Professor Steven V. Ley leads the Yusuf Hamied Department of Chemistry at the University of Cambridge, focusing on transformative research in flow chemistry, organic synthesis, and green chemistry. His work emphasizes sustainable methodologies and the integration of advanced technologies like microcontrollers and automation to revolutionize chemical processes. In 2018, he received the prestigious Arthur C. Cope Award—the first UK-based recipient—recognizing groundbreaking contributions to organic chemistry. Research interests include developing continuous flow systems for hazardous reaction management, immobilized reagents, and machine-assisted synthesis. Collaborations span academia and industry, notably through spin-off company New Path Molecular , which applies cutting-edge synthesis techniques to pharmaceuticals and agrochemicals. Key publications highlight innovations in flow chemistry applications, sustainable process design, and automation. His work bridges chemistry with engineering, aiming to address global challenges in resource efficiency and environmental impact. Awards: Arthur C. Cope Award (2018) Lab/Teams: Active research group at the University of Cambridge; collaborates with New Path Molecular on commercial applications.
Elsa A. Olivetti is the Jerry McAfee (1940) Professor in Engineering and Professor of Materials Science and Engineering at MIT, and a MacVicar Faculty Fellow. She leads the Olivetti Group, focusing on sustainable materials design, recycling strategies, and computational models for environmental and economic impact assessment. Her work bridges materials science with sustainability, emphasizing circular economy principles and decarbonization. Education: B.S. in Engineering Science from University of Virginia (2000); Ph.D. in Materials Science and Engineering from MIT (2007). Her doctoral research centered on lithium-ion battery electrode materials. She joined MIT’s Department of Materials Science and Engineering (DMSE) in 2014 as an Assistant Professor, later advancing to full Professor. She co-directs the MIT Climate & Sustainability Consortium and chairs the MIT Climate Nucleus. Research interests include: sustainable materials systems, recycling-friendly material design, waste mining, and AI-driven materials discovery. She develops models for cost prediction, environmental impact analysis, and policy-relevant supply chain dynamics. Notable contributions include high-throughput zeolite design and battery recycling frameworks. Awards include the Bose Teaching Award (2021), NSF Early Career Award (2018), and Minerals, Metals & Materials Society Early Career Fellowship (2019). Her work emphasizes education and curriculum development, including courses for MIT’s Climate Scholars program. Labs/Teams: Olivetti Group (MIT), MIT Climate & Sustainability Consortium. Active in global sustainability initiatives, focusing on materials for energy transition and climate resilience.
Thomas Ouldridge is a Royal Society University Research Fellow and Reader in Biomolecular Systems at the Department of Bioengineering, Faculty of Engineering, Imperial College London. He leads the 'Principles of Biomolecular Systems' group, which focuses on theoretical and computational modeling of complex biochemical systems, particularly exploring the interplay between molecular details and emergent behaviors like sensing, replication, and self-assembly. His work integrates natural systems analysis with synthetic biology applications, aiming to engineer artificial analogs of biological processes. His research spans interdisciplinary areas including stochastic thermodynamics, DNA-based computation, and molecular reaction networks. Key affiliations include the Physics of Life, Synthetic Biology Hub, and the Leverhulme Centre for Cellular Bionics. He has contributed to over 60 peer-reviewed articles since 2009, with recent work emphasizing energy-efficient molecular information processing and thermodynamic limits of biochemical systems. Awards: Royal Society University Research Fellowship (current). Labs/Teams: Principles of Biomolecular Systems Group, collaborating with multiple centers including the Centre for Synthetic Biology and Institute of Chemical Biology. Grants/Positions: Maintains research funding through the Royal Society and UKRI grants, focusing on non-equilibrium biomolecular systems and synthetic biology tools. Recent publications highlight advances in DNA templating networks, stochastic thermodynamic modeling of computation, and optimal protocols for molecular copying systems. His work bridges foundational physics with applied biotechnology, aiming to push the boundaries of synthetic biological engineering.
