جستجوی استاد راهنمای دانشگاهی

• Semiconductor Physics
• Gallium Nitride (GaN) Electronics
• Two-Dimensional Materials
• +۶ مورد دیگر

Tomas Palacios is a Professor of Electrical Engineering at the Massachusetts Institute of Technology (MIT) , where he directs the Center for Graphene Devices and 2D Systems and leads the Microsystems Technology Laboratories (MTL). His research focuses on pushing the boundaries of microelectronics through novel semiconductor materials and device architectures, including Gallium Nitride (GaN) and 2D materials like graphene and molybdenum disulfide (MoS2). Professor, MIT Electrical Engineering and Computer Science Director, MIT Center for Graphene Devices and 2D Systems Clarence J. LeBel Professor, MIT Faculty Director, Northeast Microelectronics Internship Program (NMIP) Research Interests span multiple cutting-edge domains: High-frequency electronics (>300 GHz) for 6G and quantum applications High-voltage power devices (600V–10kV) for energy conversion Post-silicon logic devices using 2D materials High-temperature electronics (e.g., Venus rover applications) Distributed neural networks on large-area 2D materials Graphene-based biosensors and chemical detection systems Scientific Contributions include: Double recipient of the IEEE George Smith Award for groundbreaking GaN transistor work Co-invented first MoS2 electronic circuits Developed world’s first Wi-Fi-to-electricity conversion antenna Led MIT’s Microsystems Technology Laboratories since 2021 Advising Philosophy emphasizes cross-layer expertise, with students gaining experience from materials synthesis to system-level prototyping. His lab has incubated startups like Vertical Horizons , focused on GaN power devices for AI and EVs.

Scott Hauckمشاهده پروفایل

استاد

• Reconfigurable Computing
• FPGA Architecture
• Quantum Computing Control
• +۵ مورد دیگر

Affiliation & Education Scott Hauck is a Professor at the University of Washington's Department of Electrical & Computer Engineering and an Adjunct Professor in Computer Science & Engineering. He leads the Adaptive Computing Machines and Emulators (ACME) Lab . He earned his BS in EECS from UC Berkeley (1990), and MS/PhD in CSE from the University of Washington (1992/1995). Research Focus Dr. Hauck specializes in FPGA-based reconfigurable computing with applications in: Quantum Computing: FPGA controllers for trapped-ion quantum systems enabling precise laser control and quantum state readout. Medical Imaging: Portable radiation sensors for personalized cancer therapy and PET scanner enhancements. High-Energy Physics: FPGA readout systems for ATLAS pixel detectors at CERN's Large Hadron Collider. AI Acceleration: Real-time machine learning inference for scientific applications via projects like hls4ml. His work bridges hardware innovation with computational physics, emphasizing real-time processing and low-latency systems. Publication Trends Recent research focuses on FPGA-accelerated machine learning for particle physics (e.g., transformer networks for LHC trigger systems) and quantum computing instrumentation. Earlier work established foundations in reconfigurable computing architectures and medical imaging electronics. Awards & Recognition Distinguished Teaching Award, University of Washington (2010) Advising & Funding Leads the ACME Lab with extensive funding from NSF, DARPA, NIH, DOE, and industry partners including Intel, Xilinx, and Microsoft. Mentored over 30 MS/PhD students in VLSI, reconfigurable systems, and scientific computing. Collaborations & Labs Directs the ACME Lab (EE1-307), collaborating with UW Radiology (Prof. Robert Miyaoka), UW Physics (Prof. Shih-Chieh Hsu), and Drexel University (Prof. Josh Agar). Projects include quantum control systems, LHC readout electronics, and medical sensor networks.

• High-speed data converters
• Wireless transceivers
• Digital phase-locked loops
• +۷ مورد دیگر

Shiyu Su is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Waterloo. His research focuses on high-speed data converters, wireless transceivers, digital phase-locked loops (PLL), and AI-assisted analog/mixed-signal design automation. He holds a Ph.D. from the University of Southern California (2019) and teaches courses such as ECE 340 (Electronic Circuits 2) and ECE 432 (Radio Frequency Integrated Devices and Circuits). Education: B.S. from Beijing University of Post and Telecommunication (China) and Queen Mary, University of London (UK), 2011; M.S. and Ph.D. from USC, 2013 and 2019, all in electrical engineering. Research Interests: High-speed ADCs/DACs RF/mm-wave transceivers Time-approximation filters (TAF) Analog/mixed-signal design automation Memristor-based computing Biomedical interfaces Key Awards: IEEE SSCS Predoctoral Achievement Award (2017–2018) Best Student Paper Award at IEEE RFIC (2022) Ming Hsieh Institute Scholar (2019–2020) Lab Focus: The Shiyu Su Lab develops integrated circuits for communications, sensing, and computing, with a focus on AI-driven methodologies and digital-analog co-design. Collaborations include work with Prof. Wei Wu (USC) on memristor-based systems.

