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Xiaohang LiView profile

Associate Professor

• Wide Bandgap Semiconductors
• III-Nitride Materials
• Optoelectronics
• +6 more

Xiaohang Li is an Associate Professor in the Department of Electrical and Computer Engineering at the King Abdullah University of Science and Technology (KAUST) , where he serves as Principal Investigator of the Advanced Semiconductor Laboratory (ASL) . He holds a Ph.D. (2015), M.S. (2011), and B.S. (2008) in Electrical Engineering and Applied Physics from Georgia Institute of Technology, Lehigh University, and Huazhong University of Science and Technology, respectively. Research Focus: Professor Li specializes in (ultra)wide bandgap semiconductors (III-nitrides, III-oxides) for next-generation devices, including LEDs, lasers, transistors, and sensors. His work spans materials growth, simulation, fabrication, and characterization, targeting applications in energy, communications, and biomedical industries. Key Trends in Publications: Recent articles highlight advancements in InGaN micro-LED fabrication, β-Ga2O3 flexible transistors, AlN MOSFETs, and machine learning-optimized plasmonic structures. Themes include wide bandgap materials , 3D integration , thermal management , and flexible electronics . Scientific Awards: Harold M. Manasevit Young Investigator Award (2018) IEEE North Jersey Section ED/CAS MTT/AP Chapters Award (2019) Georgia Tech representative, Global Young Scientist Summit (2015) IEEE Photonics Society Graduate Student Fellowship (2014) D. J. Lovell Scholarship (2013) Mentorship: Since joining KAUST, Li has advised over 100 students and leads the ASL team in pioneering semiconductor research. His group focuses on fundamental and applied studies of wide bandgap materials to drive innovations in energy and health sectors.

Zheshen ZhangView profile

Professor

• Quantum Engineering
• Quantum Computing
• Quantum Communication
• +4 more

Prof. Zheshen Zhang is a Professor in the Department of Electrical and Computer Engineering at the University of Michigan College of Engineering . He leads the Quantum Engineering Lab , focusing on harnessing quantum mechanical resources like entanglement to advance sensing, communication, and computing systems. Academic Rank: Professor Institution: University of Michigan School: College of Engineering Department: Electrical and Computer Engineering Research Interests: His work spans quantum engineering, emphasizing: Quantum computing architectures using continuous-variable cluster states Quantum communication via entanglement-assisted protocols Quantum sensing for precision metrology and dark matter detection Hybrid photonic circuits with Scandium Aluminum Nitride and Silicon Nitride Application of machine learning to quantum information processing Publications Trends: Recent articles highlight: Advances in integrated photonics for scalable quantum devices Development of entanglement-enhanced sensors for covert and precision applications Exploration of exceptional points in optical cavities for metrology Quantum network prototypes enabling open-access quantum computing Machine learning integration with quantum data acquisition

Kaushik NayakView profile

Associate Professor

• Semiconductor Devices
• Mesoscopic Electronics
• Nanoelectronics
• +3 more

Kaushik Nayak is an Associate Professor in the Department of Electrical Engineering at the Indian Institute of Technology Hyderabad . His research spans semiconductor device physics, mesoscopic electronics, and electro-thermal effects in nanoscale transistors, with recent work on diamond MOSFETs, 2D material contacts, and thermal resistance in nano-sheet FETs. Ph.D., Indian Institute of Technology Bombay M. Tech., Microelectronics, IIT Bombay B.E., Electronics & Telecommunication, Utkal University He teaches advanced courses on semiconductor device modeling, mesoscopic electronics, and electromagnetic wave propagation. His publications focus on nanoelectronics, device variability, and high-temperature operations. Contact: knayak@ee.iith.ac.in .

