Sara Vinco is an Associate Professor at the Department of Control and Computer Engineering (DAUIN), Politecnico di Torino, Italy. She specializes in battery simulation, digital twins, and energy-efficient design automation for heterogeneous embedded systems, aligning with Industrial and Information Engineering (Area 0009) and ERC sectors including Computer Architecture and Machine Learning . Her research focuses on advancing cyber-physical systems through simulation frameworks like SystemC-AMS, enabling holistic modeling of analog, digital, and thermal domains. Key projects include data-driven digital twins for EV batteries and low-area digital circuits in industrial/medical applications, supported by commercial contracts such as C-based virtual prototyping. Her recent publications (2022-2023) emphasize machine learning for battery SOH/SOC estimation , energy monitoring in production lines , and multi-domain fault modeling . These works span journals like IEEE Transactions and conferences including DATE and ISLPED. Awarded the FFABR 2017 grant and IEEE FDL Best Paper Award 2011 , she also chairs editorial boards for IEEE Transactions on CAD and DATE Conference. She supervises PhD students Giovanni Pollo (Digital Circuits) and Khaled Alamin (EV Battery Twins), reflecting her leadership in smart systems design.
Dr. Andrea S. Carlini is an Assistant Professor of Materials in the Department of Chemistry & Biochemistry at the University of California, Santa Barbara (UCSB). Her research focuses on structurally dynamic biomaterials and devices, aiming to bridge biochemical signals with soft materials for smart biomedical applications. She holds a PhD from UC San Diego and completed a postdoc at Northwestern University’s Querrey Simpson Institute for Bioelectronics. B.S. in Chemistry & Biological Sciences (Virginia Tech, 2012) M.S. in Chemistry & Biochemistry (UC San Diego, 2014) Ph.D. in Chemistry & Biochemistry (UC San Diego, 2018) Her research is organized into three core areas: (1) stimuli-responsive materials for disease monitoring, (2) 4D shape-changing peptides/polymers, and (3) soft wearable devices for quantitative health feedback. Recent work includes thermal sensors for vascular access and enzyme-responsive biomaterials for tissue engineering. Published articles span bio-electrochemical systems, wearable sensors, and smart hydrogels. Her NSF GRFP Fellowship supported early work on myocardial tissue engineering. The Carlini Group collaborates broadly across UCSB’s interdisciplinary environment. Labs/Teams: Carlini Group (UCSB) Focus: Bioelectronics, biomedical devices, and dynamic materials
Professor Antonio Griffo holds the position of Professor of Power Electronics and Electric Drives at the University of Sheffield's School of Electrical and Electronic Engineering. He leads the Electrical Machines and Drives Research Group and is involved in the High Reliability Drives Group. His academic journey includes a MSc (2003) and PhD (2007) in Electrical Engineering from the University of Naples, followed by research roles at Bristol and Sheffield Universities before becoming a Lecturer in 2013 and later a Professor. His research focuses on advanced control of electric drives, SiC-based power electronics for aerospace/renewables, fault detection in machines, and thermal management. Key projects include modeling hybrid AC/DC power systems for 'More Electric Aircraft', sensorless control techniques, and real-time simulation methodologies. He has pioneered work on SiC converter reliability, insulation monitoring, and condition-based maintenance systems. Publications (15+ in top journals like IEEE Transactions) emphasize innovative solutions for power electronics challenges, including voltage stress mitigation, thermal modeling, and fault tolerance. His work bridges theory and application, addressing critical issues in aerospace, renewable energy, and electric vehicle systems. Griffo also contributes to educational advancements through modular training platforms for power electronics education. Labs/Teams: Active in the Electrical Machines and Drives Research Group, focusing on high-reliability drive systems and sustainable energy technologies. Collaborates with industry on projects like the EPSRC Offshore Wind Prosperity Partnership.
