جست‌وجوی استاد و پژوهشگر

• Electrical Engineering
• Semiconductor Devices
• Optical Communications
• +۳ مورد دیگر

Matthew Heins is an Associate Professor of Instruction in Electrical Engineering at The University of Texas at Dallas, affiliated with the Erik Jonsson School of Engineering and Computer Science. He holds an office in ECSN 4.608 and can be reached via email at Matthew.Heins@utdallas.edu or phone at +1 (972) 883-3846. His research interests focus on high-speed electronics, semiconductor device applications, and advanced amplifier design for optical and microwave systems. Key areas include transimpedance amplifiers, GaAs-based HEMT technologies, and millimeter-wave systems. Matthew has contributed to the development of high-performance integrated circuits for fiber-optic communications, coherent optical receivers, and low-noise amplifiers operating at Ka-band and beyond. His publications highlight advancements in SiGe BiCMOS technology for optical receivers (2017), quad-channel transimpedance amplifiers (2016), and X-band GaAs mHEMT LNAs (2004). Earlier work includes pioneering contributions to metamorphic HEMT applications (2003), 40-Gb/s optical driver amplifiers (2002), and W-band HBT frequency sources (1999). Matthew’s research emphasizes practical implementations of semiconductor devices in high-frequency systems, blending theoretical analysis with hands-on circuit design. No awards or grants are explicitly mentioned in the text, though his extensive publication record reflects sustained contributions to the field.

Richard Silberglittمشاهده پروفایل

استاد

• Emerging Technologies
• Science, Technology, and Innovation Policy
• Energy Security
• +۱۷ مورد دیگر

Richard Silberglitt is a Senior Physical Scientist at RAND and Professor of Policy Analysis at the RAND School of Public Policy, where he conducts interdisciplinary research at the nexus of science, technology, and public policy. With over 50 years of experience across academia, government, and industry, he is a leading expert in technology foresight, energy systems, and R&D portfolio management. His research focuses on emerging technologies , science and innovation policy , energy security , and nanotechnology . He has developed influential methodologies such as an energy scenario analysis framework and the PortMan portfolio management system, both widely applied in U.S. and international contexts. His work supports strategic planning in defense, public safety, and economic development. His recent publications reflect a strong trend toward technology foresight , critical materials supply chains , quantum technology assessment , and public sector innovation . These works often employ scenario planning, Delphi methods, and data-driven analytics to inform high-stakes policy decisions. Member, American Physical Society Member, Materials Research Society Member, American Ceramic Society As a subject matter expert, Silberglitt has advised the United Nations, U.S. Department of Defense, National Security Agency, and multiple federal agencies. He has chaired the International Advisory Board of the APEC Center for Technology Foresight and delivered testimony to U.S. Congressional committees on critical materials and technology policy. His research has been supported by grants and contracts from the U.S. Army, Navy, CDC, and National Institute of Justice. He has led major initiatives on law enforcement technology, transportation safety ( The Road to Zero ), and international technology foresight. His work often involves collaborative teams and cross-sector partnerships to address complex technological and policy challenges.

Zaman Noorمشاهده پروفایل

مدرس ارشد

• Distributed Systems
• Large Scale Computation
• Big Data Analytics
• +۷ مورد دیگر

Zaman Noor is a Senior Lecturer in the Department of Computer Science and Engineering at The University of Texas at Arlington (UTA), where he has been serving since September 2022. He previously held an Adjunct Professor role at the same institution from January to August 2022 and was a Lecturer at Port City International University from 2015 to 2016. PhD in Computer Science, The University of Texas at Arlington, 2021 BS in Computer Science, Chittagong University of Engineering, 2019 His research focuses on Distributed Systems , Large Scale Computation , and Big Data Analytics , with an emphasis on optimizing array-based programming models for distributed environments such as Spark SQL. He explores compiler techniques for translating high-level array operations into efficient distributed queries, enabling scalable data analytics. Zaman Noor's publications reveal a strong trend in bridging programming languages with database systems, particularly through the translation of array-based loops and graph programs into optimized SQL-based distributed execution plans. His work intersects computer science, database systems, and high-performance computing, targeting applications in cloud computing, big data management, and machine learning infrastructure. He has received recognition for his research, including the Best Paper Award at IEEE BigData Congress in 2018 . Best Paper Award, IEEE BigData Congress, July 6, 2018 Zaman Noor advises master's students, including Priyank Gupta, and serves on thesis committees. He is actively involved in teaching and curriculum development, offering courses in distributed systems, cloud computing, and algorithms. He also contributes to academic service as a faculty advisor for the Google Developer Student Club and The Cornerstone, and as a member of the Publicity Committee. He leads and participates in educational initiatives and student mentorship, particularly in cloud computing and big data technologies, and supports student research through thesis supervision and committee roles. Zaman Noor is affiliated with research and teaching teams focused on data-intensive systems and distributed computing. His GitHub profile indicates engagement with open-source machine learning frameworks, including contributions to TensorFlow-related repositories, reflecting his interest in practical implementations of large-scale computation.

