Hadi Esmaeilzadeh is an Associate Professor at the University of California, San Diego in the Department of Computer Science and Engineering . He previously served as an Assistant Professor at Georgia Tech (2013-2017) and holds the Halicioğlu Chair in Computer Architecture . As founder/director of the Alternative Computing Technologies (ACT) Laboratory and associate director of UCSD's Center for Machine Integrated Computing and Security (MICS) , his research drives cross-stack solutions for next-generation computer systems. Early tenure recipient at UCSD Coined the term "dark silicon" in computer architecture Developed Tabla/DnnWeaver open-source frameworks Research Interests span: Approximate Computing Neural Acceleration FPGA/ASIC Hardware Design Machine Learning Systems Dark Silicon Challenges Security/Privacy in Accelerated Systems Scientific Recognition : 4 CACM Research Highlights 4 IEEE Micro Top Picks Distinguished Paper Award (HPCA 2016) Inducted to ISCA Hall of Fame (2018) Teaching : Developed courses on accelerator design (CSE 240D) and alternative computing (CS 8803 ACT) Advocates for hands-on FPGA-based learning in Processor Design with FPGAs courses
Devis Tuia serves as Associate Professor at the Swiss Federal Institute of Technology Lausanne (EPFL), holding appointments in the Institute of Environmental Engineering (IIE) within the School of Architecture, Civil and Environmental Engineering (ENAC). He leads the Environmental Computational Science and Earth Observation Laboratory (ECEO) since 2020 and contributes to EPFL's Doctoral Program in Civil and Environmental Engineering. His academic journey began in Lausanne with studies at UNIL and EPFL, culminating in a PhD in remote sensing from UNIL. Postdoctoral research followed at institutions in Valencia, Boulder, and EPFL, focusing on machine learning model adaptation. He progressed from Research Assistant Professor at University of Zurich to Associate and Full Professor at Wageningen University before joining EPFL. Tuia's research bridges Earth observation with artificial intelligence, specializing in interpretable deep learning for environmental applications. His lab develops algorithms for making remote sensing accessible, with particular emphasis on digital wildlife conservation through automated censuses using drone and satellite imagery. Current projects tackle the 'black box' problem in environmental modeling while advancing spatial intelligence for sustainable urban development. His 2023-2025 publication portfolio reveals three dominant trends: (1) species distribution modeling using incomplete observations, (2) multimodal fusion of satellite/drone data with textual descriptions, and (3) interpretable AI frameworks for environmental decision-making. This work consistently addresses real-world challenges like wildfire forecasting and biodiversity monitoring. As an educator, Tuia supervises 12 current PhD students and has graduated 4 former EPFL doctoral candidates. His teaching portfolio includes Frontiers of Deep Learning for Engineers , Sensing and Spatial Modeling for Earth Observation , and Image Processing for Earth Observation courses. The ECEO laboratory maintains active collaborations with ESA-NASA initiatives and conservation organizations globally.
Antonio González is a full Professor at the Barcelona School of Informatics (FIB), Universitat Politècnica de Catalunya (UPC), where he is affiliated with the Department of Computer Architecture. He leads the Microarchitecture and Compilers (ARCO) research group, focusing on robust and energy-efficient computing systems, including general-purpose processors, GPUs, and cognitive computing architectures. His research spans computer architecture, microarchitecture, compilers, GPU design, and deep learning acceleration, with a strong emphasis on energy efficiency and memory optimization. His work integrates hardware and compiler techniques to enhance performance in modern computing systems. The recent publications highlight a strong trend in energy-efficient DNN acceleration, GPU microarchitecture, memory systems, and near-data processing. His team explores novel quantization methods, adaptive caching, and architectural extensions for vision and AI workloads, published in top-tier venues such as MICRO, ISCA, and IEEE/ACM journals. ACM Fellow (2020) ICREA Academia Award (2014, 2019, 2024) UPC Duran Farell Award (2008) HiPEAC 2024 Paper Award (twice) González has supervised several PhD students and leads competitive R&D+i projects, including those funded by ICREA and the European Research Council. His research group collaborates extensively within UPC and with international institutions. He is actively involved in advancing computer architecture through innovation in simulation, hardware design, and AI acceleration. He is a member of IEEE and ACM, and his work continues to influence both academic and industrial developments in high-performance and energy-efficient computing.
