Research
Preprints and in submission
- Verchand, K.A., Pensia, A., Haque, S., Kuditipudi, R. (2026), High-dimensional estimation with missing data: Statistical and computational limits, preprint.
- Celentano, M., Cheng, C., Pananjady, A., and Verchand, K.A. (2025), State evolution beyond first-order methods I: Rigorous predictions and finite-sample guarantees,preprint.
- Chandrasekher, K.A., El Alaoui, A., and Montanari, A. (2020), Imputation for High-Dimensional Linear Regression, preprint.
- Mardia, J., Asi, H., Chandrasekher, K.A. (2020), Finding Planted Cliques in Sublinear Time, preprint.
Publications
- Ma, T., Verchand, K.A., Berrett, T.B., Wang, T., and Samworth, R.J. (2026+), Estimation beyond Missing (Completely) at Random, Annals of Statistics (to appear).
- Verchand, K.A. and Montanari, A. (2026+), High-dimensional logistic regression with missing data: Imputation, regularization, and universality, SIAM Journal on Mathematics of Data Science (SIMODS), (to appear).
- Lou, M., Verchand, K.A., Fridovich-Keil, S., Pananjady, A. (2026), Accurate, provable and fast polychromatic tomographic reconstruction: A variational inequality approach, SIAM Journal on Imaging Sciences (SIIMS).
- Lou, M., Verchand, K.A., and Pananjady, A. (2025), Hyperparameter tuning via trajectory predictions: Stochastic prox-linear methods in matrix sensing, Mathematical Programming: Series B.
- Ma, T., Verchand, K.A., and Samworth, R.J. (2025+), High-probability minimax lower bounds, Statistical Science (to appear).
- Mardia, J., Verchand, K.A., and Wein, A.S. (2024), Low-degree phase transitions for detecting a planted clique in sublinear time, Conference on Learning Theory (COLT).
- Chandrasekher, K.A., Lou, M., and Panajady, A. (2024), Alternating minimization for generalized rank one matrix sensing: Sharp predictions from a random initialization, Information and Inference: A Journal of the IMA.
- Chandrasekher, K.A., Pananjady, A., and Thrampoulidis, C. (2023), Sharp global convergence guarantees for iterative nonconvex optimization: A Gaussian process perspective, Annals of Statistics. Runner-up: Best paper prize for young researchers in continuous optimization, Mathematical Optimization Society
- Cheng, G., Chandrasekher, K.A., and Walrand, J. (2019), Static & Dynamic Appointment Scheduling with Stochastic Gradient Descent, American Control Conference (ACC).
- Lee, K., Chandrasekher, K.A., Pedarsani, R., and Ramchandran, K. (2019), SAFFRON: A Fast, Efficient, and Robust Framework for Group Testing Based on Sparse-Graph Codes, IEEE Transactions on Signal Processing.
- Lazar, D., Chandrasekher, K.A., Pedarsani, R., and Sadigh, D. (2018), Maximizing Road Capacity Using Cars that Influence People, Conference on Decision and Control (CDC).
- Chandrasekher, K.A., Lee, K., Kairouz, P., Pedarsani, R., and Ramchandran, K. (2017), Asynchronous and Noncoherent Neighbor Discovery for the IoT Using Sparse-Graph Codes, International Conference on Communications (ICC).
- Chandrasekher, K.A., Ocal, O., and Ramchandran, K. (2017), Density Evolution on a Class of Smeared Random Graphs: A Theoretical Framework for Fast MRI, International Symposium on Information Theory (ISIT).