Morteza Fayaziمشاهده پروفایل

استادیار

• Electronic Design Automation (EDA)
• Machine Learning (ML) in Analog Circuit Design
• System-on-Chip (SoC) Design Automation
• +۳ مورد دیگر

Morteza Fayazi is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Utah, with an adjunct position in the Kahlert School of Computing. His research focuses on Electronic Design Automation (EDA), applying machine learning to automate analog and mixed-signal circuit design, and developing high-performance computing systems. He holds a B.Sc. from Sharif University of Technology, and M.S.E./Ph.D. degrees from the University of Michigan. His research interests include AI-driven EDA, RF/circuit automation, and energy-efficient processors. Key achievements include the MEDAL lab’s work on terahertz radars, systolic-array processors (e.g., DAP and Versa), and open-source frameworks like FASCINET and Tablext. He has received awards such as the 2024 College of Engineering Dean’s ETR Fund and the 2017 Outstanding Undergraduate Thesis Award. Teaching responsibilities include multiple iterations of the Digital System Design course (ECE/CS 3700). His work spans over 15 peer-reviewed articles in IEEE Transactions, ACM, and top conferences like ICCAD and VLSI-SOC, emphasizing automation, efficiency, and AI integration in hardware design.

Charles Ahnمشاهده پروفایل

استاد

• Complex Oxides
• Nanofabrication
• Electronic Control in Correlated Oxides
• +۴ مورد دیگر

Charles Ahn is the John C. Malone Professor of Applied Physics & Materials Science at Yale University. His research focuses on fabricating and studying novel complex oxide materials using advanced techniques like molecular beam epitaxy and synchrotron x-ray scattering. His work addresses multifunctional oxides, nanofabrication, and nonvolatile logic switches for post-CMOS computing. Key research areas include electronic control of complex order parameters in correlated oxides and scanning probe microscopy-based nanofabrication. Education: Ph.D. in Applied Physics from Stanford University. Research interests span the physics and technology of complex oxides, with emphasis on electronic and structural control at the nanoscale. His group develops next-generation materials for computing and electronics, leveraging interdisciplinary approaches in materials science and condensed matter physics. Notable awards include Fellow of the American Physical Society, AVS Peter Mark Memorial Award, David and Lucile Packard Fellowship, and Alfred P. Sloan Fellowship. His lab (Ahn Lab) actively explores cutting-edge applications in oxide electronics and quantum phenomena. Grants and patents include innovations in magnetoelectronic devices and ferroelectric-based technologies. He collaborates widely, bridging experimental materials science with theoretical modeling to advance functional oxide systems.

Jing Liمشاهده پروفایل

دانشیار

• Computer Architecture
• Compiler Design
• Distributed Systems
• +۷ مورد دیگر

Dr. Jing Li is an Associate Professor and Eduardo D. Glandt Faculty Fellow at the University of Pennsylvania , holding dual appointments in the Electrical and Systems Engineering and Computer and Information Science departments. As co-director of the CyberSavvy nationwide security research center and director of the Penn Computational Intelligence Lab (PennCIL) , she pioneers innovations in non-von Neumann computing paradigms. Her research spans post-CMOS technologies, in-memory computing, and hardware-software co-design for security and AI applications. PhD in Computer Engineering, Purdue University (2009) BSc in Electrical Engineering, Shanghai Jiaotong University (2004) Research Focus: Dr. Li's work addresses fundamental challenges in computer systems across the stack. Key areas include: In-Memory Computing: Liquid Silicon architecture combining RRAM with silicon CMOS through monolithic 3D integration Security Engineering: Transforming computer security from "Art" to formal "Engineering" discipline within CyberSavvy Virtualization: Cloud FPGA abstraction layers decoupling compilation from runtime resource management Graph Analytics: Degree-aware optimization techniques for massive-scale graph processing Deep Learning Systems: Roofline model extensions for FPGA-based CNN acceleration Scientific Impact: Awarded DARPA Young Faculty Award , NSF CAREER Award , and IBM CEO Milestone Award , her team has achieved world records in energy-efficient computing (ENIAD supercomputer). With 46 U.S. patents and over 80 publications, she leads ecosystem development for emerging computing architectures through initiatives like the open-source MEG simulation platform . Community Leadership: Dr. Li serves on program committees for flagship conferences ( ISCA , FPGA Symposium ), chairs the International Memory Workshop , and contributes to the MLsys conference's inaugural committee. She actively mentors through multiple PhD openings and industry collaborations.

