aerospace Engineering. Dean West has a 30-year record as a researcher, educator, inventor, and entrepreneur, focusing on biomaterials, nanotechnology, and tissue engineering to address unmet medical needs, particularly in cancer therapy. Her education includes a B.S. from MIT (1992) and a Ph.D. from the University of Texas at Austin (1996). Before UVA, she was at Duke University as the Fitzpatrick Family Distinguished Professor of Engineering and Associate Dean for Ph.D. Education. Research Interests: Biomaterials and biosynthesis Nanotechnology and tissue engineering Cancer therapy through engineered materials Scientific Awards: Member of the National Academy of Medicine (2023) Member of the National Academy of Engineering (2016) Over 20 patents, including foundational work for Nanospectra Biosciences’ clinical trials in cancer therapy Grants & Initiatives: Leading UVA Engineering’s focus on research, experiential learning, and entrepreneurship Recipient of a $900,000 grant for character-building education initiatives Labs & Teams: Developed hydrogel platforms for tissue integration, vascularization, and drug delivery Pioneered gold nanoshell-based photothermal cancer therapy

• Quality and Reliability Engineering
• Design of Experiments
• Time Series Analysis
• +۶ مورد دیگر

Rong Pan is a Professor at the School of Computing and Augmented Intelligence, Arizona State University (ASU). He holds a Ph.D. in Industrial Engineering from Pennsylvania State University (2002), an M.S. from Florida A&M University (1999), and a B.S. in Materials Science from Shanghai Jiao Tong University (1995). His research focuses on quality and reliability engineering, design of experiments, time series analysis, and statistical learning theory. Key projects involve NSF-funded research on reliability prediction, accelerated life testing, and degradation modeling. He serves as an Associate Editor for the Journal of Quality Technology and has authored over 80 publications. Courses taught include Reliability Engineering, Design of Experiments, and Statistics for Data Analysts. His academic service includes roles as a referee for IEEE Transactions and IIE journals. Research interests emphasize statistical methods for reliability improvement, with recent work on Bayesian inference models, optimal experimental design, and machine learning applications in industrial systems. Grants include collaborations with the NSF, Arizona Department of Transportation, and Science Foundation Arizona. His work bridges theoretical advancements and practical applications in manufacturing, energy systems, and semiconductor reliability. Education: Ph.D. (2002), M.S. (1999), B.S. (1995) Key Research Areas: Reliability Engineering, Bayesian Methods, Time Series, DOE Active Grants: NSF CMMI, SUNY IT Visiting Scholar Program Teaching: IEE 573 Reliability Engineering, DSE 501 Statistics Service: Journal of Quality Technology (Associate Editor), IEEE Transactions (Referee)

• Nanophotonics
• Metasurfaces
• Inverse Design
• +۳ مورد دیگر

Jonathan Fan is an Associate Professor at Stanford University in the Department of Electrical Engineering. His teaching portfolio includes graduate and undergraduate courses in electromagnetics, integrated circuit fabrication, and specialized studies across all quarters. EE 242: Electromagnetic Waves (Autumn) EE 312: Integrated Circuit Fabrication Laboratory (Winter) ENGR 42/EE 42: Electromagnetics and Applications (Spring) 11 independent studies and thesis courses (EE 190, EE 191, EE 300, etc.) His research focuses on nanophotonics and metasurface engineering , with particular emphasis on inverse design methodologies, machine learning -driven photonic optimization, and machine learning in electromagnetic simulation. His recent publications demonstrate a strong trend toward deep learning-enabled photonic design and high-speed optimization of complex optical systems. His work spans metamaterial fabrication , nonlocal effects in metasurfaces, and multi-functional optical devices such as spaceplates for aberration correction. Key technical contributions include physics-augmented neural networks , reparameterization techniques for design constraints, and topology-optimized metasurfaces .

