جست‌وجوی استاد و پژوهشگر

Aidong Zhangمشاهده پروفایل

استاد

• Machine Learning
• Data Mining
• Bioinformatics
• +۳ مورد دیگر

Aidong Zhang is the Thomas M. Linville Professor of Computer Science at the University of Virginia, with joint appointments in Biomedical Engineering and the School of Data Science. Her research focuses on machine learning, interpretable AI, federated learning, and generative AI applications in healthcare and bioinformatics. She holds a Ph.D. in Computer Science from Purdue University. Dr. Zhang has been honored with prestigious awards including the ACM Fellow (2017), IEEE Fellow (2009), and the 2025 Distinguished Researcher Award from UVA. Her work bridges computational methods with biomedical challenges, emphasizing fairness, robustness, and explainability in AI systems. Key research areas include federated learning frameworks, concept-based models, and large language models for scientific hypothesis generation. Dr. Zhang leads a lab offering PhD positions in machine learning, bioinformatics, and health informatics. Notable grants include NSF projects on explainable AI platforms and hardware-software co-design for extreme-scale machine learning. Education: Ph.D., Computer Science, Purdue University Affiliations: School of Engineering and Applied Science, School of Data Science Grants: NSF-funded projects on federated learning, multimodal analysis, and biomedical AI Labs/Teams: Zhang's Research Group focusing on interpretable machine learning and healthcare applications

Abdol-Hossein Esfahanianمشاهده پروفایل

استاد

• Graph Theory
• Computer Networks
• Algorithm Design
• +۳ مورد دیگر

Abdol-Hossein Esfahanian is a Professor and Chairperson of the Computer Science and Engineering (CSE) Department at Michigan State University (MSU), part of the College of Engineering. He joined MSU in 1983 and has held leadership roles, including Graduate Director for 10 years and Associate Chair. His research focuses on applying graph theory to computer networks, algorithm design, and fault-tolerant computing. He has published extensively in journals like IEEE Transactions on Computers and Discrete Applied Mathematics, and serves as an editor for professional journals. Education: Ph.D. in Electrical Engineering and Computer Science from Northwestern University (1983), M.S. in Computer, Information, and Control Engineering from the University of Michigan (1977), and B.S. in Electrical Engineering from the University of Michigan (1975). Research interests include graph theory applications in network design, distributed systems, and fairness-aware algorithms. Notable awards include the Withrow Teaching Excellence Award (2005, 2015) and recognition as an IEEE Senior Lifetime Member. Teaching includes courses like CSE 835 (Algorithmic Graph Theory). He has contributed to curriculum development, emphasizing computational competencies for engineering students. His work integrates theoretical foundations with practical applications in networking and distributed systems.

Michael J. Spiveyمشاهده پروفایل

استاد

• Psycholinguistics
• Visual Perception
• Sensorimotor Processing
• +۵ مورد دیگر

Michael J. Spivey is a Professor of Cognitive Science at the University of California, Merced, affiliated with the School of Social Sciences, Humanities and Arts. His research focuses on understanding human cognition through embodied and dynamic systems approaches, with particular expertise in eye-tracking methodologies and real-time language processing. As a faculty member in the Cognitive and Information Sciences department, he contributes to interdisciplinary research that bridges psychology, linguistics, and neuroscience. Dr. Spivey's educational background includes: Ph.D. in Brain and Cognitive Sciences from the University of Rochester (1996) M.A. in Brain and Cognitive Sciences from the University of Rochester (1995) B.A. from the University of California, Santa Cruz (1991) His research interests span psycholinguistics, visual perception, sensorimotor processing, embodied cognition, and dynamical systems theory. Dr. Spivey investigates how cognitive processes unfold in real-time through eye movements and other behavioral measures, challenging traditional modular views of cognition. His work emphasizes the continuous interaction between perception, action, and cognition, demonstrating how language processing is deeply embedded in our sensorimotor experiences. He has made significant contributions to understanding the time course of language comprehension, the role of visual context in linguistic processing, and the dynamic nature of cognitive representations. Analysis of Dr. Spivey's recent publications reveals a strong focus on embodied and dynamic approaches to cognition. His work spans diverse areas including eye-tracking and mouse-tracking methodologies, team cognition, creativity research, bilingual language processing, and the application of foraging theory to cognitive processes. A recurring theme across these publications is the investigation of real-time cognitive dynamics using continuous behavioral measures. His research increasingly incorporates ecological approaches, examining cognition in more naturalistic contexts while maintaining experimental rigor. The interdisciplinary nature of his work is evident in collaborations across psychology, linguistics, neuroscience, and even music cognition. Dr. Spivey is affiliated with the Center for Human Adaptive Systems and Environments and collaborates with researchers at Western University, Northwestern University, Brown University, and UCLA. His work with students likely focuses on training in advanced methodologies for measuring real-time cognitive processes.