• Quantum Computing
• MEMS Sensors
• Analog Circuit Design
• +۵ مورد دیگر

Hua Chen is an Associate Researcher and PhD Supervisor at the School of Quantum Science and Engineering at Southern University of Science and Technology (SUSTech), with extensive experience in quantum computing circuit design and MEMS sensor interfaces. Previously, he served as an Associate Researcher at the Institute of Microelectronics, Chinese Academy of Sciences (2021-2022) and as an Assistant Researcher there from 2017-2021, building on his industry experience as an RF/Analog IC Design Engineer at Southwest Integrated Circuit Design Co., Ltd (2007-2010). Education: Ph.D. in Microelectronics and Solid-State Electronics, University of Chinese Academy of Sciences (2014-2017) M.Eng. in Electronic and Communication Engineering, University of Chinese Academy of Sciences (2011-2014) B.Eng. in Microelectronics, Chongqing University of Posts and Telecommunications (2003-2007) Dr. Chen's research focuses on interface circuit design for quantum computing and MEMS sensors, with particular expertise in high performance analog/RF/mixed-signal IC design. His recent work has shifted toward cryogenic circuit design for quantum bit control and readout systems, representing a strategic pivot toward China's national priority in quantum information technology. His publications reveal a consistent trajectory from MEMS gyroscopes and oscillators toward quantum computing hardware support circuits, demonstrating his ability to adapt expertise to emerging technological frontiers. Dr. Chen's research output shows a clear evolution from traditional MEMS sensor interfaces toward quantum cryogenic electronics, with his 15 most recent publications heavily concentrated in cryogenic circuit design for quantum applications. This represents a strategic shift aligning with China's substantial investment in quantum computing research, particularly in the development of control and readout electronics for scalable quantum systems. His significant honors include: Special talent of Shenzhen's Pengcheng Peacock Plan (January 2024) IEEE Senior Member (February 2022) Intellectual Property Specialist of Chinese Academy of Sciences (January 2020) Outstanding Employee (Top 10%) at Institute of Microelectronics CAS (2020) Multiple Graduate Student Scholarships from Institute of Microelectronics CAS (2015-2017) As a PhD Supervisor at SUSTech, Dr. Chen mentors graduate students in quantum circuit design while leading research funded through multiple sources including a Beijing Natural Science Foundation General Project (RMB 200,000) for RF MEMS disk oscillator drive circuits and a Youth Project (RMB 100,000) for MEMS gyroscope phase alignment. He has also participated in major national projects totaling over RMB 27 million, demonstrating his integration into China's strategic research initiatives. Dr. Chen leads a research group at the International Quantum Academy in Shenzhen focused on cryogenic CMOS integrated circuit design for quantum computing applications. His team develops specialized low-temperature measurement and control chips that address critical challenges in scaling quantum computing systems, with particular emphasis on MEMS-based quantum frequency references and cryogenic amplifiers that operate at temperatures near absolute zero.

Ava Hedayatipourمشاهده پروفایل

استادیار

• Analog Integrated Circuit Design
• Bio-implantable and Biomedical Devices
• Low-Power and Low-Noise Designs
• +۳ مورد دیگر

