جستجوی استاد راهنمای دانشگاهی

Sheng Shen is a Professor in the Mechanical Engineering Department at Carnegie Mellon University (CMU) , with courtesy appointments in the Departments of Electrical and Computer Engineering and Materials Science and Engineering . He earned his Ph.D. in Mechanical Engineering (Minor in Electrical Engineering) from Massachusetts Institute of Technology (MIT) , and his B.S. and M.S. from Huazhong University of Science and Technology in China. Prior to joining CMU in 2011, he conducted postdoctoral research at UC-Berkeley . Education: Ph.D., Mechanical Engineering, MIT (2010) B.S. & M.S., Power Engineering & Engineering Thermophysics, Huazhong University of Science and Technology (2000 & 2003) Research interests include nanophotonics , nanoscale energy transport and conversion , nanofabrication , and advanced manufacturing , with applications in thermal management , light sources and devices , thermal emission control , solar energy conversion , infrared sensing , and multifunctional materials . His work leverages interdisciplinary expertise in thermal and optical measurements , material synthesis , device fabrication , and theoretical modeling . Recent publications highlight advancements in infrared radiation control , thermal interface materials , metasurface engineering , and graphene-based nanosystems . His scientific awards include: NSF CAREER Award DARPA Director's Fellowship DARPA Young Faculty Award Elsevier/JQSRT Raymond Viskanta Award CMU Dean's Early Career Fellowship Philomathia Foundation Research Fellowship Hewlett-Packard Best Paper Award Best Paper Award, Julius Springer Forum Advising spans Ph.D. and postdoctoral researchers in nanoscale energy systems, with alumni contributing to solar energy conversion , infrared sensing , and flexible electronics . His lab receives funding from ARL, DARPA, DOE, DTRA, NASA, NSF, and ONR , and recently secured a DURIP award for instrumentation.

• Nanophotonics
• Nanoscale Energy Transport and Conversion
• Advanced Manufacturing
• +۷ مورد دیگر

Subhasish Mitra is the William E. Ayer Professor of Electrical Engineering and Computer Science at Stanford University, holding dual appointments in both departments. He leads the Stanford Robust Systems Group and serves on the leadership team of the Microelectronics Commons AI Hardware Hub under the US CHIPS and Science Act. His research spans Robust Computing, NanoSystems, Electronic Design Automation (EDA), and Neurosciences, with breakthroughs in X-Compact test compression, carbon nanotube computing, and 3D integration. He has held international roles like the Carnot Chair at CEA-LETI and Visiting Professorships globally. Education & Honors: Recipient of over 40 awards including the IEEE Computer Society’s Harry H. Goode Memorial Award, ACM/IEEE’s A. Richard Newton Technical Impact Award, and the Intel Achievement Award. He earned top academic accolades from IIT Kharagpur and Jadavpur University, and is a Fellow of ACM and IEEE. Research Impact: Pioneered first-of-their-kind systems like the carbon nanotube computer and monolithic 3D integration. His work on robust computing techniques like QED validation and X-Compact compression has industry-wide adoption, saving billions in manufacturing costs. Collaborates with industry leaders like Intel, Google, and Samsung. Publications & Grants: Over 400 publications, including award-winning papers in DAC, ISSCC, and IEEE journals. Leads grants from NSF, DoE, and industry partnerships. His lab explores cutting-edge topics like neuromorphic computing, 3D thermal scaffolding, and AI hardware acceleration. Administration & Outreach: Serves as Associate Chair (Faculty Affairs) for Stanford’s Computer Science Department. Recognized by students for mentorship, and frequently invited to global forums like the World Economic Forum and National Academy of Engineering.

• Robust Computing
• NanoSystems
• Electronic Design Automation (EDA)
• +۳ مورد دیگر

Rakesh Kumar is a Professor and John Bardeen Faculty Scholar in the Electrical and Computer Engineering Department at the University of Illinois at Urbana-Champaign. His work focuses on computer architecture, system-level design automation, and low-power computing. PhD in Computer Engineering from University of California, San Diego BS in Electrical Engineering from IIT Kharagpur His research spans all layers of the computing stack, with key contributions to flexible computer systems , waferscale computing , error-resilient architectures , and approximate computing . He has pioneered work on voltage-reliability tradeoffs and peak power management techniques. Recent publications highlight trends in space microdatacenters , printed microprocessors , and neural graph accelerators . His work on plastic chips was recognized as one of the three biggest semiconductor headlines of 2022 by IEEE Spectrum. IEEE Fellow (2024) ISCA Influential Paper Award MICRO Test-of-Time Award ICCAD Ten Year Retrospective Most Influential Paper Award Best Paper Awards at CASES, SELSE, HPCA He has received teaching accolades including the Stanley H. Pierce Faculty Award and Ronald W. Pratt Outstanding Teaching Award . His research group explores hardware-software co-design for emerging applications in AI, IoT, and sustainable computing.