Dr. Abdullah Bal is a researcher at Georgia State University's College of Arts & Sciences, Department of Computer Science, with over 25 years of academic experience. He holds a Ph.D. in Electrical Engineering from Yildiz Technical University (2002) and has taught graduate and undergraduate courses in algorithms, machine learning, and optical pattern recognition. B.Sc., Electronics and Communication Engineering, Istanbul Technical University (1993) M.Sc., Electrical Engineering, Yildiz Technical University (1997) Ph.D., Electrical Engineering, Yildiz Technical University (2002) His research focuses on data science, machine learning, and hyperspectral imaging applications in fields ranging from forensic analysis to historical structure preservation. He has led projects funded by the U.S. Army Research Office and the Scientific and Technological Research Council of Turkey, including real-time target detection systems and digital imaging for historical structures. Recent publications demonstrate his expertise in kernel-based transforms, ensemble learning, and hyperspectral data analysis. His work spans food safety inspection, infrared target tracking, and biometric verification systems. Faculty Outstanding Research Publication Award (2006) Turkish Air Force Academy Science Competition Winner (2009) Best Paper Award at ICFCT (2016) Previously, he chaired YTU's Informatics Department (2009-2016) and participated in academic governance through the Electrical and Electronics College Executive Committee (2012-2015). You can contact him at abal@gsu.edu in room 739, 25 Park Place.
Di Shi is an Associate Professor at the Klipsch School of Electrical and Computer Engineering , New Mexico State University (NMSU), holding the Paul W. and Valerie Klipsch Distinguished Professorship. He previously founded the AI energy startup AInergy, LLC and held leadership roles at GEIRI North America, NEC Laboratories America, and Arizona State University. Education: PhD in Electrical Engineering, Arizona State University (2012) MS in Electrical Engineering, Arizona State University (2009) BS in Electrical Engineering, Xi'an Jiaotong University (2007) His research focuses on power system data analytics , energy storage , artificial intelligence , and IoT applications for grid stability and renewable integration. His work bridges theoretical innovation with real-world deployment, including software adopted by 15 utility companies. Recent publications highlight his leadership in deep reinforcement learning for grid control, blockchain frameworks for energy management, and tensor decomposition for efficient load modeling. He has secured a $6M NSF grant for AI-driven digital twinning to address climate-aware energy resilience. Awards & Recognition: 2025 Paul W. and Valerie Klipsch Distinguished Professorship 2024 University Research Council Mid-Career Award 2024 IET Fellow Multiple IEEE Best Paper Awards (2019–2022) 2019 L2RPN AI Competition Championship He serves as an editor for IEEE Transactions on Power Systems , IET Generation, Transmission & Distribution , and other journals, and leads the IEEE Task Force on IoT for Power Systems . His team’s patents cover AI-driven load modeling , energy storage scheduling , and state estimation , with 42 granted or pending.
Prof. Baker Mohammad serves as Professor and Director of the System on Chip Lab in the Department of Computer and Information Engineering at Khalifa University. With over 15 years of industrial experience at Intel and Qualcomm designing microprocessors and DSP chips, he bridges academic research with real-world engineering challenges in high-performance computing and low-power systems. His educational background includes: Ph.D. in Electrical and Computer Engineering, University of Texas at Austin (2008) M.S. in Electrical and Computer Engineering, Arizona State University B.S. in Electrical Engineering, University of New Mexico Dr. Mohammad's research spans cutting-edge domains where VLSI design converges with AI acceleration and emerging memory technologies . His work pioneers Memristor applications in environmental sensing (radiation, vacuum, glucose) and neuromorphic computing, while advancing energy harvesting systems for wearable electronics. The integration of in-memory computing with security primitives represents a paradigm shift in hardware design, moving beyond traditional CMOS limitations. His publication trajectory reveals accelerating focus on self-powered neuromorphic systems and RRAM-based architectures, with recent work (2021-2023) emphasizing hardware-software co-design for edge AI. Over 75% of his recent publications involve cross-disciplinary collaborations spanning materials science, chemistry, and biomedical engineering. Notable scientific recognition includes: IEEE TVLSI Best Paper Award 2016 IEEE MWSCAS Myrill B. Reed Best Paper Award Qualcomm Qstar Award for Performance Leadership KUSTAR IP Excellence Award Multiple SRC Techon Best Session Papers As a dedicated mentor, he has supervised over 15 graduate students while securing competitive funding from Khalifa University, ADEK, Qualcomm, Tii, and UAE space agencies. His grant portfolio demonstrates exceptional translational impact, converting fundamental research in memristive devices into drone flight computers and medical sensors. Current projects integrate academic rigor with industrial deployment timelines. The System on Chip Lab operates as a multidisciplinary hub where semiconductor physicists collaborate with AI researchers to develop RISC-V-based secure processors and piezoelectric nanogenerator systems. Recent expansions include partnerships with Tii for aerospace applications and medical device startups for glucose monitoring technology.