Dr. Huadong Mo is a Senior Lecturer at the School of Systems and Computing, University of New South Wales (UNSW) Canberra, Australia. He holds a B.E. degree in automation from the University of Science and Technology of China (2012) and a Ph.D. in systems engineering and engineering management from the City University of Hong Kong (2016). Prior to his current position, he was a research associate at ETH Zurich's Reliability and Risk Engineering Lab (2016-2019) and a Lecturer at UNSW Canberra (2019-2021). Dr. Mo's educational background includes a strong foundation in systems engineering with international experience across China, Switzerland, and Australia. His career trajectory demonstrates a progression from academic research to faculty positions with increasing responsibilities in teaching and research leadership. His research focuses on enhancing the resilience, performance, and security of complex systems using learning-based algorithms, primarily in power and energy systems, cyber-physical systems, and manufacturing systems. He applies data analytics to understand system evolution under uncertainties, with particular emphasis on prognostics and health management, sustainable transportation, robust operation of power systems under extreme events, and reinforcement learning-based asset management. His work bridges theoretical advances with practical applications in critical infrastructure. Analysis of Dr. Mo's recent publications reveals a strong focus on energy systems, particularly in the integration of machine learning with power grid management, battery storage systems, and resilience against cyber threats. His research shows a clear trajectory toward increasingly complex system integration, with growing emphasis on multi-vector energy communities, cross-domain prediction, and uncertainty-aware energy management. The interdisciplinary nature of his work spans electrical engineering, computer science, and operations research. 2024 IEEE SMC Early Career Award 2023 Visiting Research Fellowship (Jean d'Alembert Pour Fellowship) Gold Medal in 2024 China International College Student Innovation Competition (as supervisor) Arc PGC Supervisor Award (2021) IEEE SMC Outstanding Chapter Award (2021) Alumni Achievement Award from City University of Hong Kong (2019) Dr. Mo actively supervises numerous HDR students working on cutting-edge research topics including battery health monitoring, quantum control, reinforcement learning for power systems, and explainable AI for energy management. He leads multiple significant research grants totaling over 3 million AUD, including projects funded by ARC, Energy Innovation Fund, and international collaborations with institutions like ETH Zurich, Cambridge, and Tsinghua University. His research group maintains strong international connections, facilitating student exchanges and collaborative research. As Postgraduate Course Coordinator of Systems Engineering and Chair of IEEE SMC ACT Chapter, Dr. Mo plays a significant role in academic leadership and professional community building. His research team collaborates with industry partners on practical implementations of their theoretical work, particularly in the energy sector.
Professor Ian Metcalfe is a distinguished academic at Newcastle University, specializing in advanced materials for energy applications, particularly in the areas of membrane technology, chemical looping processes, and catalysis. His research spans multiple interdisciplinary fields with significant implications for carbon capture, hydrogen production, and sustainable energy systems. Professor Metcalfe's research primarily focuses on membrane technology for gas separation, particularly CO 2 capture and hydrogen production . His work extensively investigates chemical looping processes using various oxygen carrier materials, particularly perovskite-based materials . A significant portion of his recent research explores nanoparticle exsolution for creating highly stable and active catalysts. His research group has made notable contributions to understanding the thermodynamics of non-stoichiometric materials and developing novel membrane configurations for enhanced gas separation. Analysis of Professor Metcalfe's recent publications (2023-2025) reveals a strong focus on CO 2 separation technologies , particularly using molten-carbonate membranes with innovative support structures. His work on exsolution has expanded to include room-temperature processes using plasma techniques and applications in methane reforming. The research shows increasing emphasis on direct air capture technologies and ammonia synthesis via chemical looping, indicating strategic expansion into emerging energy storage and carbon utilization areas. Professor Metcalfe maintains extensive collaborations with researchers including Dr. Wenting Hu, Dr. Evangelos Papaioannou, Dr. Dragos Neagu, and Dr. Greg Mutch. His research has significant implications for decarbonization technologies and sustainable energy systems, particularly in hard-to-abate sectors where efficient CO 2 separation and clean hydrogen production are critical.
