Exploiting the Right: Inferring Ideological Alignment in Online Influence Campaigns Using Shared Images. - Amogh Joshi 004; Cody Buntain (ICWSM Workshops, 2022)

Standardizing and Centralizing Datasets to Enable Efficient Training of Agricultural Deep Learning Models. - Amogh Joshi 004; Dario Guevara; Mason Earles (CoRR, 2022)

An Open Source Simulation Toolbox for Annotation of Images and Point Clouds in Agricultural Scenarios. - Dario Guevara; Amogh Joshi 004; Pranav Raja; Elizabeth Forrestel; Brian N. Bailey; Mason Earles (ISVC (1), 2023)

Examining Similar and Ideologically Correlated Imagery in Online Political Communication. - Amogh Joshi 004; Cody Buntain (ICWSM, 2024)

Understanding the Limits of Vision Language Models Through the Lens of the Binding Problem. - Declan Campbell; Sunayana Rane; Tyler Giallanza; Nicolò De Sabbata; Kia Ghods; Amogh Joshi 004; Alexander Ku; Steven Frankland; Tom Griffiths 001; Jonathan D. Cohen 003; Taylor W. Webb (NeurIPS, 2024)

Neural Light Spheres for Implicit Image Stitching and View Synthesis. - Ilya Chugunov; Amogh Joshi 004; Kiran Murthy; Francois Bleibel; Felix Heide (SIGGRAPH Asia, 2024)

iNatAg: Multi-Class Classification Models Enabled by a Large-Scale Benchmark Dataset with 4.7M Images of 2, 959 Crop and Weed Species. - Naitik Jain; Amogh Joshi 004; Mason Earles (CVPR Workshops, 2025)

iNatAg: Multi-Class Classification Models Enabled by a Large-Scale Benchmark Dataset with 4.7M Images of 2,959 Crop and Weed Species. - Naitik Jain; Amogh Joshi 004; Mason Earles (CoRR, 2025)