• Condensed Matter Physics
• Quantum Materials
• Quantum Devices
• +3 more

Hugh Churchill is a Professor in the Department of Physics at the University of Arkansas, College of Arts & Sciences. His research focuses on quantum materials and devices, particularly condensed matter physics with applications in 2D systems and quantum transport. Education: PhD in Physics from Harvard University, BA in Physics and BM in Music Performance from Oberlin College Recent research trends include studies on 2D materials like transition metal dichalcogenides and black phosphorus, investigating quantum transport phenomena, supercurrent tuning, strain engineering for exciton control, and applications of machine learning in quantum material discovery. His work also explores THz emission mechanisms and quantum noise mitigation strategies. Arkansas Research Alliance Fellow Presidential Early Career Award for Scientists and Engineers NSF CAREER Award ORAU Powe Junior Faculty Award AFOSR Young Investigator Connor Faculty Fellowship Hugh teaches graduate and undergraduate courses in quantum mechanics, modern physics, and 2D materials, including PHYS 5413 Quantum Mechanics I and PHYS 6713 Condensed Matter Physics II.

Vincent John Mooney IIIView profile

Associate Professor

• Hardware-Software Co-Design
• Cyber Physical Systems Security
• System Level Design
• +7 more

Vincent John Mooney III is an Associate Professor at the School of Electrical and Computer Engineering and an Adjunct Associate Professor at the School of Computer Science, Georgia Institute of Technology. His research focuses on Hardware-Software Co-Design , Cyber Physical Systems Security , and Low-Power Architectures . He has authored numerous publications on topics such as probabilistic computing, hardware security, and embedded systems design. Dr. Mooney has received prestigious awards including the NSF Career Award , National Semiconductor Fellowship , and ARCS Best Paper Award . Education: Ph.D. in Electrical Engineering (1998), Stanford University MA in Philosophy (1997), Stanford University MS in Electrical Engineering (1994), Stanford University Certificate of Graduate Study (1992), University of Navarra BS in Electrical Engineering and Computer Science (1991), Yale University Research interests span hardware/software codesign, cybersecurity in embedded systems, and synthesis of reconfigurable architectures. His recent work includes Gridtrust for decentralized supply chain cybersecurity and COPPER for computation obfuscation. Dr. Mooney has supervised numerous Ph.D. students and held leadership roles in conferences such as HOST and CASES . Scientific awards include NSF Career Award (2000) National Semiconductor Fellowship (1997-1998) AT&T Engineering Scholarship Program (1987-1991) NCAA Postgraduate Scholar (1991) Senior Member, IEEE (2003) ARCS 2012 Best Paper Award Advising and grants highlight his mentorship of students like Jun Cheol Park and Yudong Tan , along with grants such as the U.S. Air Force Summer Faculty Fellowship (2007). He leads the Hardware/Software Codesign for Security Group at Georgia Tech and has contributed to advancements in real-time operating systems and deadlock detection algorithms.

