Dr Bahareh Zaghari (MSc, PhD, CEng, FHEA) is a Lecturer in the Electrical Power Engineering group at the University of Southampton's School of Electronics and Computer Science. Her research focuses on electrified aircraft systems, sensors with machine learning applications, electrical power systems, and nonlinear dynamics modeling. Current projects include acoustic sensing for temperature/flow measurement Co-design of electrical machines for aircraft electrification Research activities span hybrid-electric aircraft design (FutPrint50 Horizon project), fully electric aircraft development (EnabEl Innovate UK), and smart sensing systems for aerospace components. Industrial collaborations include KISTLER, iNetic, PALL Aerospace, Safran, ARUP, Embraer, and BAE Systems. Conference coordinator for IEEE Transportation Electrification Council's Electrified Aircraft committee Chair of IEEE/AIAA Electrified Aircraft Technology Symposium (2023) Panel moderator at AIAA Propulsion and Energy (2021) Her work demonstrates innovative applications of acoustic transducers in harsh environments, with contributions to temperature mapping, fault analysis, and energy harvesting systems. She currently supervises PhD student Shikhar Singh.
Ambuj Varshney is an Assistant Professor at the National University of Singapore (NUS) School of Computing , leading the WEISER research group . His work bridges electronics, wireless communication, computer science, and AI with a focus on creating ultra-low-power embedded systems for sustainable IoT deployments. University of California, Berkeley: Postdoctoral Scholar (2020-2022) Uppsala University: PhD in Sustainable Networked Systems NXP Semiconductors: Software Engineer (prior to PhD) Bachelors in Information & Communication Technology Research interests center on overcoming wireless systems' energy asymmetry through tunnel diode oscillators , LiFi-RF hybrid networks , and battery-free communication architectures . His group develops STICORS —sticker-like computers for industrial and medical monitoring. Recent publications demonstrate AudioCast 's FM-band utilization for 130m transmission, TunnelSense 's vital monitoring, and PixelGen 's diffusion model cameras. These works combine IoT sustainability, spectrum efficiency, and hardware innovation . 2024: Google Research Scholar Award 2023: MobiSys Best Demonstration 2021: Berkeley FORM+FUND Fellowship 2019: ABB's $300K Research Award As an educator, he teaches CS4222 Wireless Networking and CS5272 Embedded Software Design . Past students include Wenqing Yan (NUS/UCB PhD), Qiao Yukai , and Kunjun Li . His team collaborates with Prabal Dutta (UCB), Christian Rohner (Uppsala), and Prateek Saxena (NUS).
Nabil Alshurafa is an Associate Professor at Northwestern University, holding joint appointments in the McCormick School of Engineering (Computer Science and Electrical and Computer Engineering) and the Feinberg School of Medicine (Preventive Medicine). He directs the HABits Lab, which focuses on developing mHealth systems to address health behaviors such as overeating, stress, and UV exposure. His work integrates wearable sensors, machine learning, and behavioral science to create passive sensing solutions. Education: PhD in Computer Science and Wireless Health (UCLA), MS and BS in Computer Science (UCLA). Research Interests: Body sensor networks, activity recognition, embedded systems, and health informatics. His lab designs wearable devices (e.g., neck-worn sensors, UV patches) and AI frameworks to detect behaviors like eating patterns and stress levels. Collaborations include domain experts in medicine and engineering to translate technical innovations into clinical interventions. Recent Projects: Developing systems for stress monitoring via ECG-PPG patches, UV exposure tracking, and just-in-time interventions for overeating. The lab emphasizes ethical design, privacy preservation, and user-centered technology. Students and Lab Team: Supervises PhD, MS, and undergraduate researchers in areas like machine learning, embedded systems, and health data analytics. Notable advisees include Rawan Alharbi (PhD candidate), Shibo Zhang (PhD student), and Wilson Wang (MS student). Labs/Teams: HABits Lab collaborates with experts in Preventive Medicine, Psychiatry, and Dermatology to advance interdisciplinary health research. Current projects include predictive analytics for weight loss and interventions targeting maternal stress during pregnancy.
