Jin Ma is a Professor in the Department of Mathematics at the University of Southern California (USC), where he has served since 2007. He previously held professorships at Purdue University (1994–2008). His research focuses on stochastic analysis, stochastic differential equations, mathematical finance, and control theory. He directs USC's Mathematical Finance Program and serves on editorial boards for journals like Probability, Uncertainty and Quantitative Risk and SIAM Journal on Control and Optimization . Ma received his Ph.D. in Mathematics from the University of Minnesota (1992) and M.S./B.S. in Applied Mathematics from Fudan University (1985/1982). His work bridges theoretical stochastic analysis and applied domains like finance and insurance, with notable contributions to forward-backward SDEs and mean-field games. Research Highlights: Developed frameworks for stochastic control and backward SDEs in financial and insurance contexts. Advanced mean-field game models for limit order book dynamics and equilibrium analysis. Explored set-valued stochastic differential equations and their applications in risk management. Grants & Advising: Advised numerous graduate students in stochastic processes and mathematical finance. Research supported by NSF grants and industry collaborations.
Sibel Pamukcu is a Professor in the Department of Civil and Environmental Engineering at Lehigh University, affiliated with the P.C. Rossin College of Engineering and Applied Science. Her research focuses on electroremediation of soils and groundwater, advanced geo-materials, and sensor systems for subsurface monitoring. She has held leadership roles including interim Chair of Civil and Environmental Engineering and co-director of Lehigh’s ADVANCE grant. Pamukcu teaches courses in geotechnical and environmental engineering at both undergraduate and graduate levels. Education: Ph.D. in Civil Engineering, Louisiana State University M.S. in Civil Engineering, Louisiana State University B.S. in Civil Engineering, Bogazici University, Turkey Research Interests: Her work spans electrochemical methods for environmental detoxification, development of polymer-enhanced sands, and distributed sensor networks for real-time subsurface monitoring. Key focus areas include: In-situ destruction of contaminants in clay-rich soils Enhanced oil recovery via electric fields Wireless and fiber-optic sensor systems for hazard mitigation Grants & Awards: Principal investigator on over 60 grants from NSF, DOE, DOD, and others. Notable awards include the Alfred Noble Robinson Award (Lehigh University) and the ASCE Civil Engineering Foundation Grant Award. Her lab holds multiple patents on soil remediation techniques and polymer-coated sands. Advising & Service: Supervised 9 Ph.D. and 25+ M.S. students Appointed to Martindale Center faculty and Sigma Xi leadership Contributed to 50+ peer-reviewed articles and 100+ technical publications Labs & Teams: Leads interdisciplinary initiatives in electrokinetic remediation and underground sensing, collaborating with industry partners like Electric Power Research Institute and the Pennsylvania Department of Transportation.
Bilal Farooq is an Associate Professor and Program Director for the Master of Engineering in Interdisciplinary Engineering (MEIE) at Toronto Metropolitan University, holding the Canada Research Chair in Disruptive Transportation Technologies and Services within the Department of Civil Engineering. His educational background includes a PhD from the University of Toronto (2011), MASc from Lahore University of Management Sciences (2004), and BSc from the University of Engineering and Technology (2001). Dr. Farooq's research pioneers disruptive transportation solutions through cyber-physical systems, AI/machine learning applications, behavioral modeling, and optimization techniques. His work specifically targets on-demand multimodal systems, sustainable urban transportation, urban air mobility, automated vehicles, and extended reality applications, addressing critical urban mobility challenges with human-centered approaches. Analysis of his recent publications reveals a strong trend toward quantum-enhanced computational methods, privacy-preserving federated learning frameworks, and sustainability-focused decarbonization strategies across transportation domains, with increasing emphasis on human factors and real-world implementation. Notable scientific awards include: Ontario Early Researcher Award (2018) Canada Research Chair (2017) MassMotion Academic Pedestrian Modelling Project of the Year (2016) Québec Early Researcher Award (2014) Dr. Farooq actively supervises graduate students and secures significant research funding through his Canada Research Chair position and Early Researcher Awards. He directs the Laboratory of Innovations in Transportation (LiTrans), which develops interdisciplinary solutions integrating mathematics, engineering, computer science, and economics to address emerging transportation challenges. LiTrans focuses on disruptive transportation technologies, complete streets design, cyber-physical systems, pedestrian dynamics, resilience, and climate change impacts, collaborating with industry and government partners to translate research into practical urban mobility innovations for smart cities worldwide.
