1) Noroozi, Majid, Ramchandra Rimal, and Marianna Pensky. "Estimation and clustering in popularity adjusted block model." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 83.2 (2021): 293-317.

2) Noroozi, Majid, Marianna Pensky, and Ramchandra Rimal. "Sparse Popularity Adjusted Stochastic Block Model." Journal of Machine Learning Research 22.193 (2021): 1-36.

3) Rimal, Ramchandra, and Marianna Pensky. "Density deconvolution with small berkson errors." Mathematical Methods of Statistics 28.3 (2019): 208-227.

4) Pokhrel, Nawa Raj, Keshab Raj Dahal, Ramchandra Rimal, Hum Nath Bhandari, Rajendra KC Khatri, Binod Rimal, and William Edward Hahn. “Predicting nepse index price using deep learning models.”Machine Learning with Applications 9 (2022): 100385.

5) Bhandari, Hum Nath, Binod Rimal, Nawa Raj Pokhrel, Ramchandra Rimal, Keshab R. Dahal, and Rajendra KC Khatri. “Predicting stock market index using LSTM.” Machine Learning with Applications (2022): 100320.

6) Bhandari, Hum Nath, Binod Rimal, Nawa Raj Pokhrel, Ramchandra Rimal, and Keshab R. Dahal. “LSTM-SDM: An integrated framework of LSTM implementation for sequential data modeling.” Software Impacts (2022): 100396

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Selected Presentations

 “Sparse Popularity Adjusted Stochastic Block Model”, 2021 Symposium on Data Science and Statistics; Virtual, June 02 - 04, 2021(Refereed).

“Sparse Popularity Adjusted Stochastic Block Model”, 52nd Southeastern International Conference on Combinatorics, Graph Theory and Computing;Florida Atlantic University, Boca Raton, FL, March 12, 2021.

“Estimation and Clustering in Sparse Popularity Adjusted Block Model”, 15th Annual International Conference on Mathematics: Teaching, Theory & Applications; Athens, Greece, Jun 28 - 30, 2021(Accepted).

“Estimation in the Sparse Popularity Adjusted Stochastic Block Model”, AMS Special Session on Stochastic Spatial Models, I; 2020 Joint Mathematics Meeting, Denver, Colorado, Jan 15 - 18, 2020.

“Estimation in Popularity Adjusted Stochastic Block Model”, Contributed Paper Session, 75 - Probability and Statistics; Joint Statistical Meeting, Denver, Colorado, July 27 - Aug 1, 2019.

“Estimation in Popularity Adjusted Stochastic Block Model”, Contributing Paper Session, 31st Cumberland Conference on Combinatorics, Graph Theory and Computing, UCF, Orlando, Florida, May 19, 2019.

“Estimation in Popularity Adjusted Stochastic Block Model”, AMS Special Session on Network Science, II, #1145: Joint Mathematics Meetings, Baltimore, Maryland, January 16 - 19, 2019.

“Density Estimation with Small Berkson Errors”, Contributed Paper Session II, #1144 AMS Fall Western Sectional Meeting, San Francisco, California, October 27 - 28, 2018.

“Estimation of Coastal Hydrodynamics with Machine Learning”, 24th Industrial Mathematical and Statistical Modeling (IMSM) Workshop, Wednesday, July 25, 2018.

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“Estimation with small Berkson Errors”, Contributed Paper Session, 2018 Joint Meetings of the Florida Section of the Mathematical Association of America and the Florida Two-Year College Mathematics Association, Florida Atlantic University, Feb 9 - 10, 2018.

“Estimation with Berkson Errors”, Special Session on Modern Statistical Methods for Structured Data, I, #1133 AMS Fall Southeastern Sectional Meeting, University of Central Florida, September 23 - 24, 2017.

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Fall 2020

MATH 2050-002- Probability and Statistics: 3 credit hours 

MATH 2050-004- Probability and Statistics: 3 credit hours

Spring 2021

MATH 6612-001-Problems in Mathematics: 3 credit hours

DATA 3550-001 - Applied Predictive Modeling: 3 credit hours

MATH 2050-003- Probability and Statistics: 3 credit hours

Summer 2021

Math 1810 - Applied Calculus I: 3 credit hours

Fall 2021

DATA 3550-001 - Applied Predictive Modeling: 3 credit hours

MATH 2050-003- Probability and Statistics: 3 credit hours

Spring 2022

DATA 3550-001 - Applied Predictive Modeling: 3 credit hours

DATA 6320-D01 - Predictive Modeling: 3 credit hours

MATH 2110 - Data Analysis: 1 credit hours

Fall 2022

DATA 3550-001 - Applied Predictive Modeling: 3 credit hours

DATA 6990-001 - Topics Seminar in Data Science: 3 credit hours

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