Dr. Ramchandra Rimal

Assistant Professor of Mathematics (Data Science )

Dr. Ramchandra Rimal
+1 (615) 904-82
Room 325 A, Kirksey Old Main (KOM)
MTSU Box 34, Murfreesboro, TN 37132
Office Hours

Monday 8 am – 9 am

Wednesday 6 pm – 8 pm

Saturday 8:00 am - 10:00 am, and 

 others by appointment.

The office hours are determined based on the nature of the course being taught(online, remote, or in-person) and the student's availability!

 

Degree Information

  • PHD, University of Central Florida (2020)
  • MS, University of Central Florida (2017)
  • BS, Tribhuvan University (2009)

Areas of Expertise

Machine Learning

Statistical Network Models

Classification and Clustering

Data Science

Biography

Ramchandra Rimal is an Assistant Professor in the Department of Mathematical Sciences. His research interests include Machine Learning, Statistical Network Models, and Classification and Clustering problems. These research areas have applications to the problems in diverse areas, from business to biological sciences. He focuses on developing new modeling frameworks and applying them to real-world problems.   

Publications

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 deconvoluti...

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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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Presentations

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...

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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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Awards

 

1) 2019 Graduate Research Excellence Award from UCF Department of Mathematical Sciences

2) 2018 Graduate Teaching Assistant Award from UCF Department of Mathematical Sciences

Courses

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

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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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