Kevin A. Shinpaugh is Collegiate Professor in the Department of Aerospace and Ocean Engineering at Virginia Tech’s College of Engineering. Since 2019 he has led instruction and research in spacecraft design and propulsion, leveraging decades of experience in high-performance computing and space-systems engineering. Education Ph.D., Aerospace Engineering, Virginia Tech (1994) M.S., Aerospace Engineering, Virginia Tech (1989) B.S., Aerospace Engineering, Virginia Tech (1986) Research Focus Dr. Shinpaugh’s scholarship centers on the intersection of high-performance computing (HPC) and space systems engineering . He develops and applies advanced computational techniques to spacecraft design, propulsion analysis, and mission planning. His work spans numerical simulation of complex aerospace systems, optimization of propulsion architectures, and creation of scalable HPC frameworks that enable rapid design iteration for spacecraft and launch vehicles. Publication Trends Across more than thirty refereed papers and design-competition reports, a clear trajectory emerges: early contributions in experimental fluid-mechanics instrumentation (laser-Doppler velocimetry, fiber-optic sensors) evolved into large-scale computational studies of space systems, and most recently into student-led mission-concept designs for CubeSats, lunar exploration, and interplanetary missions. Keywords consistently include spacecraft design, propulsion, deployable structures, and mission architecture. Service & Committees Chair, Virginia Tech HPC User Committee (2004–2011) Member, VT HPC Advisory Board (2007–present) NSF TeraGrid/XSEDE Campus Champion for Virginia Tech (2006–2013) IBM HPC/AI Customer Advisory Council DC (2019–present) Member, VT AOE Seminar Committee (2019–present) Laboratory & Computing Resources Dr. Shinpaugh has long stewarded Virginia Tech’s high-performance computing ecosystem. He directs students and collaborators in leveraging the university’s Advanced Research Computing (ARC) clusters, as well as national facilities through XSEDE and DoD HPCMP, to execute spacecraft-design simulations and propulsion analyses at scale.
Professor Cedo Maksimovic is a leading academic in the Department of Civil and Environmental Engineering at Imperial College London, Faculty of Engineering. He is a Principal Research Fellow and heads the Urban Water Research Group (UWRG), with affiliations to the Environmental and Water Resource Engineering group, Grantham Institute, Space Lab, and Urban Systems Lab. His research focuses on urban water systems , including storm drainage, urban flooding, water supply, and the interaction between urban infrastructure and the environment. He has pioneered work in applied fluid mechanics , smart water infrastructure , and flood risk management , with innovations such as the AOFD method for urban surface flood modelling and intelligent sensor networks recognized by the ICE Telford Gold Medal. His recent research, as reflected in publications, spans urban pluvial flooding , leakage detection , integrated urban water management , and blue-green infrastructure . These works emphasize computational modelling, real-time monitoring, and climate resilience in urban environments. UNESCO/IAHR Lecturer of the Year 2001 ICE Telford Gold Medal (WINES project team) Prof. Maksimovic has led major projects funded by EPSRC, EU (Climate-KIC, Interreg), UNESCO, and ERANET_CRUE. He advises postgraduate students and has created international educational initiatives like the EDUCATE programme. He also serves as Editor-in-Chief of the Urban Water Book Series and co-founded the Urban Water journal. He leads the UNESCO-endorsed IRTCUD/CUW network with centres in Banjaluka, Belgrade, Cairo, Kuala Lumpur, London, Porto Alegre, Tehran, and Trondheim, promoting global collaboration in urban water research and education.
