Institute for Computational and Mathematical Engineering (ICME)
Showing 1-89 of 89 Results
-
Haoxuan Chen
Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2022
BioPersonal website: https://haoxuanstevec00.github.io/
-
Jikai Jin
Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2023
BioI am currently a Ph.D. student of the The Institute for Computational and Mathematical Engineering (ICME) at Stanford university. Prior to joining Stanford, I obtained my bachelor degree in computational mathematics at the School of Mathematical Sciences, Peking University, fortunately having Prof. Liwei Wang as my research advisor. My research is highly interdisciplinary across machine learning, statistics, operations research. While primarily focusing on theoretical aspects, the ultimate goal of my research is to develop state-of-the-art solutions for important real-world problems.
-
Riley Juenemann
Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2021
BioThird-year Computational and Mathematical Engineering (ICME) PhD Candidate @ Stanford University passionate about research at the intersection of mathematics, computing, and biology.
-
Ava Kouhana
Masters Student in Computational and Mathematical Engineering, admitted Autumn 2024
BioHi ! I am an ICME master's degree student at Stanford University, currently conduction research in Professor Gordon Wetzstein’s Computational Imaging Lab.
Prior to Stanford, I dedicated six months conducting research at Harvard under the supervision of Dr. Mengyu Wang, focusing primarily on Computer Vision tasks like Image Segmentation and Vision-Language Models. Before joining ICME , I have had the opportunity to work for six months supervised by Stanford Professor Craig Levin, researching the application of Diffusion Models for image super-resolution.
My research interests primarily revolve around computer vision, deep learning, and generative AI, with a growing interest for 3D modeling and video generation. -
Zetian Li
Masters Student in Computational and Mathematical Engineering, admitted Autumn 2024
Current Research and Scholarly InterestsStatistical Learning, Machine Learning, Bayesian Statistics, Probability Theory
-
Adrienne Propp
Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2021
BioI am a fourth year PhD student in ICME (the Institute for Computational and Mathematical Engineering). Prior to Stanford, I was working as a technical analyst at the RAND Corporation where I spent most of my time designing microsimulations and other models to investigate topics in healthcare, education, disaster relief, and international relations.
My research interests lie at the intersection of mathematics, data, and modeling, which has led me to a focus on scientific machine learning (SciML). Specifically, I am working on developing new graph-based surrogate modeling methods for low-data regimes. I am grateful to be advised by Daniel Tartakovsky, During my PhD, I have also collaborated with Jenny Suckale to model volcanic lava fountaining, and Susan Athey and Sanath Kumar Krishnamurthy to design improved algorithms for contextual bandits.
Past research projects have ranged from computational models of the heart to inverse modeling to predict satellite performance. -
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.