• Computer Architecture
• System-Level Design Automation
• Low Power Computing
• +۳ مورد دیگر

Sumeet Kumar Gupta is an Associate Professor in the Department of Electrical and Computer Engineering at Purdue University. His academic career spans from his current role to a prior Assistant Professorship at Pennsylvania State University (2014-2017) and an engineering position at Qualcomm Inc. (2012-2014). He holds a PhD in Electrical and Computer Engineering from Purdue University (2012), an M.S. from the same institution (2008), and a B.Tech in Electrical Engineering from IIT Delhi (2006). B.Tech, Electrical Engineering, IIT Delhi (2006) M.S., Electrical and Computer Engineering, Purdue University (2008) PhD, Electrical and Computer Engineering, Purdue University (2012) Dr. Gupta's research focuses on neuromorphic computing, low power variation-aware VLSI design in emerging nanotechnologies, device-circuit co-design, and nano-scale device modeling/simulations. His work addresses challenges in ferroelectric materials, crossbar arrays for deep neural networks, and energy-efficient AI hardware. Recent publications (2025-2024) highlight trends in: Ferroelectric HfO2/HZO thin films Compute-in-memory architectures Variability/stochasticity analysis Machine learning for device optimization Interconnect resistance/temperature effects AI hardware fault tolerance Scientific Awards & Recognitions: DARPA Young Faculty Award (2016) Early Career Professorship, Penn State (2014) 6th TSMC Outstanding Student Research Bronze Award (2012) Magoon Award (Purdue) Outstanding Teaching Assistant Award (Purdue, 2007) Intel PhD Fellowship (2009) His professional journey includes academic appointments at Purdue University (2020-present, Associate Professor) and Pennsylvania State University (2014-2017, Assistant Professor) after industry experience at Qualcomm Inc. (2012-2014). He maintains IEEE and EDS membership while publishing over 100 refereed works.

• Neuromorphic Computing
• Low Power VLSI Design
• Emerging Nanotechnologies
• +۳ مورد دیگر

Kristofer Pister is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He co-directs the Berkeley Sensor and Actuator Center (BSAC) and the Ubiquitous Swarm Lab. His career spans groundbreaking innovations in Micro/Nano Electro Mechanical Systems (MEMS), Control Systems, and Low-Power Circuits, with a focus on Smart Dust and synthetic insects. Education: Ph.D. and M.S. in EECS from UC Berkeley (1992, 1989); B.A. in Applied Physics from UC San Diego (1986). His research areas include MEMS , Control Systems , Robotics , and Integrated Circuits , with recent work on self-powered micro-sensors, crystal-free radios, and interplanetary swarm networks. Key awards include the ISA Albert F. Sperry Founder Award (2009) , Alexander Schwarzkopf Prize (2006) , and the NSF CAREER Award (1996) . He has authored numerous influential publications in wireless sensor networks and microrobotics. His lab, Ubiquitous Swarm Lab , explores distributed robotics and swarm intelligence. Pister emphasizes open collaboration in research, ethical conduct in academia, and efficient resource utilization for graduate students.

• Micro/Nano Electro Mechanical Systems (MEMS)
• Control Systems
• Robotics
• +۶ مورد دیگر