Prof. Alexander Pretschner is a Professor of Software & Systems Engineering at the Technical University of Munich (TUM) and Founding Director of the Bavarian Research Institute for Digital Transformation (bidt). He also serves as Scientific Director of fortiss, a Bavarian research institute for software-intensive systems. His research focuses on software engineering, testing, information security, and ethical software development. Pretschner holds a PhD from TUM and has held academic positions at Karlsruhe Institute of Technology (KIT) and TU Kaiserslautern. He is a co-editor of several prestigious journals, including IEEE Transactions on Reliability and the Journal of Software Testing, Verification and Reliability. Education: PhD in Computer Science, Technical University of Munich MSc in Computer Science, University of Kansas (on Fulbright Scholarship) Diplom in Computer Science, RWTH Aachen University Research Interests: His work spans testing methodologies, secure software design, and ethical considerations in agile development. Notable contributions include frameworks for metamorphic testing, distributed data usage control, and accountability mechanisms for cyber-physical systems. Awards: IBM Faculty Award (2012, 2013) Google Focused Research Award (2011, 2012) EARTO Innovation Prize (2014) 2nd Platz Supervisory Award (2020) Advising & Grants: Pretschner has supervised numerous PhD and Master’s students, contributing to over 200 publications. He leads projects like EDAP (Ethical Deliberation in Agile Processes) and collaborates with industry partners on cybersecurity and AI ethics initiatives. Labs & Teams: His work is anchored in bidt, fortiss, and TUM’s Chair of Software & Systems Engineering, focusing on societal impacts of digitalization and trustworthy AI systems.
Alexei A. Efros is the Howard Friesen Professor in the EECS Department at UC Berkeley, affiliated with the Berkeley Artificial Intelligence Research (BAIR) Lab. Previously, he spent a decade at CMU's Robotics Institute and held a postdoc at the University of Oxford under Andrew Zisserman. He collaborates with INRIA/École Normale Supérieure in Paris. His research focuses on self-supervised learning, generative models, and visual data mining, with applications to robotics, computational photography, and art. Education & Academic Roles: Postdoc at Oxford (with Andrew Zisserman), faculty at CMU (2005–2015), currently at UC Berkeley. Teaches courses like CS 180/280A (Computer Vision) and CS 280 (Graduate Computer Vision). Research Interests: Self-supervised learning, generative models (e.g., diffusion models, inpainting), visual commonsense, and cross-modal reasoning. His work bridges computer vision and graphics, emphasizing data-driven approaches. Recent projects include Visual Jenga, Diffusion Models as Data Mining Tools, and Prioritized Generative Replay. Grants & Labs: Leads the Efros Research Group, advised over 40 PhD students (e.g., Jun-Yan Zhu, Tinghui Zhou). Collaborates with institutions like INRIA and NVIDIA. Active in grants related to AI, vision, and robotics. Labs/Teams: BAIR Lab (UC Berkeley), former affiliations with CMU Robotics Institute and Willow Team (INRIA/ENS Paris). Current lab focuses on generative AI, 3D perception, and visual reasoning.
Atul N. Parikh is a Professor in the Departments of Biomedical Engineering and Materials Science and Engineering at the University of California Davis. His work bridges physical and biological sciences, focusing on understanding cellular mechanisms and designing bio-inspired synthetic materials. Key research areas include membrane dynamics, phase separation in vesicles, and the creation of synthetic protocells to explore life's fundamental processes. Education details are not explicitly provided in the text. His research emphasizes far-from-equilibrium systems and non-equilibrium self-assembly, aiming to develop materials capable of complex functions like memory and self-repair. Recent studies explore lipid phase separation, osmotic stress responses, and surfactant-mediated membrane modulations. Notable projects include the development of lipid nanoconstructs for drug delivery, osmo-regulated vesicle systems, and understanding microbial membrane interactions. His work has applications in biomedical engineering, material science, and synthetic biology. Lab activities focus on experimental approaches combining microscopy, biophysical characterization, and synthetic material fabrication. Collaborative efforts involve interdisciplinary teams addressing challenges in membrane biology and functional materials design.