Dr. Ava Hedayatipour is an Assistant Professor of Electrical Engineering at California State University at Long Beach (CSULB), where she joined in Fall 2020. She holds a Ph.D. from the University of Tennessee, Knoxville (2020), and degrees from Iran University of Science and Technology (B.S., 2012) and Shahid Rajaee Teacher Training University (M.S., 2015). Her research focuses on analog/mixed-signal circuit design, bio-implantable devices, low-power systems, and hardware security. Notable contributions include a first-of-its-kind integrated secure multimodal sensor and a flexible paper electrode for remote electrochemical experiments. Education: Ph.D., Electrical Engineering, University of Tennessee, Knoxville, 2020 M.S., Electrical Engineering, Shahid Rajaee Teacher Training University, Iran, 2015 B.S., Electrical Engineering, Iran University of Science and Technology, 2012 Research Interests: Analog and mixed-signal circuit design Biomedical devices and lab-on-chip applications Low-power, low-noise microelectronics Hardware security for IoT and biomedical sensors Flexible electrodes for wearable systems Awards: University of Tennessee Fellowship Award (2019) Outstanding Teaching Assistant Award (2018) BEST PAPER AWARD at IEEE DCAS 2025 2nd Place Winner at IEEE BIOCAS 2023 Innovation Challenge Advising & Grants: Lead CSULB LEAP program project on medical imaging braces Funded NSF project on chaotic analog security (2018–present) Collaborated with industry partners like Applied Medical and Synaptics Labs & Teams: Next Generation Wearable Lab at CSULB Focus on sensor design, hardware security, and biomedical applications

Yiorgos Makrisمشاهده پروفایل

استاد

• Hardware Security
• Trusted Systems
• Machine Learning in Semiconductor Manufacturing
• +۹ مورد دیگر

Yiorgos Makris is a Professor in the Department of Electrical and Computer Engineering at the Erik Jonsson School of Engineering & Computer Science, The University of Texas at Dallas, since July 2011. Previously, he was a faculty member at Yale University for over a decade. He holds a Ph.D. in Computer Engineering from the University of California, San Diego, and a Diploma in Computer Engineering from the University of Patras, Greece. Education: Ph.D. in Computer Engineering, University of California, San Diego (2001) M.S. in Computer Engineering, University of California, San Diego (1997) Diploma in Computer Engineering and Informatics, University of Patras, Greece (1995) Research Interests: Hardware Security and Trustworthiness Statistical Side-Channel Fingerprinting Machine Learning in Semiconductor Manufacturing Trusted and Reliable Integrated Circuits Hardware Trojans in Wireless Cryptographic ICs On-Die Learning and Emergent Technologies His work focuses on enhancing hardware security through statistical methods, machine learning, and formal verification, with applications in analog/RF ICs, post-production calibration, and secure IC design. Key Contributions: Co-Founder and Site-PI of NSF CHEST I/UCRC (Hardware and Embedded System Security and Trust) Leader of the Safety, Security, and Healthcare Thrust at TxACE (Texas Analog Center of Excellence) Director of the Trusted and RELiable Architectures (TRELA) Lab Grants and Funding: NSF, NIH, SRC, ARO, AFRL, AFWERX, DARPA, DOE, and industry partnerships with Boeing, Northrop Grumman, IBM, Intel, Qualcomm, etc. Recent grants include projects on DNA storage security, analog neural networks, and malicious hardware detection. Awards and Recognition: IEEE Fellow (2025) Best Paper Awards at DATE'13, VTS'15, DCAS'22 Best Hardware Demonstration Awards at HOST'16 and HOST'18 Erik Jonsson School Faculty Research Award (2020) Labs and Teams: TRELA Lab (Focus: Secure Hardware Design, Trusted Architectures) CHEST I/UCRC (Industry-University Collaboration)

