Kamiar Rahnama Rad

Education

Ph.D., Statistics, Columbia University

M.S., Electrical Engineering, UCLA

B.S., Electrical Engineering, Sharif University

Journal Articles

(2024). Theoretical Analysis of Leave-one-out Cross Validation for Non-differentiable Penalties under High-dimensional Settings. IEEE Transactions on Information Theory,

(2024). Scalable implementations of approximate leave-one-out cross validation for risk estimation. In Progress.

(2024). Theoretical Analysis of Leave-one-out Cross Validation for Non-differentiable Penalties under High-dimensional Settings Theoretical Analysis of Leave-one-out Cross Validation for Non-differentiable Penalties under High-dimensional Settings. In Progress.

(2023). A mixed method exploration of individual and network-level factors and Type 2 Diabetes Mellitus (T2DM) among Mexican American adults in New York City. Plos One,

Rahnama Rad, K., Yue, Y., & Mejia, A. (2023). Scalable and Fully Bayesian Trend Filtering on Large Graphs. Journal of Machine Learning Research (Revise and resubmit),

Xu, J., Maleki, A., Rahnama Rad, K., & Hsu, D. (2021). Consistent Risk Estimation in High-Dimensional Linear Regression. IEEE Transactions on Information Theory, 67(9). 5997-6030.

Rahnama Rad, K., & Maleki, A. (2020). A scalable estimate of the out-of-sample prediction error via approximate leave-one-out. Journal of the Royal Statistical Society: Series B, 84(2). 965-996.

Rahnama Rad, K., Machado, T., & Paninski, L. (2017). Robust and scalable Bayesian analysis of spatial neural tuning function data. The Annals of Applied Statistics, 11(2). 598-637.

Pnevmatikakis, E., Rahnama Rad, K., Huggins, J., & Paninski, L. (2014). Fast Kalman filtering and forward-backward smoothing via a low-rank perturbative approach. Journal of Computational and Graphical Statistics, 23(2). 316-339.

Molavi, P., Jadbabaie, A., Rahnama Rad, K., & Tahbaz-Salehi, A. (2013). Reaching Consensus with increasing information. IEEE Journal of Selected Topics in Signal Processing, 7(2). 358-370.

(2011). Nearly sharp sufficient conditions on exact sparsity pattern recovery. IEEE Transactions Information Theory, 57(7). 4672- 4679.

(2010). Efficient estimation of two-dimensional firing rate surfaces via gaussian process methods. Network: Computation in Neural Systems, 21. 142- 168.

(2009). Mean-field approximations for coupled populations of generalized linear model spiking neurons with Markov refractoriness. Neural Computation, 21. 1203-1243.

(2009). A new look at state-space models for neural data. Journal of Computational Neuroscience, 29. 107-126.

Rahnama Rad, K., & Nasiri-Kenari, M. (2004). Iterative detection for V-BLAST MIMO communication systems based on expectation maximisation algorithm. IEE Electronics Letters, 40(11). 684-685.

Conference Proceedings

Zhou, H., Auddy, A., Rahnama Rad, K., & Maleki, A. (2024). Approximate Leave-one-out Cross Validation for Regression with l1 Regularizers. International Conference on Artificial Intelligence and Statistics (AISTATS).

Rahnama Rad, K., Zhou, W., & Maleki, A. (2020). Error bounds in estimating the out-of-sample prediction error using leave-one-out cross validation in high-dimensions. International Conference on Artificial Intelligence and Statistics.

Molavi, P., Rahnama Rad, K., Tahbaz, A., & Jadbabaie, A. (2012). On consensus and exponentially fast social learning. American Control Conference (ACC).

Paninski, L., Rahnama Rad, K., & Vidne, M. (2012). Robust particle filters via sequential pairwise reparameterized Gibbs sampling. Information Sciences and Systems (CISS).

Rahnama Rad, K., & Paninski, L. (2011). Information rates and optimal decoding in large neural populations. Advances in Neural Information Processing Systems.

Rahnama Rad, K., & Tahbaz-Salehi, A. (2010). Distributed parameter estimation in networks. IEEE Decision and Control.

Rahnama Rad, K., & Nasiri-Kenari, M. (2004). Expectation maximization based detection for V-BLAST MIMO communication systems and performance evaluation. Spread Spectrum Techniques and Applications, IEEE Eighth International Symposium on.