Ian C. BruceView profile

Professor

• Auditory modeling
• Hearing aids
• Cochlear implants
• +4 more

Dr. Ian Bruce is a Professor in the Department of Electrical and Computer Engineering at McMaster University, Hamilton, ON, Canada. He has been with the department since 2002, conducting interdisciplinary research that bridges electrical engineering with auditory neuroscience. His work has significant implications for hearing technologies and auditory rehabilitation. Education: B.E. (electrical and electronic) from The University of Melbourne (1991) Ph.D. from the Department of Otolaryngology, The University of Melbourne Dr. Bruce's research program focuses on auditory modeling, hearing aids, cochlear implants, tinnitus, neural coding of speech, and digital speech processing. His work centers on understanding the physiological mechanisms of auditory processing and applying this knowledge to develop improved hearing technologies. He has pioneered computational models of the auditory periphery that accurately predict speech intelligibility for hearing-impaired listeners, directly informing hearing aid and cochlear implant design. Analysis of Dr. Bruce's recent publications (2019-2025) reveals a consistent focus on cochlear implants and auditory nerve modeling, with increasing integration of machine learning techniques. His work demonstrates a sophisticated balance between physiological accuracy and computational efficiency, with recent papers exploring WaveNet-based approximations of cochlear models and DNN-based auditory processing. A significant portion of his research examines the relationship between neural responses and perceptual outcomes in hearing-impaired individuals, particularly regarding temporal processing and speech understanding. Scientific Awards and Recognitions: Fellow of the Acoustical Society of America Member of the Association for Research in Otolaryngology Registered Professional Engineer in Ontario Associate Editor of the Journal of the Acoustical Society of America Dr. Bruce has mentored numerous graduate students through various capstone design projects across multiple engineering disciplines including biomedical, electrical, mechanical, and software engineering. His teaching portfolio includes specialized courses in biomedical signals and systems, cellular bioelectricity, models of the neuron, and advanced signal processing. He has consistently supervised M.Eng. projects and independent studies, demonstrating commitment to training the next generation of engineers in auditory technology development. Dr. Bruce's research is conducted within McMaster University's interdisciplinary biomedical engineering framework, collaborating with clinicians and researchers in otolaryngology and audiology. His laboratory work focuses on developing and validating computational models that simulate auditory nerve responses to both natural and prosthetic stimulation, with direct applications to improving cochlear implant performance and hearing aid algorithms for real-world listening environments.

Matthias BucherView profile

Professor

• Analog/RF Integrated Circuits Design
• MOSFET Compact Modeling
• Wide-band Device Characterization
• +3 more

Matthias Bucher is a Professor at the Department of Electronics and Computer Engineering, Technical University of Crete. He specializes in analog/RF integrated circuits design, MOSFET compact modeling, and device characterization. His research focuses on nanoscale CMOS, wide-band semiconductor devices, and high-voltage MOSFETs. He leads the Electronics Laboratory and teaches courses such as Electronics II and CMOS Analog IC Design. Education: Ph.D. in Electrical Engineering, Swiss Federal Institute of Technology (EPFL), 1999 M.S. in Electrical Engineering, Swiss Federal Institute of Technology, 1993 Research Interests: Prof. Bucher’s work emphasizes charge-based compact models (e.g., EKV3), RF device modeling, and noise analysis in MOSFETs/JFETs. His contributions include open-source tools for Verilog-A modeling and parameter extraction methodologies for advanced CMOS technologies. Labs/Teams: He directs the Electronics Laboratory , focusing on nanoelectronics and high-reliability circuits. His team collaborates on semiconductor device modeling for aerospace and industrial applications. Grants/Awards: While not explicitly listed, his extensive publication record and leadership in open-source projects indicate sustained recognition in semiconductor research communities.

• Plasma Physics
• Laser-Plasma Acceleration
• Nanomaterials in Accelerators
• +3 more

Aakash Sahai is an Assistant Research Professor in the CEDC-Electrical Engineering department at the University of Colorado Denver - Denver Campus. His research focuses on advancing plasma physics, laser-plasma interactions, and nanoplasmonic technologies for high-energy particle acceleration. He is actively involved in designing novel accelerator concepts, such as nanostructure-based plasmonic accelerators capable of achieving extreme electric fields (PetaVolts/meter). His work bridges theoretical, computational, and experimental approaches to address challenges in high-gradient acceleration, plasma wakefields, and extreme nanoscience. Key research interests include laser-driven plasma acceleration, plasmonic field enhancement in nanostructures, and applications of particle beams in medical and high-energy physics. He collaborates on projects like the EuPRAXIA design study, aiming to develop compact, cost-efficient particle sources. His contributions span experimental setups, computational modeling, and innovative methodologies for radio transmission through plasmas and particle beam processing. Notable achievements include pioneering studies on relativistic surface plasmons, PetaVolt plasmonics, and optimizing laser-plasma interactions for proton/ion acceleration. His research has implications for next-generation accelerators, compact X-ray sources, and advanced plasma diagnostics. Sahai’s interdisciplinary approach integrates electrical engineering, material science, and high-energy physics to push the boundaries of accelerator technology. Advising and grants: No formal advisees or grant details listed. His work is supported by collaborations and institutional resources, including participation in national and international initiatives like Snowmass workshops. Labs/Teams: Active contributor to the EuPRAXIA consortium and affiliated with plasma physics and accelerator research groups at University of Colorado Denver.