Vladimir Zhdankinمشاهده پروفایل

استادیار

• plasma physics
• astrophysics
• turbulence
• +۹ مورد دیگر

Vladimir Zhdankin is an Assistant Professor of Physics at the University of Wisconsin-Madison , where he leads the Zhdankin Group. He received his Ph.D. and B.S. in Physics from UW-Madison in 2015 and 2011, respectively. His career includes postdoctoral appointments as a NASA Einstein Postdoctoral Fellow (2018-2021) and Flatiron Research Fellow (2021-2023). Research Interests : Theoretical and computational plasma physics Relativistic plasma turbulence and instabilities Nonthermal particle acceleration and radiative processes Nonequilibrium statistical mechanics of collisionless plasmas Coherent structures in astrophysical systems Scientific Awards : NASA Einstein Postdoctoral Fellowship Flatiron Research Fellowship Advising & Collaborations : Current group members: Braden Buck, Miguel Castelan Tanner, Petr Ugarov, Cristian Vega (joint with Prof. Rogerio Jorge), Louis Henderson Collaborators: Dmitri Uzdensky, Matthew Kunz, Alexander Philippov, Stanislav Boldyrev

• rhetorical theory
• history of rhetoric
• digital rhetorics
• +۱۲ مورد دیگر

David Blakesley is Professor of Rhetorics, Communication, and Information Design and Campbell Chair in Technical Communication at Clemson University's College of Arts and Humanities, Department of Interdisciplinary Studies. He currently serves as President of the Faculty Senate for 2023-2024. His academic career spans over three decades with significant contributions to rhetorical theory, digital publishing, and technical communication. Blakesley earned his PhD in Rhetoric, Linguistics, and Literature from the University of Southern California (1990), an MA in English from San Diego State University (1986), and a BA in English from the same institution (1983). His research spans multiple interconnected fields including rhetorical theory, history of rhetoric, digital and visual rhetorics, print and digital publishing, film theory and production, technical communication, and digital humanities. He has focused particularly on Kenneth Burke's rhetorical theory and its applications across various media. His scholarly work demonstrates a consistent trajectory from traditional rhetorical theory toward digital applications and publishing innovations. Over the past decade, his research has increasingly focused on the intersection of digital technologies with rhetorical practices, particularly examining how digital platforms transform scholarly communication and publishing. His 2022 TUGboat article on production versus distribution in publishing exemplifies his ongoing interest in how technological shifts impact academic and professional communication. Fellow, Rhetoric Society of America George Yoos Distinguished Service Award (RSA) Charles Moran Award for Distinguished Contributions to the Field of Computers and Composition Blakesley has secured significant funding including the $11 million Clemson-Adobe Partnership for Campus-Wide Creative Cloud implementation. He serves as Editor of KB Journal, Co-Editor of WAC Journal, and General Editor of The Writing Instructor. His editorial work has been instrumental in shaping scholarly communication in rhetoric and composition studies, particularly through his leadership in digital publishing initiatives.

• Topology of Complex Systems
• Higher-order Networks
• Topological Neuroscience
• +۷ مورد دیگر