Yang Weng is an Associate Professor at the School of Electrical, Computer and Energy Engineering, Arizona State University. He leads the U.S.-Israel International Consortium on Energy Cyber Initiative on Cybersecurity R&D and directs a research lab focused on smart grid resilience and machine learning applications. Previously, he was a TomKat Postdoctoral Scholar at Stanford University. Education: Ph.D. in Electrical and Computer Engineering, Carnegie Mellon University M.S. in Machine Learning, Carnegie Mellon University Research: His interdisciplinary work bridges power systems, machine learning, and cybersecurity, emphasizing renewable integration, grid optimization, and cyber-physical resilience. Key themes include physics-informed AI, adversarial robustness in energy infrastructure, and real-time control algorithms for dynamic grids. Publications: Recent articles (2024–2025) demonstrate strong trends in AI-driven grid security, adaptive control under uncertainty, and climate-impact modeling. Dominant domains include neural network applications for stability guarantees, cyber-attack mitigation, and data-efficient renewable integration. Awards: NSF CAREER Award (2021), Amazon Research Award (2023) Best Paper Awards at IEEE SmartGridComm (2012, 2013), PES GM (2014), PMAPS (2016) IEEE Senior Member, Sun Award (ASU), Centennial Award (ASU) Grants & Leadership: Secured DOE, NSF, and AFOSR funding for projects on AI-enhanced grid resilience. Advises PhD/postdoc candidates and chairs the U.S.-Israel Energy Center consortium. Organized international workshops (e.g., ICRDE 2023) and validated research via hardware-in-the-loop experiments. Lab & Team: Directs a research group developing deployable ML solutions for utilities (e.g., OPAL-RT collaborations). Focus areas: cybersecurity toolchains, reinforcement learning for grid control, and anomaly detection architectures.
Sebastian Schemm is a Heisenberg Fellow at the Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge, a position regarded as equivalent to a non-permanent Associate Professor. He leads research within the Atmosphere-Ocean Dynamics group and previously held an ERC Starting Grant-funded Assistant Professorship (without tenure track) at ETH Zurich. Education and Career Path PhD (2013) and MSc (2010), ETH Zurich, Switzerland Postdoctoral researcher, University of Bergen, Norway (2014–2017) Postdoctoral researcher, Laboratoire de Météorologie Dynamique, ENS Paris (2017–2018) Assistant Professor (ERC Starting Grant), ETH Zurich (2020–2024) Heisenberg Fellow, DAMTP, University of Cambridge (2025–present) Research Focus Schemm’s work centres on atmospheric and climate dynamics, spanning turbulence to planetary scales. Core themes include the physics of extratropical cyclone life cycles, jet-stream and storm-track dynamics, Rossby waves and teleconnection patterns, high-resolution atmospheric modelling, and the integration of machine-learning techniques for parameter estimation, data assimilation, and kilometre-scale global simulations. He also contributes to large-scale initiatives such as ECMWF’s WeatherGenerator. Scientific Awards and Editorial Service DFG Heisenberg Fellowship (2025) ERC Starting Grant (2020–2024) European Meteorological Society Young Researcher Medal (2019) Co-Editor, Weather and Climate Dynamics (EGU) Co-Editor, Quarterly Journal of the Royal Meteorological Society PhD Supervision & Funding He currently supervises PhD students at both Cambridge and ETH Zurich, with funding streams including the Cambridge CREATES Doctoral Training Partnership and Swiss/EU grants. Ongoing students explore reinforcement-learning parameterisations, jet-stream–storm-track relationships, mid-latitude eddy energetics, machine-learning ensemble forecasting, and Bayesian parameter estimation in LES. Active Projects EU Horizon project WeatherGenerator (led by ECMWF) PASC HiRAD-Gen : High-Resolution Atmospheric Downscaling Using Generative Models
Beverley J. McKeon is a Professor of Mechanical Engineering at Stanford University, previously holding the Theodore von Kármán Professorship in Aeronautics at Caltech. Her research focuses on fluid mechanics, particularly turbulence, flow control, and boundary layer dynamics. She earned her B.A. and M.Eng. from the University of Cambridge, and her Ph.D. from Princeton University. McKeon's work integrates experimental and theoretical approaches to manipulate wall-bounded flows for drag reduction and performance enhancement. Her research interests include resolvent analysis, high Reynolds number turbulence, and the application of machine learning to fluid dynamics. She has led interdisciplinary projects on morphing surfaces and viscoelastic turbulence. Awarded the Vannevar Bush Faculty Fellowship and PECASE, McKeon has been recognized for her teaching and mentoring. Her honors include Fellowships from the APS and AIAA. She chairs editorial boards for journals like Physical Review Fluids and has served on national committees for theoretical and applied mechanics. Her academic leadership includes roles as Deputy Chair of Caltech’s Division of Engineering and Applied Science and Associate Director of GALCIT. She advises numerous students and collaborates globally on initiatives like the Stories of Women in Fluids.