• Nonlinear Optics
• Topological Photonics
• Photonic Crystals
• +3 more

Jicheng Jin is a Postdoctoral Researcher in the Department of Physics and Astronomy at the University of Pennsylvania, affiliated with the College of Arts & Sciences. His research focuses on topological photonics, nonlinear optics, and advanced photonic materials like AlScN and lithium niobate. Key interests include Floquet engineering, topological insulators of light, and novel optical devices. Research Interests: Nonlinear optical phenomena in photonic crystals Floquet topological phases and their experimental realization CMOS-compatible materials for integrated photonics Surface-emitting THz/FIR sources Bound states in the continuum and high-Q resonances Recent studies emphasize topological edge states, nonlinear frequency conversion, and device applications of 2D van der Waals heterostructures. His work bridges theoretical predictions with experimental validations in advanced photonic systems. Notable trends in publications include exploration of AlScN-based electro-optic phase shifters, Floquet Chern insulators, and geometric phase effects in magnon polaritons. No scientific awards are explicitly mentioned in the provided materials. Advising and grants remain unspecified in the current dataset. Lab affiliations include collaborations related to the Department's photonics and condensed matter physics groups.

Elsa Prada NuñezView profile

Researcher

• Condensed Matter Theory
• Quantum Materials
• Topological Insulators
• +14 more

Elsa Prada Nuñez is a Senior Researcher at the Institute of Materials Science of Madrid (ICMM) under the Spanish National Research Council (CSIC) . She leads the Quantum Dynamics of Materials (QUDYMA) group and currently serves as Head of the Theory Department. Her academic career spans multiple institutions, including the Universidad Autónoma de Madrid (UAM), Karlsruhe University, and Lancaster University, with a focus on condensed matter theory and quantum materials. PhD in Physics from UAM (2006) Tenured Scientist at ICMM-CSIC (2020-2025) Senior Researcher at ICMM-CSIC (2025-present) Her research explores quantum phenomena in low-dimensional materials and nanostructures, particularly topological insulators, Majorana zero modes in hybrid nanowires, graphene and 2D crystals, disorder effects, magnetotransport, spintronics, quantum pumping, straintronics, and exciton dynamics. She has directed over 20 students across PhD, Master's, and undergraduate levels, including notable projects on full-shell hybrid nanowires and twisted bilayer graphene. Recent publications highlight advancements in Josephson junctions, Majorana detection, and topological superconductivity in full-shell nanowires. Awards include the Young Female Scientist 2021 from the Royal Academy of Sciences of Spain and Mastercard, and the 2022 Certamen Universitario 'Arquímedes' First Award as a tutor. She has secured significant grants from the Spanish government and European collaborations for projects on quantum materials and topological superconductivity. Principal Investigator for €139,150 Spanish government grant (PID2021-125343NB-I00) Lead on €5,000 ICMM-CSIC grant (2020-2021) Participant in €3.48M EU AndQC project (2019-2023)