• Cybersecurity
• Computer Networking
• Network Measurement
• +۷ مورد دیگر

Kirill Igorevich Levchenko is an Associate Professor at the University of Illinois at Urbana-Champaign, holding joint appointments in the Department of Electrical and Computer Engineering, the Siebel School of Computing and Data Science, the Information Trust Institute, and the Coordinated Science Laboratory. His interdisciplinary work bridges systems, networking, and security. His research focuses on cybersecurity , network measurement , programmable networks , and online misinformation . He investigates real-world network infrastructure vulnerabilities, develops secure protocols for emerging technologies like UAVs, and studies human interactions with digital deception. His work combines empirical measurement with system design. Recent publications reveal a strong trend in networking systems and security , particularly in programmable data planes, measurement frameworks like PacketLab, and defenses against botnets and spam. He also explores the societal impact of technology through studies on fake media and user perception. Yi-Min Wang and Pi-Yu Chung Faculty Fellow Dr. Levchenko leads research projects in network security and trust, likely supported by grants from federal and private agencies, though specific grants are not listed. His publications suggest active mentorship of graduate students, particularly in systems and networking. He contributes to major conferences in computer security and internet measurement. He is affiliated with the Coordinated Science Lab and the Information Trust Institute , both leading interdisciplinary research centers at UIUC focused on advanced computing and cybersecurity.

Eman El-Sheikhمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Cybersecurity
• +۶ مورد دیگر

Dr. Eman El-Sheikh is a Professor and Associate Vice President at the University of West Florida (UWF), affiliated with the Hal Marcus College of Science and Engineering, the Department of Computer Science, and the Center for Cybersecurity. She is a globally recognized leader in artificial intelligence, machine learning, and cybersecurity, with over 30 years of experience and more than $26 million in competitive research funding. Ph.D. in Computer Science and AI, Michigan State University (2002) M.Sc. in Computer Science, Michigan State University (1995) B.Sc. in Computer Science, American University in Cairo (1992) Her research centers on the integration of Artificial Intelligence and Machine Learning in cybersecurity, with a strong emphasis on education, workforce development, and ethical AI. She leads the development of AI-Cyber curricula for national adoption and is a pioneer in experiential learning models such as the Cybersecurity for All® and CyberSkills2Work® programs. The trends in her recent publications reflect a consistent focus on AI-driven cybersecurity solutions, secure AI systems, national workforce development, experiential education, and inclusive cyber training. Her work bridges technical innovation with policy, leadership, and pedagogy, demonstrating a holistic approach to strengthening the cybersecurity ecosystem. Dr. El-Sheikh has received numerous prestigious honors, including: 2024 GISEC Global Educator of the Year 2024 and 2025 Cybersecurity Woman of the Year (World Finalist) UWF Million Dollar Research Hall of Fame ($25M and $1M levels) 2020 Women Leaders in Cybersecurity, Security Magazine Achievement Award for Advancing Cybersecurity Education (SAM 2016) NSF/NSA AI-Cyber Task Force Member (2024–2025) Founder, Women in Cybersecurity (WiCyS) Florida Affiliate She has led major grant-funded initiatives such as the National Cybersecurity Workforce Development Program and CyberSkills2Work®, securing over $26 million to advance cybersecurity education and training. She mentors students and emerging professionals, particularly through diversity and inclusion efforts. Dr. El-Sheikh also serves as Chief Strategic Alliance Officer and USA Ambassador for the Global Council for Responsible AI, influencing national and global AI policy and education standards. She founded and leads the Artificial Intelligence Research Group at UWF and is actively involved in national task forces and advisory boards, including the Florida Cybersecurity Task Force and the WiCyS Florida Affiliate Advisory Board. Her leadership extends to shaping the future of AI and cybersecurity through collaborative research, innovation in higher education, and strategic partnerships.

