Ngoc Cuong Nguyen is a Principal Research Scientist in the Department of Aeronautics and Astronautics at MIT and a member of the MIT Center for Computational Engineering. His research focuses on computational mechanics, numerical simulation, and advanced numerical methods such as hybridizable discontinuous Galerkin (HDG) methods for multi-scale and multi-physics problems. Education: PhD in High Performance Computation for Engineered Systems (2005), National University of Singapore BEng in Aeronautical Engineering (2001), Ho Chi Minh City University of Technology Research Interests: Computational Mechanics, Molecular Mechanics, Nanophotonics Numerical Simulation & Optimization, Scientific Computing, Machine Learning Reduced Basis Methods, High-Order Methods (e.g., HDG), Uncertainty Quantification Key Projects: Development of HDG methods for fluid dynamics, structural mechanics, and electromagnetics Plasmonic nanostructure simulations using quantum hydrodynamic models Space weather modeling via GPU-accelerated HDG approaches Optimization of photonic crystals and nanostructured materials Large-eddy simulation (LES) of hypersonic flows and buffet phenomena Labs & Teams: Active contributor to the MIT Center for Computational Engineering, leading projects in numerical methods, computational fluid dynamics, and interdisciplinary applications of advanced simulation techniques.
Jonathan Skelton is a Senior Lecturer in Computational and Theoretical Chemistry at the University of Manchester's School of Chemistry. He holds a Ph.D. in Computational Chemistry from the University of Cambridge (2010–2013) and a B.A. and M.Sc. in Natural Sciences from Trinity College, Cambridge (2006–2010). His research focuses on lattice dynamics and computational modeling of materials, particularly thermoelectrics, to enhance energy efficiency and sustainability. Education Ph.D. in Computational Chemistry, University of Cambridge (2010–2013) M.Sc. and B.A. Natural Sciences, Trinity College, University of Cambridge (2006–2010) Research Interests : Lattice dynamics, density-functional theory (DFT), thermoelectric materials, thermal transport, and computational materials design. His work emphasizes structural dynamics' role in material properties and the development of open-source tools for broader accessibility. Recent Research Trends : Recent publications explore thermoelectric properties of oxides (e.g., LaCoO₃), lanthanide frameworks, and 2D materials. His studies highlight advances in thermal conductivity reduction and phonon engineering for energy applications. Awards & Memberships : Associate Fellow of the UK Higher Education Academy, Member of the Royal Society of Chemistry. Grants & Supervision : Advised multiple PhD theses on topics like actinide systems and functional perovskites. Active in reviewing and conference participation. Labs & Collaborations : Works on open-source software development and collaborates globally on energy materials research.
SHEN Lei is a researcher at the National University of Singapore (NUS), affiliated with the Department of Physics. With a PhD in Physics from NUS, he specializes in Multiscale Modeling and Simulation and Materials Informatics , leveraging machine learning and computational methods for advanced materials discovery. Research Focus: Density functional theory, molecular dynamics, finite element analysis, and data-driven design of materials. Teaching: Modules include Mechanics and Waves (PC1433), Applied Quantum Mechanics (PC2130B), and Mechanical Properties of Materials (ESP2109). His work spans spintronics, ferroelectricity, and energy storage materials, with recent publications on interatomic potentials, sliding heterostructures, and battery anodes. He has received the Teaching Commendation Award and declined the Lee Kuan Yew Postdoctoral Fellowship . Notable Trends: Recent articles emphasize machine learning in materials science, van der Waals heterostructures, quantum transport, and medical image analysis. Subfields include Rashba spin-orbit coupling, piezoelectric tensor modeling, and defect-informed neural networks. Scientific Awards: Teaching Commendation Award (AY15/16; AY16/17) Lee Kuan Yew Postdoctoral Fellowship (2014) (declined)
Jingling Xue is a Scientia Professor at the School of Computer Science and Engineering at the University of New South Wales (UNSW) in Sydney, Australia. As an IEEE Fellow of the Computer Society, he leads the Programming Languages and Compilers research group, focusing on practical applications of compiler optimization and program analysis techniques. His work bridges theoretical foundations with real-world software systems, particularly in developing open-source tools for large-scale program analysis. Professor Xue received his B.Eng and M.Eng degrees from Tsinghua University in 1984 and 1987, respectively, followed by a PhD from the University of Edinburgh in 1992. His academic journey has established him as a leading figure in programming languages and