• Machine Learning
• Deep Learning
• Anomaly Detection
• +۵ مورد دیگر

Peter Karsmakers serves as Associate Professor at KU Leuven's Department of Computer Science within the Faculty of Engineering Technology, based at the Geel Campus. He coordinates the Declarative Languages and Artificial Intelligence (DTAI) research group and holds leadership roles including coordinator of Research and Education for Computer Science across Geel and Diepenbeek Campuses. Karsmakers earned his PhD in Engineering Science in May 2010, focusing on kernel-based learning algorithms for sparse modeling and efficient predictions from large datasets. His doctoral work established foundations for his current research trajectory in resource-constrained machine learning systems. His research integrates machine learning with signal processing for real-time sensor data interpretation, specializing in anomaly detection from acoustic, radar, and accelerometer signals on embedded devices. Current projects address industrial condition monitoring, elderly care systems, and livestock facility monitoring through three main tracks: acoustic monitoring (e.g., SINS, WATCHDOG), radar-based systems (e.g., FARADAY, NextPerception), and smart electronics for power converters. Recent publications demonstrate strong trends in constraint-guided deep learning architectures for industrial applications, cross-environment robustness in sensor systems, and domain-knowledge integration to reduce data requirements. His work consistently bridges theoretical machine learning with practical implementations in resource-constrained environments. No scientific awards or fellowships were mentioned in the provided materials. Karsmakers supervises over 10 master's theses annually and coordinates a research team of 10 PhD students and a post-doc within DTAI-ADVISE. He has secured approximately 2.3 million euros in funding through VLAIO, EU-ECSEL, and bilateral industry contracts, including 10 active projects such as AutoEdgeML (2024-2028) and Fault Tolerant Neural Networks for Space Applications (2024-2027). He leads the DTAI-ADVISE research group focused on developing software that attaches semantics to sensor data on resource-constrained devices. The team operates across multiple campuses with specialized labs for acoustic monitoring (Geel), radar-based systems (in collaboration with ESAT-TELEMIC), and smart electronics (with Electrical Engineering department), maintaining strong industry partnerships with companies in healthcare, manufacturing, and agriculture sectors.

Erica R.H. Fuchsمشاهده پروفایل

استاد

• Technology Policy
• Global Manufacturing
• Innovation Policy
• +۳ مورد دیگر

Erica R.H. Fuchs is the Kavčić-Moura Professor in the Department of Engineering and Public Policy at Carnegie Mellon University (CMU), directing the Critical Technology Initiative (CTI). She holds courtesy faculty positions in Materials Science and Engineering and the Heinz College. Her research focuses on emerging technology development, commercialization, and global manufacturing, with emphasis on national policy implications. Fuchs leads interdisciplinary efforts at CTI to advance critical technology assessment frameworks, ensuring smart investments and policies for national security and economic competitiveness. Formerly, she directed the Manufacturing Futures Initiative, now an endowed institute at CMU. Education: She earned a Ph.D. (2006), M.Sc. (2003), and B.Sc. (1999) in Engineering Systems, Technology Policy, and Materials Science from MIT. Post-MIT, she was a UNIDO fellow in Beijing. Research Interests: Global innovation policy, technology commercialization pathways, and the intersection of technology with economic and security objectives. She authored influential works like the 2021 report *Securing America’s Future*, advocating for a National Technology Strategy to align multi-objective outcomes (security, equity, prosperity). Key Contributions: Catalyzed the National Network for Critical Technology Assessment, mobilizing 13+ universities to address technology gaps. Testified in Congress on supply chain resilience, EV battery policies, and semiconductor strategies. Advises entities like the National Semiconductor Technology Center and MIT’s Institute for Data, Systems, and Society. Affiliations: National Bureau of Economic Research (Research Associate), National Institute of Standards and Technology (NSTC Board), and Brookings Institution panelist. Her work is frequently cited in *Axios*, *Bloomberg*, and the *New York Times*.

• Embedded Systems
• Compilers
• Hardware-Software Co-Design
• +۵ مورد دیگر

Prof. Dr. rer. nat. Rainer Leupers is a faculty member at RWTH Aachen University, chairing the Department of Software for Systems on Silicon. His research focuses on embedded systems, hardware-software co-design, virtual prototyping, and security in computing-in-memory architectures. He has published extensively on RRAM accelerators, logic locking, and neuromorphic security. Chair of Software for Systems on Silicon Research in hardware security and deep learning accelerators Recent publications on cross-tool virtual frameworks and thermal side-channel attacks His work bridges system-level modeling with practical security implementations, emphasizing reliability and performance in heterogeneous computing environments. Key trends in his 2025-2023 articles include compute-in-memory optimization, neural network inference efficiency, and security vulnerabilities in emerging hardware. Awards and formal recognitions are not explicitly detailed in the provided materials. He has not directly mentioned advising students or research grants in the given text fragments. The chair's contact information includes an office at ICT Cube 1, Electrical Engineering, Aachen, with direct email and website links.