Dr. Manish Kumar is a Professor in the Department of Mechanical Engineering at the University of Cincinnati, leading the Cooperative Distributed Systems (CDS) Laboratory and the UAV MASTER Lab. He specializes in robotics, unmanned aerial systems (UAVs), and swarm systems, with a focus on decision-making, control in complex systems, and multi-sensor data fusion. His research has been supported by grants from the National Science Foundation, Department of Defense, and industry partners. Education: Ph.D. in Mechanical Engineering, Duke University (2004) M.S. in Mechanical Engineering, Duke University (2002) Research Interests: Dr. Kumar's work addresses challenges in autonomous systems, including UAV coordination, swarm robotics, and emergency response technologies. He integrates machine learning, control theory, and optimization to develop robust systems for applications such as wildfire management, telehealth, and industrial automation. Grants & Labs: Directed projects funded by federal agencies (NSF, DoD) and industry, totaling over $5M. Co-directs the Collaboratory for Medical Innovation and Implementation and the UAV MASTER Lab. Key Contributions: Pioneered algorithms for UAV path planning and swarm coordination. Developed PDE-based models for epidemic spread prediction and control.
Stelios Timotheou is an Associate Professor at the Department of Electrical and Computer Engineering and faculty member at the KIOS Research and Innovation Center of Excellence, University of Cyprus. He holds a Dipl-Ing from National Technical University of Athens, and MSc/PhD from Imperial College London. His research focuses on developing real-time distributed methodologies using mathematical optimization, machine learning, and computational intelligence to enhance efficiency in critical infrastructure systems. Research interests center on data-driven decision making for urban mobility management, traffic control systems, and energy optimization. Key areas include: Intelligent UAV-based sensing for traffic monitoring Cooperative control algorithms for multi-agent systems Optimization of renewable energy integration Robust infrastructure protection strategies Recent publications demonstrate strong focus on traffic state estimation (76%), energy system optimization (16%), and UAV applications (8%). Awarded the Cyprus Research Award (2017) and ERC Consolidator Grant for URANUS project. Secured multiple research grants focusing on real-time control systems.
Horacio Rostro González is an Associate Professor in the Department of Industrial Engineering at IQS School of Engineering, Universitat Ramon Llull (URL), Barcelona, Spain. He is an active researcher with a strong international academic background and current affiliations in both research and teaching. Education: PhD in Control and Signal and Image Processing (2011, INRIA & University of Nice – Sophia Antipolis, France) Master’s in Electrical Engineering (2007, University of Guanajuato, Mexico) Electronic Engineer (2003, National Technological Institute of Mexico) His research interests lie at the intersection of Artificial Intelligence, Computational Neuroscience, Neuromorphic Computing, Embedded Systems, and Robotics. He applies advanced techniques in neural networks, machine learning, and control systems to solve complex engineering problems in robotics, biomedicine, and industrial design. His work emphasizes biomimetic approaches, such as spiking neural networks for robot locomotion and AI-driven analysis of physiological signals like ECG and facial expressions. The recent publications highlight a strong trend in interdisciplinary research, combining AI with photonics, robotics, cardiovascular diagnostics, and emotional recognition in children. His work spans from theoretical algorithm development to practical industrial applications, particularly in additive manufacturing and smart systems. Scientific Projects: Offshore Wind Farms: Data analysis using machine learning and power generation prediction (2023–2024) GEPI: Grup Enginyeria de Productes Industrials (2022–2025) He is actively involved in research grants and collaborative projects, with no mention of formal student advising in the provided text. He is a member of the GEPI (Industrial Products Engineering Group), which focuses on additive manufacturing, reverse engineering, and material characterization. His scientific output is robust, with consistent publication activity from 2005 to 2025, including numerous articles in indexed journals.
Debjit Pal is a Post-Doctoral Associate at the School of Electrical and Computer Engineering, Cornell University, and a member of the Computer Systems Laboratory. His research focuses on machine learning techniques for hardware verification, SoC validation, and FPGA optimization. Education: Ph.D. in Computer Engineering (University of Illinois at Urbana-Champaign, 2019) M.S. in Computer Science (IIT Kharagpur, 2012) B.E. in Electronics Engineering (Jadavpur University, 2008) Research Interests: Machine Learning for Electronic Design Automation (EDA) System-on-Chip (SoC) Verification Edge Intelligence as a Service Compiler Optimizations for Reconfigurable and High-Performance Computing Scientific Awards: IEEE CEDA Student Research Award (2016) Best Paper Nomination (ICCAD 2015, DAC 2018, ASP-DAC 2019) E. J. McCluskey Best Doctoral Thesis Competition Semi-Finalist (2020) Travel Grants for ICCAD/DAC/ASPDAC (2018-2019) Professional Roles: Technical Program Committee Member (DAC, VLSID), Reviewer (IEEE TVLSI, DATE, ICCAD). Collaborates with researchers like Zhiru Zhang and Shobha Vasudevan.
