جست‌وجوی استاد و پژوهشگر

Massimiliano Di Ventraمشاهده پروفایل

استاد

• Quantum Transport
• Nanoscale Systems
• Memristive Systems
• +۲ مورد دیگر

Massimiliano Di Ventra is Professor of Physics at UC San Diego, specializing in theoretical nanoscale physics. He received his PhD from École Polytechnique Fédérale de Lausanne (1997) and has pioneered research in memristive systems and quantum transport. His honors include the 2020 Feynman Prize in Nanotechnology and election as Foreign Member of the Academia Europaea. Research areas: Theory of quantum transport in nanoscale systems Development of memristors, memcapacitors and meminductors DNA sequencing via quantum tunneling in nanopores Non-equilibrium statistical mechanics Recent work focuses on memory effects in nanostructures for unconventional computing applications. His group demonstrated associative memory in memristive neural networks and developed stochastic methods for quantum molecular dynamics. Publications show consistent focus on bridging fundamental physics with nanotechnology applications. He has proposed novel approaches to DNA sequencing using quantum tunneling and investigates non-equilibrium phenomena in nanoscale systems.

Kai Liuمشاهده پروفایل

استاد

• Nanomagnetism
• Spintronics
• Nanostructured Materials
• +۷ مورد دیگر

Prof. Kai Liu is a Professor and Robert L. McDevitt Chair in Physics at Georgetown University. He holds a Ph.D. from Johns Hopkins University (1998) and transitioned to Georgetown after serving as faculty at UC Davis from 2001 to 2018, where he became a Full Professor in 2008. His research focuses on nanomagnetism, spintronics, and nanostructured materials, with emphasis on topological spin textures like skyrmions, magneto-ionics, and 3D magnetic networks. Key achievements include advancements in high entropy materials, hydrogen storage foams, and neuromorphic computing devices. He is a Fellow of multiple prestigious institutions, including APS, IEEE, and the National Academy of Inventors. Education: Ph.D. in Physics (Johns Hopkins, 1998); Postdoctoral Research (UC San Diego). Professional Roles: Chair of IUPAP Commission on Magnetism (2021–24); Former Program Co-Chair for MMM 2007 and Intermag 2011. Research Labs: GNuLab, Georgetown Institute for Soft Matter. Research Interests: Synthesis and characterization of nanostructured materials, including magnetic nanowire foams for filtration, magneto-ionic devices, and skyrmion-based spintronics. His work bridges fundamental physics with applications in energy-efficient electronics and neuromorphic computing. Awards: Alfred P. Sloan Fellowship (2005), UC Davis Chancellor’s Fellowship (2007), and multiple Fellowships (IoP, APS, IEEE, AAAS, NAI). Key contributions include room-temperature skyrmions and tunable magnetic anisotropy in thin films. Grants/Advising: Leads NSF-funded projects on magneto-ionics and nanowire networks; advises students in experimental condensed matter physics. Collaborates internationally on magnetic materials and device applications.

Gen Yinمشاهده پروفایل

استادیار

• Spintronics
• Topological Materials
• Condensed Matter Physics
• +۳ مورد دیگر

Prof. Gen Yin is an Assistant Professor in the Department of Physics at Georgetown University, part of the College of Arts & Sciences. He holds a Ph.D. in Electrical and Computer Engineering from the University of California, Riverside (2015), followed by postdoctoral research at UCLA (2016–2020) as a postdoctoral researcher and Assistant Project Scientist. His research focuses on theoretical and computational studies of charge, spin, and heat transport in solid-state materials, emphasizing topology, spin structures, and spin-orbit coupling. Key projects include van der Waals spintronics, topological insulators, antiferromagnetic spintronics, and AI-driven physics research. His group develops tools like density functional theory, machine learning, and micromagnetic simulations, collaborating closely with experimentalists. Research interests span transport theory, topological magnetism, quantum phenomena, and AI-assisted material discovery. Recent work includes studies on quantum anomalous Hall effects, Berry phase modulation, and high-entropy alloys. His team explores applications in next-generation devices, such as neuromorphic computing and magnetic memory. Publications highlight advancements in topological materials, machine learning for magnetometry, and nanowire networks. Collaborators include institutions like UCLA, MIT, and national labs. Advising includes Ph.D. students and postdocs focusing on computational and experimental physics. Labs are based in the Reiss Science Building at Georgetown.