Stephen Brown is a Professor at the University of Toronto within the Department of Electrical and Computer Engineering under the Faculty of Applied Science and Engineering. He earned his B.A.Sc and M.A.Sc in Electrical Engineering from the University of Toronto and New Brunswick, respectively, and a Ph.D. in Electrical Engineering from the University of Toronto (1992). His career spans over two decades in academia and industry collaboration. Education : B.A.Sc, University of New Brunswick M.A.Sc, University of Toronto Ph.D, University of Toronto Professor Brown’s research focuses on field-programmable gate arrays (FPGAs) , CAD algorithms , and computer architecture , with applications in machine learning and high-level synthesis . He is a principal investigator in the LegUp project , an open-source high-level synthesis framework that bridges software and hardware design. His work also extends to optimizing FPGA interconnect delays, physical synthesis, and logic block architectures. Key trends in his publications include advancements in high-level synthesis tools, FPGA architecture evaluation, and timing-driven design methodologies. His contributions often intersect with design automation , resource sharing , and embedded systems . Scientific Awards : NSERC 1992 Doctoral Prize Hart Professorship for Innovation in Teaching (2017) Multiple teaching excellence awards Best Paper Award at ICCAD 1990 Best Paper Award nomination at Canadian Conference on VLSI (1989) As Director of the FPGA University Program at Intel Corporation, he leads industry-academia initiatives. His teaching portfolio includes courses like ECE253 (Digital Logic) and ECE1733F (Switching Theory).
Dr. Matthias Cuntz is a Senior Researcher at the Department of Computational Hydrosystems within the Helmholtz Centre for Environmental Research (UFZ) in Leipzig, Germany. He leads the Regional Ecophysiology group and focuses on integrating stable isotopes, remote sensing, and computational modeling to study terrestrial ecosystems. Research Interests: Energy, water, and trace gas exchange in ecosystems; stable isotope applications; global water-carbon cycle modeling; eddy-covariance flux analysis; sap flow dynamics. Key Projects: Involved in TERENO (Terrestrial Environmental Observatories) and ICOS (Integrated Carbon Observation System) for long-term ecological monitoring. Recent Publications address soil freeze-thaw processes, Amazon forest carbon dynamics, hydrological model calibration, and isotopic partitioning of evapotranspiration. His work spans computational hydrology, remote sensing validation, and uncertainty quantification in environmental models. Contact: Email: matthias.cuntz@ufz.de Personal Webpage: www.macu.de
Marco Cuturi is a Research Scientist at Apple ML Research in Paris and Professor of Statistics at CREST-ENSAE, Institut Polytechnique de Paris. His work bridges machine learning , optimal transport , and optimization , with applications in time-series analysis , kernels , and multiresolution methods . He has held academic roles at Kyoto University and Princeton University, and previously worked in the financial industry. Research Interests: Optimal transport theory and computational methods Kernel design for structured data and histograms Time-series alignment and soft-DTW Entropic regularization in optimization Applications to computer vision and genomics Teaching: Cuturi has taught courses on linear optimization at Princeton, geometric methods in machine learning at Kyoto, and scientific English. He has also organized machine learning summer schools in Kyoto, Les Houches, and other international venues. Recent Trends: His 2024-2025 publications focus on entropic optimal transport solvers, disentangled representation learning via Gromov-Monge gaps, and applications to text-to-image diffusion models. Collaborative work with institutions like Google Research, MIT, and University of Tokyo highlights his interdisciplinary impact.
Sean Z. Qian is a Professor at Carnegie Mellon University with joint appointments in the Department of Civil and Environmental Engineering (College of Engineering), Heinz College of Information Systems and Public Policy, and the Department of Electrical and Computer Engineering. He directs the Mobility Data Analytics Center (MAC) and founded the spinoff firm TraffiQure Technologies in 2020 to commercialize AI/ML technologies in infrastructure and mobility services. His academic credentials include: 2012: MS in Statistics, Stanford University 2011: Ph.D. in Civil Engineering, University of California, Davis 2006: MS in Civil Engineering, Tsinghua University 2004: BS in Civil Engineering, Tsinghua University Qian's research centers on large-scale dynamic network modeling and data analytics for multi-modal transportation systems, applying AI, network flow theory, and economics to address aging infrastructure challenges. His work spans Infrastructure Resilience under climate stress, Urban Systems Interdependency , and Intelligent Transportation Systems , with emphasis on sustainable network optimization and cyber-physical-social system integration. Key methodologies include remote sensing, transportation economics, and digital twin technologies for infrastructure management. His notable scientific awards include: NSF CAREER Award (2018) Greenshields Prize, Transportation Research Board (2017) Research funding has been secured from diverse sources: Federal Agencies: National Science Foundation (NSF), U.S. Department of Energy (DOE), U.S. Department of Transportation (DOT) State Agencies: Pennsylvania Department of Transportation (PennDOT), Maryland Department of Transportation (MDOT), Pennsylvania Department of Community and Economic Development (DCED) Industry Partners: IBM, Honda Research Institute, Fujitsu Inc. Foundations: Benedum Foundation, Hillman Foundation As Director of the Mobility Data Analytics Center (MAC), Qian leads collaborations with Fujitsu on digital twin technologies for infrastructure management in southwestern Pennsylvania, while actively mentoring graduate students and recruiting Ph.D. candidates with strong quantitative backgrounds for Fall 2025.
