129 citations · 586 across the 26 of their papers we have counts for
26 papers
AAAI-26 Dual Submissions: Novel Challenges
Kiri L. Wagstaff, Joydeep Biswas, Erich Merrill +4
Dual submissions, in which identical or substantially similar papers are simultaneously submitted to one or more archival venues, without cross-citation or disclosure, are a growin…
Position: Every Ground Truth is a Human Construction, not an Objective Truth
Charlotte Högberg, Ericka Johnson, Kiri L. Wagstaff
Ground truth datasets play a fundamental role as reference values in the training and evaluation of machine learning models. This position paper argues that ground truths are not n…
AI-Assisted Peer Review at Scale: The AAAI-26 AI Review Pilot
Joydeep Biswas, Sheila Schoepp, Gautham Vasan +10
Scientific peer review faces mounting strain as submission volumes surge, making it increasingly difficult to sustain review quality, consistency, and timeliness. Recent advances i…
Realistic Handwritten Multi-Digit Writer (MDW) Number Recognition Challenges
Kiri L. Wagstaff
Isolated digit classification has served as a motivating problem for decades of machine learning research. In real settings, numbers often occur as multiple digits, all written by…
Evaluating Terrain-Dependent Performance for Martian Frost Detection in Visible Satellite Observations
Gary Doran, Serina Diniega, Steven Lu +2
Seasonal frosting and defrosting on the surface of Mars is hypothesized to drive both climate processes and the formation and evolution of geomorphological features such as gullies…
Interactive Mars Image Content-Based Search with Interpretable Machine Learning
Bhavan Vasu, Steven Lu, Emily Dunkel +3
The NASA Planetary Data System (PDS) hosts millions of images of planets, moons, and other bodies collected throughout many missions. The ever-expanding nature of data and user eng…
Data-Driven Approaches to Searches for the Technosignatures of Advanced Civilizations
T. Joseph W. Lazio, S. G. Djorgovski, Andrew Howard +19
Humanity has wondered whether we are alone for millennia. The discovery of life elsewhere in the Universe, particularly intelligent life, would have profound effects, comparable to…
Cosmic Microwave Background Recovery: A Graph-Based Bayesian Convolutional Network Approach
Jadie Adams, Steven Lu, Krzysztof M. Gorski +2
The cosmic microwave background (CMB) is a significant source of knowledge about the origin and evolution of our universe. However, observations of the CMB are contaminated by fore…
Using Machine Learning to Reduce Observational Biases When Detecting New Impacts on Mars
Kiri L. Wagstaff, Ingrid J. Daubar, Gary Doran +5
The current inventory of recent (fresh) impacts on Mars shows a strong bias towards areas of low thermal inertia. These areas are generally visually bright, and impacts create dark…
Hidden Heterogeneity: When to Choose Similarity-Based Calibration
Kiri L. Wagstaff, Thomas G. Dietterich
Trustworthy classifiers are essential to the adoption of machine learning predictions in many real-world settings. The predicted probability of possible outcomes can inform high-st…
Integrating Novelty Detection Capabilities with MSL Mastcam Operations to Enhance Data Analysis
Paul Horton, Hannah R. Kerner, Samantha Jacob +3
While innovations in scientific instrumentation have pushed the boundaries of Mars rover mission capabilities, the increase in data complexity has pressured Mars Science Laboratory…
Mars Image Content Classification: Three Years of NASA Deployment and Recent Advances
Kiri Wagstaff, Steven Lu, Emily Dunkel +8
The NASA Planetary Data System hosts millions of images acquired from the planet Mars. To help users quickly find images of interest, we have developed and deployed content-based c…
Visualizing Image Content to Explain Novel Image Discovery
Jake H. Lee, Kiri L. Wagstaff
The initial analysis of any large data set can be divided into two phases: (1) the identification of common trends or patterns and (2) the identification of anomalies or outliers t…
Interpretable Discovery in Large Image Data Sets
Kiri L. Wagstaff, Jake Lee
Automated detection of new, interesting, unusual, or anomalous images within large data sets has great value for applications from surveillance (e.g., airport security) to science…
A Machine Learning Classifier for Fast Radio Burst Detection at the VLBA
Kiri L. Wagstaff, Benyang Tang, David R. Thompson +6
Time domain radio astronomy observing campaigns frequently generate large volumes of data. Our goal is to develop automated methods that can identify events of interest buried with…
Limits on Fast Radio Bursts from Four Years of the V-FASTR Experiment
Sarah Burke-Spolaor, Cathryn M. Trott, Walter F. Brisken +7
The V-FASTR experiment on the Very Long Baseline Array was designed to detect dispersed pulses of milliseconds duration, such as fast radio bursts (FRBs). We use all V-FASTR data t…
Online classification for time-domain astronomy
Kitty K. Lo, Tara Murphy, Umaa Rebbapragada +1
The advent of synoptic sky surveys has spurred the development of techniques for real-time classification of astronomical sources in order to ensure timely follow-up with appropria…
Comparing the WFC3 IR Grism Stare and Spatial-Scan Observations for Exoplanet Characterization
Mark R. Swain, Pieter Deroo, Kiri L. Wagstaff
We report on a detailed study of the measurement stability for WFC3 IR grism stare and spatial scan observations. The excess measurement noise for both modes is established by comp…
Real Time Event Detection in Astronomical Data Streams: Lessons from the VLBA
David R. Thompson, Sarah Burke-Spolaor, Adam T. Deller +5
A new generation of observational science instruments is dramatically increasing collected data volumes in a range of fields. These instruments include the Square Kilometre Array (…
A framework for interpreting fast radio transients search experiments: application to the V-FASTR experiment
Cathryn M. Trott, Steven J. Tingay, Randall B. Wayth +8
We define a framework for determining constraints on the detection rate of fast transient events from a population of underlying sources, with a view to incorporating beam shape, f…
VAST: An ASKAP Survey for Variables and Slow Transients
Tara Murphy, Shami Chatterjee, David L. Kaplan +36
The Australian Square Kilometre Array Pathfinder (ASKAP) will give us an unprecedented opportunity to investigate the transient sky at radio wavelengths. In this paper we present V…
Machine Learning that Matters
Kiri Wagstaff
Much of current machine learning (ML) research has lost its connection to problems of import to the larger world of science and society. From this perspective, there exist glaring…
Limits on the event rates of fast radio transients from the V-FASTR experiment
Randall B. Wayth, Steven J. Tingay, Adam T. Deller +4
We present the first results from the V-FASTR experiment, a commensal search for fast transient radio bursts using the Very Long Baseline Array (VLBA). V-FASTR is unique in that th…
V-FASTR: The VLBA Fast Radio Transients Experiment
Randall B. Wayth, Walter F. Brisken, Adam T. Deller +4
Recent discoveries of dispersed, non-periodic impulsive radio signals with single-dish radio telescopes have sparked significant interest in exploring the relatively uncharted spac…