Anna Choromanska is an Associate Professor in the Department of Electrical and Computer Engineering at NYU Tandon School of Engineering, with affiliations to NYU Center for Data Science (CDS), NYU Center for Urban Science and Progress (CUSP), NYU Center for Advanced Technology in Communications (CATT), and the C2SMART Center. Her research focuses on deep learning optimization, generalization, and applications in autonomous driving and large-scale data analysis. She holds an Alfred P. Sloan Fellowship and NSF CAREER Award, and her work impacts industries like NVIDIA and Facebook. She directs the Learning Systems Laboratory (LSL), emphasizing interdisciplinary experimental/theoretical work. Research Interests: Machine Learning fundamentals, DL optimization, continual learning, autonomous vehicle systems, large data analysis. Her lab explores DNN learning dynamics, training architecture design, and scalable algorithms. Professional Impact: Over 50 invited talks, workshop organization for top ML conferences, and contributions to open-source projects like Vowpal Wabbit. Her algorithms are deployed in production systems at Facebook and Baidu. Awards: NSF CAREER Award Alfred P. Sloan Fellowship IBM Global University Program Academic Award (2x) Columbia University Presidential Fellowship Advising & Labs: Leads LSL, supervising interdisciplinary projects in optimization and autonomy. Actively involved in NYU's Modern AI seminar series and industry partnerships through CATT. Personal Interests: Accomplished pianist, salsa dancer, and fashion design enthusiast with notable performances and certifications in dance and music.
N.K. Anand is a Distinguished Professor of Mechanical Engineering at Texas A&M University, holding the James J. Cain III Regents Professorship. He leads research in advanced computational methods and thermal-hydraulic systems, with affiliations to Multidisciplinary Engineering and Nuclear Engineering programs. His work focuses on physics-informed machine learning, finite volume methods, and aerosol transport in nuclear reactor contexts. Education: PhD (Mechanical Engineering, Purdue University, 1983), M.S. (Kansas State University, 1979), and B.E. (Bangalore University, 1978). Awards include the ASME James Harry Potter Gold Medal (2020) and multiple teaching/administrative excellence awards from Texas A&M. Research emphasizes fluid dynamics modeling (e.g., PINNs for periodic flows, turbulent deposition studies), heat pipe systems, and nuclear reactor thermal-hydraulics. His Versatile Test Reactor (VTR) contributions include cartridge loop designs and aerosol transport experiments. Active in high-temperature reactor safety, with facilities studying pebble beds, helical coil exchangers, and HTGR upper plenum dynamics. Publications span physics-informed ML applications, finite volume techniques, and nuclear thermal systems. Grants supported development of advanced CFD tools and reactor safety infrastructure. His lab collaborates on international nuclear energy projects and emerging AI-driven simulation methodologies.
