School of Engineering
Showing 2,301-2,400 of 3,023 Results
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Karan P. Singh
Ph.D. Student in Electrical Engineering, admitted Autumn 2023
BioI am an second-year electrical engineering PhD student at Stanford University and NSF Graduate Research Fellow, advised by Dr. Ehsan Adeli. I am broadly interested in applied ML, and am currently working on foundation models for resting-state functional MRI.
Previously, I studied electrical engineering at Cal Poly SLO and was the youngest engineering graduate in the school's history. I then worked as a post-baccalaureate researcher in Dr. Kim Butts Pauly's lab here at Stanford, where I applied machine learning to transcranial ultrasound neuromodulation, a non-invasive therapeutic modality with the potential to cure neurological diseases such as epilepsy, Alzheimer's, and even addiction. My primary focus during this time was using ML to improve therapy planning accuracy and efficiency.
Outside of academia, I enjoy playing the piano, badminton, working out, and cooking! I am also the co-founder and co-president of the Stanford Piano Society. -
Adrien Specht
Ph.D. Student in Computational and Mathematical Engineering, admitted Spring 2024
BioI'm a PhD student in the Institute for Computational and Mathematical Engineering (ICME) at Stanford University, mentored by Prof. Mignot. My research is at the intersection of artificial intelligence and sleep medicine, focusing on developing predictive models for circadian rhythms and sleep debt from proteomics data. I adopt a problem-oriented approach, selecting methods based on the data and research questions at hand. My techniques range from linear regression to sophisticated deep learning frameworks, aiming to extract maximal insights from the data. I also explore the use of unsupervised and semi-supervised learning, and am interested in the applications of multimodal and foundation models in biology.
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Kavya Sreedhar
Ph.D. Student in Electrical Engineering, admitted Autumn 2019
BioKavya Sreedhar is an electrical engineering PhD candidate advised by Mark Horowitz. Her research interests include architecture design and developing hardware accelerators for cryptography and machine learning applications. On the cryptography side, she has worked on designing a fast extended GCD accelerator for constant-time modular inversion and verifiable delay functions. On the deep learning side, she is working on dynamically adapting the execution of state-of-the-art models for use in real-time systems and accelerating dynamic transformer models for computer vision in an ongoing collaboration with NVIDIA. She previously worked with the Agile Hardware (AHA) Project in developing Lake, a parameterizable memory generator that can be configured at runtime to support different image processing and machine learning applications. As part of her research, she has worked on taping out three chips in SKY130nm, GF12nm, and TSMC16nm. Kavya is supported by the Quad Fellowship (2023 to 2024) and Stanford's Knight-Hennessy Graduate Fellowship (2019 to 2022). She received a B.S. in Electrical Engineering and BEM (Business, Economics, & Management) from Caltech in 2019 and a M.S. in Electrical Engineering from Stanford in 2021.
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Skyler St. Pierre
Ph.D. Student in Mechanical Engineering, admitted Autumn 2020
Current Research and Scholarly Interestsbiomechanics, machine learning, computational modeling
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Ryan Stice-Lusvardi
Ph.D. Student in Management Science and Engineering, admitted Autumn 2017
BioRyan is interested in examining how changing applications and increasing adoption of data analytics are shaping the future of work and organizations. In her current research, she seeks to illuminate the ways in which assumptions and values of data analysts are embedded in analytic practices and how this shapes data insights and decisions. Her previous research has examined the impact of perceived analytic ability on HR performance evaluations and the emergence of HR analytics. Ryan has lived in Hong Kong, Thailand, Singapore, France, and South Korea. She enjoys repurposing vintage doors, bouts of gardening, and eating gourmet takeout.
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Maxwell Bradley Strange
Ph.D. Student in Electrical Engineering, admitted Autumn 2018
BioMax is a Ph.D. student in Electrical Engineering advised by Mark Horowitz. His research focuses on developing infrastructure and tools to facilitate agile hardware development as part of the ongoing efforts by the Stanford AHA! Research Center. His research interests also include domain-specific hardware architectures, hardware/software co-design, and embedded systems design. He graduated from the University of Wisconsin in 2017 with a B.S. in Computer Engineering and Computer Science.