Giovanni Petri is a Professor in the Network Science Institute at Northeastern University London, where he joined in June 2023. Previously, he held positions at CENTAI as a Principal Researcher and at IMT Lucca as a Guest Scholar, with earlier affiliations at ISI Foundation and Imperial College London. His educational background includes a PhD in Complex Networks from Imperial College London (2012), an MSc in Theoretical Physics from the University of Pisa (2008), and a BSc in Physics from the University of Pisa (2005). Petri's research spans the analysis of neuroimaging data and AI systems with topological techniques, the formalization of cognitive control models with tools of statistical mechanics and network theory, and the study of the predictability of socio-technical systems. His work in Topological Neuroscience explores brain architecture using algebraic topology, while his research in Cognitive Neuroscience focuses on neural mechanisms underlying human cognition. He is particularly known for his work on higher-order networks, using mathematical frameworks like hypergraphs and simplicial complexes to model systems with multi-way interactions. His recent publications (2023-2025) demonstrate a strong focus on higher-order network theory applied to neuroscience, with particular emphasis on topological approaches to brain connectivity, social contagion models, and the physics of complex systems. These works reveal consistent themes in understanding how multi-body interactions shape system dynamics across biological, social, and technological domains. European Research Council Consolidator Grant (RUNES: Reconstruction and unification of neural and ecological systems, 2024) As Principal Investigator of the NPLab, Petri advises numerous PhD and postdoctoral researchers including Marilyn Gatica, Andrea Santoro, and Simone Poetto. His RUNES project, funded by the ERC Consolidator Grant, represents a significant research initiative. The lab maintains active collaborations with CENTAI, Project CETI (Cetacean Translation Initiative), and various international institutions. The NPLab investigates the role of topology and geometry in the collective dynamics of complex systems, ranging from neuroscience to society, using statistical mechanics, algebraic topology, and innovative computational approaches. Current projects include Topological Neuroscience, Cognitive Neuroscience, Higher-order Networks, Project CETI, and RUNES.

• Secure Computing
• Hardware/Software Co-Design
• Machine Learning
• +۶ مورد دیگر

Farinaz Koushanfar is a Professor in the Department of Electrical and Computer Engineering at the Jacobs School of Engineering, University of California San Diego (UCSD) . She holds the Siavouche Nemat-Nasser Endowed Chair and serves as Founding Co-Director of the Center for Machine-Intelligence, Computing and Security . Her affiliations include NSF Trust-Hub (Co-PI) and NSF TILOS AI Institute . She also serves on the Editorial Board of The Proceedings of the IEEE . Research Focus: Prof. Koushanfar leads research in secure and efficient computing , including robust/safe AI , hardware/system security , AI-based optimization , and cryptographically secure privacy-preserving computing . Her work pioneered logic obfuscation/locking for chip security, automated co-design of AI systems , watermarking/tracing of deep learning models , and physical proofs of provenance . She explores co-design with cryptographic constructs for privacy preservation and manages nonlinearities in ciphertext domains. Article Trends: Recent publications show expertise in neural watermarking (deepfakes, media authentication), zero-knowledge proof frameworks , Trojan attack defenses in ML models, secure federated learning , and hardware acceleration of cryptographic protocols . Her work combines machine learning , cryptography , and physical design security across 2022-2025 publications. Scientific Awards: Fellow of ACM Fellow of IEEE Fellow of National Academy of Inventors (NAI) Fellow of Kavli Foundation of NAS Inducted to NAI 2024 Fellows Advising & Leadership: She has advised multiple PhD students who became faculty at top universities (e.g., Stanford, Purdue). She chairs conferences like ACM WiSec 2024 and co-led the NSF SaTC decadal review. Her lab ( ACES Lab ) produces award-winning graduates like Bita Rouhani (DAC Under-40 Innovators) and Shehzeen Hussain (UCSD Best Dissertation Award).

Beichuan Zhangمشاهده پروفایل

استاد

• Computer networks
• Internet routing architecture and protocols
• Internet topology
• +۱ مورد دیگر

Beichuan Zhang serves as Associate Department Head and Professor in the Department of Computer Science at the University of Arizona, maintaining office GS 723 with contact details 520-621-4817 and bzhang@cs.arizona.edu. His academic leadership spans network architecture research and departmental administration within the university's computing ecosystem. Zhang holds a Ph.D. from the University of California at Los Angeles (2003), establishing his foundation in advanced networking systems. His doctoral work catalyzed a career focused on internet infrastructure evolution. Research centers on computer networks with specific expertise in Internet routing architecture, protocols, topology, and multicast systems. Zhang is a principal investigator in Named Data Networking (NDN), driving innovations in stateful forwarding planes, in-network caching (e.g., Nb-cache, BLEnD), and congestion control mechanisms. His work bridges theoretical networking models with practical implementations for wireless, satellite, and live-streaming environments. Analysis of 2021-2025 publications reveals strategic expansion into Low Earth Orbit satellite networks, where Zhang pioneers NDN adaptations for handover resilience and outage detection. Concurrently, his group optimizes video streaming protocols and wireless performance through interest bundling techniques. This dual trajectory demonstrates systematic progression from terrestrial networking to space-ground integrated architectures.