Dr. Yujie Tang is an Assistant Professor in the Faculty of Computer Science at Dalhousie University, Canada, where she has been serving since September 2022. Prior to this, she was an Assistant Professor at Algoma University (2019–2022) and a Post-Doctoral Fellow at the University of Waterloo (2017–2019). Her academic journey includes a PhD from the University of Waterloo and earlier degrees from Harbin Institute of Technology and Lanzhou Jiaotong University. PhD – University of Waterloo (2017) M.E. – Harbin Institute of Technology, Shenzhen, China B.E. – Lanzhou Jiaotong University, Lanzhou, China Her research focuses on intelligent networking and computing technologies for future IoT and 5G/6G systems. Key areas include Internet of Vehicles (IoV), AI-empowered edge computing, resource management in heterogeneous networks, software-defined networking, and UAV-assisted communications. She employs machine learning and optimization techniques to design energy-efficient and high-performance network protocols. The most recent publications reflect a strong trend in applying AI and machine learning to solve complex problems in vehicular networks, edge caching, and spectrum management. Her work spans top-tier IEEE journals such as IEEE Transactions on Vehicular Technology , IEEE Internet of Things Journal , and IEEE JSAC , with a clear emphasis on real-world deployable solutions for next-generation wireless systems. Faculty Research Startup Fund, Dalhousie University, 2022 NSERC Discovery Grant, 2021–2026 Algoma University Research Fund, 2021 Faculty Research Startup Fund, Algoma University, 2019 Best Speaker Award, University of Waterloo, 2017 Faculty of Engineering Award (4 times), University of Waterloo, 2013–2017 University of Waterloo Graduate Scholarship, 2013–2015 International Doctoral Student Award, 2012–2016 Graduate Research Studentship (twice), 2011–2012 Provost Doctoral Entrance Award for Women, 2011 Dr. Tang actively supervises graduate and undergraduate students and has secured competitive research grants, including the NSERC Discovery Grant. She serves on the technical program committees of major IEEE conferences such as INFOCOM, GLOBECOM, and ICC, and regularly reviews for top journals like IEEE JSAC , IEEE TWC , and IEEE TVT . She currently leads a research group focusing on B5G/6G networks, IoV, and edge computing, and she is actively recruiting new students and visiting scholars. Her research group operates within the Faculty of Computer Science at Dalhousie University, where she leads projects in intelligent resource management, AI-driven networking, and integration of space-air-ground networks. She is a member of IEEE, IEEE Communications Society, and IEEE Vehicular Technology Society.
Professor Anil Seth is a leading cognitive and computational neuroscientist at the University of Sussex, where he holds a professorship in the School of Engineering and Informatics. He is Director of the Sussex Centre for Consciousness Science and Co-Director of the CIFAR Program on Brain, Mind, and Consciousness and the Leverhulme Doctoral Scholarship Programme. His research bridges neuroscience, psychology, philosophy, and AI to investigate the biological basis of consciousness and selfhood. His research interests include: Predictive processing approaches to perception Virtual and augmented reality in self-experience studies Mathematical modeling of perception and emergence Machine learning applications in subjective perception modeling Interoception and selfhood Neural mechanisms of conscious experience The recent publications reflect a strong focus on consciousness, neural dynamics, predictive models, and interdisciplinary approaches. Trends include the use of computational modeling, neurophenomenology, causal analysis, and the ethical implications of emerging neurotechnologies. His work increasingly integrates large language models and data-driven methods for analyzing subjective experience. His scientific awards include: Segerfalk Award Perspectives Award Highly Cited Researcher (Web of Science, 2019–2022) Royal Society Michael Faraday Prize (2023) Seth has secured substantial research funding from the European Research Council (ERC), EPSRC, Wellcome Trust, CIFAR, and the Sackler Foundation. He has supervised numerous research projects and doctoral students through interdisciplinary programs. He leads the Dreamachine project and has been an Engagement Fellow with the Wellcome Trust. He serves on editorial boards including Philosophical Transactions of the Royal Society B and is Editor-in-Chief of Neuroscience of Consciousness . He leads a multidisciplinary research group at the Sackler Centre, bringing together psychologists, mathematicians, neuroscientists, computer scientists, and philosophers. His team conducts innovative research using virtual reality, neuroimaging, and computational modeling to explore the nature of consciousness and self.