Ulrich Tallarek serves as Professor of Analytical Chemistry in the Faculty of Chemistry at Philipps University of Marburg, where he has held a W3 professorship since 2011. He also serves on the Board of Directors for the Materials Science Center at the university, a position he has held since 2007. His research group focuses on the fundamental understanding of transport phenomena in porous media with applications spanning chromatography, battery technology, and microfluidic systems. The group maintains strong collaborations with institutions worldwide and secures substantial research funding for advanced computational and experimental work. Professor Tallarek's research interests center on functional porous solids, with specific focus on morphology-transport-performance relationships. His work bridges multiple scales from molecular dynamics simulations of solute behavior in nanopores to macroscopic transport in chromatographic columns and battery electrodes. Key research areas include diffusion in hierarchical porous media, electrokinetic phenomena in microfluidic systems, molecular simulation of chromatographic processes, and advanced characterization of porous materials using tomography and other techniques. His group has pioneered multiscale simulation approaches that connect molecular-level surface chemistry to macroscopic transport properties. The research output demonstrates consistent focus on understanding fundamental transport mechanisms in porous systems, with recent publications emphasizing multiscale simulation techniques, molecular dynamics studies of solvent effects in chromatography, advanced characterization of mesoporous structures, and applications to separation science and energy storage. The work shows strong integration of computational modeling with experimental validation across multiple length scales. 2003: Desty Memorial Prize for Innovation in Separation Science, The Royal Institution of Great Britain, London 2006: Young Scientist Award from DECHEMA e.V. 2011: Named Discussion Leader at the 2011 Gordon Research Conference on Physics & Chemistry of Microfluidics 2011–2012: Chairman of the German Chemical Society (GDCh), Marburg 2013: Finalist, World Technology Awards, for category Environment 2013: Named as one of the 100 most influential analytical scientists in the world (The Analytical Scientist Power List) 2017: Recipient of the Silver Jubilee Medal 2017, The Chromatographic Society, UK Professor Tallarek's research has been supported by numerous grants enabling high-performance computing resources, advanced instrumentation, and international collaborations. His group maintains strong ties with industry partners in separation science and analytical instrumentation. The Tallarek Research Group includes postdoctoral researchers, PhD students, and technical staff working across experimental and computational domains. Current projects focus on molecular simulation of chromatographic processes, advanced characterization of porous battery electrodes, and development of novel separation methodologies. The Tallarek Research Group operates state-of-the-art facilities for computational modeling, including access to high-performance computing resources at Forschungszentrum Jülich. The group also maintains experimental capabilities for chromatographic analysis, materials characterization, and microfluidic device development. Their work on physically reconstructed porous media has established new standards for connecting microstructure to transport properties in complex materials systems.
Dr. Vahid Hosseini is an Associate Professor and Graduate Program Chair in the School of Sustainable Energy Engineering at Simon Fraser University (SFU). His research focuses on sustainable energy systems, urban air pollution, and clean mobility solutions. He holds a Ph.D. in Mechanical Engineering from the University of Alberta (2008), and M.A.Sc. and B.Eng. degrees from Sharif University of Technology (Iran). His academic roles include leadership in graduate academic programs and engineering education. Key research areas include thermo-fluid systems analysis, vehicle emissions reduction, and urban air quality modeling. He is actively involved in projects addressing real-world driving emissions, emission inventory development, and the impact of cold climates on transportation energy consumption and pollution. Notable contributions include studies on retrofit emission control devices for motorcycles, high-emitter vehicle identification, and policy recommendations for emission reduction. His work integrates experimental methods, computational fluid dynamics (CFD), and machine learning to tackle complex environmental challenges. Teaching interests span thermodynamics, fluid mechanics, and air pollution control engineering. Current courses include SEE 325 D100 Mechanical Design and Finite Element Analysis . Research highlights include collaborations on Tehran’s air quality management, particulate matter (PM2.5) source apportionment, and the development of high-resolution emission inventories. He contributes to international conferences and journals, with a focus on practical solutions for sustainable urban transportation systems.