Mehmetcan Akbulutمشاهده پروفایل

استاد پژوهشی

• Photonics
• Directed Energy
• Remote Sensing
• +۱۲ مورد دیگر

Mehmetcan Akbulut is an Associate Research Professor at the Wyant College of Optical Sciences , The University of Arizona. His office is located in GCRB 447. Research Focus: Photonics subsystems and systems development with applications in Directed Energy, Remote Sensing, Quantum Optical Networks, Optical Computing, Ultrafast & RF Photonics, and Biophotonics. Key Research Areas: High-energy fiber lasers, coherent beam combining, LIDAR systems, quantum communications, optical computing architectures, and photonic biosensors. Recent publications highlight advancements in injection-locked fiber lasers, SBS suppression techniques, optical Ising machines, and frequency comb applications. His work intersects academic and applied research through collaborations like the Center for Quantum Networks and CIAN projects. The Photonics & Systems Laboratory explores technologies for Directed Energy, wind shear sensing, distributed fiber sensing, and AI-driven biomedical imaging systems.

• Cloud Computing
• Data Center Optimization
• Resource Allocation
• +۴ مورد دیگر

Bhuvan Urgaonkar is a Professor in the Department of Computer Science and Engineering at Penn State University's College of Engineering. His research centers on optimizing cloud computing systems through innovative approaches to resource allocation, cost efficiency, and energy management. Current research focuses on Burstable Instance Scaling Serverless Computing Optimization Distributed Storage Systems Multi-resource Fair Allocation Cloud Economics Recent publications highlight advancements in autoscaling techniques, serverless architecture design, and trace modeling for high-load scenarios. These works emphasize practical solutions for cost-effective resource utilization in public cloud environments. Scientific Awards: CNS: Core: Small: Consistent, Geo-Distributed Data Stores on the Public Cloud (NSF, 2022-2025) CNS Core: Small: Principled Methodologies for Automated Cost-Effective Service Blending (NSF, 2021-2024) PPoSS: Cross-Layer Design for HPC in the Cloud (NSF, 2020-2022) CSR: Burstable Instances for Cost-Efficacy (NSF, 2017-2020) CSR: Student Travel Support for SIGMETRICS (NSF, 2016-2017)

B. Montgomery Pettittمشاهده پروفایل

استاد

• Biophysics
• Chemical Physics
• Computational Science
• +۷ مورد دیگر

B. Montgomery Pettitt is a Professor in the Department of Biochemistry and Molecular Biology at the University of Texas Medical Branch (UTMB). His research spans biophysics, chemical physics, and computational science, focusing on DNA compaction in bacteriophages, protein folding mechanisms, and multiscale modeling of biomolecular systems. Education: BS in Chemistry and Mathematics from University of Houston (1975), PhD in Physical Chemistry from University of Houston (1980) Postdoctoral Training: University of Texas (1980-1983), Harvard University (1983-1985) Research interests center on thermodynamic barriers in viral DNA packaging, protein solubility and phase transitions, and multiscale computational methods linking atomic and macroscopic properties. His work has implications for genomics, nanotechnology, and therapeutic delivery systems. Key publication themes include DNA conformational dynamics, protein collapse thermodynamics, solvation energetics, ion pair interactions in protein-DNA complexes, and validation of continuum-solvent models. These studies employ computational approaches and experimental data integration. His laboratory develops theoretical frameworks and computational tools to analyze solute-solvent interactions, leveraging proximal distribution functions and activity models to understand biological processes across disparate length and time scales.

Cynthia D. Rudinمشاهده پروفایل

استاد

• Interpretable Machine Learning
• Sparse Models
• Causal Inference
• +۷ مورد دیگر

