Quantitative Methods for Natural Language Processing — 6.861

Fall 2022, Fall 2023, Fall 2024

How can computers understand and leverage text data and human language? Natural language processing (NLP) addresses this question, and in this course students study modern, advanced Machine Learning approaches. Students will also work on a significant, novel research project. Enrollment of 350-425 students.

Modeling with Machine Learning — 6.C01

Spring 2023

We introduce a wide-array of machine learning concepts, including foundational items to recent state-of-the-art advances. Students simultaneously enroll in an approved, supplement course of their respective majors, whereby the demonstrate applications of this coursework. Enrollment of 200 students.

Intro to Machine Learning — 6.39 (formerly 6.036)

Spring 2024

We introduce a wide-array of machine learning concepts, including foundational items to recent state-of-the-art advances (e.g., Transformers and Reinforcement Learning). Enrollment is 400 students.

Deep Learning for Natural Language Processing (NLP) — AC295/CS287

Fall 2021

I’m very excited to create this new graduate course! Enroll capped to 55 students. Topics include language models, transformers, machine translation, summarization, coreference resolution, and many other NLP tasks. Students will also work on a significant research project.

Master’s Capstone Research Course — AC297r

Fall 2019, Spring 2020, Fall 2020, Spring 2021, Spring 2022

I work with a wide variety of organizations (e.g., large software companies, non-profits, government, academia, start-ups) to craft real-world machine learning projects for this 40-person class. I oversee all projects as students work in teams to research, develop, and effectively communicate solutions to their projects. I also cover supplemental skills such as public speaking and being able to work well with a team. Course website.

Supervised Reading and Research — CS91r

Spring 2020, Spring 2021, Summer 2021

I supervise undergraduates on independent research, where the final deliverable is a short research paper.

Introduction to Data Science — CS109A/CS209A

Fall 2019, Fall 2020

For roughly 400 students, I co-teach Introductory Data Science, which can be taken for graduate or undergraduate credit. The course is the first-half of a year-long introduction to data science. Topics include obtaining and cleaning data, exploratory data analysis, visualization, regression, classification, PCA, boosting, trees, and neural networks.

Advanced Data Science — CS109B/CS209B

Spring 2020, Spring 2021

For roughly 250 students, I co-teach Advanced Data Science, which can be taken for graduate or undergraduate credit. The course is the second-half of a year-long introduction to data science. Topics include advanced methods for data wrangling, data visualization, deep neural networks, statistical modeling, and prediction. Students learn to implement CNNs, LSTMs, seq2seq, reinforcement learning, transformers, basic Bayesian methods, nonlinear statistical models, unsupervised learning, and generative models such as variational autoencoders and GANs.

Introduction to Computation for Humanities and Social Sciences

Fall 2018

As the sole instructor, I re-designed this course, which introduces non-Computer-Science students to computation via Python. Created all course content, gave lectures, held office hours, and managed 5 TAs. Course material includes introducing variables, data types, functions, data structures, text analysis, APIs, and data visualization.

Introduction to Computational Linguistics

Spring 2014 (Teaching Assistant)

Responsibilities included grading all assignments and holding weekly office hours.

Instructor: Dr. Eugene Charniak.

Assembly Language

Spring 2007 (Teaching Assistant)

Responsibilities included teaching two-hour discussion sections each Friday, holding office hours, assisting students with weekly projects, and grading assignments. The architecture of focus was MIPS.

Instructor: Dr. David Smallberg

Artificial Intelligence

Winter 2007 (Teaching Assistant)

I helped grade all quizzes, tests, homework, and projects. Software assignments were written in LISP.

Instructor: Dr. Bruce Rosen.

Data Structures and Algorithms

Fall 2006 (Teaching Assistant)

Responsibilities included grading all tests and quizzes, creating weekly labs assignments, and assisting students in each lab session.

Instructor: Dr. Ronaldo Menezes.