Teaching experience

Find here highlights of my official teaching opportunities through my research and teaching appointments at the Massachusetts Institute of Technology (MIT) and the Pontificia Universidad Católica de Chile (PUC).

I have received guest lecturer and workshop lead invites from multiple institutions, among others:

• Massachusetts Institute of Technology (MIT), USA
• Pontificia Universidad Católica de Chile (PUC), Chile
• Johannes Kepler University Linz (JKU), Austria
• Korea Advanced Institute of Science and Technology (KAIST), Korea
• Universidad de la Sabana, Colombia

Furthermore, in January 2023, I led an international academic team to host several workshops throughout a 10-day summer research camp on Human-Centered Machine Learning, Natural Language Processing, Data Visualization, and Causal Inference for the MIT–Chile Research Workshops series. I raised 30K USD to host more than 100 students and fly speakers from MIT and Harvard University to Chile (Santiago and Concepción). See highlights here.

Website: public-thought.media.mit.edu

Lead TA – Ph.D. Teaching Assistant at MAS Fall 2020

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Ph.D. Teaching Assistant at EECS Fall 2019

▪ Serving as TA for undergraduate (introductory: 6.036) and graduate (applied: 6.862) machine learning.

▪ Guiding grad students in their projects and developing material for new problem sets related to deep learning. Worked under professors Leslie Kaelbling, Tamara Broderick, Duane Boning, Patrick Jaillet, and Jacob Andreas, among others.

Led MAS and the MIT Media Lab in 2021 and 2023.

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https://tll.mit.edu/programming/faculty/new-faculty-teaching-program/#

https://tll.mit.edu/programming/grad-student-programming/kaufman-teaching-certificate-program/

For all the following, I led the design and implementation of the syllabus and planning for the full classes. Driving the the courses process end-to-end, working with my own TAs and students throughout.

Adjunct Professor - 2th- and 6th-year university students. In this course, we study (and mainly implement) advanced topics in Computer Programming such as object-oriented design, data structures, functional programming, threading, simulation, metaprogramming, input/output, unit testing and graphical interfaces. As a former TA for this class, I supported this published book "Advanced Computer Programming in Python", see acknowledgments. Available in Amazon.

Adjunct Professor - 4th- and 6th-year university students (undergrad and grad level). Analysis and implementation of basic techniques and algorithms in data mining. We study data warehouses, ETL, data preprocessing, data visualization, association rules, linear regression, classification algorithms (logistic regression, decision trees, random forest, KNN), clustering methods (k-means, hierarchical, and gaussian mixtures with EM).

Adjunct Professor - Minimum course for the students of the Master in information technologies and data management. This course gives the students a deep understanding of the Data Mining and Data Warehousing principles. It starts presenting strategies for building warehouse systems; then the lessons go through applications for visualizing and querying data by non-experts. The primary objective is to drive management decisions and marketing plans using these techniques. Mainly the customer relationship management (CRM).

2013 - 2015 | Teacher’s Chief Assistant

2015 - 2016 | Course Coordinator

2013 - 2014 C# | 2015 - 2016 Python

• I led the implementation of the first flip classroom environment in the Computer Science Department at PUC. I was in charge of creating assessments and weekly practical activities for more than a hundred students per term.
• I oversaw a 25-member team. Together we gave feedback and graded students.
• From C# to Python: I supported this class' published book "Advanced Computer Programming in Python", see acknowledgments. Available in Amazon.
• I also served as TA for some introductory courses related to computer programming.

2017 | Lead TA

• We studied machine learning from a probabilistic perspective. 
• Bayesian learning: the beta-binomial and the Dirichlet-multinomial model.
• Monte Carlo Inference: I also learned and developed customized implementations of rejection sampling, importance sampling.
• MCMC inference: we solved problems implementing Gibbs sampling, metropolis hastings, annealing methods.
• Variational inference: KL divergence, the mean field method, and Variational Bayes EM.

2016 | Lead TA

• 4th- and 6th-year university students. Analysis and implementation of basic techniques and algorithms in data mining. We study data warehouses, ETL, data preprocessing, data visualization, association rules, linear regression, classification algorithms (logistic regression, decision trees, random forest, KNN), clustering methods (k-means, hierarchical, and gaussian mixtures with EM).

2015 - 2016 | TA

• This course teaches the fundamental data structures and their main algorithms. We evaluated complexity on memory and time. We also studied the main techniques for solving discrete optimizations.
• We implemented and evaluated: linked lists, queues, heaps, hash tables, trees (binary, red-black, B), graphs (BFS, DFS), dictionaries, prioritized queues, disjoint sets, greedy algorithms (Dijkstra, minimum cost coverage), divide to conquer, dynamic programming, and sorting algorithms.

2015 - 2016 | Lead TA

• The course introduces the basis of stochastic modeling systems. This course presents basic techniques and concepts under the most widely used analytics models in operations research for representing probabilistic systems.
• We studied Poisson processes, discrete-time Markov chains, and continuous-time Markov chains. We also learned process simulation.

2017 | Lead TA

• This course teaches widely used tools and applications for automatic analysis and data mining processes. Starting by presenting strategies for building warehouse systems, the lessons go through applications for visualizing and querying data by non-experts.
• This subject gave me a big picture of every piece of software and model I developed during my time as a data scientist. Now I know how they fit together.