compiler technology. Xue's research spans programming languages, compiler technology, and program analysis with emphasis on practical relevance. His current projects include compiler techniques for improving parallelism and locality, pointer/alias analysis for million-line-scale programs, and static/dynamic analysis for detecting bugs and security vulnerabilities in real-world applications like web browsers and Android apps. His group actively develops open-source tools to support scientific replicability and reproducibility in these areas. His recent publications demonstrate a strong focus on applying program analysis techniques to modern challenges including AI compilers, homomorphic encryption, security vulnerability detection, and graph processing systems. The work shows evolution from traditional compiler optimization to addressing emerging domains like privacy-preserving computation and deep learning systems while maintaining rigorous theoretical foundations. Scientific Awards: Best Paper Award at CGO'13 Best Paper Award at CGO'16 Distinguished Paper Award at ECOOP'16 Distinguished Paper Award at ICSE'18 Distinguished Paper Award at ISSTA'19 Distinguished Paper Award at ASE'19 Distinguished Artifact Award at ISSTA'23 Best Artifact Award at FSE'23 Distinguished Paper Award at ASE'23 Test-of-Time Award at CGO'21 Professor Xue has successfully supervised 30 PhD students to completion, many of whom now work as professors or researchers in academia and industry. He has served as Program Chair for major conferences including LCTES'13, CC'18, CGO'20, and General Chair for LCTES'20. His group currently focuses on memory safety in Rust, smart contract analysis, AI compilers, compilation for privacy-preserving computation, and adversarial attacks in deep learning. The Programming Languages and Compilers group maintains strong connections with industry partners, translating theoretical advances into practical tools for real-world software development challenges. Their work on pointer analysis, memory safety, and compiler optimizations continues to influence both academic research and industrial practice.
Stefano Martiniani is an Assistant Professor of Physics, Chemistry, Mathematics, and Neuroscience at New York University, affiliated with the Center for Soft Matter Research and the Simons Center for Computational Physical Chemistry. His interdisciplinary research explores computational physics of complex systems, including neural circuit theories, non-equilibrium statistical mechanics, and AI-driven materials discovery. He has pioneered methods for analyzing high-dimensional energy landscapes and received prestigious awards like the NSF CAREER Award (2024) and IUPAP Early Career Prize (2023). Education: PhD in Physics (2017), University of Cambridge MPhil in Physics (2013), University of Cambridge BSc in Physics (2012), Imperial College London Research Interests: His work bridges statistical physics and artificial intelligence, focusing on: Engineering disordered materials with tailored spectral properties Quantifying entropy production in active matter Developing open science frameworks like ColabFit for machine learning interatomic potentials Neural circuit models for cortical communication Grants & Collaborations: Funded by NSF, NIH, Chan Zuckerberg Initiative, and Simons Foundation. Leads interdisciplinary teams in computational physics, AI, and materials science. Labs/Initiatives: Core member of NYU's Center for Soft Matter Research; develops software tools like FReSCo and KLIFF-Torch for computational materials science.
Dallas Trinkle is the Ivan Racheff Professor and Associate Head of the Department of Materials Science and Engineering at the University of Illinois, Urbana-Champaign. He holds a Ph.D. in Physics from Ohio State University (2003) and joined UIUC's faculty in 2006 after postdoctoral work at the Air Force Research Laboratory. Affiliations: Willett Faculty Scholar, NCSA Faculty Fellow, Center for Advanced Study Associate. Research Focus: Computational materials science, including defect properties (dislocations, point defects), mechanical behavior at atomic scales, and diffusion mechanisms using density-functional theory and machine learning. Key Achievements: NSF CAREER Award (2009), TMS Brimacombe Medal (2019), and over 150 peer-reviewed publications. His research group develops atomistic methods to study material defects and their impact on mechanical/thermal properties. Projects include magnesium alloy strengthening, hydrogen diffusion in palladium, and oxygen diffusion in titanium. They also create open-source computational tools like the magnesium solute database. Teaching: Courses on atomic-scale simulations, plasticity, and computational materials engineering (MSE 485, MSE 584, CSE 498 DM). Current Group:** Includes 7 Ph.D. students and a research scientist. Former advisees hold roles at institutions like MIT, Lawrence Berkeley Lab, and industry leaders such as MathWorks and General Motors.