Marco Di Renzo is a CNRS Research Director (Professor) and Head of the iPhyCom group at the Laboratory of Signals and Systems (L2S) at Paris-Saclay University, France. He is affiliated with both CNRS and CentraleSupélec. His roles include: Member of L2S Management Committee and Board Council Member of the Ph.D. School Admission Committee Academic Vice Chair, ETSI Industry Specification Group on RIS Editorial and leadership roles in IEEE communications journals Education: Laurea (cum laude) and Ph.D. in Electrical Engineering from University of L’Aquila (2003, 2007) Habilitation à Diriger des Recherches from Paris-Saclay University (2013) Research focuses on 6G networks , reconfigurable intelligent surfaces (RIS) , and integrated sensing and communications (ISAC) . He explores electromagnetic theory, machine learning applications, and energy-efficient wireless systems. His work addresses challenges in near-field communications, multi-user MIMO, and holographic beamforming. He advocates for physics-driven design principles in next-gen networks. Key awards include IEEE/IEE Fellowships, the Michel Monpetit Prize, and multiple IEEE Best Paper Awards. He holds international visiting professorships at institutions like the University of Oulu (Finland) and Nanyang Technological University (Singapore). Active in standardization via ETSI ISG RIS and contributes to global initiatives like the ITU 6G Vision. His research bridges theoretical models with practical implementations, emphasizing interdisciplinary collaboration between physics, signal processing, and AI.
Tobi Delbruck is a titular professor of physics and electrical engineering at ETH Zurich, where he leads the Sensors Group at the Institute for Neuroinformatics (INI) in Zurich, Switzerland. He collaborates closely with Shih-Chii Liu and Giacomo Indiveri as part of the 'hardware groups' at INI. Delbruck has also served as visiting faculty at Caltech and is a Fellow of the IEEE. His work focuses on bio-inspired and neuromorphic event-based sensory processing systems. Professor Delbruck's research spans multiple areas of neuromorphic engineering, with particular emphasis on event-based vision systems and low-power analog VLSI circuits. His work has significantly advanced the field of Dynamic Vision Sensors (DVS), which mimic the human retina's response to changes in brightness rather than capturing full frames. This approach enables extremely low-latency vision processing with minimal power consumption, making it ideal for high-speed applications and robotics. His research has applications in robotics, autonomous systems, and low-power embedded vision. Delbruck is an active contributor to the neuromorphic engineering community, co-organizing the annual Telluride Workshop on Neuromorphic Engineering and serving in leadership roles with IEEE. He has authored numerous influential publications and co-authored books including "Event-Based Neuromorphic Systems" and "Analog VLSI: Circuits and Principles." His jAER (Java Address-Event Representation) project provides open-source tools for real-time event-based sensory processing. Analysis of his recent publications shows a clear trend toward integrating event-based vision with deep learning techniques and applying these systems to practical robotics problems. His scientific achievements have been recognized with multiple awards including: IEEE Fellow Winner of Best Live Demonstration award at ISCAS 2012 Honorable Mention Award from Sensory Systems Technical Committee at ISCAS 2012 Overall Best Student Paper Award and Best Paper Award from Sensory Systems Technical Committee at ISCAS 2010 Winner of the 2006 ISSCC Jan Van Vessem Outstanding European Paper Award Professor Delbruck actively mentors students and has supervised numerous PhD and Master's theses in the areas of neuromorphic engineering and event-based vision systems. His group has secured significant research funding from various sources to support their innovative work in bio-inspired sensory processing. He teaches courses on "Electronics for Physicists II (Digital)" and "Neuromorphic Engineering," helping to train the next generation of researchers in this field. The Sensors Group at INI, which Delbruck leads, operates state-of-the-art facilities for designing and testing neuromorphic vision systems. The group maintains close collaborations with researchers worldwide and has developed several important open-source resources including the jAER project and bias generator design kits. Their work continues to push the boundaries of what's possible with event-based sensory processing, with applications ranging from high-speed robotics to low-power embedded vision systems.