• Environmental and Power Reactor Health Physics
• Radiation Detection
• Nuclear Security
• +۱ مورد دیگر

Jason Harris is a Professor in Health Sciences Education at Purdue University with a courtesy appointment in the College of Engineering, Department of Nuclear Engineering. He serves as a key researcher at the Center for Radiological and Nuclear Security (CRANS) and maintains active leadership roles in major nuclear professional organizations. His academic credentials include: Ph.D. in Health Physics from Purdue University (2007) M.S. in Nuclear Engineering from the University of Illinois at Urbana-Champaign (2002) B.S. in Biology and Chemistry from the University of Tampa (1995) Dr. Harris's research centers on Environmental and Power Reactor Health Physics , Radiation Detection , Nuclear Security , and Nuclear Science Education and Training . His work pioneers methodologies for integrating nuclear safety and security frameworks, developing quantitative risk assessment tools that address terrorism scenarios while maintaining operational safety standards in nuclear facilities. Analysis of his 2020-2024 publications reveals a dominant focus on nuclear security risk quantification, with 80% of works developing facility risk indices and safety-security integration tools. His research consistently applies advanced computational methods including Monte Carlo simulations, game theory, and analytical hierarchy processes to model adversarial behavior and optimize defense strategies against radiological threats. Professional leadership includes: ABET Program Evaluator for Health Physics Chair of Health Physics Program Directors Organization (HPPDO) Chair of Academic Education Committee (Health Physics Society) Member-at-Large, Executive Committee (Institute of Nuclear Materials Management) Former Chair, International Nuclear Security Education Network (IAEA) At CRANS, Dr. Harris directs research on practical security assessment tools, including graphical user interface implementations for risk index calculation and terrorism scenario modeling. His work bridges theoretical security frameworks with operational implementation in nuclear facilities worldwide.

Vadim Backmanمشاهده پروفایل

استاد

• Physical Genomics
• Chromatin Engineering
• Nanotechnology
• +۵ مورد دیگر

Dr. Vadim Backman is the Sachs Family Professor of Biomedical Engineering and Medicine at Northwestern University's McCormick School of Engineering and Applied Sciences and Feinberg School of Medicine. He holds additional roles as Professor of Medicine (Hematology/Oncology) and Biochemistry and Molecular Genetics, Associate Director of Research Technology and Infrastructure at the Robert H. Lurie Comprehensive Cancer Center, and Director of the Center for Physical Genomics and Engineering. He earned his Ph.D. in Medical Engineering from Harvard-MIT and M.S./B.S. in Physics from St. Petersburg Polytechnic Institute. His research focuses on physical and biological science intersections, developing nanoscale imaging and computational technologies to study chromatin dynamics and their role in disease. Key areas include cancer diagnostics/therapeutics, chromatin engineering, and genome nanoimaging. Dr. Backman has published over 230 papers, holds 20+ patents, and leads large-scale projects like NCI Bioengineering Research Partnerships. Education: Ph.D. (Harvard-MIT), M.S. (MIT), M.S./B.S. (St. Petersburg Polytechnic Institute) Affiliations: PhD Programs in Applied Physics and Interdisciplinary Biological Sciences Research emphasizes chromatin's role in disease, with clinical translation for diagnostics and therapy. His lab develops technologies like nano-CHIA and ChromSTEM, advancing understanding of genomic organization and epigenetic regulation. Awards include the Cozzarelli Prize and MIT Technology Review's Top 100 Innovators. Awards: Cozzarelli Prize (2017), AIMBE Fellowship (2009), NSF CAREER Award (2003) Grants and collaborations include managing multi-investigator projects and co-founding biotech companies. Courses taught: BME 302 (Quantitative Systems Physiology), BME 429 (Advanced Physical and Applied Optics).

He Wangمشاهده پروفایل

دانشیار

• Computer Graphics
• Computer Vision
• Machine Learning
• +۶ مورد دیگر

Dr. He Wang is an Associate Professor in the Department of Computer Science at University College London (UCL), affiliated with the Virtual Environment and Computer Graphics (VECG) group and the UCL Centre for Artificial Intelligence. He holds a Visiting Professorship at the University of Leeds and previously served as an Associate Professor and Lecturer there, as well as a Senior Research Associate at Disney Research Los Angeles. His research focuses on computer graphics, vision, and machine learning, with notable contributions to crowd simulation, generative models, and physics-informed neural networks. Dr. Wang earned his BEng from Zhejiang University and his PhD from the University of Edinburgh, followed by postdoctoral work at the University of Edinburgh's School of Informatics. He has been recognized as a Turing Fellow and serves as an Academic Advisor to the Commonwealth Scholarship Council and an Associate Editor of Computer Graphics Forum . His research spans cutting-edge topics including 3D reconstruction, adversarial attacks on motion recognition, and AI-driven groundwater modeling. He has supervised six PhD students to completion and actively engages in collaborative projects, consultancy, and grant evaluations. His lab welcomes students through dedicated recruitment channels.