Jacob Gissinger is an Assistant Professor in the Department of Chemical Engineering and Materials Science at Stevens Institute of Technology's Charles V. Schaefer, Jr. School of Engineering and Science. He leads the Gissinger Group, conducting computational research on polymer degradation, mechanical failure, and dynamic response of materials using molecular dynamics and machine learning. Education: PhD in Materials Science and Engineering from University of Colorado Boulder (2020) BS in Chemical and Biomolecular Engineering and Materials Science from University of Pennsylvania (2014) Research Focus: His work spans reactive dynamics in extreme environments, machine learning applications for material design, and collaborative development of high-performance polymer composites. Core research areas include molecular modeling of degradation processes and computational prediction of material behavior. Professional Experience: NASA Postdoctoral Fellow at Langley Research Center (2021-2023) Core developer of LAMMPS molecular dynamics software Developer of REACTER protocol for chemical reaction modeling Professional Affiliations: American Chemical Society, American Institute of Chemical Engineers, American Physical Society, Materials Research Society. Teaching: Currently instructs CHE 632 - Advanced Momentum Transfer.
Dr. Alice Allen is a Project Leader at the Max Planck Institute of Polymer Research , specializing in machine learning methods for molecular simulations. She holds a PhD in Physics from the University of Cambridge and completed her undergraduate degree in Physics at Imperial College London. Allen previously worked as a research associate at the University of Cambridge and the University of Luxembourg, followed by a postdoctoral position at Los Alamos National Laboratory. Education: BSc in Physics (Imperial College London) PhD in Physics (University of Cambridge) Her research focuses on developing machine learning models for interatomic potentials , enabling accurate and efficient simulations of reactive processes, biological molecules, and material properties. She has published extensively on topics such as permutationally invariant polynomials, data-driven force fields, and meta-learning approaches for foundation models in interatomic potential development. Recent publications highlight her work on integrating experimental data into machine learning potentials, enhancing the transferability of empirical valence bonds, and creating meta-learning frameworks for foundational interatomic models. These studies span applications in molecular dynamics , thermodynamics , and multi-scale simulations . Research Themes: Machine Learning for Atomistic Simulations Reactive Process Modeling Transferable Force Fields Interpretable Models for Material Properties Collaborative Meta-Learning Frameworks Alice Allen leads the Gräter Groups at the institute, where her team advances predictive methodologies for chemical reactions and material science applications. No specific scientific awards or student advisees are mentioned in the provided text.
Stefano Curtarolo is the Edmund T. Pratt Jr. School Distinguished Professor of Mechanical Engineering and Materials Science at Duke University’s Pratt School of Engineering. He also holds professorships in the Department of Physics and Electrical and Computer Engineering, and serves as Director of the Center for Extreme Materials (2025–present). Education: M.S. in Physics, University of Padua (1995, 1998) M.S. in Physics, Pennsylvania State University (1999) Sc.D. in Physics, Massachusetts Institute of Technology (2003) Research Interests: Curtarolo’s work bridges Materials Science , Physics , and Artificial Intelligence , focusing on Autonomous Materials Design , High-Entropy Disordered Systems , and Materials for Extreme Environments . His methodologies integrate Quantum Mechanics , Machine Learning , and High-Throughput Computing . Recent Research Trends: His 2024–2025 publications highlight advancements in High-Entropy Ceramics , including Soliquidy (a geometric descriptor for disorder), Dual-Phase Ceramic Synthesis , and Machine Learning Interatomic Potentials . These works emphasize disorder engineering , plasticity enhancement , and AI-driven material discovery for Aerospace and Deep Space Exploration applications. Scientific Awards: European Academy of Sciences (2024) Clarivate Highly Cited Researcher (2021) Friedrich Wilhelm Bessel Research Award (2016) DOD MURI Awards (2013, 2015) NSF CAREER (2008) ONR Young Investigator (2008) PECASE (2007) Grants & Leadership: Current projects include DETER (2024–2027, Missouri S&T) and Transition States Algorithms (2024–2027, ONR). He co-leads NRT-HDR (2020–2026, NSF). Labs & Teams: Curtarolo directs the Center for Extreme Materials and leads the AFLOW consortium , a global materials design ecosystem. His lab combines machine learning , quantum simulations , and experimental validation for multiscale materials discovery .
