Teaching
Teaching philosophy
For students to thrive and be successful, it is important for them to understand why they are learning something and to feel invested in learning the concepts being taught. The motivation for a topic should come first, not as an afterthought. My teaching style is interactive and problem-based as I encourage students to engage with the material and think about how it relates to things they are learning and experiencing outside the classroom. Students can find limitless resources to learn computer science and AI concepts online. I feel that my role as their instructor is to not just teach them the concepts in the course syllabus but, as with some machine learning models, equip them with better meta-learning tools that enable them to learn new concepts and tools more efficiently in the future.
Courses at ASU
- Spring 2024: Machine Learning for Remote Sensing (CSE 598)
- Fall 2023: Foundations of Machine Learning (CSE 475)
- Spring 2023: Data Mining (CSE 572)
- Fall 2022: Data Mining (CSE 572)
Courses elsewhere
- Spring 2022: Open Source GIS (GEOG 670, University of Maryland)
- Spring 2015: CS for People Who Don’t Know CS (Yet!) (UNC Chapel Hill)
Guest lectures
- November 2023: GEOG272 Introduction to Earth Observation Science (University of Maryland, Instructor: Catherine Nakalembe)
- July 2023: Introduction to AI for Agriculture (Climate Change AI Summer School), [link]
- Feb 2023: Machine Learning Challenges in the Real World (Oregon State Univ., Instructor: Kiri Wagstaff)
- Aug 2022, 2023: Computer Vision for Ecology (CV4E) Summer School (Caltech) [2022 link]
- Fall 2019, 2021, 2022: Coding for Exploration (Arizona State Univ., Instructor: JD Das)
- July 2021: PRAIRIE AI summer school (PAISS)
- Spring 2021: Remote Sensing for Sustainable Development (Univ. of Strasbourg, Instructor: Inbal Becker-Reshef)
- Spring 2020: Remote Sensing (UMD, Instructor: Mong-Han Huang)
Tutorials and capacity building
- May 2023: Machine Learning for Remote Sensing Summit, hosted in Kigali, Rwanda at CMU Africa and led by ASU, NASA Harvest, and CMU (https://nasaharvest.github.io/ml-for-remote-sensing/iclr2023/tutorial)
- Feb 2023: ‘Āina Data Stewards program kickoff, hosted at University of Hawaii Maui College and led by ASU, UMD, and NASA Harvest/ACRES consortium team members (https://nasaharvest.github.io/mauinui#data)
- Sep 2022: Argentina Multilateral training program led by NASA Harvest, University of Maryland (UMD), the Buenos Aires Grain Exchange (Bolsa de Cereales), the Argentinian Ministry of Education, and the International Development Bank [article]
- Aug 2022: Scalable Cropland Mapping Workshop, hosted at University of Maryland for Regional Centre on Mapping Resources and Development (RCMRD) [agenda]
- Jun 2022: Machine Learning for Remote Sensing and Applications in Agriculture and Food Security tutorial (invited) at Computer Vision and Pattern Recognition (CVPR) [agenda]
- Mar 2022: AI and Earth Observations for Agriculture tutorials led by UMD and NASA Harvest team members at Regional Centre on Mapping Resources and Development (RCMRD) and Rwanda Space Agency (RSA)
- Sep 2021: Crop Yield Modeling Workshop, Regional Center for Mapping of Resources for Development (RCMRD) (NASA Harvest and NASA SERVIR, PI: Nakalembe) [article]
- Aug-Dec 2021, 2022: Course on remote sensing and ML technologies for agriculture, part of Educational program to strengthen agricultural and agroindustrial educational establishments of the Province of Buenos Aires, on agricultural estimates and new applied technologies (NASA Harvest and Bolsa de Cereales) [article]
Outreach
- 2020-: From Prison Cells to Ph.D. (mentor)
- 2019-2022: Brooke Owens Fellowship (reviewer/mentor)
- 2018-2019: ASU Prison Education Program, Adobe Mountain School (instructor)
- 2016-2019: Girls Who Code, Maie Bartlett Heard K-8 School (instructor)