Kamiar Rahnama Rad

Assc Professor

Zicklin School of Business

Department: Paul Chook Dept InfoSys & Stat

Areas of expertise:

Email Address: kamiar.rad@baruch.cuny.edu

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Education

Ph.D., Statistics, Columbia University

M.S., Electrical Engineering, UCLA

B.S., Electrical Engineering, Sharif University

SemesterCourse PrefixCourse NumberCourse Name
Fall 2023STA9891Machine Learning/Data Mining
Fall 2023STAT70400Quant Analy for Bus Decisions
Fall 2022STAT70400Quant Analy for Bus Decisions
Fall 2022BUS89500Independent Study
Fall 2022STA9891Machine Learning/Data Mining
Spring 2022STA3920Data Mining for Bus Analytics
Spring 2022STA9890Stat Learning for Data Mining
Spring 2022CIS3920Data Mining for Bus Analytics
Fall 2021STAT70400Quant Analy for Bus Decisions
Fall 2021STA2000Business Statistics I
Fall 2021STA9891Machine Learning/Data Mining
Spring 2021STAT70500Multivariate Statistical Meth
Spring 2021STA9890Stat Learning for Data Mining
Fall 2020STAT70400Quant Analy for Bus Decisions
Fall 2020STA9891Machine Learning/Data Mining
Spring 2020STA2000Business Statistics I
Spring 2020STA9890Stat Learning for Data Mining
Fall 2019STA9891Machine Learning/Data Mining
Spring 2019BUS89500Independent Study
Fall 2018STA2000Business Statistics I
Fall 2018STA2000Business Statistics I
Fall 2018STA9891Machine Learning/Data Mining
Fall 2018BUS89500Independent Study
Spring 2018STA9890Stat Learning for Data Mining
Spring 2018STA2000Business Statistics I
Fall 2017STA9715Applied Probability
Fall 2017STA9690Adv Data Mining for Bus App
Spring 2017STA2000Business Statistics I
Fall 2016STA9690Adv Data Mining for Bus App
Fall 2016STA2000Business Statistics I
Spring 2016STA2000Business Statistics I
Fall 2015STA9794Special Topics in Statistical
Fall 2015STA2000Business Statistics I
Spring 2015STA2000Business Statistics I
Fall 2014STA2000Business Statistics I
Fall 2014STA9706Anal Of Cat & Ord
Spring 2014STA2000Business Statistics I
Fall 2013STA9706Anal Of Cat & Ord
Fall 2013STA2000Business Statistics I

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.

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

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

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, 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, 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, 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, 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. (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., & 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.

TitleFunding Agency SponsorStart DateEnd DateAwarded DateTotal FundingStatus
A Scalable machine learning methodology to uncover the neural representation of space in the brainEugene Lang Fellowship06/01/201906/30/202004/08/20195388Completed
Social Networks, acculturation, and food behaviors and values among Mexican-American familiesNational Institutes of Health07/01/201806/30/202309/17/202054889Completed
Network reconstruction from samples of a dynamical system: Theory and ApplicationsPSC-CUNY 4507/01/201406/30/201504/15/20143500Completed
Collaborative Research: Consistent risk estimation under high-dimensional asymptoticsNational Science Foundation07/01/201806/30/202108/01/2018120241Funded - In Progress
Honor / AwardOrganization SponsorDate ReceivedDescription
Membership of Sigma Xi, The Scientific Research Honor Society2020
Teaching ExcellenceZicklin School of Business2019
Faculty FellowshipColumbia University2006
Bronze Medal, International Physics OlympiadUniversity of Leicester, UK2000
Gold Medal, National Physics OlympiadTehran, Iran1999

College

Committee NamePosition RoleStart DateEnd Date
Student Technology Fee CommitteeCommittee MemberPresent
Faculty SenateCommittee MemberPresent
Equity AdvocatePresent
Search CommitteeCommittee MemberPresent
Meeting with the McAllister & Quinn TeamAttendee, MeetingPresent
Undergraduate Curriculum CommitteeCommittee MemberPresent
BBA in SQM majorFaculty Advisor1/31/2023
Zicklin Undergraduate New Student Welcoming OrientationFaculty Mentor10/31/2021
Search CommitteeCommittee Chair5/31/2021
SEEK Freshman Year Career Journey & Alumni Networking eventFaculty Advisor3/31/2021
Strategic Care Workflow & Training Team for the implementation of EAB12/31/2020
Pitney Bowes Data Science Seminar Series12/31/2020
Majors/Minors Fair 11/30/2019
Third Annual Symposium on Data AnalyticsCommittee Member10/31/2019
Second Annual Symposium on Data AnalyticsCommittee Member10/31/2018
MS/MBA Information SessionFaculty Mentor10/31/2018
Hiring Research Programs Director in the Office of Sponsored Programs and ResearchInterviewer8/31/2018
Faculty-led scholarship panel for New Faculty Orientation8/15/2018
Faculty Elective Panel - Full-Time MBA Program - New Student Orientation 8/13/2018
Faculty Search Committee3/31/2018
HSBC Quant Risk Group: Dinner 3/26/2018
The First Annual Symposium on Business Analytics: Research and PedagogyCommittee Member10/31/2017
BBA major in data/business analyticsAttendee, Meeting12/31/2015
MS program in Data Science12/31/2015
MS in Business Analytics Group12/31/2015
Statistics Undergraduate Majors (STA, DATA SCIENCE, OR)12/31/2015
Undergraduate research adviserFaculty Mentor6/30/2015

University

Committee NamePosition RoleStart DateEnd Date
Aligning Gateway Statistics Course Across CUNYCommittee Member1/1/2019Present
Doctoral Faculty1/1/2018Present
CUNY Knowledge Convergence for Data ScienceFaculty Advisor1/1/201612/31/2019

Professional

OrganizationPosition RoleOrganization StateOrganization CountryStart DateEnd DateAudience
Journal of the Royal Statistical Society: Series BReviewer, Journal Article1/1/2024PresentInternational
The 36th Annual Conference on Learning Theory (COLT 2023) Session Chair2/1/20225/31/2022International
The 35th Annual Conference on Learning Theory (COLT 2022) Reviewer, Conference Paper2/1/20225/31/2022International
The 34th Annual Conference on Learning Theory (COLT 2021) Reviewer, Conference Paper2/1/20215/31/2021
IEEE Transactions on Information TheoryReviewer, Journal Article1/1/202012/31/2020
Journal of the Royal Statistical Society: Series CReviewer, Journal Article1/1/201812/31/2018
Journal of Time Series AnalysisReviewer, Journal Article1/1/201812/31/2018
Norwegian University of Science and TechnologyPrepare/Grade Certification ExamsNorway1/1/201312/31/2015
Norwegian University of Science and TechnologyPrepare/Grade Certification ExamsNorway10/1/201510/31/2015
Journal of Statistical Mechanics: Theory and Experiment Reviewer, Journal Article1/1/201412/31/2014
Physical Review EReviewer, Journal Article1/1/201412/31/2014
IEEE International Symposium on Information TheoryReviewer, Conference Paper1/1/201312/31/2014
IEEE Transactions on Information TheoryReviewer, Journal Article1/1/201312/31/2014
Norwegian University of Science and TechnologyPrepare/Grade Certification ExamsNorway2/1/20142/28/2014
Annals of Applied ProbabilityReviewer, Journal Article1/1/201212/31/2013
Norwegian University of Science and TechnologyExternal censor on master thesis Norway8/1/20138/31/2013