Jeff ZhangView profile

Assistant Professor

• Deep learning
• Computer architecture
• Embedded systems
• +5 more

Jeff Zhang is an Assistant Professor in the School of Electrical, Computer and Energy Engineering at Arizona State University. He joined ASU in January 2023 after completing a postdoctoral fellowship at Harvard University. His research spans deep learning, computer architecture, embedded systems, and EDA, with particular emphasis on energy-efficient and fault-tolerant design for AI/ML systems and hardware accelerators. Education: Ph.D., New York University M.Eng., B.Eng., Hunan University Dr. Zhang's research bridges theoretical machine learning with practical hardware implementation, developing novel architectures that optimize performance, power consumption, and reliability. He has pioneered approaches for hardware acceleration of large language models, efficient sparse matrix operations, and novel memory technologies for AI workloads. His work has received multiple awards including IEEE Top Picks in Test and Reliability (2023) and IEEE Micro Best Paper Award (2022). His recent publications demonstrate a strong trend toward heterogeneous computing, with significant work in chiplet-based AI accelerators, photonic computing for AI, and 2.5D/3D integration techniques. The research spans from high-level compiler frameworks to circuit-level innovations, with a consistent theme of co-designing algorithms and hardware for optimal AI performance. His work on the SODA toolchain has been particularly influential in bridging Python to silicon. Scientific Awards: IEEE Top Picks in Test and Reliability, IEEE ITC, 2023 Best Paper Award, IEEE Micro, 2022 Best Paper Award Candidate, IEEE DATE, 2022 Best Presentation Award Nomination, ACM SIGDA DATE PhD Forum, 2020 Best Paper Award Nomination, IEEE VLSI Test Symposium, 2018 Ernst Weber Ph.D. Fellowship, New York University, 2015, 2016 Dr. Zhang actively mentors a diverse group of graduate and undergraduate students, with several alumni now working at leading technology companies including Apple, TSMC, and Ansys. His research is supported by prestigious grants from NSF, Sandia National Labs, and industry partners. He serves on technical program committees of numerous top conferences and has organized special sessions on emerging topics like Gen AI for Chip Design and LLM-Aided Design. Dr. Zhang leads a vibrant research group that collaborates extensively with industry partners and national laboratories. Current projects focus on next-generation AI hardware, including chiplet-based systems, photonic accelerators, and novel memory technologies for large language models. His group has developed several open-source tools and frameworks, including the SODA toolchain for bridging Python to silicon.

Qin LiView profile

Associate Professor

• Numerical Analysis
• Scientific Computing
• Inverse Problems
• +8 more

Qin Li is an Associate Professor in the Mathematics Department at the University of Wisconsin-Madison. She holds affiliations with the Wisconsin Institutes for Discovery and serves as a senior PI at the Institute for Foundations of Data Science. Her research focuses on numerical analysis, scientific computing, and inverse problems, with a strong emphasis on kinetic theory and multiscale PDEs. Her work spans computational methods for inverse transport and radiative transfer equations, Bayesian approaches in optical tomography, and optimization techniques for solving stochastic and deterministic PDEs. Recent publications highlight applications of diffusion models, Wasserstein gradient flow, and random sampling in inverse problems, as well as control theory for Vlasov-Poisson systems and reconstruction of chemotaxis kernels. She leads a research group within the Mathematics Department and has received funding from the National Science Foundation (NSF), the Office of Naval Research (ONR), and the Wisconsin Alumni Research Foundation (WARF). Her lab, Kinetic At Madison, explores nonlinear hyperbolic PDEs and their applications. She also contributes to teaching as a TA Supervisor.

Marco BernardiView profile

Professor

• Theoretical Materials Physics
• Computational Materials Science
• Electron Transport
• +7 more

Marco Bernardi is a Professor of Applied Physics, Physics and Materials Science at the California Institute of Technology (Caltech). His research focuses on theoretical and computational materials physics , developing first-principles methods to investigate electron transport, ultrafast dynamics, and light-matter interactions in materials. His work has applications in electronics, optoelectronics, ultrafast spectroscopy, energy technologies, and quantum devices. Education : Ph.D. in Materials Science from MIT (2013), M.S. from University of Rome Tor Vergata (2008), B.S. from University of Rome La Sapienza (2004). Research Interests : Electron-phonon interactions, polarons, spin relaxation and decoherence, nonequilibrium electron dynamics, quantum materials, and software development for materials simulations ( PERTURBO code). Scientific Awards : NSF CAREER Award (2018) AFOSR Young Investigator Award (2017) Psi-K Volker Heine Young Investigator Award (2015) Intel Ph.D. Fellowship (2013) Franco Strazzabosco Award (2020) Teaching : Offers graduate courses at Caltech including Structure and Bonding in Materials (MS 131) , Computational Solid State Physics (APh/MS 256) , and Introduction to Computational Methods (APh/MS 141) . Group Members : Mentors current graduate students and postdocs in developing advanced computational techniques for materials research, with former advisees now in academic and industry positions.