Presentations

Rahnama Rad, K. (2020, June 30). Scalable estimation of the out-of-sample prediction error via approximate leave-one-out in the high-dimensional regime. Statistics seminars at Department of Mathematical Sciences, NTNU. : Norwegian University of Science and Technology.

Rahnama Rad, K. (2019, December 31). Scalable estimation of out-of-sample prediction error via approximate leave-one-out with applications to neural data analysis. Columbia University, Department of Statistics, Weekly Seminar.

Rahnama Rad, K. (2019, October 31). A scalable estimate of the out-of-sample prediction error via approximate leave-one-out. Workshop on Science of Data Science, International Centre for Theoretical Physics. Trieste, Italy

Rahnama Rad, K. (2019, December 31). Scalable estimation of out-of-sample prediction error via approximate leave-one-out with applications to neural data analysis. Bell Labs Weekly Seminar.

Rahnama Rad, K. (2019, July 31). Approximate leave-one-out cross-validation for nonparametric Bayesian Gaussian Process methods with applications to neural data. Numerical Computations: Theory and Algorithms.

Rahnama Rad, K. (2019, January 31). Scalable adaptive learning of grid fields¿. Invited Talk at the Kavli Institute for Systems Neuroscience / Centre for Neural Computation. Trondheim, Norway: Norwegian University of Science and Technology.

Rahnama Rad, K. (2018, October 31). Track A VC 14-270 Chair: Radhika Jain Efficient and scalable implementations of approximate leave-one-out cross validation. Second Annual Symposium on Data Analytics.

Rahnama Rad, K. (2018, July 31). Convex Optimization. Summer Lectures at the Kavli Institute for Systems Neuroscience / Centre for Neural Computation. Trondheim, Norway: Norwegian University of Science and Technology.

Rahnama Rad, K. (2018, March 28). A scalable estimate of the extra-sample prediction error via approximate leave-one-out. Flatiron Institute | Simons Foundation Numerical Algorithms Group. 162 5th Ave, New York, NY 10010, USA: Flatiron Institute | Simons Foundation.

Rahnama Rad, K. (2017, October 13). Scalable prediction error estimation for big data. First Annual Symposium on Business Analytics: Research and Pedagogy.

Rahnama Rad, K. (2017, June 15). Scalable and Robust Model Estimation and Assessment. QPRC 2017: The 34th Quality and Productivity Research Conference. The University of Connecticut: American Statistical Association.

Rahnama Rad, K. (2017, May 31). Scalable and Robust Model Estimation and Predictive Performance Assessment. Research Seminar. Baruch College: Department of Information Systems and Statistics.

Jahani, J., Rahnama Rad, K., & Johnson, G. (2014, May 15). Dynamic Noise Reduction in MRI. International Society for Magnetic Resonance in Medicine. Milan, Italy: GE Healthcare, Philips Medical Systems, Siemens.

Rahnama Rad, K. (2013, August 31). Concentration Inequalities and Large Deviation Theory. Summer Lectures at the Kavli Institute for Systems Neuroscience / Centre for Neural Computation,. Trondheim, Norway: Norwegian University of Science and Technology.

Rahnama Rad, K., & Tahbaz-Salehi, A. (2013, February 25). Asymptotically efficient estimation based on local message passing and observations. Information Theory and Applications Workshop. San Diego, CA: IEEE Information Theory Society.

Rahnama Rad, K. (2012, December 31). . Risk Seminar. : Department of Statistics, Columbia University.

Paninski, L., Rahnama Rad, K., & Huggins, J. (2011, December 31). Fast low-SNR high-dimensional optimal filtering, applied to inference of dynamic receptive fields. Computational and System Neuroscience.

Rahnama Rad, K., & Paninski, L. (2010, December 31). Information processing of temporally correlated stimuli in a large population of neurons. Computational and System Neuroscience.

Kontoyiannis, I., Rahnama Rad, K., & Gitzenis, S. (2010, December 31). Sparse superposition codes for Gaussian vector quantization. IEEE Information Theory Workshop.

Rahnama Rad, K., & Paninski, L. (2009, December 31). Efficient two dimensional estimation of firing rate surfaces. Computational and System Neuroscience.

Toyoizumi, T., Rahnama Rad, K., & Paninski, L. (2009, December 31). Mean field approximation for a network of coupled GLM neurons. Computational and System Neuroscience.

Rahnama Rad, K. (2009, December 31). . Time Series Analysis in Neuroscience Workshop. : Columbia University.

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