Subash Jonnalagadda, Ph.D., is a Professor and Department Head of Chemistry & Biochemistry at Rowan University's College of Science & Mathematics, also affiliated with the Biological & Biomedical Sciences program. He holds a B.S. from Pondicherry University, M.S. from University of Hyderabad, and Ph.D. in Organic Chemistry from Purdue University, with postdoctoral training at University of Pennsylvania and University of Minnesota. Recipient of Rowan University's Wall of Fame Teaching Award (2013, 2016) Eli Lilly International Graduate Scholar (2000-2005) Research focuses on: Medicinal Chemistry: Developing boron-based small molecules (e.g., benzoboroxoles) and betulinic acid derivatives as anti-cancer agents Biomass Valorization: Converting cellulose into chemicals like hydroxymethylfurfural for bio-based polymers Publications emphasize anti-cancer drug design, nanocarrier systems, and enzyme inhibition strategies. Advised over 50 graduate/undergraduate students, many progressing to academic and pharmaceutical careers. Collaborates with Rowan School of Osteopathic Medicine on Alzheimer's drug candidates.
Olivia Di Matteo serves as an Assistant Professor in the Department of Electrical and Computer Engineering within UBC's Faculty of Applied Science, leading the Quantum Software and Algorithms Research (QSAR) group since her January 2022 appointment. Her academic foundation includes a BSc from Lakehead University and MSc/PhD in Physics (Quantum Information) from the University of Waterloo, completed in 2019. Dr. Di Matteo's research centers on quantum software engineering , with pioneering work in quantum compilation , circuit optimization , and debugging tools . She champions open-source quantum frameworks and develops accessible educational resources to democratize quantum computing. Analysis of her 15 most recent publications (2021-2025) reveals dominant trends in quantum programming infrastructure, particularly circuit analysis (33%), bug classification (20%), and qubit network optimization (15%), with strong emphasis on practical software tooling over theoretical physics. No scientific awards were documented in the source materials. She advises graduate students in the QSAR group while contributing to open-source quantum ecosystems through projects like PennyLane and The Ionizer transpiler, and teaches courses including CPEN 400Q (Gate-model quantum computing) and ELEC 221 (Signals and Systems). The QSAR group operates at the intersection of quantum software development and education, focusing on making quantum programming accessible through visual tools, real-time debugging environments, and hardware-agnostic compilation techniques.
Franklin Goldsmith serves as Associate Professor of Engineering within Brown University's School of Engineering, where his research bridges fundamental chemical kinetics with practical combustion applications. His work directly impacts energy conversion technologies and emission reduction strategies through rigorous investigation of reaction mechanisms. His academic foundation includes: PhD in Chemical Engineering from Massachusetts Institute of Technology (2010) BS in Chemical Engineering from North Carolina State University (2003) BA in Chemistry from University of North Carolina at Chapel Hill (1998) Goldsmith's research program centers on radical reaction kinetics and low-temperature oxidation phenomena , employing both computational master equation modeling and experimental techniques like shock tube spectroscopy and synchrotron photoionization. His investigations into non-Boltzmann energy distributions and pressure-dependent rate coefficients have established new frameworks for understanding ignition chemistry. The Thermochemistry for Combustion Database project exemplifies his commitment to foundational data resources for the field. Analysis of his publication record reveals three dominant research thrusts: (1) detailed kinetic modeling of hydrocarbon oxidation, particularly propane systems; (2) development of computational methodologies for pressure-dependent rate estimation; and (3) fundamental studies of radical-molecule interactions. His work consistently integrates high-precision experimental validation with theoretical frameworks, as evidenced by collaborations with national laboratories. Goldsmith teaches Brown's core chemical engineering curriculum including ENGN 1120 (Reaction Kinetics and Reactor Design) and ENGN 1130 (Chemical Engineering Thermodynamics), alongside specialized graduate courses in heterogeneous catalysis (ENGN 2751) and chemically reacting flow (ENGN 2910Q). His educational approach emphasizes the connection between molecular-scale kinetics and reactor design principles. His research group maintains active collaborations with Argonne National Laboratory (Klippenstein), MIT (Green), and Sandia National Laboratories (Taatjes), focusing on multiscale informatics for complex reaction systems. Current projects investigate biomass-derived fuel combustion and catalytic partial oxidation mechanisms using spatially resolved experimental techniques.