Jonathan Viventiمشاهده پروفایل

دانشیار

• Neural Engineering
• Brain-Computer Interfaces
• Flexible Electronics
• +۴ مورد دیگر

Jonathan Viventi is the Hawkins Family Associate Professor of Biomedical Engineering at Duke University, with additional appointments as Assistant Professor in Neurosurgery and Neurobiology, and as a Faculty Network Member of the Duke Institute for Brain Sciences. His research focuses on developing flexible, high-resolution neural interfaces for diagnosing and treating neurological disorders, particularly epilepsy, and advancing brain-machine interfaces. Education: Ph.D. in Engineering from the University of Pennsylvania (2010). Research Interests: Dr. Viventi's work centers on flexible electronics for high-resolution brain interfacing . Key areas include: Micro-electrocorticography (μECoG) arrays with thousands of channels Wireless, fully implantable neural prosthetics for speech restoration Real-time seizure detection and prediction systems Chronic biointegration of soft electronic materials Translational applications in epilepsy, stroke, and Parkinson's disease Scientific Awards: MIT Technology Review Innovators Under 35 (2014) Popular Science Brilliant 10 (2014) Grants & Funding: Dr. Viventi leads multiple NIH-funded projects including: A Wireless µECoG Prosthesis for Speech (2021-2026) Neuro-CROWN: Optimized Ultra-Flexible CMOS Electrode Arrays (2021-2025) Next-Generation Wireless Intracranial Arrays for Post-traumatic Epilepsy (2021-2025) Laboratory & Teams: His lab develops cutting-edge neural interface technologies, collaborating across engineering, neuroscience, and clinical departments at Duke to translate flexible electronics into therapeutic devices.

Ahmedullah Azizمشاهده پروفایل

استادیار

• Mixed-Signal VLSI Circuits
• Non-Volatile Memory
• Beyond CMOS Device Design
• +۴ مورد دیگر

Ahmedullah Aziz is an Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville, within the Tickle College of Engineering. He earned his Ph.D. in Electrical and Computer Engineering from Purdue University (2019), an M.S. from Pennsylvania State University (2016), and a B.S. from Bangladesh University of Engineering & Technology (2013). His research focuses on mixed-signal VLSI circuits, non-volatile memory, and beyond-CMOS device design, emphasizing emerging technologies like ferroelectrics and spintronics. He leads the NorDIC Lab and has published over 60 articles in journals, conferences, and patents. Education: PhD, Electrical & Computer Engineering, Purdue University, 2019 MS, Electrical Engineering, Pennsylvania State University, 2016 BS, Electrical & Electronic Engineering, BUET, 2013 Research Interests: Aziz explores device-circuit-system co-design techniques, particularly in cryogenic neuromorphic systems, superconducting memory, and ferroelectric-based circuits. His work bridges material innovation with practical applications, aiming to advance energy-efficient and high-performance computing. Awards: EDAA Outstanding Dissertation Award (2019/2020) Outstanding Graduate Student Research Award (Purdue, 2019) Samsung 'Icon' Award (2013) Best Publication Awards (SRC-DARPA STARnet, 2015/2016) Advising & Grants: While specific grants are not detailed, his research has been supported by prestigious institutions. He has advised/co-advised projects in emerging technologies but no listed students. His work extends to reviewing IEEE journals, conference TPC roles, and editorial contributions. Labs & Teams: He directs the NorDIC Lab, focusing on next-generation computing and device innovation.

• Nano-CMOS modeling
• Physical design
• Nanotechnology
• +۶ مورد دیگر

Yu Cao is an Adjunct Professor in the School of Electrical, Computer and Energy Engineering at Arizona State University (ASU). He holds a Ph.D. in Electrical Engineering from the University of California, Berkeley (2002), an M.A. in Biophysics (1999), and a B.S. in Physics from Peking University (1996). His research focuses on nanoscale technology modeling, variability and reliability in electronics, and hardware design for on-chip learning. Key interests include predictive technology modeling, post-silicon integration, and energy-efficient semiconductor devices. Dr. Cao has led multiple grants funded by NSF, Samsung, Semiconductor Research Corporation, and academic institutions. His work emphasizes bridging technology and design automation gaps in nanometer-scale integration. Courses taught include VLSI design, digital systems, and graduate research supervision. He has authored two books and numerous articles on nano-CMOS modeling and physical design. His recent research advances include stable, efficient organic light-emitting diodes (OLEDs) and electrochromic devices leveraging phosphorescent molecular aggregates. These innovations address challenges in color stability, energy efficiency, and device lifetime. Notable grants include NSF-funded projects on hardware and algorithms for on-chip learning (2015–2016) and low-power bio-signal processing (2015–2016). His contributions span semiconductor reliability, nanoelectronic device applications, and predictive circuit simulation techniques.