Experience

University of Michigan College of Engineering

• United States
• Higher Education
• 700 & Above Employee

• Graduate Student Research Assistant

  • Jan 2019 - Present

• Graduate Student Instructor

  • Sep 2018 - Dec 2018

  - Course: EECS 445 Introduction to Machine Learning- Primary instructor: Professor Jenna Wiens- Led weekly discussion to help students review concepts taught in lectures- Worked closely with Professor Wiens and other instructional aides to develop projects and homeworks for the class- Managed the class piazza and held office hours to help students with their homeworks and projects- Topic covered in class: Perceptrons, Kernelized SVMs, Ordinary Least Square Regression, Logistic Regression, Multilayer Perceptrons, Convolutional Neural Networks, Gaussian Mixture Models, Bayesian Networks, Hidden Markov Models. Show less

• Undergraduate Research Assistant

  • Sep 2017 - Aug 2018

  - Research Assistant at the MLD3 lab (Machine Learning for Data Driven Decisions), PI: Prof. Jenna Wiens- Collaborated with other research assistants to develop data driven methods for GVHD (Graft-vs-Host Disease) risk stratification models

• Instructional Aide

  • Sep 2017 - Dec 2017

  - Course: EECS 445 Introduction to Machine Learning- Primary instructor: Professor Jenna Wiens- Led weekly discussion to help students review concepts taught in lectures- Worked closely with Professor Wiens and other instructional aides to develop projects and homeworks for the class- Managed the class piazza and held office hours to help students with their homeworks and projects- Topic covered in class: Perceptrons, Kernelized SVMs, Ordinary Least Square Regression, Logistic Regression, Multilayer Perceptrons, Convolutional Neural Networks, Gaussian Mixture Models, Bayesian Networks, Hidden Markov Models. Show less



University of Michigan School of Public Health

• United States
• Higher Education
• 300 - 400 Employee

• Research Group Mentor, Big Data Summer Institute

  • Jun 2019 - Jul 2019



University of Michigan School of Public Health

• United States
• Higher Education
• 300 - 400 Employee

• Research Group Mentor, Big Data Summer Institute

  • Jun 2018 - Jul 2018

  Today, hospitals collect an immense amount of data pertaining to their patients. Put to good use, these data could help improve healthcare. In this project group, students will learn to apply machine learning approaches to real (i.e., messy) health data for patient risk stratification for adverse health outcomes (e.g., in-hospital mortality). Students will explore a variety of approaches ranging from supervised learning (e.g., deep learning) to unsupervised learning (e.g., graph mining). These techniques will be explored in a range of settings across multiple modalities (e.g., graphs images, waveforms, and text). Implementation will be largely conducted in Python, but will rely on external packages/libraries. Students will be guided through the full "data intensive science" pipeline from data extraction to preprocessing, model selection, evaluation, interpretation and visualization of results. Students will see firsthand the data science opportunities that exist in healthcare. Show less



University of Michigan

• United States
• Higher Education
• 700 & Above Employee

• Guest Lecturer, ESSI Summer Camp

  • Jun 2018 - Jun 2018

  This is part of the lecture series for ESSI (Exercise & Sports Science Initiative) Summer Camp at UMich, which is a data science summer camp for high-school students interested in sport analytics. On June 26, I lectured about unsupervised learning and clustering techniques (k-means, hierarchical clustering), and then guided participants through an analysis of NBA player statistics and identifying different positions and skill sets. This is part of the lecture series for ESSI (Exercise & Sports Science Initiative) Summer Camp at UMich, which is a data science summer camp for high-school students interested in sport analytics. On June 26, I lectured about unsupervised learning and clustering techniques (k-means, hierarchical clustering), and then guided participants through an analysis of NBA player statistics and identifying different positions and skill sets.



Kiavi

• United States
• Financial Services
• 200 - 300 Employee

• Software Engineer Intern

  • May 2017 - Aug 2017

  Developed features for the loan origination platform using Rails and React; worked on platform integration with Amazon Web Services (Lambda and EC2). Developed features for the loan origination platform using Rails and React; worked on platform integration with Amazon Web Services (Lambda and EC2).



University of Michigan

• United States
• Higher Education
• 700 & Above Employee

• Mathematics Tutor

  • Sep 2016 - Apr 2017

  As a math lab tutor, I provide walk-in tutoring service to other students for various college mathematics courses, ranging up to linear algebra and advanced calculus. As a math lab tutor, I provide walk-in tutoring service to other students for various college mathematics courses, ranging up to linear algebra and advanced calculus.



Conifer Technology, Inc

• Technology, Information and Internet

• Technology Specialist

  • Feb 2016 - Jan 2017