Roozbeh Torkzadeh is a researcher with expertise in smart grids, synchrophasor technologies, and power quality. He completed his M.Sc. (Hon.) in Electrical Engineering at Yazd University (2013) and earned his Ph.D. from the Department of Electrical Engineering at Eindhoven University of Technology (TU/e) in 2023. His Ph.D. project was funded by Netbeheer Nederland. He has held research roles at Universidad Loyola Andalucía (Spain) and TenneT TSO BV (Netherlands), and is currently a guest researcher at TU/e while working as a Senior Electrical Engineer at Shell Global Solutions International, focusing on grid connection, renewable integration, and decarbonization. Education: M.Sc. (Hon.) in Electrical Engineering, Yazd University (2013) Ph.D. in Electrical Engineering, Eindhoven University of Technology (2023) Roozbeh’s research spans power systems, renewable energy integration, and grid stability. He has contributed to voltage dip assessment, energy transition impacts on network quality, and PMU placement optimization. His work emphasizes practical challenges in wide-area measurement systems, fault current injection, and synthetic inertia control for low-inertia grids. Recent publications highlight trends in energy transition-driven grid modifications, voltage dip analysis, and synchrophasor applications. Key subfields include network modeling, renewable energy markets, and hydrogen scheduling. Scientific Awards: Next Generation Network (NGN) Showcase Presentation Award (CIGRE, 2021) His Ph.D. grant from Netbeheer Nederland and collaborations with Dutch grid operators underscore his industry-academia engagement. Current roles in Shell and TU/e reflect his focus on electrification and decarbonization projects.
Maiken Mikkelsen is the James N. and Elizabeth H. Barton Associate Professor of Electrical and Computer Engineering at Duke University, promoted to Professor in 2025. She holds a secondary appointment as Associate Professor of Physics (2023–present) within Trinity College of Arts & Sciences. Her research bridges Nanophotonics , Quantum Materials , and Ultrafast Spectroscopy , focusing on plasmonic nanostructures and nonlinear metasurfaces for quantum optics and optoelectronic applications. Education: Ph.D. in Physics (University of California, Santa Barbara, 2009), B.S. in Physics (University of Copenhagen, 2004), postdoctoral work at University of California, Berkeley. Her work explores Plasmonics and Quantum Optics to engineer nanoscale light-matter interactions, enabling transformative technologies in Single-Photon Sources , Ultrafast Photodetectors , and Active Metasurfaces . Recent projects include real-time tunable lasing and polarization-controlled nanocavity systems. Her 2016–2025 publications highlight breakthroughs in plasmonic fluorescence enhancement, hot electron dynamics, and room-temperature quantum devices. Grants include Nano Solutions On-Chip (Triad National Security, LLC, 2025–2029) and Meta-Imaging (Air Force Office of Scientific Research, 2021–2026). Her lab, jointly based in Electrical & Computer Engineering and Physics, has graduated PhD students Eunso Shin and Hengming Li, and actively engages in STEM outreach initiatives.