Yinzhi Cao is an Associate Professor at the Johns Hopkins University Department of Computer Science . He serves as Technical Director of the Johns Hopkins Information Security Institute and is affiliated with the Data Science and Artificial Intelligence Institute and the Institute for Assured Autonomy . Cao joined JHU in 2018 from Lehigh University, where he was an Assistant Professor. Doctor of Philosophy (PhD) in Computer Science, Northwestern University (2014) Bachelor of Engineering (BE) in Electronic Engineering, Tsinghua University (2008) Research Interests focus on security and privacy of web, mobile, and machine learning systems . Key projects include Vulnerability Analysis of Web Applications and Security, Privacy, and Fairness Analysis of ML Systems . His work addresses prototype pollution in JavaScript, node.js vulnerabilities, browser fingerprinting, federated learning privacy, and automated exploit generation. Scientific Recognition includes the NSF CAREER Award (2021) DARPA Young Faculty Award (2022) & Director's Fellowship (2024) Amazon Research Awards (2022, 2017) IEEE Security & Privacy Test of Time Award (2025) Distinguished Paper Awards at IEEE S&P 2025, CCS 2023, USENIX Security 2022 Advising & Grants highlight mentorship of 20+ PhD and Master’s students across institutions. Major grants include $1.2M collaborative CICI TCR grant (2024-2026) with Dr. John Aucott $750K DARPA YFA grant (2022-2025) $500K NSF SaTC grant (2022-2025) NSF EAGER grant (2016-2017) Labs & Teams : Affiliated with Johns Hopkins Information Security Institute , Data Science AI Institute , and Institute for Assured Autonomy . Collaborates with institutions like Columbia, UC Santa Barbara, and SRI International. His group investigates real-world vulnerabilities in over 2,500 websites and NPM packages, uncovering 80+ zero-day issues.
Gregory M. Shaver is the Reilly Professor of Mechanical Engineering and Director of Herrick Laboratories at Purdue University's School of Mechanical Engineering. He holds a Ph.D. (2005) and M.S. (2004) in Mechanical Engineering from Stanford University, and a B.S. (2000) from Purdue University where he graduated with highest distinction. His research focuses on model-based control of sustainable transportation systems, with emphasis on: Commercial vehicle powertrain optimization Internal combustion engine and after-treatment controls Flexible valve actuation for diesel/natural gas engines Connected/automated vehicle systems Battery modeling for energy storage Fundamental areas include thermodynamics, combustion, and control systems, applied to sustainable energy and transportation challenges. Publication analysis reveals consistent focus on engine efficiency innovations (cylinder deactivation, valve control), electrified transportation (hybrid systems, battery modeling), and emission reduction strategies. Recent work emphasizes real-world applications in medium-duty vehicles and thermal management. Awards and honors: 2014 Early Career Excellence in Research Award (Purdue Engineering) 2014 University Faculty Scholar 2011 Max Bentele SAE Award for Engine Technology Innovation Purdue BSME with Highest Distinction (2000) He leads research initiatives at Herrick Laboratories, supervising graduate students in projects funded by industry and government grants. Current work explores AI-enabled control for hybrid vehicles and low-emission combustion strategies.
David Eaton is a Professor and former NSERC/Chevron Industrial Research Chair in Microseismic System Dynamics at the University of Calgary's Department of Geoscience. He holds a PhD in Geophysics from the University of Calgary (1992) and has published the textbook 'Passive Seismic Monitoring of Induced Seismicity'. Educational Background: PhD Geophysics, University of Calgary, 1992 MSc Geophysics, University of Calgary, 1988 BSc Geology and Physics, Queen's University, 1984 His research focuses on induced seismicity characterization, microseismic monitoring technology development, distributed acoustic sensing applications, physics-informed machine learning approaches, and lithospheric structure analysis. Current projects investigate earthquake triggering mechanisms during hydraulic fracturing and geothermal energy development. Publications show consistent focus on induced seismicity source characterization, monitoring methodologies, and geophysical applications for energy resource development. Recent work integrates machine learning with seismic monitoring to understand geological controls on induced seismicity. Scientific Awards: NSERC Synergy Award for Innovation (2020) J. Tuzo Wilson Medal, Canadian Geophysical Union (2020) CSEG Distinguished Lecturer (2019) Schulich School of Engineering Distinguished Collaborator (2019) University of Calgary Great Supervisor Award (2016) He leads the CREATE-REDEVELOP program training future leaders in responsible resource development and directs the microseismic research laboratory.