Dr. Bob Beitle Jr. is a Professor of Chemical Engineering and Senior Associate Vice Chancellor for Research and Innovation at the University of Arkansas. He joined the department in 1993, earned tenure in 1998, and was promoted to Full Professor in 2006. His research spans biochemical engineering , bioseparation , fermentation , and adaptive technology for the disabled , with significant work on protein purification, catalytic nanoparticles, and sustainable bioprocesses. Education: BS, MS, PhD in Chemical Engineering from the University of Pittsburgh (1987, 1991, 1993) Dr. Beitle's research combines experimental and computational approaches, focusing on peptide-directed nanoparticle synthesis and biocatalysis . His recent publications highlight advancements in MOF-based separations , CO2 capture materials , and viral detection platforms . He has secured grants like the CAREER Award and led projects in industrial partnerships and student development . Scientific contributions include multiple patents in bioseparation and software interfaces. Awards span decades: teaching honors (1988–2007) and mentorship recognition . He serves on the Cell and Molecular Biology Program Advisory Committee and the Executive Committee for the Biochemical Technology Division of ACS . Lab initiatives involve genomic data-driven affinity tail design and membrane-assisted fermentation systems .
Terese Løvås serves as Vice Dean of Research and Innovation at the Faculty of Engineering, Norwegian University of Science and Technology (NTNU), where she leads strategic development of research and innovation activities. She concurrently holds the position of Professor of Combustion and Thermodynamics within the Department of Energy and Process Engineering. Her leadership responsibilities include oversight of Centers of Excellence, Horizon Europe projects, and PhD researcher training. Her research focuses on combustion engineering and alternative fuel technologies , particularly investigating ammonia and hydrogen combustion for zero-emission engines, biomass gasification processes, and reactive multiphase flow modeling. She heads the Engine Lab at NTNU and teaches Thermodynamics, Heat, and Combustion courses. Her work bridges theoretical modeling with experimental validation in sustainable energy systems. Løvås actively contributes to major research initiatives including LowEmission (SFI center), ACTIVATE (ammonia-powered agricultural vehicles), AMAZE (ammonia zero-emission), and CAHEMA (marine ammonia/hydrogen engines). Her publications reveal strong trends in ammonia combustion chemistry , emissions reduction , and advanced computational modeling for sustainable fuel systems, with increasing focus on nitrogen oxide formation mechanisms and dual-fuel strategies. Member of the Board of Directors, Combustion Institute (2022–present) Joint Editor, Proceedings of the Combustion Institute (2019–present) Alumni Fellow in Engineering, Churchill College, Cambridge University As Vice Dean, she manages NTNU's Research and Innovation Committee and represents the faculty in NTNU's Research and Innovation Committee. She supervises multiple PhD candidates and leads international collaborations through projects funded by the Norwegian Research Council, Nordic Energy Research, and EU programs. Her laboratory work focuses on optical engine diagnostics and advanced combustion testing. Løvås maintains active industry engagement through her leadership in the ComKin Research Group and membership in the Institute of Physics and Scandinavian-Nordic Section of the Combustion Institute. Her current work emphasizes practical implementation of ammonia-fueled engine technologies for marine and agricultural applications.
Joyce Pham is an Assistant Professor in the Department of Chemistry and Biochemistry at California State University–San Bernardino (CSUSB), part of the College of Natural Sciences. She teaches courses in general, inorganic, materials, and solid-state chemistry, and leads an active research group focused on the synthesis and characterization of extended inorganic solids. Bachelor of Science in Chemistry, University of California, Davis (2012) Ph.D. in Chemistry, Iowa State University (2018) Postdoctoral Fellow, Max Planck Institute for Chemical Physics of Solids, Dresden, Germany (2018–2020) Her research lies at the intersection of solid-state, inorganic, and materials chemistry, emphasizing crystallography, chemical bonding, and electronic structure analysis. Using X-ray diffraction and computational modeling, her group investigates metal-rich compounds, quasicrystals, and complex intermetallics to uncover fundamental structure–property relationships. She integrates research into teaching to foster scientific curiosity and critical thinking. Although no publications are listed in the provided text, her research agenda is deeply rooted in experimental and computational solid-state chemistry, with a clear trajectory toward discovery of novel materials and dissemination through national conferences and collaborative networks. 2010 ACS Undergraduate Award in Inorganic Chemistry 2018 Alpha Chi Sigma Research Award 2016 ISU Teaching Excellence Award 2014 Cotton-Uphaus Award CSUSB Faculty Senate Exceptional Service to Students Award (ESSA) Joyce Pham actively mentors undergraduate and visiting researchers through her “Solid State Chemistry Phamily,” many of whom have secured prestigious summer research opportunities at Princeton University, Pacific Northwest National Lab, and Ames National Laboratory via programs funded by DOE-BES-FAIR, NSF-CREST, and CSUSB initiatives. Her research is supported by grants from the NSF, DOE, CSU-VETI, and multiple CSUSB offices including Academic Affairs, Research Development, and Student Research. She collaborates widely with regional institutions and utilizes high-performance computing resources for electronic structure calculations. She leads the “Solid State Chemistry Phamily,” a vibrant undergraduate research group that engages students in hands-on synthesis, structural analysis, and computational modeling of advanced materials. The group participates in regional and national conferences and maintains strong partnerships with national labs and universities.