Marek Petrikمشاهده پروفایل

دانشیار

• Reinforcement Learning
• Robust Decision Making
• Risk-Averse Optimization
• +۱۳ مورد دیگر

Marek Petrik is an Associate Professor in the Department of Computer Science at the University of New Hampshire, where he is also a member of the artificial intelligence research group. Prior to joining UNH, he was a Research Staff Member at IBM’s T. J. Watson Research Center. He received his Ph.D. in Computer Science from the University of Massachusetts Amherst in 2010 under the supervision of Shlomo Zilberstein. In 2022–2023, he was a visiting faculty researcher at Google Research. Education: Ph.D. in Computer Science, University of Massachusetts Amherst, 2010 His research focuses on robust, data-driven decision making , particularly in reinforcement learning with limited data, risk aversion, and Bayesian models of uncertainty. His work spans theoretical advances in robust Markov decision processes (MDPs), policy optimization, and imitation learning, with applications in natural resource management, agriculture, renewable energy, and space systems. Recent publications emphasize provable robustness, percentile optimization, and safe offline reinforcement learning. His recent work (2023–2025) demonstrates strong trends in risk-averse reinforcement learning , robust policy gradients , offline learning under uncertainty , and Bayesian approaches to decision-making . He frequently publishes in top venues such as NeurIPS, ICML, UAI, AAAI, and journals like JMLR and Mathematics of Operations Research. Scientific Awards: Best Student Paper Award, Uncertainty in Artificial Intelligence (UAI), 2015 Marek Petrik has advised numerous students and collaborators, particularly in the areas of robust RL and safe decision-making. His research has been supported by academic and industry collaborations, including IBM and Google. He is not currently accepting new PhD students. He is actively involved in developing methods for controlling invasive species , pest monitoring , and optimizing environmental systems . Labs and Research Groups: Artificial Intelligence Research Group, University of New Hampshire Former member: Mathematical Sciences Department, IBM T. J. Watson Research Center Visiting researcher: Google Research, 2022–2023

Jimmie Floresمشاهده پروفایل

استاد

• Project Management
• Organizational Development
• Educational Technology
• +۷ مورد دیگر

Dr. Jimmie Flores is an accomplished academic and educator with extensive expertise in business administration, project management, and organizational development. He holds two doctoral degrees and seven master’s degrees, reflecting a deep commitment to lifelong learning and interdisciplinary education. His teaching spans Bachelor’s and Master’s programs in General Business, Management, and Project Management, where he integrates real-world experience with academic rigor. Doctor of Management in Information Systems and Technology, University of Phoenix (2009) Ph.D. in Human and Organizational Development, Fielding Graduate University (2006) Ed.M. in Curriculum, Instruction, & Assessment, Regis University (2009) M.S. in Educational Technology, DeVry University (2008) M.S. in Nonprofit Management, Regis University (2006) M.S. in Management, Regis University (2006) M.S. in Computer Information Technology, Regis University (2003) B.A. in Finance and Marketing, University of St. Thomas (1992) B.A. in Corporate Financial Management, St. Mary’s University (1990) Dr. Flores's research and professional interests center on enhancing educational outcomes through technology integration, global insights, and leadership development. His work bridges academia and industry, drawing from his experience at Shell Oil Company and as a project management trainer for organizations including Rackspace, the U.S. Air Force, and Verizon Wireless. He emphasizes a coaching mindset and practical application in his instructional approach. While no formal publications are listed, his educational background and teaching portfolio suggest a strong focus on applied management sciences, organizational behavior, and digital learning environments. His career reflects a dedication to empowering diverse learners and improving performance through structured, technology-enhanced education. Dr. Flores has no listed scientific awards or fellowships in the provided text. He advises students in business and management disciplines and brings significant industry experience into the classroom. Though no formal grants are mentioned, his external training engagements indicate ongoing professional development and consulting activities. He leads project management training teams based in Asia and Central America, demonstrating international collaboration and operational oversight. Dr. Flores also served as a collegiate basketball referee for 25 years in major conferences such as the Big 12, Mountain West, and WAC, showcasing leadership and discipline outside academia.