Soheil Salehi is a tenure-track Assistant Professor in the Department of Electrical and Computer Engineering at the University of Arizona, with a joint appointment in Systems and Industrial Engineering. He is the Director of the Privacy-preserving, Intelligent, and Secure Computing (PRISM) Lab, established in August 2022. Prior to this, he was an NSF-Sponsored Computing Innovation Fellow and Postdoctoral Research Fellow at the University of California, Davis. Ph.D., Electrical and Computer Engineering, University of Central Florida, 2020 M.S., Electrical and Computer Engineering, University of Central Florida, 2016 B.S., Isfahan University of Technology, Iran, 2014 Dr. Salehi's research focuses on the intersection of hardware, AI, and security. His work spans hardware and AI-enabled security in IoT , Generative AI for hardware design and security , neuromorphic and biologically-inspired AI hardware , emerging spin-based devices , reconfigurable architectures , low-power VLSI circuits , and digital twins and mixed reality for semiconductor workforce development . He also explores the application of Generative AI in personalized education . His recent publications, spanning 2023–2025, reveal a strong trend toward integrating AI and machine learning into hardware security and design. Key themes include automated secure IC design flows , AI-driven hardware obfuscation , firmware and side-channel attack analysis , security in neuromorphic and spiking neural networks , and educational frameworks using digital twins and generative models . His work appears in top venues like DAC, ICCAD, USENIX Security, IEEE TCAS-I, and ISCAS. Outstanding Reviewer Award, IEEE/ACM Design Automation Conference (DAC), 2023 Best Presentation of the Symposium Award, UC Davis Postdoctoral Research Symposium, 2021 UCF Excellence by a Graduate Teaching Assistant (University-Level), 2016 Nominated for 30-under-30 Award, UCF, 2020 Nominated for Postdoctoral Research Excellence Award, UC Davis, 2022 Dr. Salehi has secured significant research funding as PI and Co-PI, including a $300K NSF SaTC EAGER grant on Generative AI-based Personalized Cybersecurity Tutor, a $174,000 University of Arizona PIF Award, and multiple RII grants totaling over $198K. He has also received industry funding from CHEST. He actively mentors students and leads the PRISM Lab, which focuses on privacy-preserving and intelligent secure computing. His service includes roles as Technical Program Committee (TPC) Member and Session Chair at premier conferences such as DAC, ICCAD, CCS, NDSS, and GLSVLSI. The PRISM Lab, under his direction, conducts cutting-edge research in secure and intelligent hardware systems, with applications in IoT, edge computing, and workforce development. The lab emphasizes interdisciplinary collaboration and innovation in both research and education.
Alfred O. Hero, III is the John H. Holland Distinguished University Professor of Electrical Engineering and Computer Science and the R. Jamison and Betty Williams Professor of Engineering at the University of Michigan, Ann Arbor. He is currently on leave from the University of Michigan as a Program Director in the CISE Directorate at the National Science Foundation. His primary appointment is in the Department of Electrical Engineering and Computer Science (EECS), with secondary appointments in the Department of Biomedical Engineering and the Department of Statistics. He is also affiliated with the UM Center for Computational Medicine and Bioinformatics (CCMB), the UM Graduate Program in Applied and Interdisciplinary Mathematics (AIM), the UM Applied Physics Program, and the Michigan Institute for Data Science (MIDAS), which he co-founded from 2015-2018. Hero's research focuses on data science, developing theory and algorithms for multimodality data collection, fusion, analysis and visualization that use statistical machine learning and distributed optimization. His work has applications in wearable technologies for personalized health and predictive medicine, spatio-temporal networks in biology, climate, and social discourse, anomaly detection, and data analysis for international security. His recent research interests include high dimensional spatio-temporal data analysis, multimodal data integration, statistical signal processing, and machine learning, with particular emphasis on predictive mathematical models for biological and physical sciences, social networks, network security and forensics, and personalized health and disease. His recent publications demonstrate a strong focus on high-dimensional statistical methods, contrastive learning, neural network optimization, change detection in temporal graphs, and applications in microbiome analysis and epidemic modeling. The research spans theoretical foundations in information theory and statistical learning while addressing practical applications across multiple domains. Scientific Awards: IEEE Signal Processing Society Best Paper Award (1998) Best Original Paper Award from Journal of Flow Cytometry (2008) Best Magazine Paper Award from IEEE Signal Processing Society (2010) SPIE Best Student Paper Award (2011) IEEE ICASSP Best Student Paper Award (2011) IEEE Signal Processing Society Technical Achievement Award (2014) IEEE Signal Processing Society Society Award (2015) IEEE Fourier Award (2020) University of Michigan Distinguished Faculty Achievement Award (2011) Hero has advised over 60 PhD students and 30 postdocs in areas including modeling, computation, and inference for large scale time varying data in the biosciences. He has received significant research funding from the Department of Energy, Army Research Office, Air Force Office of Scientific Research, and National Science Foundation. He has held leadership positions including President of the IEEE Signal Processing Society (2006-2007), Director of Division IX (Signals and Applications) on the IEEE Board of Directors (2009-2011), and Chair of the Committee on Applied and Theoretical Statistics of the US National Academies (2018-2020).