• Machine Learning
• Deep Learning
• Neural Networks
• +۳ مورد دیگر

Claudio Gallicchio is an Associate Professor of Deep Learning at the Department of Computer Science, University of Pisa, Italy. His research focuses on Machine Learning, Deep Learning, Neural Networks, and Dynamical Systems, with specialization in Dynamical Neural Networks, Reservoir Computing, and Graph Learning. He actively contributes to IEEE Task Forces on Reservoir Computing and Randomized Neural Networks, and is a member of several AI societies including AAAI and ENNS. He holds a Ph.D. in Computer Science from the University of Pisa. His work emphasizes addressing challenges in graph neural networks—such as oversquashing—and advancing energy-efficient neural architectures using memristive systems. He supervises students on topics like Recurrent Neural Networks, Neural ODEs, and Deep Learning for Digital Humanities. Gallicchio's recent publications explore Reservoir Computing applications in industrial processes, non-dissipative graph networks, and federated learning. His research bridges theoretical foundations (e.g., Hamiltonian architectural bias) with practical implementations (e.g., memristor-based models). He has pioneered frameworks like GRAMA for adaptive graph time-series analysis and the TEACHING platform for human-centric autonomous systems.

Sunil Khatriمشاهده پروفایل

استاد

• VLSI Design
• Secure Computing Systems
• Machine Learning Hardware
• +۷ مورد دیگر

Sunil Khatri is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University, affiliated with the College of Engineering and the Computer Engineering and Systems Group. He holds a Ph.D. from the University of California, Berkeley (1999), an M.S. from the University of Texas at Austin (1989), and a B.S. from the Indian Institute of Technology Kanpur (1987). His academic progression includes roles as Assistant Professor (2000–2004) at the University of Colorado Boulder and Associate Professor (2010–2015) at Texas A&M. Educational Background: Ph.D., University of California, Berkeley, 1999 M.S., University of Texas at Austin, 1989 B.S., Indian Institute of Technology Kanpur, 1987 Research Interests: Intelligent and Secure Computing Systems: Focused on hardware architectures for machine learning (e.g., neuromorphic circuits, NoCs, sub-threshold circuits) and secure hardware design (blockchain, physically unclonable functions). Logic Synthesis and Applications: Includes VLSI CAD, gene regulatory network modeling, noise-based logic, and Boolean satisfiability (SAT) solvers. Interdisciplinary Extensions: Explores routing table compression, radar signal processors, optical networking (DWDM), and LDPC decoders. Research Trends in Publications: Recent work emphasizes neuromorphic hardware acceleration (e.g., CapsPDNet for insulator discharge prediction, FFIR filters for IoT), secure protocols (multi-factor authentication frameworks), and energy-efficient circuits (flash-based designs, low-voltage regulators). His contributions span VLSI, machine learning, and cybersecurity, often leveraging FPGA/GPU acceleration. Grants & Labs: Leads projects in the Computer Engineering and Systems Group, focusing on secure computing, machine learning hardware, and interdisciplinary VLSI applications. Active in grant-driven research with a focus on practical implementations in industry.

Anxiao Jiangمشاهده پروفایل

استاد

• Information Theory
• Coding for Flash Memories
• Algorithms
• +۳ مورد دیگر

Prof. Anxiao (Andrew) Jiang is a Professor in the Department of Computer Science & Engineering at Texas A&M University, with a courtesy appointment in Electrical and Computer Engineering. He holds positions in both departments and has affiliations with research groups focused on coding theory and data storage systems. His research spans information theory, coding for non-volatile memories, algorithms, machine learning, and wireless networks. Ph.D. in Electrical Engineering from Caltech (2004) M.S. in Electrical Engineering from Caltech (2000) B.Sc. in Electronic Engineering from Tsinghua University (1999) His research interests emphasize coding for flash memories, error correction, and machine learning applications. Key contributions include innovations in rank modulation, trajectory codes, and joint source-channel decoding. Recent work explores deep learning and analog computing error mitigation. Publications highlight advancements in error-correcting codes, secure storage systems, and AI-driven data analysis. His work on fingerprint denoising won 2nd place in the ECCV 2018 competition. Awards include the NSF CAREER Award (2008) and IEEE Best Paper Award (2009). Jiang advises numerous graduate and undergraduate students, focusing on coding theory, machine learning, and storage systems. His lab develops practical solutions for next-generation memory technologies and robust neural networks.