Cynthia D. Rudin is the Gilbert, Louis, and Edward Lehrman Distinguished Professor of Computer Science at Duke University, with joint appointments in the Departments of Electrical and Computer Engineering, Statistical Science, Mathematics, and Biostatistics & Bioinformatics. She directs the Interpretable Machine Learning Lab and has held previous positions at MIT, Columbia, and NYU. Her educational background includes: Undergraduate degree from the University at Buffalo PhD from Princeton University (2004) Research Interests: Dr. Rudin's research focuses on interpretable machine learning and its applications across multiple domains. Her work emphasizes creating machine learning models whose reasoning processes people can understand, which includes algorithms for extremely sparse models, interpretable neural networks, interpretable matching methods for causal inference, and dimension reduction for data visualization. She applies these techniques to critical societal problems in healthcare, criminal justice, materials science, and other domains. Her lab has developed practical code for sparse models such as decision lists, decision trees, and additive models that provably optimize accuracy and sparsity. Dr. Rudin's recent publications (2024-2025) demonstrate a strong focus on interpretable AI applications across diverse fields including healthcare (mortality risk scores, breast cancer prediction), materials science (metamaterials design), and environmental justice (location-based health analysis). Her work consistently emphasizes practical implementations with real-world impact, particularly in high-stakes decision-making domains where model transparency is critical. Scientific Awards: Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity (2022) - often described as the "Nobel Prize of AI" INFORMS Society on Data Mining Prize (2024) Guggenheim Fellowship (2022) Three-time winner of the INFORMS Innovative Applications in Analytics Award (2013, 2016, 2019) Winner of the 2023 John M. Chambers Statistical Software Award for PaCMAP Winner of the 2024 Award for Innovation in Statistical Programming and Analytics Dr. Rudin has advised numerous PhD students and postdocs who have co-authored significant publications with her. Her lab has received substantial funding for projects applying interpretable machine learning to healthcare (seizure prediction in ICU patients), criminal justice (crime series analysis), and energy infrastructure (underground electrical distribution networks). Her work on the Series Finder algorithm has been adapted by the NYPD and has been running live in NYC since 2016. She directs the Interpretable Machine Learning Lab at Duke, which includes the Almost-Matching-Exactly Lab focused on interpretable causal inference. Her team develops practical code implementations for all their research, emphasizing usability and real-world application in critical domains.

Chuanfei Dongمشاهده پروفایل

استادیار

• Star-Terrestrial Planet Interactions
• Magnetic Reconnection
• Turbulence
• +۵ مورد دیگر

Chuanfei Dong is an Assistant Professor of Astronomy at Boston University's College of Arts & Sciences and of Electrical and Computer Engineering at the College of Engineering. His research focuses on understanding plasma physics and its applications to space science, planetary atmospheres, and fusion energy. Dong joined BU in January 2023 after working as a staff scientist at the Princeton Plasma Physics Laboratory. Education: B.S. in Space Science from University of Science and Technology of China M.S. in Earth and Atmospheric Sciences from Georgia Institute of Technology M.S.E. in Nuclear Engineering and Radiological Sciences from University of Michigan M.S. in Planetary and Space Sciences from University of Michigan Ph.D. in Scientific Computing from University of Michigan Research Interests: Dr. Dong's research spans multiple disciplines within space physics and plasma science. His primary interests include Star-Terrestrial Planet Interactions in our Solar System and beyond, magnetic reconnection and turbulence phenomena, wave-particle interactions in space plasmas, and applications of physics-informed machine learning to plasma problems. He also investigates high-intensity laser-plasma interactions with applications to fusion energy research. His work bridges the gap between theoretical plasma physics and observational space science, with particular focus on planetary atmospheres, solar wind interactions, and exoplanet habitability. Dong's interdisciplinary approach combines computational modeling, observational data analysis, and theoretical frameworks to address fundamental questions in space physics. Research Trends: Dong's recent publications demonstrate a strong focus on applying advanced computational techniques to space plasma physics problems. His work spans solar system bodies including Earth, Mars, Mercury, and the Moon, with increasing attention to exoplanet systems. A notable trend is the integration of machine learning approaches with traditional plasma physics modeling, particularly for complex phenomena like Landau damping and magnetic reconnection. His research has significant implications for understanding atmospheric evolution, space weather, and potential habitability of planetary bodies. Scientific Awards: DOE Early Career Research Award (2023) - $875,000 grant for plasma turbulence research Alfred P. Sloan Research Fellow (2024) Metcalf Travel Award Advising and Grants: Dr. Dong mentors undergraduate research assistants and plans to expand his research group with the support of his DOE Early Career Award, which will fund a graduate student and postdoctoral researcher. His research is supported by the Department of Energy and has connections to NASA missions including MAVEN (Mars) and BepiColombo (Mercury). Dong is also involved with the Mauve telescope project as BU institutional PI. His work has been featured in numerous media outlets including Phys.org, Science Daily, and German TV program zdf/3sat. Labs and Teams: Dr. Dong leads a research group focused on computational plasma physics at Boston University. He collaborates with researchers at Princeton Plasma Physics Laboratory and is involved with multiple NASA missions. His team develops advanced computational models to simulate space plasma phenomena, with particular expertise in magnetohydrodynamics (MHD), particle-in-cell methods, and physics-informed machine learning approaches. Dong is also affiliated with BU's Hariri Institute for Computing.

