I broadly work on the foundations of machine learning -- specifically
the study and analysis of empirical phenomena.
In particular I have studied the robustness of neural networks under various distribution shifts, and the connections between
neural networks and classical kernel machines.
I currently work in a special projects team at Amazon.
Previously I spent 9 wonderful years at UC Berkeley, where I had the pleasure of working under Ben Recht
You can contact me at vaishaal at gmail dot com
Taori, R., Dave, A., Shankar, V., Carlini, N., Recht, B., Schmidt, L. (2020). Measuring Robustness to Natural Distribution Shifts in Image Classification. arXiv preprint arXiv:2007.06444 pdf
Shankar, V.*, Dave, A.*, Roelofs, R., Ramanan, D., Recht, B., & Schmidt, L. (2019). Do Image Classifiers Generalize Across Time? arXiv preprint arXiv:1906.02168. pdf
Shankar, V., Krauth, K., Pu, Q., Jonas, E., Venkataraman, S., Stoica, I., Recht, B., & Ragan-Kelley, J. (2018). numpywren: serverless linear algebra. arXiv preprint arXiv:1810.09679 pdf
Shankar, V., Fang, A., Guo, W., Fridovich-Keil, S., Schmidt, L., Ragan-Kelley, J., & Recht, B. (2020). Neural Kernels Without Tangents. In Proceedings of International Conference on Machine Learning (ICML) pdf
Shankar, V*., Roelofs, B*, Mania, H., Fang, A., Recht, B., Schmidt, L. (2020). Evaluating Machine Accuracy on ImageNet. In Proceedings of International Conference on Machine Learning (ICML) 2020. pdf
Roelofs, R.,Fridovich-Keil, S. Miller, J., Shankar, V., Hardt, M., Recht, B., & Schmidt, L. (2019). A Meta-Analysis of Overfitting in Machine Learning. In Advances in Neural Information Processing Systems (pp. 9175-9185).
Recht, B., Roelofs, R., Schmidt, L. & Shankar, V.. (2019). Do ImageNet Classifiers Generalize to ImageNet?. Proceedings of the 36th International Conference on Machine Learning pdf
Jonas, E., Bobra, M., Shankar, V., Hoeksema, J. T., & Recht, B. (2018). Flare prediction using photospheric and coronal image data. Solar Physics, 293(3), 48.
Morrow, A., Shankar, V, Petersohn, D., Yosef, N., Recht, B., Joseph, A.D (2016, December). Convolutional Kitchen Sinks for Transcription Factor Binding Site Prediction. NIPS Workshop on Machine Learning in Computational Biology pdf talk
Shankar, V., Zhang, J., Chen, J., Dinh, C., Clements, M., & Zakhor, A. (2016, February). Approximate Subgraph Isomorphism for Image Localization. International Symposium on Electronic Imaging
Shankar, V., & Culler, D. (2015, March). A Modern Student Experience in Systems Programming. In Proceedings of the Second (2015) ACM Conference on Learning@ Scale (pp. 233-236). ACM.
Kantchelian, A., Tschantz, M. C., Afroz, S., Miller, B., Shankar, V., Bachwani, V., Bachwani, R., Joseph, A.D. & Tygar, J. D. (2015, October). Better malware ground truth: Techniques for weighting anti-virus vendor labels. In Proceedings of the Eigth ACM Workshop on Artificial Intelligence and Security (pp. 45-56). ACM.
Miller, B., Kantchelian, A., Tschantz, M.C., Afroz, S., Bahwani, R., Faizullabhoy, R., Huang, L., Shankar, V., Wu, T., Yiu, G., Joseph, A.D. & Tygar, J. D (2016 July). Reviewer Integration and Performance Measurement for Malware Detection. 13th Conference on Detection of Intrusions and Malware & Vulnerability Assessment
Iliopoulos, F., Moulous, V., Shankar, V., & Simchowitz, M. Gradients for the Loss!.
Git for advanced beginners - Git slide deck I made a couple years ago
Tutorials - A list of tutorials on various mathy things curated by my good friend Achal Dave
Is Thirteen? - I have nothing to do with this but very useful tool.