Teaching

Course materials

For current and potential CS master students at NAU, here is a list of progression plans with various specialties including machine learning, which should be useful for planning what classes you take.

• Fall 2024, UdeS IFT 187, Éléments de bases de données, supplementary course materials.
• Fall 2024, UdeS IFT 603/712, Techniques d’Apprentissage, web site adapted from materials by Pierre-Marc Jodoin.
• Spring 2024, NAU SES graduate seminar on R Package development, my slides.
• Fall 2023, NAU CS 470, Artificial Intelligence, my course materials.
• Fall 2023, NAU CS 599, Deep Learning, my course materials.
• Fall 2023, NAU CS 599, Unsupervised Learning, my course materials.
• Spring 2023, Introduction to Deep Learning in R, a 2 hour lecture for Research Bazaar Arizona, my course materials.
• Spring 2023, NAU CS 470, Artificial Intelligence, my course materials.
• Fall 2022, NAU CS 499-002 (5893), Deep Learning, my course materials.
• Fall 2022, NAU CS 105/205/305, Computing Tools, my course materials.
• Summer 2022, Introduction to Machine Learning and Neural Networks, with an application to earth system modeling. 60 minute lecture for summer school on “New Advances in Land Carbon Cycle Modeling” (5th year), my course materials.
• Spring 2022, NAU CS570, Advanced Intelligent Systems (Deep Learning). my course materials
• Fall 2021, NAU CS499/599, Unsupervised Learning. my course materials
• Summer 2021, Introduction to Machine Learning and Neural Networks, with an application to earth system modeling. 60 minute lecture for summer school on “New Advances in Land Carbon Cycle Modeling” (4th year). code, slides, video, quiz
• Spring 2021, NAU CS470/570, Artificial Intelligence, my course materials
• Fall 2020, NAU CS499/599, Unsupervised Learning. my course materials
• Summer 2020, Introduction to Machine Learning and Neural Networks, 90 minute lecture for summer school on “New Advances in Land Carbon Cycle Modeling” (3rd year). my course materials
• Spring 2020, NAU CS499-3, Deep Learning. my course materials
• Fall 2019, NAU CS599-6 / EE599-4, Machine learning research. my course materials
• Spring 2019, NAU CS499-007, Machine learning algorithms. my course materials
• Fall 2018, Open source software engineering class at NAU. my course materials
• Summer 2017, International useR 2017 conference, Brussels, Belgium, Tutorial on Introduction to Supervised changepoint detection, my course materials, video.
• Spring 2017, Université de Montréal, Criminology Department, Introduction to R for Criminology, my course materials.
• Summer 2016, International useR 2016 conference, Stanford, CA, Tutorial on Understanding and Creating Interactive Graphics, my course materials.
• Spring 2011, Mines ParisTech, teaching assistant for Fabien Moutarde’s Machine Learning class, my course materials.

Screencasts

Topics without links below are on my TODO list.

• Convolutional neural networks in R
• Number of hidden units is a regularization parameter (R keras)
• Neural networks using keras in R
• Make an R package with C++ code
• Machine learning basics in R
• Regular expressions for text parsing and data reshaping
• R and Emacs Speaks Statistics
  • On Monsoon
• emacs and python
• Interpreting machine learning models with feature selection
• Data manipulation with data.table, LatinR Oct’23 slide source, updated slides.
• Interactive data visualization with the grammar of graphics
• git/github

NAU students mentored

“Science is a cooperative enterprise, spanning the generations. It’s the passing of a torch from teacher to student to teacher. A community of minds reaching back to antiquity and forward to the stars.” Neil DeGrasse Tyson, Cosmos (2014), Episode 1: Standing Up in the Milky Way.

For the students below, my role is primary advisor, unless another primary advisor is mentioned.