Dr. Priya Vashishta is a Professor with joint appointments in Computer Science, Materials Science, and Physics at the University of Southern California. His research integrates computational methods across multiple scales to address fundamental challenges in materials science and nanotechnology. Research focuses on multiscale simulation frameworks combining quantum molecular dynamics, machine learning interatomic potentials, and high-performance computing. Key areas include: energy storage materials, 2D materials characterization, catalytic processes, and AI-driven materials discovery. Publication trends reveal consistent development of computational methodologies: machine learning potentials for materials simulation, neural network quantum dynamics, GPU-accelerated computing, and multiscale modeling techniques. Recent work emphasizes practical applications in energy storage, nanoscale electronics, and advanced manufacturing. Research group activities include development of open-source simulation packages (PND, Allegro) and leadership in the MAGICS computational materials center. Contributions span fundamental theoretical frameworks to applied materials engineering.
Stephen T. Lam is an Assistant Professor of Chemical Engineering at the University of Massachusetts Lowell, where he also serves as Director of the Nuclear Engineering Program and RHSA Mentor within the Francis College of Engineering. His research focuses on accelerating materials development for nuclear and clean energy applications through computational methods. His educational background includes a Ph.D. in Nuclear Science and Engineering (2020) and an MS in Nuclear Science and Engineering (2017) from the Massachusetts Institute of Technology, and a BS in Chemical Engineering (2013) from the University of British Columbia. Prior to his academic career, Lam worked in the petroleum and chemical processing industries and holds a Professional Engineer license in Canada. Ph.D.: Nuclear Science and Engineering (2020), Massachusetts Institute of Technology MS: Nuclear Science and Engineering (2017), Massachusetts Institute of Technology BS: Chemical Engineering (2013), University of British Columbia Lam's research integrates multi-scale simulation, experimental validation, and data analytics to address materials challenges in nuclear and clean energy systems. His work spans molten salt chemistry for advanced reactors, neural network interatomic potentials, and fusion energy materials. He leads the Lam Research Group, which combines predictive simulation, data analytics, and experiments to accelerate materials development. His recent publications demonstrate expertise in molten salt structure, neural network potentials, and fusion materials, with emphasis on fluoride and chloride salt systems for nuclear applications. The research shows strong interdisciplinary collaboration across computational chemistry, materials science, and nuclear engineering. Among his notable recognitions are the Early Career Research Award (2024) from the U.S. Department of Energy and the Distinguished Faculty Advancement Award (2024) from the U.S. Nuclear Regulatory. He has also received multiple early-career research awards from national laboratories and professional societies. Early Career Research Award (2024), U.S. Department of Energy Distinguished Faculty Advancement Award (2024), U.S. Nuclear Regulatory Best Presentation (2017), Tokyo Institute of Technology Natural Sciences and Engineering Research Council of Canada Postgraduate Scholarship (2017) Lam actively mentors graduate students and postdoctoral researchers, with current advisees working on AI-assisted materials design, molten salt chemistry, and fusion energy applications. He teaches courses in nuclear materials, nuclear science and engineering, transport phenomena, and fundamentals of electricity. The Lam Research Group maintains strong collaborations with national laboratories and industry partners, focusing on developing computational tools to improve understanding of material properties and accelerate materials discovery for next-generation energy technologies.
