Assoc Prof Ng Teng Yong is an Associate Professor at the School of Mechanical & Aerospace Engineering (NTU), specializing in numerical modeling and simulation. With a background as Research Manager at A*STAR Institute of High Performance Computing, his work spans materials science, nanotechnology, and aerospace engineering. Current focus on graphene-based desalination membranes Expertise in molecular dynamics simulations Investigates nanoscale fluid mechanics and structural dynamics Recent publications highlight advancements in energy-efficient electrodialysis, smart robotics, and nonlinear vibration analysis. His interdisciplinary approach integrates computational methods with experimental validation in additive manufacturing and soft material mechanics.
Andrew D. White is an Associate Professor of Chemical Engineering at the Hajim School of Engineering & Applied Sciences, University of Rochester. He holds a PhD from the University of Washington (2013). His research focuses on automating scientific discovery through AI, particularly leveraging large language models (LLMs) and deep learning techniques in chemistry. His lab develops agents that integrate literature analysis, hypothesis generation, and experimental design to advance fields like molecular dynamics and drug discovery. Education: PhD in Chemical Engineering, University of Washington, 2013 BS/MS (not explicitly stated in text, inferred from career timeline) Research Interests: Large language models for scientific automation Deep learning applications in chemistry and materials science Molecular dynamics simulations Scientific agents and autonomous systems Publications: His work includes groundbreaking studies on closed-loop AI systems for chemistry, federated learning in molecular property prediction, and multi-agent systems for drug discovery. Recent highlights include the Robin system and ChemCrow tools. Awards: Recipient of the NSF Career Award (2018), NIH Outstanding Investigator Award (2020), and the Curtis Teaching Award (2019). He also advises biotech companies and serves on the National Academy of Sciences' Chemical Sciences Roundtable. Grants & Funding: Supported by DOE, NSF (multiple grants including CBET-1751471), NIH (R35GM137966), and LLNL projects. Collaborates with institutions like Argonne National Lab and Qubit Pharmaceuticals. Labs & Teams: Leads the White Lab at Rochester and co-founded FutureHouse, a nonprofit advancing AI-driven scientific discovery. Supervises a multidisciplinary team of PhD students and postdocs in computational chemistry, AI, and biophysics.
R. Edwin García is a Professor at the School of Materials Engineering at Purdue University, where he has been faculty since 2005. He holds appointments in the Materials Engineering department within Purdue's College of Engineering, specifically in the School of Materials Engineering located in the Neil Armstrong Hall of Engineering at Purdue's West Lafayette campus. His educational background includes: B.S. in Physics from the National University of Mexico (1996) M.S. in Materials Science and Engineering from Massachusetts Institute of Technology (2000) Ph.D. in Materials Science and Engineering with a minor in Applied Mathematics from Massachusetts Institute of Technology (2003) Professor García's research focuses on the design of materials and devices through the development of a fundamental understanding of the solid state physics of individual phases, their short and long range interactions, and associated microstructural properties and time evolution. His current research emphasizes establishing relationships between material properties and resultant performance and degradation in electrochemical systems. He integrates computational approaches ranging from kinetic Monte Carlo, phase field and level set methods, to finite elements, finite volumes, and symbolic computing. His work particularly addresses microstructure design, crystallographic texture, and grain boundary science and engineering to control the topology of underlying phases and establish practical relations between processing, microstructure, and material properties. His recent publications demonstrate a strong focus on lithium-ion battery technology, ferroelectric materials, and computational modeling of material behaviors. The research trends show increasing integration of machine learning with traditional computational methods, exploration of novel sintering techniques like flash sintering, and deeper investigation into the fundamental mechanisms of material degradation in energy storage systems. His work spans multiple length scales from atomistic to continuum modeling, reflecting a comprehensive approach to materials design and analysis. Professor García teaches several courses including MSE 230 (Structure and Properties of Materials), MSE 350 (Thermodynamics of Materials), MSE 597G (Modeling and Simulation of Materials), MSE 597I (Introduction to Computational Materials), and MSE 597N (Physical Properties of Crystals). He mentors graduate students in areas related to computational materials science, battery technology, and microstructural evolution. His research group, the Laboratory of Computational Microstructures, focuses on developing home-grown analytical theories and algorithms to resolve relevant time and length scales in materials systems. The group's work has significant implications for portable power sources, including rechargeable batteries and fuel cells, as well as for ferroelectric ceramic applications.