Houman Savoji is an Associate Professor in the Department of Pharmacology and Physiology at the Faculty of Medicine, University of Montreal. He is also a full-time researcher at the CHU Sainte-Justine Research Center and principal investigator in regenerative medicine, organs-on-chip, and bioprinting at TransMedTech Institute. Dr. Savoji received his PhD in Biomedical Engineering from the Institute of Biomedical Engineering at Polytechnique Montréal in 2016. He then completed a postdoctoral fellowship at the Institute of Biomaterials and Biomedical Engineering at the University of Toronto. His research expertise combines advanced manufacturing technologies (micro- and nano-fabrication, 3D bioprinting, microfluidics, cell electrospinning) with functional and composite materials for applications in tissue engineering, regenerative medicine, and organs-on-chip. His work focuses on the design, development, optimization, implementation, and characterization of innovative functional biomaterials using emerging engineering technologies, with particular emphasis on cardiac tissue engineering and biomimetic pulmonary heart valves for pediatric patients. Dr. Savoji has published extensively on biomaterials, tissue engineering, 3D bioprinting, and organ-on-chip technologies. His recent publications demonstrate expertise in viscoelastic characterization of soft tissues, engineering immune responses to biomaterials, ceramic engineering for biomedical applications, and advanced 3D bioprinting techniques for cardiac and vascular tissue engineering. 2017-2020, Postdoctoral Research Grant, CIHR 2017-2019, Postdoctoral Research Grant, FRQNT 2017-2018, Human Society of International Grant, Human Toxicity Assessment Project 2016, CR-CHUM Research Center Award 2015, Star Student-Researcher Award, FRQNT 2014-2015, Jane and Frank Warchol Fellowship, Society of Vacuum Coaters Foundation 2013, Institute of Textile Science Award 2012-2015, Excellence Doctoral Scholarship for Foreign Students, FRQNT Dr. Savoji has supervised Master's students including Ines Barrakad (2024) working on 'Advanced manufacturing technologies versus molding of corneal implants: 3D printing vs molding of a Keratoprosthesis' and Zineb Ajji (2023) researching 'Development of perfusable patches by 3D bioprinting for potential application in cardiac tissue regeneration.' He has secured numerous research grants from organizations including CIHR, NSERC, FRQNT, FRQS, MITACS, and others for projects related to 3D bioprinting of cardiac tissues, biomimetic heart valves, and other tissue engineering applications. The Savoji Laboratory, located within the Department of Pharmacology and Physiology and Institute of Biomedical Engineering of the Faculty of Medicine of the University of Montreal, the Research Center of the CHU Sainte-Justine (CHUSJ), and the TransMedTech Institute, focuses on multidisciplinary research involving 3D bioprinting using stem-cell derived human cardiac cells to fabricate functional cardiac tissues for transplantation and drug discovery applications.

• Bioengineering
• Tissue Engineering
• Regenerative Medicine
• +۴ مورد دیگر

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.

• Hardware Security
• AI-Enabled Security
• Internet of Things (IoT) Security
• +۱۵ مورد دیگر

Jiang Hu is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University, holding the Eric D. Rubin '06 Endowed Professorship. He also serves as Co-Director of Graduate Programs and is affiliated with the Computer Science & Engineering department. His research focuses on VLSI design automation, machine learning applications, and hardware security. He has held roles as editor for IEEE Transactions on CAD and ACM Transactions on Design Automation, and chaired the 2012 ACM International Symposium on Physical Design. Education: B.S. in Optical Engineering (Zhejiang University, 1990), M.S. in Physics (1997), and Ph.D. in Electrical Engineering (University of Minnesota, 2001). He worked at IBM Microelectronics before joining Texas A&M in 2002. Research interests include energy-efficient VLSI circuits, on-chip communication fabrics, analog layout automation, and AI-driven EDA. Recent work emphasizes machine learning for design closure, privacy-preserving frameworks, and systolic array-based architectures. Awards: IEEE Fellow (2016) Humboldt Research Fellowship (2012) Multiple best paper awards at DAC, ICCAD, and ASPDAC Advising and grants: Leads initiatives like the SLICE project, NSF workshops on ML-EDA infrastructure, and serves as Editor-in-Chief of ACM TODAES since 2024. His work bridges academic research and industry applications in EDA and semiconductor design. Labs/Teams: Active contributor to open-source tools like ALIGN for analog layout generation and collaborations on machine learning for EDA commons.

• VLSI Design Automation
• Hardware Security
• Machine Learning Applications
• +۳ مورد دیگر

