Student Research
Ask Big Questions, Solve Big Problems
Computational mathematics is at the heart of many engineering and science disciplines
Students of the Institute for Computational and Mathematical Engineering (ICME) are a diverse, interdisciplinary group, with interests and research conducted at the intersection of applied math, statistics, computer science and applications.
ICME Students design state-of-the-art mathematical and computational models, methods, and algorithms for engineering and science applications for bioinformatics, geosciences, computational finance, and more. Student's research advances disciplinary fields by designing and improving computational approaches in collaboration with engineers and scientists.
Xpo Research Symposium
Every May, ICME holds its Xpo Research Symposium, providing an up-close and inside look at current research and future plans for ICME faculty and students. This is a unique opportunity to see how computational mathematics, data science, machine learning, scientific computing, and related fields are applied across a wide range of domain areas.
Real-World Impacts
At ICME, students have opportunities to make real-world impacts through project based research.
ICME Analytics Accelerator
Multidisciplinary teams of graduate students from across campus work with Stanford faculty mentors on impactful research.
Fall ICME Master’s and PhD students as well as graduate students from Stanford School of Medicine and School of Engineering focused on COVID-19 research projects.
students + mentors + faculty = impact
ICME Xplore immerses ICME graduate students in a quarter long, real-world project-based data science learning experience while offering course credit through CME291
Student Stories and Experiences
Using Machine Learning to Better Understand Text
“We’re both interested in machine learning. We care a lot about software and design, proper software architecture and making sure we can use our research in the real world. Essentially, what we’re trying to do is categorize and understand text. When we get a piece of text, we want to be able to identify what it is and what it’s talking about. Is this text positive? Negative? Biased? The way we’re approaching the problem is to build algorithms and systems that can be applied to any of those problems. We are trying to build algorithms that are generic enough that they can work in multiple domains – sentiment analysis, humor detection, political bias detection, text categorization for Wikipedia, etc. We want to enable computers to match how good humans are at understanding text. To do this, we use deep learning, a very expressive and powerful part of machine learning, to build our algorithms. Deep learning allows us to learn and imbue the notion of abstraction – something humans are good at. In terms of real-world applications, we’re looking at a lot of things like measuring customer satisfaction based on reviews or tweets, categorizing news or web pages into different topics of interest, finding the most useful reviews of a product, identifying bias in text. There are really limitless possibilities here.”
--Alfredo Lainez and Luke de Oliverira, MS students
Teaching a Computer to Analyze MRI Data
“I am working with the Cardiovascular Biomechanics Computation Lab. This lab is working to build and run simulations of the cardiovascular system, so we can better understand how it works, how it responds to surgeries, biomedical implants, etc. In order to perform these simulations, we currently take MRI or CT scan data and manually construct 3D models of the cardiovascular system. The process takes a very long time. I am working to figure out how we can automate the construction of accurate cardiovascular models to speed up this process. This comes down to an artificial intelligence question of how do we teach a computer to recognize where blood vessels are in MRI or CT scan data. Essentially, we are trying to teach a computer how to analyze MRI data, and then we’re working to optimize this process using machine learning, artificial intelligence, deep learning and comprehensive algorithms. If we’re able to successfully automate 3D cardiovascular model construction, this would make it easier to study and come to better understandings of the causes of particular cardiovascular diseases, or how particular surgeries affect the cardiovascular system. With better simulations, we could also potentially optimize surgical processes. Simulations allow a surgical team to run through a couple of potential surgery options, and then choose the best possible surgery for a patient.”
--Gabriel Maher, ICME PhD Candidate
Student Theses
ICME PhD theses are available on Stanford University’s Libraries website.
A Center of Collaboration
ICME Affiliated Faculty represent all nine Engineering departments and have strong ties outside of SoE.