Zhengyuan Zhouمشاهده پروفایل

استادیار

• Machine Learning
• Reinforcement Learning
• Stochastic Optimization
• +۱۲ مورد دیگر

Zhengyuan Zhou is an Assistant Professor in the Department of Technology, Operations, and Statistics at the Leonard N. Stern School of Business, New York University. He is also associated faculty at the Department of Computer Science and Engineering, Tandon School of Engineering, and affiliated with the NYU Center for Data Science. He joined NYU Stern in 2020 after serving as an IBM Goldstine Research Fellow and a Visiting Scholar at NYU Stern during 2019–2020. Education: Ph.D., Electrical Engineering, Stanford University, 2019 Master’s in Computer Science, Stanford University Master’s in Statistics, Stanford University Master’s in Economics, Stanford University B.A., Mathematics, UC Berkeley B.S., Electrical Engineering and Computer Sciences, UC Berkeley His research centers on the intersection of machine learning, stochastic optimization, control theory, and game theory, with a focus on data-driven decision-making. He develops algorithms for reinforcement learning, contextual bandits, and policy learning under uncertainty, with applications in inventory control, revenue management, and auction bidding. His work emphasizes sample efficiency, computational tractability, and robustness. The recent publications reflect a strong trend in distributionally robust learning , offline reinforcement learning , and multi-agent systems , particularly in settings with delayed feedback, adversarial environments, and adaptive data collection. His articles span top journals in operations research, machine learning, and control theory. Scientific Awards and Honors: IBM Goldstine Fellowship (2019–2020) INFORMS Nicholson Award Finalist (2017, 2018) NSF and ONR grants (multiple, 2021–2027) NYU Research Catalyst Prize (2023) Horizon Robotics, Bain, and JP Morgan faculty awards (2021) CRA Outstanding Undergraduate Researcher (2013) Zhou advises PhD students in operations management and has served on dissertation committees at Georgia Tech and Tsinghua University. He has received over $1.8 million in research funding from NSF, ONR, and industry partners. He is actively involved in editorial roles as Associate Editor for Management Science , Operations Research , and Mathematics of Operations Research , and as Area Chair for NeurIPS, ICML, and ICLR. He also mentors high school students through logic and cryptography programs at Stanford’s Pre-Collegiate Summer Institute.

Guangdong Zhuمشاهده پروفایل

پژوهشگر

• Concentrating Solar Power
• Geothermal Energy
• Hybrid Renewable Energy Systems
• +۳ مورد دیگر

Guangdong Zhu serves as Group Manager III-Systems Engineering and Senior Researcher at the National Renewable Energy Laboratory (NREL), leading the Thermal Energy Systems Group within the Energy Conversion and Storage Systems Center. He joined NREL in 2010 and specializes in concentrating solar power, geothermal technologies, and hybrid renewable energy systems development. His academic credentials include: Bachelor of Thermal Engineering from Northeast China Institute of Electric Power Engineering Master of Thermal Engineering from Xi'an Jiaotong University PhD in Mechanical Engineering from the University of New Mexico Zhu's research focuses on solar collector characterization, geothermal power plant modeling, and renewable energy hybridization, with expertise spanning heat transfer, fluid mechanics, stress analysis, and numerical modeling. His work addresses critical challenges in thermal energy storage and power generation efficiency for decarbonization. Recent publications (2025) reveal strong trends toward geological thermal energy storage for grid-scale applications, geothermal network optimization via HEATNETS, advanced materials testing for solar concentrators, and precision modeling of power tower flux distribution. These efforts collectively advance the reliability and scalability of renewable thermal systems. Key recognitions include: NREL Outstanding New Partnership Award (2016) NREL President’s Award (2016) As Associate Editor for the ASME Journal of Energy Resources Technology and Executive Committee Member of the ASME Advanced Energy Systems Division, Zhu actively shapes research standards while mentoring junior staff. His group manages NREL's concentrating solar power research portfolio with significant DOE funding. The Thermal Energy Systems Group operates specialized facilities for solar field testing, geothermal simulation, and hybrid system integration, supporting national initiatives for clean energy transition through experimental validation and computational modeling.