Martin D F Wong serves as the Edward C. Jordan Professor of Electrical and Computer Engineering and Executive Associate Dean for the College of Engineering at the University of Illinois at Urbana-Champaign. He is affiliated with the Coordinated Science Laboratory and has been instrumental in advancing electronic design automation research. His educational background includes: B.Sc. in Mathematics, University of Toronto (1979) MS in Mathematics, University of Illinois at Urbana-Champaign (1981) Ph.D. in Computer Science, University of Illinois at Urbana-Champaign (1987) Wong's research centers on combinatorial optimization and algorithm design for VLSI systems, with particular expertise in lithography-aware physical design, field-programmable systems, and electronic packaging. His work bridges theoretical algorithms with practical semiconductor manufacturing challenges as feature sizes shrink below 20 nanometers. Current research focuses on integrating chip design with next-generation lithography technologies including triple-patterning, self-aligned double patterning, directed self-assembly, and extreme ultraviolet processes. His publication record shows consistent contributions to electronic design automation, with emphasis on manufacturing-aware physical design algorithms and circuit optimization techniques. Recent work addresses the critical interface between circuit layout and lithography processes as semiconductor technology advances to 14nm and beyond. Scientific recognition includes: Fellow of IEEE and ACM 2000 IEEE Donald O. Peterson Best Paper Award Multiple Best Paper Awards at DAC, ICCD, and ICCAD conferences IBM Faculty Awards (2000, 2004) NSF Research Initiation Award Wong has secured significant research funding including a $450,000 NSF grant for lithography-aware physical design and has supervised over 49 PhD students. His work continues the legacy of integrated circuit innovation at Illinois, building on foundational contributions like Jack Kilby's integrated circuit invention. Current research initiatives focus on optimizing chip design for next-generation manufacturing processes where optical interference challenges require co-design of layout and fabrication. He leads research within the Coordinated Science Laboratory, focusing on electronic design automation algorithms that address the growing complexity of semiconductor manufacturing at nanometer scales.

• Combinatorial optimization
• Design and analysis of algorithms
• Field-programmable systems
• +۷ مورد دیگر

Prof. Dr. Tobias Gemmeke is a University Professor at RWTH Aachen University's Faculty of Electrical Engineering and Information Technology, leading the Chair of Integrated Digital Systems and Circuit Design. His work focuses on neuromorphic computing, hardware accelerators, and energy-efficient electronics. He has pioneered advancements in FPGA-based computational neuroscience simulators, neuromorphic processor architectures, and sensor integration for industrial and medical applications. Research interests include time-domain computing, ReRAM reliability, and co-optimization of neural networks with hardware. Notable contributions include the neuroAIx framework for accelerated neuroscience simulations and energy-efficient ASIC designs for post-quantum cryptography. He actively explores memristive devices and domain generalization techniques for edge computing. Recent publications highlight innovations in spiking neural networks, sensor systems for plain bearings, and time-domain compute-in-memory engines. His work bridges theoretical neuroscience with practical hardware implementations, emphasizing scalability and real-time performance.

• Neuromorphic Computing
• Hardware Accelerators
• VLSI Circuits
• +۵ مورد دیگر

Professor John D. Cressler is a tenured faculty member at the Georgia Institute of Technology, holding a position within the School of Electrical and Computer Engineering in the College of Engineering. His research focuses on cutting-edge semiconductor technologies, particularly silicon-germanium heterojunction bipolar transistors (SiGe HBTs) for mixed-signal applications spanning RF, microwave, mm-wave, analog, and digital domains. His research interests center on atomic-scale bandgap engineering for next-generation semiconductor devices, with emphasis on SiGe HBT technology development, radiation-hardened circuits for space applications, cryogenic electronics, and device-circuit interactions. His team explores fundamental device theory, broadband noise analysis, profile optimization, 2-D/3-D simulation, compact modeling, and radiation effects. Current projects include Europa-surface mission electronics, D-band/sub-THz systems, and radiation-tolerant receiver designs. Analysis of his 15 most recent publications (2024-2025) reveals a dominant focus on radiation-hardened electronics for space applications (40% of works), millimeter-wave circuit design (30%), and SiGe HBT reliability optimization (30%). Key trends include Europa mission electronics development, D-band/sub-THz circuit innovation, and advanced radiation mitigation techniques using SiGe BiCMOS technology. Professor Cressler teaches multiple courses including ECE 3040 (Microelectronic Circuits), ECE 3450 (Semiconductor Devices), ECE 6444 (Silicon-Based Heterostructure Devices and Circuits), and the interdisciplinary IAC 2002 course on Science, Engineering and Religion. His research is supported by industrial collaborations and Georgia Tech facilities including the Georgia Electronic Design Center (GEDC), NanoTECH, and C-STAR.