Paul Erhart is a Professor in Condensed Matter and Materials Theory at the Department of Physics, Chalmers University. He received his PhD from Technische Universität Darmstadt in 2006, followed by postdoctoral and staff positions at Lawrence Livermore National Laboratory from 2007, before joining Chalmers in 2011. His research bridges computational physics, materials science, and machine learning to tackle fundamental problems in materials design and characterization. Dr. Erhart's research focuses on computational materials science with particular emphasis on condensed matter physics, nanomaterials, and quantum materials. His work spans from developing computational methods like machine-learned potentials (GPUMD, neuroevolution potentials) to studying fundamental phenomena in perovskites, 2D materials, thermal transport, and plasmonics. He has pioneered approaches connecting simulation with experimental techniques through correlation functions and has made significant contributions to understanding phase transitions, defect physics, and electronic structure in complex materials systems. Analysis of his recent publications reveals a strong trend toward integrating machine learning with traditional computational physics methods. His work increasingly focuses on developing and applying neuroevolution potentials to study thermal properties, phase transitions, and optical phenomena in materials. There's also a clear emphasis on connecting computational results with experimental observations, particularly in neutron scattering, Raman spectroscopy, and plasmonic sensing applications. His research spans fundamental materials physics to applied areas like hydrogen sensing and sustainable materials development. Dr. Erhart has contributed to numerous software packages essential to the computational materials science community, including WulffPack for Wulff constructions, Dynasor for extracting dynamical structure factors, calorine for neuroevolution potential models, and ICET for alloy cluster expansions. His collaborative work spans multiple institutions and disciplines, reflecting the interdisciplinary nature of modern materials research. His contributions to understanding perovskite materials, thermal transport phenomena, and plasmonic systems have established him as a leading researcher in computational materials science.
Michael Feig serves as Professor in the Department of Biochemistry & Molecular Biology at Michigan State University, leading the Feig Lab within the BioMolecular Science Gateway initiative. His research bridges computational modeling and molecular biology to investigate protein behavior in cellular contexts, with particular emphasis on molecular dynamics simulations and machine learning applications. His academic background includes: Ph.D. (1999) from the University of Houston M.S. (1994) from Technical University of Berlin Feig's research program focuses on computational biophysics of protein systems, specializing in molecular dynamics simulations of crowded cellular environments, bacterial microcompartments, and intrinsically disordered proteins. His lab develops advanced modeling techniques including coarse-grained approaches (COCOMO2) and machine learning frameworks to predict protein properties and conformational landscapes. Current work explores temperature-dependent structural ensembles, enzyme cargo loading mechanisms in engineered microcompartments, and biomolecular condensate physics under shear flow. Analysis of his 15 most recent publications (2024-2025) reveals three dominant research thrusts: (1) integration of deep learning with molecular dynamics for protein structure prediction, (2) engineering of bacterial microcompartments for synthetic biology applications, and (3) fundamental studies of macromolecular crowding effects on diffusion and phase separation. His work consistently emphasizes methodological innovation with biological relevance, notably through enhancements to the CHARMM simulation platform. His scientific recognition includes: Alfred P. Sloan Fellowship (2005) As principal investigator of the Feig Lab, he directs research teams in computational biophysics projects supported by active funding mechanisms. While specific grant details aren't provided, his continuous publication pipeline and lab infrastructure indicate sustained research support. His mentorship spans graduate students in the Cell & Molecular Biology Program, with recent work involving multi-institutional collaborations on bacterial microcompartment engineering and protein phase separation. The Feig Lab operates at the intersection of high-performance computing and molecular biology, maintaining strong connections with experimental groups for method validation. Current initiatives include developing generative models for temperature-dependent protein conformations and investigating cytoplasmic protein capture mechanisms in microcompartments, with potential applications in metabolic engineering and nanobiotechnology.