Lucia Carichino is an Assistant Professor in the School of Mathematics and Statistics at Rochester Institute of Technology (RIT). She holds a PhD in Mathematics from Purdue University and a BS/MS in Mathematical Engineering from Politecnico di Milano, Italy. Her research focuses on mathematical and computational models of multiscale biological systems, particularly fluid-structure interaction in biological contexts like ocular blood flow and microswimmers. She emphasizes integrating experimental data with mathematical models to advance medical understanding. Carichino teaches courses such as Differential Equations, Linear Algebra, and oversees undergraduate research projects. In 2023, she received the National Science Foundation LEAPS-MPS award for her work on computational modeling of eye-contact lens interactions. Her research has been published in high-impact journals and presented at conferences. She actively collaborates on projects addressing glaucoma, ocular hemodynamics, and biomedical applications. Education: PhD in Mathematics, Purdue University BS and MS in Mathematical Engineering, Politecnico di Milano, Italy Research interests include fluid dynamics, numerical methods, and mathematical biology. Her work bridges theoretical models with biomedical applications, such as optimizing gene therapy delivery and analyzing ocular physiology under varying environmental conditions (e.g., altitude). She explores topics like sperm motility, computational simulations of biological systems, and the interplay between fluid dynamics and biological structures. Her recent articles highlight advancements in ocular pharmacokinetics, contact lens interactions, and altitude effects on intraocular pressure. These studies underscore her expertise in multiscale modeling and fluid-structure interaction. Carichino also contributes to educational initiatives, fostering a collaborative classroom environment. Notable awards include the NSF LEAPS-MPS award (2023). She advises student research projects and collaborates with colleagues, such as Maki, on interdisciplinary studies. Her work is supported by grants and has led to presentations at ophthalmology and mathematics conferences. Carichino’s lab focuses on computational modeling of biological systems, particularly in ophthalmology and microscale fluid dynamics. Her team develops tools to simulate complex physiological processes, aiding in medical diagnostics and treatment strategies.
Dane Morgan is a Professor in the Department of Materials Science & Engineering at the University of Wisconsin-Madison, College of Engineering. His research focuses on computational materials science for materials design, including ab initio electronic structure modeling, multiscale methods, and machine learning applications in materials discovery. His work spans nuclear materials, battery and fuel cell electrodes, and electronic materials. Education : PhD, 1998, University of California, Berkeley MS, 1994, University of California, Berkeley BA, 1992, Swarthmore College Research Interests : Computational materials science, ab initio methods for electronic structure and thermokinetics, machine learning for materials discovery, electrochemical systems modeling, and applications in nuclear materials, batteries, and electronic materials. His work integrates advanced computational techniques with experimental validation. Scientific Awards : 2024 APL Materials, Editors Pick 2023 Microscopy and Microanalysis Best Paper Award (Instrumentation and Software category) 2023 IEEE Transactions on Plasma Science Best Paper Award 2023 Kellet Mid-Career Award 2015 TMS Materials Genome Initiative Ambassador 2006 3M Technical Nontenured Faculty Grant
Long Cai is a Professor at the California Institute of Technology, affiliated with the Biology and Biological Engineering department. He pioneered the field of spatial genomics and co-developed transformative technologies such as seqFISH and MEMOIR. Research Interests: His work focuses on decoding biological systems through spatial genomics, integrating molecular imaging with computational analysis to uncover cellular organization in tissues. Key areas include developmental biology, neuroscience, kidney regeneration, and cancer biology. Publications: Recent studies highlight applications of spatial transcriptomics in kidney disease, brain nuclear architecture, and multi-omics tissue mapping. His research emphasizes creating high-resolution atlases of cellular dynamics. Scientific Awards: NIH Director’s Pioneer Award (2022) Labs & Collaborations: He leads the Cai Lab, which develops cutting-edge imaging tools in collaboration with the Elowitz Lab and other interdisciplinary teams.