James Massey is a Senior Research Fellow at the University of Cambridge, affiliated with the Department of Engineering under the Energy Group. His research focuses on computational fluid dynamics (CFD), turbulent reacting flows, and hydrogen combustion, supported by funding from Mitsubishi Heavy Industries (MHI). He holds a PhD in Engineering (2015-2019) and an MEng in Mechanical Engineering (2011-2015) from The University of Manchester. PhD in Engineering, University of Cambridge (2015-2019) MEng in Mechanical Engineering, The University of Manchester (2011-2015) His work spans hydrogen combustion , thermo-acoustics , and large eddy simulation (LES) , targeting emissions prediction, flame stabilization, and combustion instability. Key themes include mitigating CO/NOx emissions, analyzing swirl-stabilized flames, and developing skeletal mechanisms for hydrogen-hydrocarbon blends. Recent publications emphasize multi-regime combustion modeling , thermo-acoustic instability analysis , and machine learning applications in LES. His research often involves cross-institutional collaboration with MHI and contributions to combustion physics through DNS and LES frameworks. Sugden Award (2024) for best paper in The Combustion Institute British Section James contributes to teaching as a lecturer for courses like 4A13 Combustion and Engines (2023-2025) and ETB-1 Clean Fossil Fuels (2022-2023). He is a fellow of Robinson College and an active member of the Institute of Physics Combustion Physics Group and The Combustion Institute British Section.
Nuno Santos is an Associate Professor in the Department of Computer Science and Engineering at Instituto Superior Técnico (IST), University of Lisbon, and a senior researcher at INESC-ID Lisbon. He leads the SysSec team, focusing on systems security and privacy. His research spans secure enclaves, network security, and censorship-resistant systems. Education: Ph.D. in Computer Science (2013) from Max Planck Institute for Software Systems (MPI-SWS) in affiliation with Saarland University. Visiting research stints at Vrije Universiteit Amsterdam (2018) and Technical University of Munich (2024). Research Interests: Systems security, privacy, trusted execution environments (TEEs), network security, censorship resistance, AI security, and secure cloud computing. He has pioneered work on mitigating vulnerabilities in TrustZone-based TEEs and enhancing confidential computing. Publications: Over 30+ peer-reviewed articles in top-tier venues like S&P, USENIX Security, CCS, and NDSS. Recent trends focus on AI-driven security (e.g., automated exploit generation, prompt-to-SQL injections) and confidential computing (e.g., AMD SEV-SNP analysis). Awards: IST Outstanding Teaching Award (2019/2020), 2024 Prémio Científico Universidade de Lisboa/Caixa Geral de Depósitos. Advising & Grants: Supervised MSc theses in areas like AI-powered vishing attacks and confidential VMs. Active in conference organization (e.g., USENIX Security’25 Vice Chair, IEEE EuroSP’26 Co-Chair). Labs/Teams: Leads the SysSec team at INESC-ID Lisbon, collaborating on projects like AnyTEE (TEE framework) and FlowLens (network security tool).
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.
Trevor E. Carlson is an Assistant Professor at the School of Computing, National University of Singapore (NUS), focusing on high-efficiency microarchitectures, hardware/software co-design, and secure chip design for IoT and server applications. He earned his Ph.D. in Computer Science from Ghent University (2014) and B.Sc./M.Sc. in Electrical & Computer Engineering from Carnegie Mellon University (2002/2003). Research Interests include energy-efficient processors, secure computing platforms, neuromorphic accelerators, and fast simulation methodologies. He co-developed the Sniper Multi-Core Simulator used globally for performance/power evaluation. Scientific Awards : Best Paper Award, International Conference on Embedded Computer Systems (2016) Best Paper Award, International Symposium on Performance Analysis of Systems and Software (2013) Heidelberg Laureate Forum participation (2015) HiPEAC Technology Transfer Award for Sniper Simulator (2013) Current Research involves secure Systems-on-Chip (SOCure project), hardware security for IoT, and simulation methodologies. He leads a lab with researchers working on topics like Capstone for trustless secure memory access and LABS for laser fault injection benchmarks.
