Bijaya Karki is the McDermott Inc. Endowed Professor in the Division of Computer Science and Engineering at Louisiana State University's School of Electrical Engineering and Computer Science, where he served as Division Chair (2011-2024). Research integrates high-performance computing, materials science, and geophysics to study Earth materials under extreme conditions. Funded by NASA and NSF, work focuses on magma oceans, mantle dynamics, and computational mineral physics using first-principles simulations and machine learning. Honors include AGU Fellowship (2022), NSF CAREER Award (2004), and JMGM Graphics Prize (2009). Developed novel visualization frameworks for atomistic simulation data analysis. Education: Ph.D. in Computational Physics, University of Edinburgh (1997) Diploma with Honors, ICTP Trieste (1994) M.Sc. with Distinction, Tribhuvan University (1992)
Jianwei Wang is an Associate Professor in the Department of Geology and Geophysics at Louisiana State University (LSU), within the College of Science. His research focuses on computational modeling of earth materials and processes, particularly using high-performance computing tools like first-principles calculations and molecular dynamics simulations. He holds a PhD in Earth Materials from the University of Illinois at Urbana-Champaign. Education: PhD in Earth Materials, University of Illinois at Urbana-Champaign Research Interests: Molecular modeling of geological processes at the molecular scale Nuclear materials and waste forms Materials under extreme conditions (high pressure, temperature) Mineral-water interface interactions Applications in energy and environmental solutions Grants & Opportunities: His research is supported by funding agencies such as NSF, DOE, and NRC. He actively seeks students and postdocs for projects in computational materials and geochemistry, including studies on Fe-Ni liquids under core conditions, ceramic corrosion mechanisms, and nuclear fuel chemistry. Labs & Collaborations: His work involves collaborations on advanced computational methods and experimental validation, contributing to interdisciplinary projects in energy and geoscience.
Radu Ion IFTIMIE is a Professor in the Department of Chemistry at the Faculty of Arts and Sciences, University of Montreal. His research focuses on developing computational methods and software to study complex chemical structures and reaction mechanisms in liquids and solids, with a specialization in proton transfer mechanisms and aqueous reactions. He teaches courses including CHM-1990 (General Physical Chemistry), CHM-3333 (Introduction to Molecular Modeling), and CHM-6422 (Statistical Mechanics). Research Interests: Iftimie's work combines theoretical chemistry, molecular dynamics simulations, and computational spectroscopy to investigate acid-base reactions, proton translocation in biomolecules, and material science applications (e.g., lithium-ion battery electrodes). His team develops novel software tools for first-principles molecular dynamics studies. Grants & Projects: Principal Investigator (2017–2024): Investigating Proton Translocation Mechanisms in Solution (CRSNG Grant PVX20965) Co-Investigator (2011–2016): Calcul Québec Strategic Group (FRQNT-funded) Advising: Supervised 6 graduate students (2009–2021), including studies on proton transfer kinetics, copper-catalyzed alkynylation, and LiFeV₂O₇ electrode materials. Labs/Teams: Leads a computational chemistry research group focused on advancing ab initio molecular dynamics methods and their applications in chemistry and materials science.
Maria Garcia de la Banda is a distinguished Professor at Monash University's Faculty of Information Technology, where she serves in the Department of Data Science and Artificial Intelligence (DSAI). With over 25 years of academic experience, she has held significant leadership roles including Deputy Dean (Research) until July 2022, overall Deputy Dean of the Faculty (2013-2016), and Head of the Caulfield School of Information Technology (2009-2011). She is currently a member of the ARC College of Experts and Co-Chair of the Monash-Woodside FutureLab. Her educational background includes a Doctor of Philosophy in Computer Science from the Universidad Politecnica de Madrid (awarded July 7, 1994) and an Ingeniero Informatico degree from the same institution (awarded March 1, 1992). Her PhD received the university's Best PhD Award. Garcia de la Banda's research spans multiple disciplines with a strong focus on constraint programming, combinatorial optimization, program analysis, and bioinformatics. She leads the Optimization research group within DSAI and has made significant contributions to declarative programming languages, parallelism, and automatic parallelization. Her interdisciplinary work bridges computer science with biological applications, particularly in protein structure analysis and computational drug design. Her publication record shows consistent contributions across constraint programming, optimization, and bioinformatics. Recent work demonstrates increasing interdisciplinary collaboration, with a notable expansion into bioinformatics applications alongside her core constraint programming research. She has maintained a strong presence at major conferences like CP (International Conference on Principles and Practice of Constraint Programming) while also building impactful industry collaborations. Her scientific recognition includes: Logan Fellowship (1997) - the first and only prestigious award of its kind in the Faculty of IT International Constraint Modelling Challenge winner (2005, with Peter Stuckey) Universidad Politecnica de Madrid's Best PhD Award (1994) Induction into the Monash Honour Roll (2021) Vice-Chancellor's Diversity and Inclusion Award (2020) As a research leader, Garcia de la Banda has secured over $20M in industry funding and $14M in nationally competitive funding, including $8M as Chief Investigator in 11 ARC grants (5 as lead). She has served as Area Editor of the Journal of Theory and Practice of Logic Programming since 2010 and on the Editorial Board of the Constraints journal since 2019. Her leadership extends to professional organizations, having served on the Executive Committees of both the Association of Logic Programming (2005-2008) and the Association of Constraint Programming (2017-2020), where she was President (2019-2020). She leads the Optimization research group within DSAI and collaborates extensively across Monash University and with industry partners. Her current major projects include HARNESS (Hierarchical Abstractions and Reasoning for Neuro-Symbolic Systems), the ARC Training Centre in Optimisation Technologies, and the Building 4.0 CRC project focused on better buildings through technology. These initiatives demonstrate her commitment to translating theoretical research into practical applications with real-world impact.