Eun Jung Kimمشاهده پروفایل

استاد

• Computer Architecture
• Power Efficient Systems
• Parallel/Distributed Systems
• +۳ مورد دیگر

Eun Jung Kim is a Professor in the Department of Computer Science and Engineering at Texas A&M University, affiliated with the College of Engineering. Her research focuses on computer architecture, power-efficient systems, parallel/distributed systems, cluster computing, performance evaluation, and fault-tolerant computing. Ph.D.: Computer Science and Engineering, Pennsylvania State University (2003) M.S.: Computer Science and Engineering, Pohang University of Science and Technology (1994) B.S.: Computer Science, Korea Advanced Institute of Science and Technology (1992) Her work explores innovative solutions for optimizing hardware and software systems, including energy-efficient interconnects, security enhancements, and adaptive network-on-chip designs. Notable achievements include the NSF Early CAREER Award (2009) and contributions to publications like IEEE Transactions on Parallel and Distributed Systems . Recent research trends include advancements in near-data processing, secure computing abstractions (e.g., WHISTLE), and mitigating hardware vulnerabilities (e.g., cache timing attacks). She also integrates IoT and low-cost cluster systems for environmental research applications.

• Nanotechnology
• Biosensors
• Materials Science
• +۳ مورد دیگر

Minhee Yun is a Professor affiliated with the Swanson School of Engineering at the University of Pittsburgh . Her research focuses on nanotechnology, biosensors, and advanced materials science, with a particular emphasis on graphene-based devices and conductive polymer applications. She collaborates with the Young Hee Lee lab at Sungkyunkwan University, Suwon, Korea, and has pioneered innovative fabrication techniques for nanowire sensors and flexible electronics. Education highlights include the Micron Fellowship (1999) and ERI Graduate Fellowship (1995) from Arizona State University, along with a Scholarship for Studying Abroad from Chonbuk National University (1994). Her research interests span Biosensor development for medical diagnostics Graphene and 2D material integration into electronic systems Optoelectronic modulation of nanomaterials like MoS₂ Hybrid sensor arrays for VOC/toxic gas detection Mechanisms of resistive switching in perovskite and TiO₂ films Advanced fabrication techniques for nanoscale devices Awards include multiple NASA Tech Brief Awards (2007, 2005, 2004), the Jet Propulsion Laboratory Team Leader Award (2003), and the Korea Brain Pool Scholar (2011). Her work emphasizes practical applications in healthcare, energy systems, and electronic devices. As a Principal Investigator (PI), she has led projects such as the Planck Project and collaborates on grants involving microbial fuel cells and bolometer arrays. Her labs specialize in Microfluidic biosensor integration Graphene transfer implementations Electrochemical nanowire sensor arrays and maintain active partnerships with institutions globally.

• 2D Materials
• Ion Gating
• Nanoelectronics
• +۴ مورد دیگر

Susan Fullerton is an Associate Professor and Bicentennial Board of Visitors Faculty Fellow in the Department of Chemical and Petroleum Engineering at the University of Pittsburgh's Swanson School of Engineering. She also serves as Vice Chair of Education. Her research focuses on nanoionics and electronics, particularly ion transport in 2D materials and polymer electrolytes for advanced device applications. Fullerton holds a PhD from Penn State (2009) and previously served as a Research Assistant Professor at Notre Dame's Department of Electrical Engineering before founding Pitt's Nanoionics and Electronics Lab in 2015. She has received multiple prestigious awards including the NSF CAREER Award, Alfred P. Sloan Fellowship, and AAAS Marion Milligan Mason Award. Her teaching excellence was recognized with the 2018 James Pommersheim Award. Research interests include phase transitions in 2D materials, hardware security via ion-locked polymers, and sustainable polymer design. Education: PhD in Chemical Engineering, Pennsylvania State University, 2009 Key Research Areas: Ion gating in 2D materials Non-volatile memory devices Triggerable polymer degradation Hardware security through ion locking Neuromorphic computing architectures Awards: Alfred P. Sloan Research Fellowship (2020) AAAS Marion Milligan Mason Award (2019) NSF CAREER Award (2018) James Pommersheim Teaching Award (2018) Fullerton's lab develops novel materials for electronics and security applications, including monolayer electrolytes and phase-change polymers. She leads efforts to improve STEM education through personalized learning models and diversity initiatives. Current projects explore ion-mediated semiconductor transitions and scalable nanomaterial fabrication techniques. Labs/Teams: Nanoionics and Electronics Lab (established 2015), interdisciplinary collaborations in nanomaterials engineering and educational innovation.