W. Travis Horton is an Associate Professor of Civil Engineering at Purdue University, with a courtesy appointment in Mechanical Engineering. He holds a Ph.D. from Purdue University and has over 20 years of academic and industry experience in thermal systems research. His affiliations include the Lyles School of Civil Engineering and the Ray W. Herrick Laboratories at Purdue, focusing on advanced thermal energy conversion systems and sustainable building technologies. Dr. Horton’s research emphasizes integration of HVAC systems with renewable energy, optimization of ground-source heat pumps, and innovative compressor/expander technologies. He leads projects on building energy modeling, combined heat and power systems, and waste heat recovery. His work bridges experimental facilities with computational models for system analysis and optimization. His teaching includes courses on building mechanical systems design and energy audits. He is a licensed Professional Engineer (Michigan) and holds universal refrigerant certification. Active in professional organizations like ASHRAE, his research has produced over 50 peer-reviewed articles since 2001, addressing thermal efficiency, energy systems integration, and sustainable building design.
Kimia Zamiri Azar serves as an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Central Florida, focusing on hardware security and verification methodologies. Her research bridges theoretical formal methods with practical security implementations in semiconductor design and testing. Her educational background includes: Ph.D. in Electrical and Computer Engineering, George Mason University (2021) Postdoctoral Research, University of Florida Dr. Azar's research spans hardware security with emphasis on system-level verification, VLSI design-for-trust, and advanced IC testing. She pioneers techniques in logic locking, secure heterogeneous integration, and IC supply chain security, developing frameworks for authenticated encryption in Systems-in-Package and runtime security monitoring. Her work integrates formal verification with innovative testing methodologies to address hardware trust challenges across the semiconductor lifecycle. Analysis of her recent publications reveals two dominant trends: (1) Application of large language models (LLMs) to hardware design tasks including high-level synthesis code generation and RTL optimization, and (2) Advancement of secure heterogeneous integration techniques for System-in-Package architectures with focus on counterfeit prevention and split-test security protocols. These directions address critical gaps in hardware trustworthiness amid increasingly complex semiconductor supply chains. Her scientific contributions have earned significant recognition: Best Paper Award at ICCAD 2019 Best Paper Award at ISVLSI 2020 Best Paper Award at ICCAD 2020 Best Paper Award at IEEE DCAS 2020 Best Paper Award at HOST 2022 Best Paper Award at DATE 2023 Dr. Azar secures substantial research funding from premier agencies including NSF, SRC, DARPA, AFRL, DoD (NG), and Microsemi. Her grants support projects spanning hardware security validation frameworks, secure heterogeneous integration, and AI-augmented verification methodologies. She actively mentors students in her research group, guiding publications in top venues like IEEE D&T, IEEE TC, and DAC while fostering industry-academic collaborations. Her work directly impacts semiconductor security standards through patented innovations and open-source verification tools. As an active IEEE and ACM member, she contributes to community advancement through conference organization (HOST, DATE), journal editorial roles, and workshop leadership on hardware security standards. Her research group collaborates with semiconductor industry leaders to translate theoretical security frameworks into practical design-for-trust methodologies for next-generation integrated circuits.
Negin Alemazkoor is an Assistant Professor at the University of Virginia's School of Engineering and Applied Science, specializing in interdisciplinary research on infrastructure resilience. Her work focuses on developing AI-driven methodologies for analyzing interconnected systems like power grids, urban flood models, and transportation networks under uncertainty. Key areas include enhancing grid reliability through multi-fidelity modeling, hurricane evacuation equity analysis, and precision-compression techniques for large-scale data. She co-leads a NSF-funded initiative to democratize AI education in high schools. Her research integrates graph neural networks, physics-informed models, and machine learning to address challenges in energy systems, environmental monitoring, and disaster response. Notable projects include hurricane-induced power outage risk analysis under climate change and precision guarantees for smart-meter data analytics. She emphasizes computational efficiency and multi-fidelity approaches to balance accuracy with resource constraints. Recent contributions span AI applications in flood forecasting, renewable energy integration, and infrastructure cybersecurity. Her NSF grant aims to create inclusive AI curricula, reflecting her commitment to education and societal impact. She is affiliated with UVA Engineering’s research initiatives on resilient systems and data-driven decision-making.