David Savage is a Professor of Biochemistry, Biophysics, and Structural Biology at the University of California, Berkeley, and an Investigator at the Howard Hughes Medical Institute. His lab focuses on understanding protein machinery involved in carbon fixation and compartmentalization in bacteria, particularly the carboxysome, a protein-based organelle critical for photosynthetic efficiency. Research combines biochemistry, microbiology, and synthetic biology to study CO₂ concentrating mechanisms (CCMs), enzyme engineering, and CRISPR-based genome editing tools. Key projects include reconstituting CCMs in heterologous hosts, developing novel genome editing technologies (e.g., allosteric Cas9 variants and MISER-directed evolution), and characterizing protein assembly principles in bacterial microcompartments. Savage's work bridges fundamental biology with applications in improving photosynthetic efficiency and enabling plant genetic engineering. Research methodologies include high-throughput screening, cryo-electron microscopy, and systems biology modeling. Recent advances include the first functional reconstitution of a bacterial CCM (Flamholz et al. 2020) and structural insights into Rubisco-carboxysome interactions (Blikstad et al. 2023). Ongoing efforts explore sulfur metabolism compartments and CRISPR-based diagnostic tools. Labs and collaborations: Savage Lab (http://savagelab.org), with interdisciplinary connections to molecular biology, structural biology, and synthetic biology communities. Research is supported by HHMI and NIH grants focusing on energy biology and genome engineering.
Dr. Attila Cangi is the Head of Department for Machine Learning for Materials Design at the Center for Advanced Systems Understanding (CASUS) , part of the Helmholtz-Zentrum Dresden-Rossendorf (HZDR) . His roles include leading research in computational materials science and developing scalable ML methods for electronic structure calculations. He has held permanent staff scientist positions at HZDR and Sandia National Laboratories, with postdoctoral experience at the Max Planck Institute. His research focuses on accelerating materials discovery through AI-driven simulations for energy storage, thermoelectrics, spintronics, and semiconductor modeling. Education: Ph.D. in Chemistry (Chemical and Materials Physics), University of California, Irvine (2011) M.Sc. in Physics, Rutgers University (2006) Research Interests: Dr. Cangi’s work integrates machine learning with first-principles simulations to model electronic structures, predict material properties (e.g., conductivity, magnetism), and simulate phase transitions. His lab leverages high-performance computing to address challenges in warm dense matter, plasma physics, and quantum transport phenomena. Key areas include: Development of physics-informed ML algorithms (e.g., MALA package) Electronic structure modeling at extreme conditions Design of sustainable materials for energy applications Lab & Collaborations: The Machine Learning for Materials Design group collaborates on projects like the European XFEL and employs tools such as atoMEC for average-atom modeling. Their work bridges theory and experiment, enabling predictions for novel materials under extreme environments.
Nicholas Mosey is an Associate Dean (Research) in the Faculty of Arts and Science at Queen's University and an Associate Professor in the Department of Chemistry. He leads the Mosey Group, which focuses on theoretical and computational chemistry, particularly in developing simulation methods and their application to catalysis, materials science, and tribology. His research integrates method development with high-performance computing to explore atomic-level phenomena in molecules and materials. Education: Ph.D. (2006) and B.Sc. (2001) in Chemistry from the University of Western Ontario. Postdoctoral fellowship at Princeton University (Mechanical & Aerospace Engineering). Joined Queen's University in 2008, becoming an Associate Professor in 2014. Research interests include computational chemistry, molecular modeling, reaction kinetics, catalysis mechanisms, and the interplay between mechanical forces and chemical reactions. Notable contributions include studies on anti-wear additives (ZDDP), tribological systems, and electrocatalysis. His work bridges theoretical and applied aspects, emphasizing practical applications in energy and materials. Publications span topics like catalytic materials, DFT methods, and nanoscale phenomena. The Mosey Group's work often involves collaborations across disciplines, yielding insights into surface chemistry, friction reduction, and energy-related materials. Leadership roles include overseeing research strategy within the Faculty and supervising a dynamic group of students and postdocs. Teaching focuses on general chemistry, quantum mechanics, and computational methods, emphasizing interdisciplinary connections.