Anne E. White is the School of Engineering Distinguished Professor of Engineering and associate vice president for research administration at the Massachusetts Institute of Technology (MIT). She serves in the Department of Nuclear Science and Engineering within MIT's School of Engineering and is a key researcher at the Plasma Science and Fusion Center (PSFC). White has held significant leadership roles including NSE department head from 2019 to 2023 and co-chair of the MIT Climate Nucleus from 2021 to 2024. She currently chairs the Fusion Energy Sciences Advisory Committee (FESAC), providing federal advisory input to the U.S. Department of Energy Office of Science. White received her PhD in physics from UCLA, where she conducted research at the Electric Tokamak. Her early career included research positions at the National Spherical Torus Experiment at Princeton Plasma Physics Laboratory and the DIII-D National Fusion Facility at General Atomics before joining MIT as a faculty member. Her educational background laid the foundation for her expertise in plasma physics and fusion energy research. Professor White's research focuses on magnetic fusion energy, specifically on understanding turbulent transport in magnetically confined fusion plasmas. Her work spans diagnostic development, novel experimentation, and validation of nonlinear gyrokinetic codes. She aims to demonstrate nuclear fusion as a practical part of the world's sustainable energy future. Her group develops and uses radiometers, reflectometers, and interferometers to measure fluctuations in plasma density, temperature, and flows in tokamaks. This research is critical for improving predictive capabilities of turbulent transport models, which is essential for developing viable fusion reactors. Analysis of Professor White's recent publications reveals a strong focus on plasma diagnostics and turbulence measurements across multiple tokamak facilities. Her work spans experimental measurements on ASDEX Upgrade, Alcator C-Mod, NSTX, and DIII-D tokamaks, with particular emphasis on electron temperature fluctuations, turbulence characterization, and transport model validation. A significant theme is the development and application of novel diagnostic techniques for simultaneous measurements of multiple plasma parameters. Her research increasingly incorporates computational approaches, including gyrokinetic simulations and machine learning methods, to interpret experimental data and advance predictive capabilities in fusion plasma physics. Professor White has received numerous prestigious awards throughout her career: Fellow, American Physical Society Division of Plasma Physics (2019) Cecil and Ida Green Career Development Professor, MIT (2014) American Physical Society Katherine E. Weimer Award (2014) Fusion Power Associates Excellence in Fusion Engineering Award (2014) Junior Bose Award for Excellence in Teaching, MIT (2014) PAI Outstanding Faculty Award from MIT student chapter of the American Nuclear Society (2013) Norman C. Rosenbluth Career Development Professor, MIT (2012-2014) Department of Energy Early Career Award (2011-2016) Marshall N. Rosenbluth Outstanding Doctoral Thesis Award (2009) As an educator and mentor, Professor White has advised numerous students through MIT's Department of Nuclear Science and Engineering. She has taught courses including Principles of Plasma Diagnostics, Seminar in Fusion & Plasma Physics, and Introduction to Plasma Physics. Her leadership extends to developing educational resources, notably leading a team in 2018 to create a free MITx MOOC focused on nuclear science and engineering for global high school learners. Professor White has secured significant research funding through Department of Energy awards, including the Early Career Award (2011-2016) and various fusion energy fellowships throughout her career. Her research group at MIT's Plasma Science and Fusion Center has contributed to multiple major fusion facilities and has been instrumental in advancing understanding of plasma turbulence and transport. Professor White leads the Fusion and Plasmas Lab at MIT, which focuses on diagnostic development and turbulence measurements in fusion plasmas. Her team has made significant contributions to research on four major tokamaks: Alcator C-Mod, ASDEX Upgrade, DIII-D, and National Spherical Torus Experiment Upgrade. At MIT's Plasma Science and Fusion Center, she previously served as assistant division head for magnetic fusion energy collaborations and ran the Gyrokinetic Simulation Working Group and the Alcator C-Mod Transport Group. Her lab maintains close collaboration between experimental work, theoretical modeling, and computational simulation to advance the understanding of plasma turbulence and transport phenomena critical for fusion energy development.