Mohamed Zahranمشاهده پروفایل

استاد بالینی

• Parallel computing
• Heterogeneous architectures
• Hardware/Software interaction
• +۷ مورد دیگر

Mohamed Zahran is a Clinical Professor of Computer Science at New York University's Courant Institute of Mathematical Sciences. He holds a Ph.D. in Electrical and Computer Engineering from the University of Maryland (2003) and has extensive experience in academia and industry, including roles at CUNY and The George Washington University. His research focuses on computer architecture, heterogeneous systems, parallel computing, and AI-driven hardware optimization. Education: Ph.D., Electrical and Computer Engineering, University of Maryland (2003); M.Sc., Cairo University (1999); B.Sc., Cairo University (1997). Research interests include exascale computing, GPU architectures, cache hierarchy design, and AI support for architecture. He has authored over 40 refereed papers and a book on heterogeneous computing. Awards include ACM Distinguished Speaker (2019–2025) and IEEE Distinguished Contributor (2021). Teaching: Courses on multicore processors, GPUs, parallel computing, and compilers. Advised numerous graduate and undergraduate students, including notable achievements in student research competitions.

• Theoretical Physics
• Quantum Optics
• Cavity Optomechanics
• +۱۲ مورد دیگر

Florian Marquardt is the Scientific Director and Head of the Theory Division at the Max Planck Institute for the Science of Light in Erlangen, Germany, and holds a part-time professorship at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). He leads a research group focused on the intersection of quantum physics, optics, and machine learning, contributing to foundational and applied advances in quantum technologies. His educational background includes a PhD summa cum laude from the University of Basel (2002), followed by postdoctoral research at Yale University and in the Swiss National Center for Competence in Research. He served as a Junior Professor at LMU Munich (2005–2010) and Full Professor at FAU (2010–2016) before joining MPL. Marquardt's research spans cavity optomechanics , quantum many-body physics , neuromorphic computing , and machine learning for physics . His group develops minimalist theoretical models to explain experimental phenomena and explores how AI can automate scientific discovery. He is particularly interested in energy-efficient analog computing devices inspired by neural networks and in leveraging physics to improve machine learning. The recent publications reflect a strong trend toward integrating artificial intelligence with quantum science, including automated experiment design, quantum error correction via neural networks, and neuromorphic photonics. There is also sustained work in quantum measurement, topological photonics, and nonequilibrium dynamics, showing both breadth and depth in theoretical physics. Emmy-Noether Fellowship (DFG, 2007) Walter Schottky Prize (DPG, 2009) ERC Starting Grant (2011) Marquardt actively supervises research and teaches advanced courses such as 'Machine Learning for Physicists' and 'Foundations of Quantum Mechanics' at FAU. His group participates in major research networks including the Max Planck School of Photonics, ELLIS, and the European Laboratory for Learning and Intelligent Systems. The Theory Division fosters collaborations on quantum technologies and neuromorphic systems, with strong emphasis on interpretability, generalization, and hardware implementation.

Duygu Kuzumمشاهده پروفایل

استاد

• Nanoelectronics
• Neuroelectronics
• Brain-inspired Computing
• +۵ مورد دیگر

Professor Duygu Kuzum is a faculty member in the Department of Electrical and Computer Engineering at the University of California San Diego. Her research focuses on nanoelectronic devices for brain-inspired computing, neural interfaces, and clinical neurodevices. She has developed synaptic devices emulating brain computation and transparent neural implants for high-resolution brain monitoring. Kuzum holds the Joan and Irwin Jacobs-Kavli Foundation Chancellor's Endowed Faculty Fellowship and has been recognized with awards including MIT TR35, Penn Neuroscience Innovation Award, and Texas Instruments Fellowship. Her lab, the UCSD Neuroelectronics Lab, pioneers neurotechnologies combining nanoelectronics with neuroscience to advance understanding of brain circuits and develop next-generation neural interfaces. Education: PhD in Electrical Engineering (Stanford University, 2010), Postdoc in Bioengineering (University of Pennsylvania, 2011-2015), B.S. in Electrical Engineering (Bilkent University, 2004). Research interests include neuromorphic computing, neural interface design, and bio-plausible learning systems. Key innovations include filament-free RRAM for energy-efficient neural networks, graphene-based transparent electrodes, and bioresorbable neural implants. Recent work explores functional integration of cortical organoids with host brain circuits using multimodal monitoring. Grants and Awards: NIH New Innovator Award (2020), multiple industry and foundation fellowships. Her lab collaborates on projects like E-organoid systems and closed-loop optogenetic systems. Labs/Teams: Director of UCSD Neuroelectronics Lab, affiliated with Kavli Institute for Brain and Mind. Active in developing transparent neural probes and neuromorphic brain interfaces.

