• About
• Projects
• Publications
• Teaching

2023

• AI 539: Machine Learning Challenges in the Real World (Oregon State University)

2022

• AI 539: Machine Learning Challenges in the Real World (Oregon State University)

2021

• HC 407: How to be Less Wrong: A Study in Common Misconceptions (Oregon State University, co-taught with Andy Olstad)

2020

• CS 161: Introduction to Computer Science I (Oregon State University)

2011

• CS 407H / HC 407: The Evolution of Computing and its Impact on History (honors seminar) (Oregon State University)

2009

• CS 461: Machine Learning (California State University, Los Angeles)

2008

• CS 461: Machine Learning (California State University, Los Angeles)

2007

• CS 203: Programming with Data Structures (California State University, Los Angeles)

2001

• CS 100: Introduction to Java Programming (Cornell University)

• James Bedell (Princeton University, B.S. '14, USC M.S. '17)
  • "Interactive Discovery in the Kepler Mission Dataset" (summer 2013)
  • "Anomaly detection with DEMUD in image processing and with missing data" (summer 2014)
• Ameya Daigavane (Indian Institute of Technology, '20, now at MIT)
  • "Time-Series Methods for Onboard Analysis of PIMS Data" (summer 2019)
• Andrew Dassonville (Oregon State University, B.S. '23)
  • "Improving Pilot-Controller Communication through the use of Artificial Intelligence for Real-Time Radio Analysis" (design project for the 2022 ACRP national competition); summary and interview with Andrew
• Jillian Green (California State University, L.A., '10)
  • "The Explosive Potential of Collaborative Machine Learner Networks" (summer 2009)
• Bryan He (Caltech, B.S. '15, now at Stanford)
  • "Automated Image-Based Change Detection for Instrument Planning" (summer 2012)
• Michael Kocurek (Caltech, B.S. '07, now at Google)
  • "The Optimization of Trained and Untrained Image Classification Algorithms for Use on Large Spatial Datasets" (summer 2005)
  • "Improving HARVIST: Using Progressive Trained Classifiers and Multisource Regression to Predict Crop Yield" (summer 2006)
• Jake Lee (Columbia University, B.S. '19, M.S. '20, now at JPL)
  • "Detecting and Explaining Unexpected Image Content" (summer 2017)
  • "Applying and Improving Interpretable Discovery in Large Image Data Sets" (summer 2018)
  • "Machine Learning for Planetary Science Images to Support Content-Based Search and Discovery" (summer 2019)
• Julian Panetta (Caltech, B.S. '10; NYU, Ph.D. '17, now at UC Davis)
  • "Using Image Processing and Machine Learning to Detect and Classify Mars Surface Features" (summer 2008)
  • "Detecting Mars Surface Change Using Landmark Comparison" (summer 2009)
• Alex Roper (Caltech, B.S. '09; Univ. of Michigan, M.S. '14; now at Google)
  • "Using Machine Learning to Predict Crop Yield from Multispectral Satellite Images" (summer 2007)
• Colorado Reed (University of Iowa, B.S. '12, University of Cambridge, M.Phil. '13; UC Berkeley, Ph.D. '23; now at Apple)
• Henry Shu (Caltech, B.S. '05, Cornell, M.S. '11; now at SpeedyAI)
  • "Clustering with Nonuniform Feature Space Dimensionality" (summer 2004)
• Scott Sosnowski (Case Western Reserve University, B.S. '10; M.S. '16)
• Matthew Wu (Caltech, '19)
  • "Generalizing an Automated Detection Pipeline for Possible Indicators of Subsurface Water on Mars" (summer 2017)

• Brian Bue (Rice University, Ph.D. '13; now at JPL)
  • I served as Brian's GSRP Fellowship mentor, 2007-2010, and on his dissertation committee 2010-2013.
  • Dissertation: "Adaptive Similarity Measures for Material Identification in Hyperspectral Imagery"
• Srija Chakraborty (Arizona State University, Ph.D. '19; now a postdoc at NASA Goddard)
  • JPL internship in 2018: "Spectral Anomaly Detection for Europa Clipper Onboard Science"
• Gary Doran (Case Western Reserve University, Ph.D. '14; now at JPL)
  • I served on Gary's dissertation committee 2012-2014.
  • Dissertation: "Multiple-Instance Learning from Distributions"
  • During a JPL internship in 2012, Gary investigated the use of unsupervised analysis and active learning to assess sources of radio frequency interference in data collected by large radio telescopes.
• Thamme Gowda (University of Southern California, M.S. '17, Ph.D. '22; now at Microsoft Translator)
  • During a JPL internship from 2016 to 2017, Thamme contributed many innovations and advances to the Mars Target Encyclopedia project and deep learning for planetary image classification.
• Amit Jain (California State University, L.A., M.S. '09)
  • I served as an external advisor for Amit's Master's thesis work on evaluating collaborative recommendation systems, 2008-2009.
• Hannah Kerner (Arizona State University, Ph.D. '19, now at Arizona State University)
  • I served as a member of Hannah's dissertation committee, 2018-2019.
  • Dissertation: "Machine Learning on Mars: A New Lens on Data from Planetary Exploration Missions"
  • During two JPL internships, Hannah developed deep learning methods for change detection in orbital remote sensing images for planetary science (2018) and novelty detection methods in Mars rover image data sets (2019).
• Umaa Rebbapragada (Tufts University, Ph.D. '10; now at JPL)
  • I served as a member of Umaa's dissertation committee, 2009-2010.
  • Dissertation: "Strategic Targeting of Outliers for Expert Review"
  • During a JPL internship in 2008, Umaa developed methods for collaborative learning in sensor networks.
• Qianjun Xu (University of Maryland, Baltimore County, Ph.D. '06, now at Microsoft)
  • I served as a member of Qianjun's dissertation committee, 2004-2006.
  • Dissertation: "Active Querying for Semi-supervised Clustering"
• Yuan Zhuang (University of Utah)
  • During a JPL internship in 2021, Yuan investigated methods for performing relation extraction in scientific publications for the Mars Target Encyclopedia project.