Coursework

as of May '19

Computer Science

CS 2043: UNIX and Scripting Tools, Spring 2016
CS 2112: Honors Data Structures and Object-Oriented Design, Fall 2015
CS 2800: Discrete Structures, Fall 2015
CS 3110: Functional Programming and Data Structures, Spring 2016
CS 3410: Computer Systems, Fall 2016
CS 4220: Numerical Analysis: Linear and Nonlinear Problems, Spring 2017
CS 4300: Language and Information Retrieval, Spring 2017
CS 4410: Operating Systems, Spring 2018
CS 4740: Natural Language Processing, Fall 2017
CS 4670: Introduction to Computer Vision, Spring 2018
CS 4775: Computational Genetics and Genomics, Fall 2017
CS 4780: Machine Learning, Spring 2017
CS 4786: Machine Learning for Data Science, Fall 2016
CS 4787: Principles of Large-Scale ML Systems, Spring 2019
CS 4820: Introduction to the Analysis of Algorithms, Spring 2017
CS 4850: Mathematical Foundations for the Information Age, Spring 2019
CS 6670: Graduate Computer Vision, Fall 2018
CS 6780: Advanced Machine Learning, Spring 2019
CS 6788: Advanced Topic Modeling, Fall 2018

Mathematics

MATH 2940: Linear Algebra for Engineers, Fall 2015
MATH 3210: Manifolds and Differential Forms, Fall 2016
MATH 3360: Applicable Algebra, Spring 2016
MATH 4310: Linear Algebra for Engineers, Spring 2016
MATH 4710: Basic Probability, Fall 2017

Operations Research and Information Engineering

ORIE 4742: Information Theory, Probabilistic Modeling, & Deep Learning with Scientific & Financial Applications, Spring 2018
ORIE 5310: Optimization II, Spring 2019
ORIE 6741: Bayesian Machine Learning, Fall 2017

Teaching

Undergraduate Teaching Assistant
Department of Computer Science, Cornell University
Department of Computational Biology, Cornell University

CS 4775: Computational Genetics and Genomics
Fall 2019

Computational methods for analyzing genetic and genomic data. Topics include sequence alignment, Hidden Markov Models for discovering sequence features, motif finding using Gibbs sampling, phylogenetic tree reconstruction, inferring haplotypes, and local and global ancestry inference.

CS 4780: Machine Learning for Intelligent Systems
Spring/Fall 2018

An introductory course in machine learning, with a focus on supervised learning techniques, including Naive Bayes, logistic regression, linear regression, SVM techniques, decision trees and random forests, Gaussian processes, and neural networks.

CS 4786: Machine Learning for Data Science
Fall 2017

An introductory course in machine learning, with a focus on data modeling and related methods and learning algorithms for data sciences, covering techniques for dimensionality reduction, clustering, and probabilistic modeling.

CS 2800: Discrete Structures
Spring 2016 - Fall 2016