Ivan K. Schullerمشاهده پروفایل

استاد

• Condensed Matter Physics
• Materials Science
• Nanotechnology
• +۵ مورد دیگر

Prof. Ivan K. Schuller is a Physics Professor at the University of California, San Diego, leading the Calit2 Institute's Materials and Devices division and directing the AFOSR-MURI on Integrated Nanosensors. He holds a Licenciado from the University of Chile, M.S. and Ph.D. from Northwestern University, and has been at UCSD since 1987. His research focuses on novel magnetic devices, superlattices, nanostructured magnetism, and phase transitions in materials like VO₂ and V₂O₃. He pioneered studies on metallic superlattices, heterostructures, and nanostructured materials, exploring connections between structure and properties (e.g., electrical transport, magnetism, superconductivity). Research Highlights Developing phase-change materials for neuromorphic computing and thermal management Advancing understanding of metal-insulator transitions in vanadium oxides Investigating spintronics in correlated electron systems Pioneering optical and electrical characterization techniques for nanomaterials Awards & Honors DOE Outstanding Scientific Accomplishment (1987) APS Wheatley Award (1999) Alexander von Humboldt Prize (2000) APS Adler Award (2003) MRS Medal (2003) Grants & Leadership Director of AFOSR-MURI on Integrated Nanosensors at UCSD. Previously a Senior Physicist and Group Leader at Argonne National Laboratory. Leads interdisciplinary projects merging materials science with neuromorphic computing and thermal engineering. Labs & Collaborations Thin Film Group at UCSD Physics Department. Collaborations with institutions globally, including Leuven Catholic University and Aachen Technical University.

Alex Fuerbachمشاهده پروفایل

استاد

• Mid-infrared laser sources
• Ultrafast lasers
• Integrated chip lasers
• +۴ مورد دیگر

Alex Fuerbach serves as Professor and Associate Dean for Research Training and Performance at Macquarie University's Faculty of Science and Engineering, where he leads the MQ Photonics Research Centre. His academic career spans over two decades with 219 research outputs and 36 projects to his name, including current leadership in hydrogen-boron fusion research and ultrafast mid-infrared laser development. His research focuses on mid-infrared laser sources , ultrafast lasers , integrated chip lasers , photonic device fabrication , and supercontinuum generation . His work bridges fundamental photonics with practical applications in sensing, communications, and energy research. The fingerprint analysis of his work reveals strong connections to waveguide technology (100%), fiber lasers (65%), fiber Bragg gratings (53%), and femtosecond laser systems (48%). Fuerbach's recent publications demonstrate significant contributions to chalcogenide-glass waveguide arrays, photorefractive reservoir computing, and neuromorphic photonic systems. His research trajectory shows increasing focus on mid-infrared photonics and photonic computing architectures, with 8 publications in 2024 and 5 already published in 2025. Key collaborations include work with Jackson on ultrafast laser systems and Batani on fusion research. His leadership extends beyond research through his Associate Dean role focusing on research training. Current projects include: LP22 UNSW Led: Towards non-thermal hydrogen-boron fusion (2023-2026) Pushing the limits: producing optimally short pulses at long wavelengths (2017-present) Ultrafast mid-infrared fiber laser systems (2016-present) At the MQ Photonics Research Centre, Fuerbach directs a multidisciplinary team working on advanced photonic materials and devices, with particular emphasis on translating fundamental research into practical applications for communications, sensing, and energy sectors.

Gourav Dattaمشاهده پروفایل

استادیار

• In-Sensor Computing
• In-Memory Computing
• Neuromorphic Computing
• +۶ مورد دیگر

Gourav Datta is an Assistant Professor in the Department of Electrical, Computer, and Systems Engineering at Case Western Reserve University (CWRU). He holds a PhD from the University of Southern California (USC) and a B.Tech. from IIT Kharagpur. His research focuses on energy-efficient algorithm-hardware co-design for machine learning at the edge, with specializations in In-Sensor Computing, Neuromorphic Computing, and Embedded Machine Learning. Prior to CWRU, he worked as an Applied Scientist at Amazon AGI, contributing to Amazon Nova's video understanding capabilities. Education: PhD in Electrical Engineering, USC (2023) B.Tech. in Instrumentation Engineering, IIT Kharagpur (2018) Research Interests: Energy-efficient computer vision and multimodal deep learning In-sensor computing and neuromorphic architectures Efficient model deployments on FPGAs/microcontrollers Multi-modal KV cache compression and retrieval-augmented generation Awards: 2024 William Ballhaus Best PhD Dissertation Award 2023 USC ECE Best RA Award 2022 Qualcomm Innovation Fellowship finalist 2018 USC Annenberg Fellowship Academic Service: Program committee member for ISQED, VLSID, DAC (2024–2025) Reviewer for IEEE journals and conferences including ICASSP, ICLR, and WACV Labs/Teams: Leads research on edge computing and neuromorphic systems at CWRU's ECSE department, collaborating with industry partners like Edge Impulse.