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, 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. (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, 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. (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. (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. (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.

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

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.

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. (2010, December 31). Information processing of temporally correlated stimuli in a large population of neurons. Computational and System Neuroscience.

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

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.

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