Wajiha Shireenمشاهده پروفایل

استاد

• Power Electronics
• Smart Grid
• Renewable Energy Systems
• +۵ مورد دیگر

Dr. Wajiha Shireen is a Professor in the Department of Engineering Technology at the University of Houston’s College of Technology. She has held this position since 1993 and maintains a joint appointment in the Department of Electrical and Computer Engineering. Her academic focus spans power electronics, smart grid technologies, and renewable energy systems. Education: B.S. (Bangladesh University of Engineering and Technology, 1987), M.S. (Texas A&M University, 1991), Ph.D. (Texas A&M University, 1993) Dr. Shireen’s research centers on power quality, wireless power transfer, battery energy storage systems, and the integration of renewable energy sources like solar and wind into modern grids. She has pioneered advancements in MPPT control for photovoltaics and frequency regulation in microgrids , with a particular emphasis on low-inertia grid stability . Her recent publications highlight trends in EV charging infrastructure optimization , predictive control for energy systems , and grid-following battery storage operations . These works intersect electrical engineering, renewable energy, and smart grid technologies. Scientific Awards : College of Technology Outstanding Faculty Award for Teaching (2014) College of Technology Outstanding Faculty Award for Research (1998, 2006) Featured in the Journal of Engineering Technology as a leading researcher (2001) Dr. Shireen has secured over $3 million in grants from the National Science Foundation, Department of Energy, and industry partners like Texas Instruments and Transmark Subsea. She has mentored numerous students, including co-authors Amir Hussain , Preetham Goli , and Sonal Patel , and contributed to the development of educational tools such as the Microgrid Test Bench .

• Networked Epidemic Modeling
• Graph Signal Processing
• Control Systems
• +۲ مورد دیگر

Victor M. Preciado is a Professor in the Department of Electrical and Systems Engineering at the University of Pennsylvania. His research focuses on network science , control theory , and graph signal processing . Research Interests: Modeling and controlling spreading processes on complex networks Optimization algorithms for time-varying systems Applications in public health and cyber-physical security Selected Publications: Recent work includes machine learning for operator inference (2022), hybrid systems stability analysis (2021), and pandemic modeling frameworks (2021). Earlier contributions focus on spectral analysis of epidemics (2009-2016) and geometric optimization (2014).

Jesús del Alamoمشاهده پروفایل

استاد

• Semiconductor Devices
• Transistor Physics
• III-V Compound Semiconductors
• +۷ مورد دیگر

Jesús del Alamo serves as the Donner Professor of Science within MIT’s Department of Electrical Engineering and Computer Science, leading cutting-edge research in semiconductor device physics with applications spanning logic, high-frequency, and power electronics. His work bridges fundamental materials science with practical device engineering to address next-generation computing challenges. Academic Credentials: PhD, Stanford University MS, Stanford University Research Focus: Professor del Alamo’s expertise centers on transistor physics and semiconductor device innovation, particularly III-V compound semiconductors (InGaAs, GaN) and diamond MOSFETs. Current investigations target reliability mechanisms in GaN transistors for RF/power applications, novel analog computing architectures, and electrochemical ionic synapses for neuromorphic hardware. His group pioneers atomic-scale fabrication techniques like thermal atomic layer etching for sub-5nm devices while exploring quantum confinement effects in vertical nanowires. Publication Evolution: Recent work (2023-2025) demonstrates a strategic shift toward neuromorphic computing, with 60% of publications focusing on electrochemical synapses and ferroelectric memories for AI acceleration. This builds upon decades of transistor scaling research, now converging with materials innovations in HfZrO 2 ferroelectrics and protonic conductors to enable energy-efficient analog deep learning hardware. Award Recognition: Louis D. Smullin Award for Excellence in Teaching Amar Bose Award for Excellence in Teaching Intel Outstanding Researcher Award Semiconductor Research Corporation Technical Excellence Award Semiconductor Industry Association-Semiconductor Research Corporation University Researcher Award Collaborative Leadership: He directs research within MIT’s Microsystems Technology Laboratories (MTL), collaborating with faculty including Bilge Yildiz (electrochemical systems) and Ju Li (computational materials). Current projects integrate device physics with neuromorphic algorithms, supported by semiconductor industry partnerships focused on translating fundamental discoveries into practical AI hardware solutions. Research Infrastructure: His group operates within MIT’s MTL cleanroom facilities, utilizing advanced characterization tools for in-situ device analysis and leveraging partnerships with industry leaders in semiconductor manufacturing to prototype novel transistor architectures.

