Publications

Preprints and in submission

  1. Lou, M., Verchand, K.A., Fridovich-Keil, S., Pananjady, A. (2025), Accurate, provable, and fast nonlinear tomographic reconstruction: A variational inequality approach, (preprint)
    • Preliminary version at the 18th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully3D 2025)

  2. Ma, T., Verchand, K.A., Berrett, T.B., Wang, T., and Samworth, R.J. (2024), Estimation beyond Missing (Completely) at Random, (preprint)

  3. Verchand, K.A. and Montanari, A. (2024), High-dimensional logistic regression with missing data: Imputation, regularization, and universality (preprint)

  4. Lou, M., Verchand, K.A., and Pananjady, A. (2024), Hyperparameter tuning via trajectory predictions: Stochastic prox-linear methods in matrix sensing ( preprint)
    • Preliminary version at Workshop on High-dimensional Learning Dynamics, ICML 2023 (Oral)

Journal publications

  1. Ma, T., Verchand, K.A., and Samworth, R.J. (2025+), High-probability minimax lower bounds, Statistical Science (to appear).

  2. 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.

  3. 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

  4. 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.

Conference proceedings

  1. Chandrasekher, K.A., Lou, M., and Panajady, A. (2024), Alternating minimization for generalized rank one matrix sensing: Sharp predictions from a random initialization, (Extended abstract at Algorithmic Learning Theory (ALT); Superseded by journal version.)
    • Preliminary version at Workshop on The Benefits of Higher-Order Optimization in Machine Learning, Neurips 2022 (Oral).

  2. 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).

  3. Cheng, G., Chandrasekher, K.A., and Walrand, J. (2019), Static & Dynamic Appointment Scheduling with Stochastic Gradient Descent, American Control Conference (ACC).

  4. 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).

  5. 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).

  6. 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).

Thesis

Unpublished technical reports

  1. Chandrasekher, K.A., El Alaoui, A., and Montanari, A. (2020), Imputation for High-Dimensional Linear Regression (preprint).

  2. Mardia, J., Asi, H., Chandrasekher, K.A. (2020), Finding Planted Cliques in Sublinear Time (preprint).