Yael Feldman Maggor is a Postdoctoral Fellow at KTH Royal Institute of Technology, affiliated with the Media Technology & Interaction Design Division and the Digital Futures research center. Her work bridges educational technologies, artificial intelligence, and science education, with a focus on enhancing pedagogy through innovative tools. Research Themes: Generative AI in education, self-regulated learning, learning analytics, chemistry education, and ethical considerations in AI integration. Key Projects: Contributions to the International Journal of Science Education, development of AI-driven evaluation frameworks, and pandemic-era online teaching analysis. Methodologies: Expertise in quantitative and qualitative research, educational data mining, and design of interactive learning platforms. Recent publications emphasize cross-cultural trust in AI, generative AI applications in chemistry education, and explainable AI for teacher professional development. She co-authored studies on nanotechnology courses for educators and self-regulation strategies in online learning environments.
Professor Byung S. Lee is a distinguished faculty member in the Department of Computer Science at the University of Vermont's College of Engineering and Mathematical Sciences. He joined UVM in 1999 and continues to be actively engaged in teaching, research, and service. His office is located in Innovation Hall at the Burlington campus, where he maintains regular office hours and oversees his research lab. Professor Lee holds a Ph.D. from Stanford University, an MS from Korea Advanced Institute of Science and Technology, and a BS from Seoul National University. His educational background provided the foundation for his extensive career in computer science research and education. Professor Lee's research spans multiple domains within computer science, with a particular focus on database systems, data mining, and data science. His work increasingly integrates machine learning techniques with traditional database approaches, especially in the analysis of time series data. He has made significant contributions to graph theory applications, anomaly detection methods, and environmental data analysis. His research often bridges computer science with practical applications in healthcare, environmental science, transportation, and astrophysics through interdisciplinary collaborations. An analysis of his recent publications reveals a strong trend toward time series analysis and anomaly detection, particularly applied to environmental monitoring and healthcare data. His work demonstrates a consistent evolution from foundational database research to more applied machine learning approaches, with increasing emphasis on real-world problem solving across multiple scientific domains. Professor Lee has served as primary advisor for numerous graduate students across multiple cohorts, including PhD candidates, Master's students, and postdoctoral researchers. His advising portfolio reflects the breadth of his research interests, with students working on topics ranging from graph neural networks to medical informatics applications. He has also been actively involved in professional service, serving on program committees for major conferences including SAC, PAKDD, DASFAA, and CIKM. Professor Lee leads a vibrant research laboratory that focuses on cutting-edge data science methodologies and their applications. His team collaborates extensively with researchers in environmental science, hydrology, and healthcare, demonstrating the interdisciplinary nature of modern data science research. The lab maintains active projects in time series analysis, graph analytics, and environmental monitoring systems, often working with large-scale datasets from real-world applications.
Luca Cardelli is a Principal Researcher and Assistant Director at Microsoft Research Cambridge, UK, since 1997. He holds visiting professorships at Imperial College London (Department of Computing, 2004–2009) and the University of Trento (2005–2007). He earned his PhD in Computer Science from the University of Edinburgh in 1982. His research spans type theory , molecular programming , and principles of programming languages , with applications to systems biology and concurrency theory. Notable contributions include formal frameworks for modeling biochemical systems (e.g., the stochastic π-calculus) and designing DNA-based circuits. Key achievements include the AITO Dahl-Nygaard Senior Prize (2007) and multiple Most Influential Paper Awards at POPL and ETAPS. His work bridges computer science and biology, advancing both theoretical foundations and practical molecular computing.