Ding Zhao is an Associate Professor in Mechanical Engineering at Carnegie Mellon University (CMU), with cross-appointments in Computer Science, Robotics Institute, CyLab Security & Privacy Institute, and Scott Institute for Energy Innovation. He directs the CMU Safe AI Laboratory, pioneering research in trustworthy AI for autonomous vehicles, robotics, and healthcare. His work emphasizes robustness, safety, and ethical deployment of AI systems. Education: • Ph.D. in Mechanical Engineering, University of Michigan (2016) • B.S. in Automotive Engineering, Jilin University (2010) Research Interests: Zhao's lab bridges machine learning theory and engineering to develop AI for high-stakes applications. Key areas include: trustworthy AI generalization, safety-critical decision-making, generative AI for digital twins, and physical AI in mobility/healthcare. His long-term mission is to create "trustworthy AI generalists" deployable in real-world critical systems. Awards & Honors: NSF CAREER Award MIT Technology Review 35 under 35 China IEEE George N. Saridis Best Paper Award Ford/Carnegie-Bosch/Toyota Industrial Fellowships Qualcomm Innovation Award Advising & Grants: He mentors 13+ PhD and 30+ Master’s students, with alumni at NVIDIA, Meta, Stanford, and Tsinghua University. His lab collaborates with Google, Amazon, Ford, Mayo Clinic, and received grants from NSF, DOT, Rolls-Royce, and Bosch. Courses taught include Trustworthy AI and Modern Control for Robotics . Lab & Projects: The Safe AI Lab develops: Safe Robotic Foundation Models (LocoMan), generative AI for autonomous driving (SafeBench), cardiac diagnostics (Heart-2), multi-agent safety systems, and robotic tool innovation (RoboTool). Projects target landslide monitoring, AV safety with Pittsburgh, and energy grid resilience.
Gabriel Alfonso Rincon-Mora is the Motorola Solutions Foundation Professor at the Georgia Institute of Technology's School of Electrical and Computer Engineering. A Fellow of the National Academy of Inventors, IEEE, and IET, he specializes in analog/power integrated circuits, energy-harvesting systems, and microelectronics. With over 200 articles, 44 patents, and 12 books, his work has produced 26 commercial power-chip products. His research spans: Analog and power-management ICs for efficient energy conversion Self-sustaining microsystems powered by thermal, mechanical, and environmental sources Nano-scale circuit designs for biomedical and wireless sensor applications Recent publications focus on piezoelectric energy harvesting, battery charging optimization, and low-power CMOS designs, demonstrating consistent innovation in power efficiency and miniaturization. Awards include the IEEE Charles A. Desoer Technical Achievement Award, National Hispanic in Technology Award, and recognition as one of "The 100 Most Influential Hispanics." He directs research in power IC design and mentors students through the Georgia Tech Analog Consortium. His laboratory develops integrated solutions for energy-constrained applications, including collaborations with industry partners like Texas Instruments.
Harish Ravichandar is an Assistant Professor at the School of Interactive Computing , Georgia Institute of Technology, and a core faculty member of the Institute for Robotics and Intelligent Machines (IRIM) . He leads the Structured Techniques for Algorithmic Robotics (STAR) Lab , focusing on structured computational frameworks and learning algorithms with inductive biases to enhance robot efficiency, reliability, and self-sufficiency in human-robot collaboration and complex applications like dexterous manipulation and multi-agent coordination. His research bridges robot learning , human-robot interaction , and multi-agent systems , emphasizing stable, frugal, and safe skill acquisition from human demonstrations. Key themes include intention inference , trajectory optimization , and heterogeneous team coordination , often leveraging Koopman operators , hypernetworks , and graph-based methods . Scientific recognition includes the NSF CAREER Award , IEEE MRS Best Paper Award , and Georgia Tech’s College of Computing Outstanding Post-Doctoral Research Award . His work also received the ASME DSCC Best Student Paper Award and P&W Institute Graduate Fellowship . Harish’s educational background includes a Ph.D. in Electrical and Computer Engineering from the University of Connecticut (2018) , an M.S. from the University of Florida (2014) , and a B.E. in Instrumentation and Control Engineering from Anna University (2012) . He previously held postdoctoral and research scientist roles at Georgia Tech before his current position.