Somnath Basu is a Professor in the Department of Metallurgical Engineering and Materials Science at the Indian Institute of Technology Bombay. He has been serving in academic roles since 2011, progressing from Assistant Professor to Associate Professor and then to Professor in 2022. His work is deeply rooted in process metallurgy and materials engineering, with a focus on industrial steelmaking technologies. His research interests include metal refining , thermodynamics of slag-metal reactions , phosphorus and sulfur removal , and continuous casting processes . He also explores nanofluids and their transport properties, indicating interdisciplinary engagement. His work bridges fundamental thermodynamic studies with practical industrial applications in iron and steel production. The selected publications reflect a strong trend in steelmaking process optimization , particularly in reaction kinetics , inclusion behavior , and process monitoring . His research spans both experimental investigations and thermodynamic modeling, targeting improvements in steel purity and casting efficiency. Scientific Contributions: Published in leading journals such as ISIJ International , Steel Research International , and Metallurgical and Materials Transactions B . Contributions to understanding phosphorus partitioning, nozzle clogging, and nanofluid conductivity. While no specific students or grants are listed, his long-standing academic position and publication record suggest active supervision of graduate research and involvement in funded projects related to metallurgical process innovation. His work likely supports both academic and industrial advancements in steel technology. He is affiliated with a leading research department equipped with advanced facilities for metallurgical experimentation and process simulation, though specific lab names or team structures are not mentioned in the text.
Mark C. Johnson is a Senior Lecturer at the Elmore Family School of Electrical and Computer Engineering at Purdue University, West Lafayette. He serves as Director of Instructional Laboratories and Associate Director for Design - Semiconductor Degree Program , overseeing laboratory infrastructure, CAD software administration, and curriculum development for courses like ECE337, ECE437, and ECE364. Education: Ph.D. in Electrical Engineering (1998), Purdue University M.S. in Electrical Engineering (1991), Wichita State University B.S. in Electrical Engineering (1983), Purdue University - Calumet His research focuses on electrical and computer engineering laboratory curriculum innovation , digital systems design , and CAD for VLSI . Over 15 recent publications highlight his work in SoC prototyping , low-power circuit design , and educational technology , spanning projects like FPGA filter optimization, dual-core processor experiments, and active learning strategies. Leadership Roles: Proceedings Chair (2003), MSE Program Chair (2005), MSE General Chair (2007), MSE Steering Committee Member, MSE & European Workshop on Microelectronics Education Chair, ECE Instructional Innovation Group (2004-2012) Secretary/Webmaster, ASEE Illinois/Indiana Section (2002-2011) He directs the ECE437 Computer Architecture Prototyping Lab and System on Chip Extension Technologies (SoCET) team , and co-advises the STARS semiconductor readiness program. Outside academia, he is an organist at Faith Presbyterian Church and composes keyboard music.