Daria Tsoupikovaمشاهده پروفایل

استاد

• Virtual Reality Design
• Digital Media
• Interactive Art
• +۶ مورد دیگر

Daria Tsoupikova is a Professor of Digital Media Design at the University of Illinois Chicago's School of Design. She serves as Co-Founder and Design Chair of the bachelor's in computer science and design program and is affiliate faculty in the UIC Electronic Visualization Laboratory (EVL). Her work bridges artistic expression and technological innovation, focusing on virtual reality and interactive media applications across multiple domains. Her educational background includes: MFA in Computer Art from Syracuse University BFA in Graphic Design from Moscow State Academy Positioned at the crossroads of artistic and technological innovation, Tsoupikova's research explores the potential of new media and interactivity in relation to traditional arts. Through development of virtual reality art projects and networked multiuser exhibitions for VR projection systems like the CAVE and CAVE2, her work applies computer graphics to diverse research domains including educational multimedia, cultural heritage, and virtual rehabilitation for stroke survivors. Her approach integrates theater methodologies, design principles, and coding to create immersive experiences that address global challenges while preserving cultural heritage. Analysis of her publication history reveals an evolving trajectory from foundational VR art installations toward increasingly sophisticated therapeutic applications. Her recent work demonstrates a strong focus on the intersection of art, healthcare, and virtual reality, with particular emphasis on collaborative multiuser environments that transform traditional storytelling and therapeutic practices. The progression shows movement from artistic exploration to clinically validated therapeutic interventions with measurable patient outcomes. Her notable awards include: Session's Best Paper at IMCIC 2019 and QRMS/IMCIC 2016 Fulbright Scholar Award (2011) and Intercountry Lecturing Award Communication Arts Award of Excellence and Creative Quarterly Excellence Award National Endowment for the Arts grant and Presidential Award from University of Illinois Tsoupikova has secured significant research funding from the National Science Foundation, National Institute on Disability and Rehabilitation Research (NIDILRR), and the Department of Education for projects including MARS2 (2007-2012) and MARS3 (2012-2018). She has developed innovative courses including Mobile App Design where students collaborate with professional partners such as Caterpillar, UIC medical departments, and clinics. Her work with stroke survivors at the Shirley Ryan AbilityLab has demonstrated improvements in patients' daily lives, regained trust in their hands, and increased therapy motivation. She is deeply involved with the UIC Electronic Visualization Laboratory, collaborating on advanced visualization projects including IBM partnerships exploring sustainable development goals through VR. Her leadership in establishing the computer science + design undergraduate program positions UIC as the only public university in the United States offering such a specialized degree, reflecting her commitment to interdisciplinary education at the technology-art intersection.

• Superconducting Logic
• Bio-based Architectures
• Novel Computing Mediums
• +۶ مورد دیگر

Dr. Jennifer Volk is an Assistant Professor at the College of Engineering, University of Wisconsin-Madison, specializing in Electrical & Computer Engineering. Her research focuses on leveraging novel technologies like superconductor electronics and photonics to create efficient systems for datacenters, neuromorphic computing, quantum computing, and space/sensing applications. She employs a holistic approach spanning circuit design, materials science, and computer microarchitecture. PhD (2024), University of California, Santa Barbara BS (2016), University of California, Santa Cruz Her research interests include superconducting logic , bio-based architectures , and novel computing mediums , emphasizing co-optimization of logic and circuit blocks. Her work develops design abstractions to simplify adoption of unconventional technologies. Dr. Volk's publications demonstrate expertise in superconducting circuit design, radiation-hardened CMOS for particle physics, and photonic materials. She has received numerous awards including the 2025 John D. Wiley Assistant Professorship and IEEE fellowships in applied superconductivity. 2025 John D. Wiley Assistant Professorship 2024 UC Santa Barbara President's Dissertation Year Fellowship 2023 IEEE CSC Graduate Study Fellowship in Applied Superconductivity 2022 IEEE Micro Top Picks Honorable Mention 2021 IEEE Micro Top Picks She teaches E C E 340 - Electronic Circuits I (Spring 2025). Her work bridges materials science, circuit design, and system architecture to enable next-generation computing platforms.