• Semiconductor Materials
• Laser Crystallization
• Phase Transformations
• +4 more

Professor James Im serves as Professor of Materials Science in the Departments of Earth and Environmental Engineering and Applied Physics and Applied Mathematics at Columbia University, with an office at 1106 S.W. Mudd (Mail Code 4701). His academic career spans over three decades at Columbia, where he progressed from Assistant Professor (1991-1994) to Associate Professor (1995-2002), and ultimately to full Professor (2002-present), including a tenure as Chair of the Materials Science and Engineering Program (2002-2014). His educational background includes a B.S. with Distinction in Materials Science from Cornell University (1984) and a Ph.D. in Electronic Materials from MIT (1989), followed by postdoctoral research at Caltech (1989-1991). Cornell University: B.S. Materials Science (1984) MIT: Ph.D. Electronic Materials (1989) Caltech: Postdoctoral Scholar (1989-1991) Im's research centers on ultra-rapid phase transitions in beam-irradiated thin films, specifically focusing on laser crystallization of silicon films , energy-beam-induced melting and solidification , and nucleation in discontinuous phase transitions . His work employs experimental, computational, and theoretical approaches to develop innovative semiconductor materials for advanced displays, solar cells, and integrated circuits. Notably, his invention of Sequential Lateral Solidification (SLS) technology has been licensed to major display manufacturers (Samsung, LG, Sharp) and implemented in products by Apple, Blackberry, and Nokia. Current research focuses on advancing the Spot-Beam Crystallization (SBC) platform using fiber lasers for next-generation microelectronics. His publication record spans environmental aerosol studies (2019-2024), oilfield operations technology (2002-2014), and foundational atmospheric research (1980s), reflecting interdisciplinary expertise bridging materials science, environmental engineering, and petroleum technology. The most recent works emphasize low-cost sensor development and aerosol monitoring. Professional recognition includes membership in prestigious societies: Bohmisch Physical Society Sigma Xi Alpha Sigma Mu Materials Research Society American Physical Society Im's research group maintains strong industry connections through technology licensing and collaborative projects, particularly in display manufacturing. His leadership as former department chair demonstrates administrative commitment alongside scientific innovation. The laboratory leverages state-of-the-art laser systems and beam delivery optics for materials development, with recent focus shifting toward environmental monitoring applications while maintaining core semiconductor research.

Rodrigo FreitasView profile

Professor

• Materials Science
• Computational Materials Design
• Machine Learning in Materials
• +5 more

Professor Rodrigo Freitas holds the TDK Professorship in Materials Science and Engineering at MIT. His research focuses on computational materials design, bridging atomistic simulations with mesoscale microstructural analysis. He leads the Freitas Research Group, specializing in machine learning-driven modeling of materials kinetics and solidification processes. Education: B.S. and M.S. in Physics, University of Campinas, Brazil M.S. and Ph.D. in Materials Science & Engineering, UC Berkeley Research Interests: Professor Freitas investigates microstructural evolution in metals and alloys using advanced computational methods. Key areas include solidification mechanisms, interstitial atom behavior in superalloys, and machine learning applications for materials discovery. His work emphasizes bridging atomistic and mesoscale phenomena to guide industrial applications like semiconductor manufacturing and battery design. Publications Trend: Recent work emphasizes machine learning potentials for alloy modeling, short-range order analysis in high-entropy alloys, and kinetic modeling of complex chemical systems. Themes include alloy phase stability, defect dynamics, and data-driven materials discovery. Labs/Teams: Leads the Freitas Research Group at MIT, which develops novel computational tools for materials engineering.