Jason Ostanek is an Assistant Professor at Purdue University's School of Engineering Technology and Environmental and Ecological Engineering. He directs the Applied Thermofluids Laboratory and Powertrain Technology Laboratory, focusing on battery safety and thermal management systems. Ph.D. in Mechanical Engineering from Penn State M.S. in Mechanical Engineering from Penn State B.S. in Mechanical Engineering from Virginia Tech His research explores energy storage systems, thermal runaway phenomena, heat transfer mechanisms in Li-ion batteries, fluid dynamics, and internal combustion engine thermal management. He has developed analytical models for battery degradation, thermal abuse simulations, and innovative cooling strategies for large-scale energy systems. Key publication trends show expertise in: Li-ion battery thermal runaway modeling Heat transfer in confined geometries Thermal management for energy storage systems Renewable energy forecasting Computational fluid dynamics applications Scientific awards include: 2020 Purdue Teaching Academy's Award for Exceptional Teaching and Instructional Support during the COVID-19 Pandemic 2020 SOET Outstanding Faculty in Engagement 2019 SOET Outstanding Faculty in Discovery 2015 NAVSEA Commander’s Award for Innovation 2013 ASME IGTI Young Engineer Travel Award 2007 DOD SMART Fellowship Recipient As director of Purdue's Applied Thermofluids Laboratory, he leads research on battery safety mechanisms, combustion dynamics, and thermal systems optimization. His work spans fundamental and applied research with industrial collaborators.
Dr. Roy Lederman is an Assistant Professor at the Department of Statistics and Data Science , Yale University. He is affiliated with the Quantitative Biology Institute (QBio) , the Applied Math Program , the Institute for Foundations of Data Science (FDS) , and the Wu Tsai Institute (WTI) . He was awarded the Sloan Research Fellowship (2023) . He previously held a Gibbs Assistant Professorship at Yale (2014-2015) and a postdoc at Princeton University (2015-2018) . Education: PhD in Applied Mathematics, Yale University (2014); dual BSc in Physics and Electrical Engineering, Tel-Aviv University. Teaching: Courses include Computational Tools for Data Science, Signal Processing, and Mathematical Machine Learning. Research Areas: Dr. Lederman works at the intersection of computational biology , structural biology , Bayesian inference , numerical analysis , and machine learning . His recent work focuses on cryo-EM and hyper-molecules for studying molecular heterogeneity, alternating diffusion for common variable recovery, and Zernike polynomials for 3D imaging. He also develops Hamiltonian Monte Carlo methods and randomized DNA sequencing algorithms . Publications Trends: His publications (15 most recent) emphasize structural biology and cryo-EM applications, machine learning (Bayesian deep learning, diffusion maps), numerical analysis (Fourier/Laplace transforms), and computational biology (DNA sequencing algorithms). Key sub-fields include heterogeneity analysis , manifold learning , Hamiltonian Monte Carlo , and Zernike polynomials . Scientific Awards: Sloan Research Fellow (2023) Dr. Lederman actively mentors graduate students and postdocs at Yale, and co-organizes the One World Cryo-EM seminar series . His lab develops open-source software (e.g., prolate function implementation ) and explores theoretical bounds on transforms and common variable recovery in multi-sensor experiments.
Michel Mandjes is a Professor at the University of Amsterdam's Faculty of Science and holds a Visiting Professor position at the Faculty of Economics and Business (FEB). His research focuses on stochastic processes, queueing theory, and probability theory, with applications in risk modeling, network analysis, and operations research. Recent publications highlight his contributions to multivariate Hawkes processes , Lévy-driven systems , and dynamic random graphs , emphasizing large deviations, rare event simulation, and statistical inference. His work bridges theoretical probability with practical challenges in traffic flow, financial risk, and social network modeling. The trends in his research include the development of stochastic models for network stability, appointment scheduling optimization, and inference techniques for non-stationary processes. His methodological innovations often leverage advanced probability theory and queueing frameworks to address real-world problems in transportation, healthcare, and finance.
Dr Yongle Sun is a Lecturer in Additive Manufacture at Cranfield University , specializing in cross-scale modelling of metal manufacturing processes for aerospace and energy applications. BSc & MSc in Mechanics from Xi'an Jiaotong University PhD in Mechanical Engineering from The University of Manchester His research focuses on multi-physics modelling of additive manufacturing and welding processes, with particular emphasis on residual stress/distortion prediction and mitigation. Current projects include: NEWAM (cross-scale additive manufacturing) SAM (smart manufacturing) I-Break (process innovation) With over £10M in research funding, his work bridges mechanistic models with engineering applications through collaborations with: GE Avio Aero WAAM3D Airbus EPSRC Innovate UK Key achievements include: First author of 16 leading journal papers Co-author of 35+ peer-reviewed works H-index of 23 Queen's Anniversary Prize contribution Top-cited paper in International Journal of Impact Engineering