• Statistical Genomics and Bioinformatics
• High-Dimensional Data Analysis
• Bayesian Statistics
• +۴ مورد دیگر

Shili Lin is a Professor of Statistics at The Ohio State University's Department of Statistics, within the College of Arts and Sciences. She joined the faculty in 1995 after serving as the Neyman Visiting Assistant Professor at the University of California, Berkeley. Her expertise spans statistical genomics, bioinformatics, high-dimensional data analysis, Bayesian statistics, and Monte Carlo methods. Lin collaborates extensively with medical researchers to address challenges in genomic data such as ultra-high dimensionality, complex dependencies, and sparsity, focusing on diseases like cancer, multiple sclerosis, tuberculosis, and diabetes. She has contributed to developing computational tools for analyzing chromatin interactions, methylation patterns, and metagenomic samples. Lin holds a PhD from the University of Washington (1993). Her professional roles include serving as an Associate Editor for Biometrics , Statistical Applications in Genetics and Molecular Biology , and Statistics in Biosciences , as well as an Editorial Board member for Genetic Epidemiology . She is a standing member of NIH's Biostatistical Methods and Research Design Study Section and has served on multiple NSF and NIH grant review panels. Additionally, she is President Elect of the Caucus for Women in Statistics and has been a member of the ASA Committee on AAAS representation for six years. Her research interests emphasize statistical methodologies tailored to genomic data, including model selection, epigenetic analysis, and integrative approaches for multi-omics data. Lin's work often combines theoretical advancements with practical applications, such as predicting relapse in immune-mediated disorders and improving imputation techniques for single-cell Hi-C analysis. She has pioneered software tools like TopKLists and GrammR to facilitate ranked list aggregation and metagenomic data analysis. Lin's scientific accolades include ASA Fellowship (2004), AAAS Fellowship (2009), and membership in the International Statistical Institute (2014). Her contributions to statistical genetics and epigenomics have been recognized through grants and editorial leadership roles. While her research group focuses on cutting-edge methods, no formal advisees or students are explicitly listed in the provided materials.

Rafael Brüschweilerمشاهده پروفایل

استاد

• Biophysical Chemistry
• Analytical Chemistry
• Protein Dynamics
• +۳ مورد دیگر

Rafael Brüschweiler is a Professor and Ohio Research Scholar at The Ohio State University, holding joint appointments in the Department of Chemistry and Biochemistry and the Department of Biological Chemistry and Pharmacology. He serves as the NMR Executive Director for the Ohio State Campus Chemical Instrument Center and the NSF-funded National Gateway Ultrahigh Field NMR Center. His research focuses on biophysical chemistry, analytical chemistry, and computational modeling, emphasizing protein dynamics, metabolomics, and NMR method development. He received his Ph.D. from ETH Zurich and completed a postdoc at the Scripps Research Institute. His research integrates experimental NMR, molecular dynamics simulations, and machine learning to study protein structure-function relationships, metabolic pathways, and biomolecular interactions. Key areas include the dynamics of oncogenic K-Ras, glucokinase glucose sensing, and nanoparticle-assisted NMR techniques. His work is funded by the NIH and NSF, with applications in biomedical diagnostics and drug discovery. Dr. Brüschweiler leads a multidisciplinary lab training students and postdocs in NMR spectroscopy, computational methods, and metabolomics. His lab developed tools like DEEP picker and COLMAR for automated NMR data analysis, contributing to the SECIM metabolomics center. He actively recruits students interested in protein dynamics, computational modeling, or metabolomics.