Chiheb Ben Mahmoud is a Junior Research Fellow and Swiss National Science Foundation Postdoctoral Fellow in the Department of Chemistry at the University of Oxford, working under Prof. Volker Deringer. He holds a PhD in Materials Science and Engineering from the Swiss Federal Institute of Technology (EPFL) and Master’s degrees from Ecole CentraleSupélec (France) and the University of Paris-Saclay (France). His research focuses on applying machine learning to model macroscopic properties of materials, with a particular emphasis on atomistic simulations and computational materials science. Key research interests include graph neural networks for interatomic potentials, solid-state NMR predictions, and addressing data challenges in atomistic machine learning. His work bridges machine learning methodologies with fundamental materials science problems, such as charge density wave modulation and hot-electron dynamics. He collaborates with experts like Dr. Andy Anker and Dr. Dmytro, advancing interdisciplinary approaches to materials modeling. No scientific awards are explicitly mentioned in the provided text. His advisory and grant activities are not detailed here, though his postdoctoral position suggests affiliation with the Swiss National Science Foundation. He contributes to Prof. Deringer’s research group, focusing on cutting-edge computational techniques for materials discovery and analysis.
Prof. Dr. Robert Roth is a faculty member at the Institute for Nuclear Physics (Technische Universität Darmstadt), specializing in Theoretical Nuclear and Many-Body Physics , Ab Initio Nuclear Structure Theory , and Computational Physics . He leads a research group focused on ultracold atomic gases and nuclear structure simulations. Email: robert.roth@physik.tu-darmstadt.de Office: S2|11 204, Schlossgartenstraße 2, Darmstadt Roth's research involves collaborations with the Collaborative Research Center 1245 , Helmholtz Research Academy Hesse for FAIR , and Extreme Matter Institute . His recent work explores machine learning interatomic potentials for materials science applications, including phase diagrams and alloy modeling. Notable projects include automated workflows via the pyiron framework and studies on Cu-Zr alloys and silicon oxycarbides. His computational approaches bridge quantum mechanics and macroscopic material properties.
Marco Fronzi is a researcher at the University of Sydney's Faculty of Science, focusing on advanced materials science and computational methods. His work spans thermoelectric materials, energy storage systems, and 2D crystal engineering. He has contributed to projects involving machine learning-driven material discovery, battery anode optimization, and photocatalytic applications. Fronzi leads the 2025 grant 'Conversion of CO into Methanol via an Innovative Photocatalytic and Electrocatalytic Process,' highlighting his expertise in sustainable energy technologies. Key research interests include van der Waals heterostructures, nanomaterials characterization, and predictive modeling for novel material design. His publications address challenges in thermoelectric efficiency, solar cell performance, and nanomaterial stability. While no awards are explicitly listed, his high-impact contributions to energy-related materials are notable. His research integrates experimental and computational approaches, with recent work exploring piezoelectric 2D crystals and lithium diffusion mechanisms in batteries. Collaboration with industry and international partners underscores his commitment to translating fundamental research into practical applications.
Fabio Priante is a Postdoctoral Researcher at the Department of Chemistry and Materials Science , Aalto University , specializing in Atomic Force Microscopy (AFM) , Molecular Dynamics (MD) , and Machine Learning . His research focuses on nanoscale surface interactions, particularly in lignocellulosics , chitin interfaces , and ice-water systems . Active in UN Sustainable Development Goals (SDGs) related to renewable materials and surface science. Key collaborations with RCF Academy Project , HACMAT , and LIBER Sammalkorpi II . Research Trends include: Structure determination of biomolecules on 2D nanomaterials (2025). AFM and MD simulations for hydration layer analysis (2024). Application of machine learning to AFM imaging (2024). Adsorption dynamics of lignocellulosic molecules (2023). Historical work on graphene and MoS2 exfoliation (2017-2021). His work appears in Small , ACS Nano , and Journal of Chemical Theory and Computation , with datasets hosted on Zenodo .