Anil K. Jain is a University Distinguished Professor at Michigan State University, where he has taught and conducted research for over 50 years. His work focuses on Pattern Recognition , Biometrics , and Machine Learning , with foundational contributions to fingerprint, face, and palmprint recognition. B.S., Indian Institute of Technology, Kanpur (1969) M.S. and Ph.D., The Ohio State University (1970, 1973) in Electrical Engineering His research spans Computer Vision , Deep Learning , and Biometric Security , addressing challenges in adversarial robustness , demographic bias , and generative models . Recent publications emphasize transformer-based architectures , domain adaptation , and contactless biometric systems . Scientific awards include: Inductee, National Academy of Engineering (2016) Inductee, The World Academy of Sciences (2019) BBVA Foundation Frontiers of Knowledge Award (2025) Fellowships: Guggenheim, Humboldt, Fulbright Doctor Honoris Causa: 3 universities He has authored seminal works like Introduction to Biometrics and Handbook of Face Recognition , and served as Editor-in-Chief of IEEE Transactions on Pattern Analysis and Machine Intelligence . His leadership in Forensic Science includes roles on the Defense Science Board and AAAS study teams.
Professor Hala Zreiqat AM is a leading biomedical engineer at The University of Sydney , serving as the Director of the ARC Training Centre for Innovative BioEngineering . A Fellow of all major Australian academies (AAS, ATSE, FAHMS, FRSN), she develops 3D printed bioceramics for bone regeneration while championing diversity through initiatives like the IDEAL Society and BIOTech Futures mentorship program. Her work bridges academia, clinical practice, and industry in musculoskeletal research . Research Focus: Her lab creates synthetic bone scaffolds that mimic natural bone architecture, strength, and porosity, enabling non-rejected bone regeneration via patient-matched implants. Key applications include orthopaedic, dental, and maxillofacial repair , with over $18M in competitive funding and multiple patents. Current projects explore AI-driven scaffold performance prediction and anti-senescence strategies for aging-related bone loss. Scientific Trends: Recent publications highlight 3D printed nanovoxelated ceramics , antisenescence biomaterials , and multifunctional theranostic platforms . Her team integrates machine learning for scaffold design, atom probe tomography for interface analysis, and two-photon imaging for cellular monitoring in 3D environments. 2021-2022 Fulbright Senior Scholar 2018 NSW Premier's Woman of the Year 2019 Eureka Prize for Innovative Use of Technology Fellow of Australian Academy of Science (2021) Over $18M in research funding Teaching & Leadership: She designed core courses like Tissue Engineering and Nanomaterials in Medicine , mentoring 158 students in 2020 alone. As Chair of CAAR (2020-2023), she strengthens Australia-Arab collaborations. Her lab trains early-career researchers , with alumni now in academia and industry.
Ronnie Sircar is the Eugene Higgins Professor of Operations Research and Financial Engineering at Princeton University , where he contributes to the Department of Operations Research and Financial Engineering (ORFE). His work spans financial mathematics, stochastic modeling, and applied probability, with a focus on market volatility, optimal investment strategies, and dynamic game theory. Email: sircar@princeton.edu Office: Sherrerd Hall, Room 208, Princeton, NJ 08544 His research interests include: Stochastic Volatility: Asymptotic analysis, calibration, and impact on option pricing and portfolio optimization. Mean Field Games: Applications to cryptocurrency mining, energy markets, and interbank network formation. Portfolio Theory: Forward performance processes, drawdown constraints, and risk-averse strategies. Credit Risk: Multi-name credit derivatives, CDO valuation, and risk measures. Energy Systems: Renewable reliability, unit commitment, and electricity market design. Recent publications emphasize mean field games in energy and blockchain, stochastic volatility in portfolio optimization, and machine learning applications for financial engineering. He has advised graduate students such as Giulia Crippa, Nicolas Garcia, and Burak Aydin, often collaborating with researchers including M. Soner, P. Chan, and A.M. Reppen.
Prof. Raul Fangueiro is a Full Professor and Vice-Dean at the School of Engineering, University of Minho, Portugal. As a Senior Researcher, he leads FIBRENAMICS – Institute of Innovation on Fiber-Based Materials and Composites. His work spans advanced materials (nano, smart, composites) and structures (3D, auxetic, multiscale) with applications in defense, healthcare, construction, and automotive sectors. Supervised over 20 PhD and Post-Doc researchers Scientific coordinator of 20+ national/international research projects Author of 200+ journal papers (H-index: 47), 500+ conference publications, 36 books, 40 patents Research focuses on nanotechnology , electrospinning , and sustainable material systems : Auxetic composites for personal protection Biodegradable nanofibers for medical use Smart textiles for biological/chemical resistance Recycled mineral/wood-based composites Graphene-reinforced green materials Multiscale fiber architectures Scientific recognition includes: Top 2% most influential scientist (Elsevier/Stanford 2020) Founder of AUXDEFENSE and ICNF conferences Editorial board member of leading composite journals Advisor to European Defense Agency/NATO working groups Active in industry-academia partnerships through spin-offs (Sciencentris, B4Logic, Beyond Composites, Givaware, Pixartidea) and collaborative projects with institutions like Instituto Superior Técnico, University of Aveiro, and international universities.