• Silicon-Germanium Heterojunction Bipolar Transistors (SiGe HBT)
• Mixed-Signal Semiconductor Devices
• RF and Microwave Circuit Design
• +۵ مورد دیگر

Dr. Bob Beitle Jr. is a Professor of Chemical Engineering and Senior Associate Vice Chancellor for Research and Innovation at the University of Arkansas. He joined the department in 1993, earned tenure in 1998, and was promoted to Full Professor in 2006. His research spans biochemical engineering , bioseparation , fermentation , and adaptive technology for the disabled , with significant work on protein purification, catalytic nanoparticles, and sustainable bioprocesses. Education: BS, MS, PhD in Chemical Engineering from the University of Pittsburgh (1987, 1991, 1993) Dr. Beitle's research combines experimental and computational approaches, focusing on peptide-directed nanoparticle synthesis and biocatalysis . His recent publications highlight advancements in MOF-based separations , CO2 capture materials , and viral detection platforms . He has secured grants like the CAREER Award and led projects in industrial partnerships and student development . Scientific contributions include multiple patents in bioseparation and software interfaces. Awards span decades: teaching honors (1988–2007) and mentorship recognition . He serves on the Cell and Molecular Biology Program Advisory Committee and the Executive Committee for the Biochemical Technology Division of ACS . Lab initiatives involve genomic data-driven affinity tail design and membrane-assisted fermentation systems .

• Biochemical Engineering
• Bioseparation
• Fermentation
• +۵ مورد دیگر

Nicola Nicolici is a Professor in the Department of Electrical and Computer Engineering at McMaster University. His research focuses on methods and algorithms for the design of digital integrated circuits and systems, with significant contributions in manufacturing test, post-silicon validation and debug. His work has expanded to include embedded systems, low-energy computing, and custom hardware-accelerated computing systems. Professor Nicolici's research interests span multiple areas of digital system design and validation. His early work focused on manufacturing test methodologies and power-aware testing strategies for integrated circuits. More recently, he has made significant contributions to post-silicon validation techniques, including constrained-random stimuli generation, trace signal selection, and bit-flip detection. His research has evolved to address emerging challenges in embedded computing systems, low-energy design, and specialized hardware acceleration for various applications including deep neural networks and signal processing. His recent publications reveal a strong trend toward hardware acceleration for specialized computing tasks. The research spans matrix multiplication algorithms (Strassen and Karatsuba), memory system optimization (DDR5 calibration), FPGA-based radar processing, and neural network acceleration. His work consistently bridges theoretical algorithm development with practical hardware implementation considerations, particularly focusing on precision analysis, fault tolerance, and energy efficiency. The research demonstrates a clear progression from traditional digital circuit testing to more complex system-level validation and acceleration techniques. Professor Nicolici has been actively involved in teaching courses related to system-on-chip design and test, digital systems, and embedded systems. His teaching portfolio includes advanced courses such as System-on-Chip (SOC) Design and Test and Digital Systems Design , reflecting his expertise in the field. While specific grant information isn't detailed in the provided text, his extensive publication record suggests ongoing research funding support. His research has contributed significantly to the fields of digital circuit testing, post-silicon validation, and hardware acceleration. The work has practical applications in semiconductor manufacturing, embedded systems design, and specialized computing architectures. His recent focus on neural network acceleration and memory system optimization reflects the evolving landscape of computer architecture research.

• Digital Integrated Circuit Design
• Hardware Testing
• Post-Silicon Validation
• +۷ مورد دیگر

David Wentzlaff is a Professor of Electrical and Computer Engineering at Princeton University, with associated faculty roles in Computer Science and the High Meadows Environmental Institute (HMEI). He leads research in computing architecture, green computing, and sustainable system design. As Director of Undergraduate Studies, he shapes educational programs in his field. Education: Ph.D., Electrical Engineering, MIT (2012) M.S., Electrical Engineering and Computer Science, MIT (2002) B.S., Electrical Engineering, University of Illinois at Urbana-Champaign (2000) Research Focus: Future Computing Systems: Designing manycore architectures, cloud computing infrastructure, and chiplet-based systems for exascale computing. Sustainability: Developing energy-efficient hardware, recyclable computing systems, and eco-friendly decommissioning strategies. Hardware-Software Co-Design: Exploring FPGA integration, in-memory computing, and parallel processing frameworks. Advising & Grants: Advises 8 current graduate students, focusing on topics like chiplet design, neural acceleration, and sustainable computing. Recipient of NSF grants for projects like OpenPiton (open-source manycore research platform) and CAREER awards for energy-efficient architectures. Labs & Collaborations: Leads the Wentzlaff Research Group at Princeton. Develops open-source frameworks like PRGA (FPGA prototyping) and OpenPiton (manycore processor).

• Computer Architecture
• Green Computing
• Cloud Computing
• +۶ 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

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