Zhenhua Zeng holds the position of Research Professor in the School of Chemical Engineering at Purdue University. His research focuses on advanced catalytic materials, electrochemistry, and energy conversion technologies. He specializes in designing novel catalysts for applications such as water electrolysis, fuel cells, and sustainable energy systems. His work integrates computational modeling, experimental characterization, and materials engineering to address challenges in surface chemistry, nanomaterials, and reaction mechanisms. Key research areas include platinum-based catalysts, transition metal alloys, and anion-exchange membrane technologies. Zhenhua has extensively studied the structural evolution of active sites under varying potentials and the role of defects in enhancing catalyst performance. His contributions span both fundamental understanding and applied innovations in catalysis, with a particular emphasis on renewable energy applications. His publications highlight advancements in catalyst design for oxygen reduction reactions, hydrogen evolution, and chlorine evolution processes in acidic environments. Zhenhua's work often employs cutting-edge techniques such as scanning transmission electron microscopy and first-principles calculations to visualize atomic-scale phenomena and predict catalytic behavior.
Prof. Michele Pavone is a Full Professor of Physical Chemistry at the University of Naples Federico II, leading the MUSICHEM laboratory. His academic career includes roles as Associate Professor (2015–2024) and Researcher (2008–2015). He holds a PhD in Chemical Sciences (2007) and a Laurea in Chemistry (2004), both from the University of Naples Federico II. His research focuses on computational quantum chemistry to study materials for energy applications, including solar cells, batteries, and electrocatalysts. Key contributions include theoretical insights into perovskite materials, sodium-ion battery cathodes, and photocatalytic systems. Pavone has been recognized with awards such as the 2017 Emerging Investigators distinction for energy materials and the 2016 Carla Roetti Prize. Scientific achievements span over 100 publications in journals like Journal of Materials Chemistry A and ACS Applied Materials & Interfaces , with a focus on material interfaces, defect engineering, and energy storage mechanisms. He serves as Principal Investigator for the MUSICHEM lab, collaborating internationally (e.g., Princeton University, ENSCP Paris). Labs/Teams : MUSICHEM Laboratory (University of Naples) Grants : Not explicitly listed but implied through PI roles in major research projects
Mikko Hakala is a Researcher at the School of Science, Aalto University. His expertise lies in materials science and computational physics, focusing on surface physics, electronic transport, and first-principles modeling. He holds a Master's degree from Helsinki University of Technology (2004) and a Doctoral Thesis from Aalto University (2013). Research interests include the study of metal adatoms on surfaces, interfacial oxide growth in silicon-based materials, and numerical methods for electron transport in nanostructures. His work integrates theoretical modeling with experimental insights, contributing to semiconductor device applications and nanotechnology. Notable contributions include studies on silicon-hafnia interfaces and alkali halide surface dynamics. He has organized workshops such as 'Scientific Computing in Practice' (2017) and presented at international conferences like the International Supercomputing Conference (2011). Awarded a project grant as Principal Investigator for CodeRefinery/Darst (2021), his collaborative efforts span computational materials science and interdisciplinary research. No scientific awards are explicitly listed in the provided information.
Qiang Cui is Professor of Chemistry at Boston University specializing in computational biophysics and molecular simulations. His research employs multi-scale modeling approaches to study biological systems including membrane remodeling, protein allostery, and enzyme mechanisms. Recent work advances force field development (CHARMM/DFTB+), membrane biophysics, and machine learning applications in molecular biophysics. Publications demonstrate strong methodological focus on QM/MM techniques, free energy calculations, and integrated experimental-computational approaches. Ongoing investigations include lipid membrane interactions with nanoparticles, synaptic fusion mechanisms, photosynthetic energy transfer, and allosteric regulation in proteins.