Michael Wallaceمشاهده پروفایل

استادیار

• Electrophysiology
• Motivated Behavior
• Basal Ganglia
• +۷ مورد دیگر

Michael Wallace serves as an Assistant Professor at Boston University, leading research at the intersection of basal ganglia circuitry, motivated behavior, and synaptic transmission mechanisms. His work addresses fundamental questions about neural control of goal-directed actions and their disruption in neurological disorders. Education: Ph.D. in Neurobiology from the University of North Carolina at Chapel Hill Dr. Wallace's research program centers on genetically defined circuits within the basal ganglia, investigating how these structures guide motivated behaviors and motor control. His laboratory employs a sophisticated multidisciplinary toolkit including in vivo electrophysiology, optogenetics, molecular genetics, computational modeling, and behavioral assays. Key research themes encompass neurotransmitter cotransmission (particularly GABA/glutamate interactions), synaptic vesicle dynamics, and circuit-level pathophysiology in conditions ranging from Parkinson's disease to addiction. The lab's long-term mission targets therapeutic interventions through mechanistic understanding of neural circuit dysfunction. Analysis of his publication record reveals consistent focus on multitransmitter neurons and basal ganglia microcircuitry since 2011, with increasing emphasis on entopeduncular nucleus function and neurotransmitter co-packaging mechanisms. His work demonstrates methodological evolution from anatomical and transcriptional profiling toward real-time circuit interrogation using optogenetic and electrophysiological approaches. The Wallace Lab operates as a dynamic neuroscience research hub, integrating expertise across molecular, cellular, and systems levels. Current investigations leverage cutting-edge techniques to dissect how specific basal ganglia pathways process motivational signals and motor commands, with particular attention to disease-relevant perturbations. This systems neuroscience approach bridges fundamental circuit mechanisms with translational applications for neurological and psychiatric disorders.

Sudi Bhattacharyaمشاهده پروفایل

استاد مدعو

• Generative AI
• Machine Learning
• Cloud Computing
• +۵ مورد دیگر

Sudi Bhattacharya is an Adjunct Professor in the Data Sciences and Operations department at the Marshall School of Business, University of Southern California. Concurrently, he serves as a Senior Software Development Manager at Amazon Web Services, leading multidisciplinary teams focused on cloud platforms and AI-driven business strategies. Education: MBA from University of Chicago Booth School of Business Dr. Bhattacharya specializes in leveraging data science and machine learning for business modernization. His research interests include: Generative AI applications in enterprise systems Large Language Model (LLM) integration with cloud infrastructure Data-driven decision-making frameworks Digital transformation economics AI automation in supply chain optimization He has taught advanced courses at USC since 2023, including: DSO-575: Driving Business Transformation with GenAI and ML DSO-599: Generative AI and Automation: Business and Societal Implications DSO-599: Special Topics in Data Sciences and Operations

Ahmet Bindalمشاهده پروفایل

استاد

• Nano-scale Electron Devices
• Silicon Nanowire Transistors
• VLSI Architecture
• +۴ مورد دیگر

Professor Ahmet Bindal is a faculty member in the Department of Computer Engineering at San José State University . He earned his B.S. in Electrical Engineering from Bogazici University, Turkey, followed by M.S. and Ph.D. degrees from the University of California, Los Angeles. Industry Experience : 20 years at IBM, Intel, Philips, and Cadence Design Systems. Current Research : Nano-scale electron devices, silicon nanowire transistors, robotics, and VLSI architecture. Research Trends : His work focuses on silicon nanowire transistors for VLSI, FPGA, and robotics. Key themes include low-power/high-speed integrated circuits , dynamic logic design , neuromorphic engineering , and advanced semiconductor processing . Patents and Publications : He holds four U.S. patents (three with IBM, one with Intel). His 30+ journal and conference publications span nanowire transistors, FPGA architecture, robotics, and semiconductor process modeling. Teaching Contributions : Developed an undergraduate System-on-Chip (SoC) course and a MOSFET design laboratory at SJSU. Books Authored : Fundamentals of Computer Architecture and Design (Springer, 2017). Electronics for Embedded Systems (Springer, 2017). Silicon Nanowire Transistors (Springer, 2017).