• Tung Lam Nguyen, Inf PHD, Spring 2024 - present.
• Doris Amoakohene, Inf master, Fall 2023 - present.
• Bilal Aslam, Inf PHD, Fall 2022 - present. Primary advisor: Kevin Gurney.
• Karl Harshe, MechE PHD, Fall 2022 - present. Primary advisor: Zach Lerner.
• Daniel Agyapong, Informatics and Computing PHD, Fall 2022 - present.
• Cameron Bodine, Informatics and Computing PHD, Spring 2020 - Spring 2024, then did postdoc at University of Delaware. Primary advisor: Dan Buscombe.
• Trevor Silverstein, Inf PHD, Fall 2023, then went back to teach school.
• Jadon Fowler, CS master, Fall 2022 - Spring 2023, working as software developer at Lunar Client.
• Austin Malmin, CS master, Spring 2023, now Salesforce developer at Microchip.
• Kyle Rust, CS master, Fall 2021 - Fall 2022, working as data scientist at CHS.
• Charlie Saluski, CS undergrad, Spring 2022.
• Balaji Senthilkumar, CS master, Spring 2022.
• Jacob Kaufman, CS master, Spring 2022, then went to General Dynamics.
• Amirhossein Safari, Informatics and Computing PHD, Spring 2022.
• Jonathan Wheeler, Math master, Spring 2022.
• Tayler Skirvin, CS undergrad, Spring 2022, then went to work as software developer at Cognizant.
• Brooke Sebranek, Biomedical Sciences undergrad, Fall 2021 - Spring 2022.
• Shadmaan Hye, Informatics and Computing PHD, Fall 2021, then went to University of Arizona CS as PHD student.
• Tristan Miller, BS CS research associate, Fall 2020 - Fall 2021, then went to work at a tech startup, Ezoic.
• Akhila Chowdary Kolla, CS master, Spring 2020 - Fall 2021, then went to work as software developer at Amazon.
• Alyssa Stenberg, CS master, Spring 2020, then went to J.B. Hunt as a software engineer (Machine Learning Operations team).
• Jonathan Hillman, CS undergrad, Summer 2020 - Summer 2021, then did Math master at NAU.
• John Francis “Frank” Burkhart, Math undergrad, Spring 2020 - Spring 2021, then did Bioinformatics PHD program at North Carolina State University.
• Weiheng Su, CS undergrad, Summer 2020 - Fall 2020, then worked as Machine Learning Engineer at Frontopsky, and then did PHD at Stony Brook University.
• Joseph Vargovich, CS undergrad, Fall 2018 - Fall 2020, then did CS master at NAU, then got job offer at Honeywell.
• Arnaud Liehrmann, master intern, Spring-Summer 2020, then did PHD at Centre Borelli, Paris-Saclay University.
• Darien Reyes-Tadeo, CS undergrad, Spring 2020.
• Andrew Hurst, CS undergrad, Fall 2019 - Spring 2020, then worked as Software Developer at General Motors in Atlanta.
• Jinming Yang, CS master, Fall 2019 - Spring 2020, then worked as software development engineer at Sangfor Tech (cybersecurity company in ShenZhen, China specializing in Cloud Computing & Network Security).
• Zaoyi Chi, EE master, Fall 2019 - Spring 2020.
• Atiyeh Fotoohinasab, Informatics and Computing PHD, Summer 2019 - Spring 2020, then did University of Arizona Biomedical Engineering PHD program.
• Anuraag Srivastava, CS master, Fall 2018 - Spring 2020, then went to Chicago to work as a software engineer for a company.
• Brandon Dunn, EE master, Fall 2018 - Spring 2019, then went to Raytheon.

ASU West ML day, Apr 2024

Left to right: Tung, Toby, Doris, Bilal, Danny.

Cross-country ski day, Feb 2024

Left to right: Toby, Tung, Doris, Danny.

ML group meeting, Sep 2023

Left to right: Richard, Trevor, Bilal, Danny, Toby, Doris, Karl. We are showing “six” fingers for SICCS (School of Informatics, Computing and Cyber Systems).

SICCS ML lab at ASU West ML Day, April 2023

Left to right: Bilal, Toby, Anirban, Danny, Austin, Jadon.

ML group meeting, Feb 2023

Left to right: Austin, Jadon, Danny, Toby, Bilal, Cam.

