Published:
I am excited to announce that I was selected by the ARCS Foundation as a 2025-2026 Horejsi Scholar. This is a fantastic honor and validates the importance of our work in fair and robust machine learning. I am extremely grateful to the ARCS Foundation and Mr. Stewart Horejsi whose generous donation made the award possible.
Published:
I am happy to announce that my paper entitled “Robustness to Subpopulation Shift with Domain Label Noise via Regularized Annotation of Domains” has been accepted to the Transactions on Machine Learning Research (TMLR). It’s a nice work that introduces a new two-stage last layer model intervention which encourages group-fair predictions without knowledge of the groups.
Published:
I am excited to announce that a paper on which I am first author was accepted to NeurIPS 2024. I am grateful to my collaborators: Rohan Ayyagari (ASU), Monica Welfert (ASU), Sanmi Koyejo (Stanford), Richard Nock (Google), and Lalitha Sankar (ASU). It is a really cool work on making state-of-the-art fairness interventions robust to labels noise. Find the paper here. I am looking forward to meeting with some fantastic (future) collaborators!
Published:
I am excited to announce that I was selected by the ARCS Foundation as a 2025-2026 Horejsi Scholar. This is a fantastic honor and validates the importance of our work in fair and robust machine learning. I am extremely grateful to the ARCS Foundation and Mr. Stewart Horejsi whose generous donation made the award possible.
Published:
I am excited to announce that I was selected by the ARCS Foundation as a 2025-2026 Horejsi Scholar. This is a fantastic honor and validates the importance of our work in fair and robust machine learning. I am extremely grateful to the ARCS Foundation and Mr. Stewart Horejsi whose generous donation made the award possible.
Published:
I am excited to announce that a paper on which I am first author was accepted to NeurIPS 2024. I am grateful to my collaborators: Rohan Ayyagari (ASU), Monica Welfert (ASU), Sanmi Koyejo (Stanford), Richard Nock (Google), and Lalitha Sankar (ASU). It is a really cool work on making state-of-the-art fairness interventions robust to labels noise. Find the paper here. I am looking forward to meeting with some fantastic (future) collaborators!
Published:
I am happy to announce that my paper entitled “Robustness to Subpopulation Shift with Domain Label Noise via Regularized Annotation of Domains” has been accepted to the Transactions on Machine Learning Research (TMLR). It’s a nice work that introduces a new two-stage last layer model intervention which encourages group-fair predictions without knowledge of the groups.
Published:
I am excited to announce that a paper on which I am first author was accepted to NeurIPS 2024. I am grateful to my collaborators: Rohan Ayyagari (ASU), Monica Welfert (ASU), Sanmi Koyejo (Stanford), Richard Nock (Google), and Lalitha Sankar (ASU). It is a really cool work on making state-of-the-art fairness interventions robust to labels noise. Find the paper here. I am looking forward to meeting with some fantastic (future) collaborators!
Published:
I am happy to announce that my paper entitled “Robustness to Subpopulation Shift with Domain Label Noise via Regularized Annotation of Domains” has been accepted to the Transactions on Machine Learning Research (TMLR). It’s a nice work that introduces a new two-stage last layer model intervention which encourages group-fair predictions without knowledge of the groups.
Published:
I am excited to announce that a paper on which I am first author was accepted to NeurIPS 2024. I am grateful to my collaborators: Rohan Ayyagari (ASU), Monica Welfert (ASU), Sanmi Koyejo (Stanford), Richard Nock (Google), and Lalitha Sankar (ASU). It is a really cool work on making state-of-the-art fairness interventions robust to labels noise. Find the paper here. I am looking forward to meeting with some fantastic (future) collaborators!
Published:
I am happy to announce that my paper entitled “Robustness to Subpopulation Shift with Domain Label Noise via Regularized Annotation of Domains” has been accepted to the Transactions on Machine Learning Research (TMLR). It’s a nice work that introduces a new two-stage last layer model intervention which encourages group-fair predictions without knowledge of the groups.