Yongmin Liu is a Professor in Mechanical and Industrial Engineering and Electrical & Computer Engineering at Northeastern University, and a member of the Cross-College Magnetics Center. He holds a PhD in Applied Science and Technology from UC Berkeley (2009), with earlier degrees from Nanjing University. His research focuses on nano-optics, metamaterials, plasmonics, and their applications in optical devices and systems. His interdisciplinary work bridges engineering, physics, and AI, with notable contributions to metasurface design and optical neural networks. Education: PhD, Applied Science and Technology, UC Berkeley (2009) M.S. and B.S., Physics, Nanjing University (2003, 2000) Research Interests: Nano-optics, nanoscale materials engineering, metamaterials, plasmonics, and applied physics. His group develops novel optical materials and devices for applications like super-resolution imaging, efficient light harvesting, and biomedical detection. Recent projects include AI-driven photonic materials design and meta-optical neural networks. Key Achievements: Recipient of the Søren Buus Outstanding Research Award (2024) NSF CAREER Award (2017) and ONR Young Investigator Award (2016) Elected SPIE Fellow (2023) and Optica Fellow (2023) Lab & Collaborations: Head of the Yongmin Liu Research Group, collaborating with institutions like Georgia Tech and Purdue University. Recent grants include a $1.5M NSF DMREF grant for AI-driven photonic materials and a $468K NSF grant for meta-optical neural networks.
Ashwani K. Gupta is a Distinguished University Professor at the University of Maryland, holding the Minta Martin Professorship in Engineering. He serves as Professor in the Department of Mechanical Engineering, Professor at the Institute of Physical Science and Technology, and Affiliate Professor in the Department of Aerospace Engineering. With over 45 years of experience in combustion engineering since graduating from Southampton University in 1970, Gupta has established himself as a leading authority in advanced combustion technologies. Dr. Gupta earned his Ph.D. from the University of Sheffield in 1973, followed by a D.Sc. from the same institution in 1986 and another D.Sc. from Southampton University in 2013. His academic journey includes six years at MIT as a research staff member and three years at Sheffield University as an independent research worker before joining the University of Maryland in 1983. Gupta's research focuses on revolutionizing combustion technology through innovations in swirl flows, high-temperature air combustion (HiTAC), and distributed combustion systems. His pioneering work on 'colorless distributed combustion' has enabled ultra-low emission combustion processes with significant applications in gas turbine engines and waste-to-energy conversion. His research spans biofuels, CO2 utilization, sulfur chemistry, waste conversion, and advanced laser diagnostics, addressing critical challenges in sustainable energy and environmental protection. Analyzing his recent publications reveals a strong emphasis on waste-to-energy conversion, biomass processing, and CO2-assisted technologies. Gupta's work demonstrates a clear trajectory toward sustainable energy solutions, with increasing integration of artificial intelligence for combustion optimization and emission control. His research bridges fundamental combustion science with practical engineering applications for cleaner energy systems. Among Gupta's numerous accolades are: Election to Fellowship of the Royal Academy of Engineering (2023) Honorary Fellowship of the Royal Aeronautical Society (2020) Recognition as one of the top 2% of scientists worldwide by Stanford University (2022-2024) Multiple prestigious medals from ASME and AIAA including the Soichiro Honda Medal (2018) and AIAA Air Breathing Propulsion Award (2014) Honorary doctorates from three international universities Gupta has secured substantial research funding throughout his career, resulting in over 850 technical papers, three books, 18 edited books, and 22 book chapters. He has delivered over 100 plenary/keynote/invited presentations at international conferences. His mentorship has shaped numerous graduate students who continue to contribute to the field of combustion engineering. Gupta directs the Combustion Laboratory at the University of Maryland, which serves as a hub for cutting-edge research in sustainable combustion technologies. The Combustion Laboratory, under Gupta's leadership, has become a center of excellence for advanced combustion research, particularly in distributed combustion systems, waste-to-energy conversion, and alternative fuels. The lab maintains strong collaborations with industry partners and international research institutions, facilitating technology transfer and practical implementation of research findings. Gupta's team employs state-of-the-art diagnostics and computational tools to advance fundamental understanding while developing practical engineering solutions for cleaner energy systems.