SICCS camping, Oct 2022

Left to right: Kyle, Toby, Danny, Basil.

ML group meeting, Oct 2022

Left to right: Cam, Bilal, Toby, Danny, Kyle, Gabi.

RcppDeepState project members, Oct 2021

Left to right: Toby, Alex, Akhila.

First fall semester group meeting, Aug 2021

Left to right: Tristan, Anirban, Toby, Shadmaan, Akhila, Balaji, Kyle.

Downhill ski day, Mar 2021

Left to right: Toby, Jon, Tristan, Frank, Akhila, Alyssa.

Summer Pizza Party, July 2020

Foreground: Akhila, left to right in the back: Benoît, Arnaud, Toby, Basil, John.

Winter Pizza Party, Feb 2020

Left to right: Atiyeh, Darien, Joe, Arnaud, Benoît, Toby.
We are showing “six” fingers for SICCS (School of Informatics, Computing and Cyber Systems).

Hiking in Sedona, Dec 2019

Left to right: Joe, Toby, Farnoosh, Atiyeh, Anuraag, Zaoyi.

Google Summer of Code students mentored

I have mentored the following students in coding free/open-source software.

• Siddesh Deodhar, 2024, chromote and plot layout for animint2.
• Josh Wu, 2024, new features for data.table.
• Nitish Jha, 2024, closing data.table issues.
• Arthur Pan, 2023, polars in R.
• Jocelyne Chen, 2023, animint2 documentation and bug fixes (primary mentor).
• Yufan Fei, 2022-2023, animint2: interactive grammar of graphics (primary mentor).
• Fabrizio Sandri, 2022, RcppDeepState: github action for fuzz testing C++ code in R packages (primary mentor).
• Daniel Agyapong, 2022, Rperform github action for performance testing R packages (primary mentor).
• Anirban Chetia, 2021, directlabels improvements (primary mentor).
• Diego Urgell, 2021, BinSeg efficient C++ implementation of binary segmentation (primary mentor).
• Mark Nawar, 2021, re2r back on CRAN (primary mentor).
• Sanchit Saini, 2020, rtracklayer R package improvements.
• Himanshu Singh, 2020, animint2: interactive grammar of graphics (primary mentor).
• Julian Stanley, 2020, Graphical User Interface for gfpop R package (primary mentor).
• Anirban Chetia, 2020, testComplexity R package (primary mentor).
• Anuraag Srivastava, 2019, Optimal Partitioning algorithm and opart R package (primary mentor).
• Avinash Barnwal, 2019, AFT and Binomial loss functions for xgboost (primary mentor).
• Aditya Sam, 2019, Elastic net regularized interval regression and iregnet R package (primary mentor).
• Alan Williams, 2018, SegAnnDB: machine learning system for DNA copy number analysis, blog (primary mentor).
• Vivek Kumar, 2018, animint2: interactive grammar of graphics, blog (primary mentor).
• Johan Larsson, 2018, sgdnet: SAGA algorithm for sparse linear models.
• Marlin Na, 2017, TnT: interactive genome browser.
• Rover Van, 2017, iregnet: regularized interval regression (primary mentor).
• Faizan Khan, 2016–2017, animint: interactive grammar of graphics (primary mentor).
• Abhishek Shrivastava, 2016, SegAnnDB: interactive system for labeling and machine learning in genomic data (primary mentor).
• Anuj Khare, 2016, iregnet: regularized interval regression (primary mentor).
• Qin Wenfeng, 2016, re2r: regular expressions (primary mentor).
• Akash Tandon, 2016, Rperform: performance testing R packages (primary mentor).
• Ishmael Belghazi, 2015, bigoptim: stochastic average gradient algorithm (primary mentor).
• Kevin Ferris, 2015, animint: interactive grammar of graphics (primary mentor).
• Tony Tsai, 2015, animint: interactive grammar of graphics (primary mentor).
• Carson Sievert, 2014, animint: interactive grammar of graphics (primary mentor).
• Susan VanderPlas, 2013, animint: interactive grammar of graphics (primary mentor).