Sahar Sharifzadeh is an Associate Professor in the Department of Electrical & Computer Engineering at Boston University's College of Engineering. She holds an affiliation with the Boston University Institute for Global Sustainability (IGS). Her research focuses on predicting and understanding functional material properties using first-principles electronic structure methods, aiming to design novel materials for energy and technology applications. She earned her PhD in Electrical Engineering from Princeton University, an MA from Princeton, and a BS in Electrical Engineering from UC Berkeley. Her research group develops computational frameworks to model quantum mechanical phenomena in materials, including nanotubes, semiconductors, and biological systems. Recent work emphasizes machine learning integration and defect engineering in materials. She has contributed to software tools like Nexmd v2.0 for molecular dynamics simulations. Her advising and grants focus on training researchers in interdisciplinary computational methods. She collaborates with the IGS to advance sustainable energy technologies through material innovation.
Michael Elowitz is the Roscoe Gilkey Dickinson Professor of Biology and Bioengineering at the California Institute of Technology (Caltech) and an Investigator at the Howard Hughes Medical Institute (HHMI). He leads the Elowitz Lab, which focuses on synthetic and systems biology, designing biological circuits to uncover principles of gene regulation and develop therapeutic tools. His work spans protein circuit engineering, cell signaling, and developmental biology, with applications in cell therapies and lineage tracking. Education: B.A., University of California, 1992 M.A. and Ph.D., Princeton University, 1999 Research Interests: Elowitz's lab pioneers synthetic biology approaches to create programmable genetic circuits, study noise in gene expression, and model developmental processes. Key areas include: Design of synthetic gene circuits for cell fate control Mechanisms of cell signaling and decision-making MEMOIR (Molecularly Encoded Memory In REporting) systems for lineage tracking Engineering programmable cell death pathways Key Contributions: Notable achievements include the first synthetic genetic oscillator (Repressilator), foundational work on noise in gene expression, and development of lineage recording tools. His research bridges fundamental science and translational applications, including therapies for genetic disorders like Rett syndrome. Awards & Recognition: HHMI Investigator (2008–present) AAAS Fellow (2015) Presidential Early Career Award for Scientists and Engineers (2005) Labs/Teams: The Elowitz Lab collaborates across disciplines, combining experimental and computational approaches. Key projects include synthetic embryo models, programmable cell death circuits, and MEMOIR-based lineage tracing systems.
Professor Avinash M. Dongare is a faculty member in the Department of Materials Science and Engineering at the University of Connecticut, with joint appointments in Mechanical Engineering and Physics. He holds a Ph.D. from the University of Virginia (2008). As Director for Graduate Studies, he oversees graduate programs while advancing research in extreme mechanics and materials science. His research focuses on computational modeling of materials behavior under extreme conditions (e.g., shock, high strain rates, pressures, temperatures). Key areas include: (1) atomistic simulations of phase transformations and spall failure in metals, (2) design of nanocrystalline composites for energy applications, and (3) mesoscale modeling of laser-material interactions. He employs advanced methods like molecular dynamics, density functional theory, and machine learning. Notable achievements include the NSF CAREER Award (2015), UTC Professorship (2018-2021), and TMS Young Leader Award (2015). His work bridges theory and experiment, collaborating with institutions like the US Army Research Laboratory and Oak Ridge National Lab. Over 100 peer-reviewed publications and active funding from NSF, DOE, and DOD highlight his impact in materials science. Current projects explore digital twins for materials processing, interfacial effects in layered systems, and ion-intercalation mechanisms in 2D materials. The Dongare Research Group (GEMMS) integrates computational and experimental approaches to address challenges in additive manufacturing and energy storage technologies.
Dr. Paulo Siani is a Researcher at the University of Milano-Bicocca, Italy, specializing in multi-scale modeling of biomembranes and drug design. His research focuses on coarse-graining modeling, atomistic simulations, and bioinorganic nanosystems for biomedical applications. He holds a PhD in Theoretical and Computational Chemistry from the University of São Paulo, Brazil (2018), and conducted a Visiting PhD at the University of Southern Denmark (2015–2016). Education: PhD in Theoretical and Computational Chemistry, University of São Paulo (2014–2018) MSc in Theoretical and Computational Chemistry, University of São Paulo (2012–2014) BSc in Chemistry, State University of Maringá, Brazil (2008–2012) Research Interests: Dr. Siani’s work bridges computational chemistry and nanomedicine, with emphasis on molecular dynamics simulations of lipid systems, nanoparticle-membrane interactions, and functionalized nanodevices for targeted therapies. His ERC-funded project (SMART BIOINORGANIC HYBRIDS FOR NANOMEDICINE) explores novel hybrid systems for photodynamic therapy and drug delivery. Key Projects: ERC Consolidator Grant 2016–2021: Developing bioinorganic hybrids for nanomedicine applications Labs/Teams: Active member of the NanoQLab at the University of Milano-Bicocca, focusing on computational nanomedicine and multi-scale modeling.