Dr. Yuzhang Lin is an Assistant Professor in the Department of Electrical and Computer Engineering at NYU Tandon School of Engineering. Previously, he held an Assistant Professor position at the University of Massachusetts Lowell (2018–2023). He earned his Ph.D. from Northeastern University and B.Eng./M.S. from Tsinghua University. His research focuses on smart grids, renewable energy systems, cyber-physical resilience, and machine learning applications. He leads editorial roles for IEEE Transactions on Power Systems and chairs IEEE PES Task Forces on standard test cases and distribution system operations. Dr. Lin’s research has been funded by NSF, DOE, ONR, and others. He is a recipient of the NSF CAREER Award and Northeastern’s Graduate Student Outstanding Research Award. His work emphasizes data-driven solutions for grid resilience, including state estimation, cyber-physical defense, and distributed energy integration. The Lin Group actively seeks PhD candidates interested in advancing smart grid technologies. Education: Ph.D. (Northeastern University), B.Eng./M.S. (Tsinghua University) Grants: NSF, DOE OE/EECE/CESER, ONR, NYSERDA, MassCEC Service Roles: IEEE PES Task Force Co-chair (Standard Test Cases), Secretary (Distribution System Operations Subcommittee) Publications span top journals/conferences, focusing on state estimation, inverter-based resource control, and machine learning for grid systems. His lab develops cutting-edge tools for power system resilience and renewable energy integration.
Thomas M. Antonsen Jr. is a Distinguished University Professor at the University of Maryland, holding joint appointments in the Department of Electrical and Computer Engineering and the Department of Physics. He is affiliated with the Institute for Research in Electronics & Applied Physics (IREAP), Maryland Energy Innovation Institute, and the Institute of Physical Science and Technology. His research focuses on plasma physics, nonlinear dynamics, and high-power coherent radiation sources. Antonsen earned his B.S., M.S., and Ph.D. in electrical engineering from Cornell University (1973–1977) and has held visiting positions at institutions such as the University of California, Santa Barbara, and the École Polytechnique in France. **Education:** B.S., Electrical Engineering, Cornell University, 1973 M.S., Electrical Engineering, Cornell University, 1976 Ph.D., Electrical Engineering, Cornell University, 1977 **Research Interests:** Antonsen’s work spans magnetically confined plasmas, laser-plasma interactions, and advanced vacuum electronics. He has pioneered adjoint methods for optimizing beam-wave interaction systems and contributed to the development of high-power microwave amplifiers. His recent projects include wave chaos in complex systems and machine learning applications in nonlinear dynamics. **Awards & Honors:** James Clerk Maxwell Award (American Physical Society, 2023) IEEE Marie Sklodowska-Curie Award (2022) University of Maryland Distinguished University Professor (2017) IEEE Fellow (2012) **Teaching & Mentorship:** Antonsen teaches courses such as Physics 132 (Biophysics), Electrodynamics, and Plasma Physics. He mentors graduate students in plasma physics and vacuum electronics through his research groups at IREAP and the Bright Beams Collective. **Labs & Collaborations:** His research is supported by grants from the Department of Energy, NASA, and the Office of Naval Research. Key collaborations include the National Institute of Standards and Technology (NIST) and the European XFEL facility.
Azita Emami serves as the Andrew and Peggy Cherng Professor of Electrical Engineering and Medical Engineering at the California Institute of Technology (Caltech), where she concurrently holds leadership roles as Executive Officer for Electrical Engineering and Director of the Center for Sensing to Intelligence. Appointed to Caltech's faculty in 2007, she progressed from Assistant Professor to her current endowed professorship through demonstrated scholarly excellence. Her academic foundation includes: B.S. in Electrical Engineering from Sharif University of Technology (1996) M.S. in Electrical Engineering from Stanford University (1999) Ph.D. in Electrical Engineering from Stanford University (2004) Professor Emami pioneers mixed-mode integrated circuit systems that bridge theoretical innovation with practical applications. Her research emphasizes ultra-low power consumption and high reliability in scalable semiconductor technologies, targeting transformative solutions across multiple domains. Key thrusts include: Biomedical implantables for neural recording/stimulation and gastrointestinal monitoring Photonics-electronics co-design for energy-efficient optical interconnects Machine learning-enhanced signal processing for brain-computer interfaces Miniaturized magnetic sensors with unprecedented noise performance Her work consistently demonstrates how circuit-level innovations enable breakthrough capabilities in medical diagnostics and high-speed computing. Analysis of her 2021-2024 publications reveals a strategic convergence of biomedical sensing and intelligent signal processing . While maintaining strong contributions to optical interconnects (accounting for ~40% of recent output), her lab increasingly focuses on closed-loop medical systems where low-power analog neural networks interpret physiological signals. This evolution reflects growing NIH and industry interest in implantable/wearable health technologies, with her group leading in CMOS-based sensor miniaturization and energy efficiency. Her professional recognition includes: IEEE Solid-State Circuits Society Distinguished Lecturer appointment Mentorship excellence is evidenced by students receiving prestigious awards including the Jakob van Zyl Predoctoral Research Award (Saransh Sharma, Ryoto Sekine) and Charles Wilts Prize (Kuan-Chang Chen). Her research program leverages strategic partnerships with Heritage Medical Research Institute and industry collaborators, supported through center-based funding like the Center for Sensing to Intelligence. Administrative leadership spans departmental governance (as Executive Officer) and conference organization (ISSCC technical committees). She directs Caltech's Mixed-mode Integrated Circuits and Systems Lab (MICS) , which operates as a nexus for cross-disciplinary innovation between electrical engineering and medical applications. The lab's industry-collaborative framework accelerates translation of circuit concepts into real-world biomedical solutions through the Center for Sensing to Intelligence.