Ibo van de Poel is a Full Professor at Delft University of Technology , within the Faculty of Technology, Policy and Management , specifically in the Section Philosophy . He holds the prestigious Antoni van Leeuwenhoek professorship , awarded to excellent young researchers, and is a leading figure in Design for Values , engineering ethics , and technology as social experimentation . Education: Van de Poel studied Philosophy of Science, Technology and Society at the University of Twente , with a propaedeutic exam in Mechanical Engineering . He earned his PhD in Science and Technology Studies (STS) in 1998 from the University of Twente , under the supervision of Prof. Dr. A. Rip. Research Interests: His research spans three major themes: Design for Values : Investigating how moral values can be integrated into engineering design, including empirical studies on how engineers handle value conflicts. Moral Responsibility in R&D Networks : Exploring the "problem of many hands" in large-scale research and development projects, developing formalizations and empirical methods. Ethics of Emerging Technologies : Examining ethical issues in new technologies like nanotechnology and synthetic biology , with a focus on technology as social experimentation . Publications and Impact: Van de Poel has authored over 25 international peer-reviewed articles and 15 book contributions . He is co-editor of major works including the Handbook of Philosophy of Technology and the Engineering Sciences (Elsevier, 2009), Philosophy and Engineering (Springer, 2010), and Ethics, Engineering and Technology (Wiley-Blackwell, 2011). His recent work includes leading the VICI project "New Technologies as Social Experiments" , funded by NWO. Scientific Awards: Antoni van Leeuwenhoek Professorship (TU Delft) VICI Grant for "New Technologies as Social Experiments: Conditions for Morally Responsible Experimentation" Advising and Grants: Van de Poel has led and contributed to multiple high-impact projects, including: NWO Project on "Moral Responsibility in R&D Networks" (2007–2012) EU Project on ethical issues in synthetic biology (2009–2011) VICI Project on "New Technologies as Social Experiments" (ongoing) He has also supervised PhD students and contributed to educational innovations, including the development of the AGORA web-based program and the first Dutch textbook on ethics and technology . Labs and Teams: Van de Poel is an active member of 4TU.Ethics & Technology , a federation of Dutch technical universities focused on ethics and technology. He contributes to the Simon Stevin Series in the Ethics of Technology and participates in various reading groups and PhD communities within the network.
Marcus Herrmann is a Professor of Aerospace and Mechanical Engineering at Arizona State University's School for Engineering of Matter, Transport and Energy. He is also affiliated with the Center for Negative Carbon Emissions. His research focuses on fluid mechanics, multiphase flows, atomization processes, and numerical methods for discontinuous interfaces. Herrmann holds a PhD in Mechanical Engineering from RWTH Aachen University (2001) and a Diplom (1995). His career includes a postdoctoral fellowship at Stanford University's Center for Turbulence Research (CTR) and a visiting scientist position at the University of Technology Eindhoven, Netherlands. He has secured major grants from NASA, NSF, and industry partners like Honeywell, focusing on atomization modeling, supersonic crossflows, and turbulence simulations. Research interests span computational fluid dynamics, multiphase flow simulation, and LES/DNS methodologies. His recent work emphasizes high-fidelity numerical techniques for particle-resolved simulations and phase interface dynamics. Teaching includes courses like MAE 561 (Computational Fluid Dynamics) and MAE 384 (Advanced Math Methods for Engineers). He actively advises students through research and dissertation roles. Notable projects include modeling wax deposition in pipelines and developing novel approaches for interface dynamics in turbulent flows. His work bridges fundamental fluid mechanics with industrial applications like combustion systems and porous media modeling.