Cesare Franchini is a full Professor at the University of Vienna's Faculty of Physics, leading the Computational Materials Physics research group. His work focuses on theoretical understanding and computational modeling of quantum materials using first principles methods, particularly VASP. He maintains an active research program with numerous postdocs, PhD students, and collaborations across multiple institutions including the University of Bologna. Professor Franchini's research centers on quantum materials with many interacting degrees of freedom (lattice, spin, and electron orbital) that enable novel electronic and magnetic phases. His specific interests include metal-insulator transitions, polaron physics (electron-phonon interactions), non-collinear spin orderings, topological Dirac/Weyl phases, multiferroism, and superconductivity. He has increasingly incorporated machine learning data-driven tools and diagrammatic Monte Carlo techniques into his computational approaches. Analysis of his recent publications (2024-2025) reveals a strong focus on polaron physics across multiple material systems, with significant work on hematite, titanium dioxide, and quantum paraelectrics like KTaO3. His research increasingly integrates machine learning with traditional first-principles methods, particularly for studying hydrogen diffusion, surface science phenomena, and electronic structure calculations. There's also substantial work on single-atom catalysis and the application of advanced computational techniques to understand fundamental charge transport mechanisms in energy materials. Professor Franchini actively supervises numerous PhD students and postdocs, including Andrea Angeletti, Viktor Birschitzky, Lorenzo Celiberti, and several others working on diverse aspects of computational materials physics. He leads or participates in major research projects including TACO (Taming Complexity in Materials Modeling), DCAFM (Doctoral College Advanced Functional Materials), and the recently launched Spin-orbit entangled anharmonic polarons project. His group maintains strong collaborations with experimentalists at Charles University, Technical University of Vienna, and other international institutions.
Kishalay Mitra is a Professor at the Indian Institute of Technology Hyderabad , with affiliations to the Department of Chemical Engineering , Department of Climate Change , and Department of Artificial Intelligence . He also holds visiting professorships at Washington University in St. Louis and University of Washington, Seattle . His work in the Global Optimization & Knowledge Unearthing Laboratory (GOKUL) spans interdisciplinary optimization, machine learning, and their applications in industrial-scale engineering problems. Education : Ph.D. from IIT Bombay. Research Interests : Mitra's research focuses on optimization under uncertainty , surrogate modeling , multi-objective optimization , and integrating machine learning with physics-based models . His work addresses real-world challenges in wind energy , bioenergy supply chains , chemical process control , nanoscience , and environmental modeling (e.g., PM10 spatiotemporal analysis, forest fire prediction, and carbon capture). Article Trends : His recent publications emphasize wind energy systems (layout optimization, yaw control, forecasting), materials science (precipitate growth prediction, polymerization), and industrial processes (crystallization, grinding circuits). Techniques include neural operators , Bayesian optimization , generative adversarial networks (GANs) , and explainable AI .
George T. C. Chiu is a Professor in the School of Mechanical Engineering at Purdue University, with courtesy appointments in Electrical and Computer Engineering and Psychological Sciences. He holds a 50% appointment as Assistant Dean for Global Engineering Programs and Partnerships. His research focuses on mechatronics, dynamic systems and control, functional printing, and human-machine interaction, with applications in biomedical engineering, robotics, and advanced manufacturing. Education: PhD (1994), MS (1990) University of California, Berkeley; BS (1985) National Taiwan University. Research interests emphasize application-driven solutions for printing technologies, motion control, and embedded systems. Notable projects include developing inkjet printing for biomedical materials and sensor systems. Awards include ASME Fellowship (2013) and the 2024 ASME Rabins Leadership Award. Publications span topics like inkjet drop dynamics, control systems, and biofabrication. He has led initiatives such as the Purdue FIRST Programs, fostering K-12 STEM education through robotics mentorship. Editorial roles include Editor-in-Chief of IEEE/ASME Transactions on Mechatronics (2017-2019).
Scott Geyer is an Associate Teaching Professor of Chemistry at Wake Forest University, located in Winston-Salem, NC. He holds a B.S. (2005) from the University of Virginia and a Ph.D. (2010) from the Massachusetts Institute of Technology, followed by postdoctoral research at Stanford University. Research Focus : Dr. Geyer’s research bridges chemical education and materials science. In pedagogy, he emphasizes laboratory course design to enhance student decision-making and scientific communication skills, particularly for graduate program applications. His materials research explores nanocrystal-based catalytic systems for energy applications, including water splitting, CO2 reduction, and photocatalytic processes. Key Contributions : His work includes developing trifunctional electrocatalysts for water splitting, lead-free perovskite alternatives for CO2 reduction, and scalable H2O2 electrosynthesis. His studies often combine computational modeling (e.g., DFT simulations) with experimental synthesis of nanomaterials. Awards & Recognition : No specific awards listed, though his publications reflect sustained contributions to catalysis and nanomaterial research. Advising & Grants : While no advisees are listed, his teaching role likely involves mentoring undergraduate and graduate students in laboratory practices and research methodologies. His work is supported by grants focused on sustainable energy materials. Labs & Teams : Engaged with Wake Forest’s chemistry department labs, contributing to interdisciplinary efforts in nanomaterials and sustainable chemistry.