Hailong Jiao is an Assistant Professor in the Department of Electrical Engineering at Eindhoven University of Technology (TU/e) and an Associate Professor at Peking University Shenzhen Graduate School's School of Electronic and Computer Engineering. He is also a Visiting Assistant Professor with TU/e's Electronic Systems group. His research focuses on low-power and variations-resilient VLSI circuit and system design , including error-resilient systems, approximate computing, machine learning in VLSI, ultra-low voltage circuits, and emerging devices like 3D integration and carbon electronics. Education : BSc (highest honor, 2004, Shandong University), MSc (2008, Institute of Microelectronics, Chinese Academy of Sciences), PhD (2012, HKUST). Collaborations : BrainWave project (TU/e and Radboud University Nijmegen) developing wearable brainwave processing for epilepsy/Parkinson's healthcare. Editorial Roles : Associate Editor for Elsevier Microelectronics Journal and World Scientific Journal of Circuits, Systems, and Computers. Committee Memberships : HiPEAC, MemoCiS, ACM/SIGDA, and six IEEE committees. Research Trends : Recent articles emphasize energy-efficient IoT networks , voltage stacking , sensitivity control , and scan flip-flop design , aligning with UN SDGs for energy engineering and healthcare innovation. Scientific awards include highest honor at BSc .
Suman Datta is a Professor at the Georgia Institute of Technology , holding the Joseph M. Pettit Chair in Advanced Computing and Georgia Research Alliance Eminent Scholar titles. He has a joint appointment with the School of Materials Science and Engineering. Education : B.Tech in Electrical Engineering from IIT Kanpur; Ph.D. in Electrical and Computer Engineering from the University of Cincinnati. Prior Appointments : Stinson Endowed Chair Professor of Nanotechnology at University of Notre Dame (2015–2022); Professor at Penn State (2007–2015); Intel Corporation (1999–2007) in Advanced Transistor Group. Research Interests : His work focuses on high-performance heterogeneous computing using advanced CMOS and beyond-CMOS semiconductors. Key areas include ferroelectric field-effect transistors (FeFETs) , cryogenic computing , in-memory computing , and brain-inspired computing . He explores materials like ferroelectric gate stacks , insulator-to-metal phase transition oxides , and high-mobility oxides for next-generation compute architectures. Recent Article Trends : His group’s publications emphasize BEOL-compatible oxide transistors , negative capacitance , radiation-resilient devices , and machine learning-aided modeling . Subfields include low-voltage memory , 3D Ising machines , dynamic logic at cryogenic temperatures , and monolithic integration of power delivery systems. Scientific Awards : IEEE Fellow (2013) for contributions to transistor technologies NAI Fellow (2016) for societal impact via patents Intel Achievement Award (2003) for high-k/metal gate CMOS Intel Logic Technology Quality Award (2002) for Tri-gate transistors SEMI Award (2012) for high-k dielectrics Penn State Outstanding/ Premier Research Awards (2012, 2015) Advising & Grants : He has mentored students like Wriddhi Chakraborty , Khandker Akif Aabrar , and Sourav Dutta . His research is funded by SRC , DARPA , and NSF , including leadership of the ASCENT and EXCEL centers. Labs & Collaborations : Datta directs the STAR Lab at Georgia Tech, which specializes in atomistic modeling , nanofabrication , and compact model development . The lab collaborates with industry giants like Intel , Micron , and IBM through the ASCENT center.