Prof. Nicolas Perkowski is a Professor in the Department of Mathematics at Freie Universität Berlin, specializing in Stochastic Analysis and Probability Theory. He holds roles such as Vice Spokesperson of DFG CRC/TRR 388 (since 2024) and Chair of the Master of Mathematics Examination Board. His research focuses on stochastic partial differential equations (SPDEs), rough paths, and applications in mathematical physics. Notable contributions include work on singular SPDEs, fractional processes, and the KPZ equation. Perkowski has authored/co-authored numerous publications in top journals like the Annals of Probability and Communications in Mathematical Physics, and he serves as an associate editor for several journals. His institutional responsibilities include leadership in research collaborations and academic governance. Education: PhD and diploma in stochastic population models (details not explicitly provided in text). Research Interests: Stochastic Analysis, Probability Theory, SPDEs, Mathematical Physics, Nonlinear Filtering. Recent Articles: Focus on fractional processes, SPDEs with singular terminal conditions, and stochastic sewing lemmas. His work bridges theoretical probability with applications in physics and engineering, with a strong emphasis on rigorous mathematical frameworks for complex stochastic systems.
Mike Kirby is a Professor at the Kahlert School of Computing, University of Utah. He also holds adjunct professorships in the Department of Bioengineering and the Department of Mathematics. His current roles include leadership in scientific computing and informatics initiatives, including former directorships of the Utah Informatics Initiative (2019-2023) and the Multi-Scale Multidisciplinary Modeling of Electronic Materials (MSME) Collaborative Research Alliance (2016-2022). He has extensive experience in strategic research initiatives, including serving as Assistant Vice President for Research (2024-2025). Education: Dr. Kirby earned a PhD in Applied Mathematics (2002) and MS in Computer Science (2001) from Brown University, and a BS in Applied Mathematics and Computer Science from Florida State University (1997). Research Interests: Focus on large-scale scientific computing, physics-informed machine learning, computational science and engineering, high-order numerical methods, and visualization. His work bridges applied mathematics and computer science to address real-world engineering challenges. Publications: Over 150 peer-reviewed articles, including high-impact contributions in journals like Journal of Computational Physics and SIAM Journal on Scientific Computing . Recent work emphasizes machine learning for differential equations, topology optimization under uncertainty, and multi-fidelity modeling. Awards: Recognized for leadership in computational science and informatics, including contributions to University of Utah’s Clery Compliance Program. Advising & Grants: Supervised over 50 graduate students and postdocs. Secured funding from NSF, DOE, and industry partnerships, totaling millions in research grants. Active in interdisciplinary collaborations across engineering, materials science, and medicine. Labs/Teams: Scientific Computing and Imaging (SCI) Institute, Utah Informatics Initiative, and the Center for Multiscale Modeling of Electronic Materials (MSME).
Prof. Abbas Samani is a Professor at the Department of Electrical and Computer Engineering and holds a joint appointment in the Department of Medical Biophysics at Western University . He is a core faculty member of the Biomedical Engineering Graduate Program and an Associate Scientist at Imaging Research Laboratories of Robarts Research Institute . His academic journey includes a Ph.D. from the University of Waterloo, an M.Sc. from the University of Tehran, and a B.Sc. from Amirkabir University of Technology. His research focuses on biological tissue computational modeling and its applications in medical imaging, intervention, and image analysis . He develops computer/image-assisted tools for minimally invasive disease diagnosis and therapy , targeting heart disease, cancer, and lung disease . Key projects include myocardium biomechanical modeling , handheld medical devices for breast cancer screening , and lung disease diagnostics via CT image segmentation . His recent publications emphasize ultrasound elastography , finite element modeling , and inverse problems in biomechanics , primarily in journals like IEEE Transactions on Computational Imaging and Translational Oncology . His work spans both computational modeling and medical device development . Selected Graduate Supervision : Ph.D. Candidates : Seyed Hassan Haddad, Elham Karami, Seyed Mohammad Hesabgar Graduated Ph.D. Students : Ali Sadeghi Naini, Seyed Reza Mousavi M.Sc. Students : Cristian Linte, Patrick Courtis, Joseph O'Hagan, Hatef Mehrabian, Hirad Karimi, Hosein Amooshahi, Seyed Mohammad Hesabgar, Nastaran Ghadarghadr, Shadi Shavakh, Ehsan Salamati, Ehsan Omidi Teaching Contributions : Graduate: BME9519B/CAMI9519B/ECE9202B/ECE9022B - Advanced Image Processing and Analysis , MBP9530A - Human Biomechanics and Biomedical Applications Undergraduate: ECE4438B - Advanced Image Processing and Analysis , ES1050 - Introductory Engineering Design and Innovation Studio , MBP3330F - Human Biomechanics and Biomedical Applications Research Affiliations : Robarts Research Institute - Associate Scientist at Imaging Research Laboratories Western University - Core Faculty, Biomedical Engineering Graduate Program
