Urvashi Arora

Visiting Assistant Professor
Mathematics

Mathematics & Physics

Contact Information

(718) 862-7985

uarora01@manhattan.edu

Office Location

HAY 505

Overview

I am Urvashi Arora, an Assistant Professor with a strong background in mathematics, data science, and statistical analysis. I hold a Ph.D. in Mathematics from the Indian Institute of Technology Roorkee and MS in Statistics and Data Science from the University of Houston. I joined the Department of Mathematics and Physics at Manhattan University in fall 2024, where I teach a range of courses, including Calculus and Precalculus, to students from various disciplines and work on machine learning and control theory research. My teaching philosophy centers on fostering critical thinking and analytical problem-solving skills, while making complex mathematical concepts accessible and engaging for students.   

My research interests include the controllability of dynamical systems and the application of machine learning across various domains. I am committed to creating a collaborative and inclusive learning environment, inspiring the next generation of mathematicians.

 

Education

Ph.D. Mathematics, Indian Institute of Technology Roorkee, India                   MS in Statistics and Data Science, University of Houston, TX, USA                                                                                                                                                                                                                                                                                        

Courses Taught

  • MATH 100 Pre-Calculus Mathematics
  • MATH 155 Calc For The Life Sciences I
  • MATH 156 Calc For The Life Sciences Ii
  • MATH 185 Calculus I
  • MATH 186 Calculus Ii
  • MATH 187 Honors Calculus I

Research & Scholarly Activities

  • Urvashi Arora and N.Sukavanam, Approximate controllability of second order semilinear stochastic system with nonlocal conditions, Applied Mathematics and Computation 258 (2015), 111-119.

  • Urvashi Arora and N.Sukavanam, Controllability of Retarded Semilinear Fractional System with Nonlocal Conditions, IMA Journal of Mathematical Control and Information, 35 (2018), no. 3, 689-705.

  • Urvashi Arora and N.Sukavanam, Approximate controllability of impulsive semilinear stochastic system with delay in state, Stochastic Analysis and Applications 34 (2016), no. 6, 1111-1123.

  • Urvashi Arora and N.Sukavanam, Controllability of Fractional System with Nonlinear Term having Integral Contractor, IMA Journal of Mathematical Control and Information, 36 (2019), no. 1, 271-283.

  • Urvashi Arora and N.Sukavanam, Approximate controllability of second order semilinear stochastic system with variable delay in control and with nonlocal conditions, Rendiconti del Circolo Matematico di Palermo 65 (2016), no. 2, 307-322.

  • Urvashi Arora and N.Sukavanam, Approximate Controllability of Non-densely De_ned Semi-linear Control System with Non Local Conditions, Nonlinear Dynamics and Systems Theory, 17 (1) (2017), 5-18.

  • Divya Ahluwalia, N.Sukavanam and Urvashi Arora, Approximate controllability of abstract semilinear stochastic control systems with nonlocal conditions, Cogent Mathematics, 2016.

  • Anurag Shukla, N. Sukavanam, D. N. Pandey and Urvashi Arora, Approximate Controllability of Second-Order Semilinear Control System, Circuits, Systems and Signal Processing 35(2016), no. 9, 3339-3354.

  • Anurag Shukla, Urvashi Arora and N.Sukavanam, Approximate controllability of retarded semilinear stochastic system with non local conditions, Journal of Applied Mathematics and Computing 49 (2015), no. 1-2, 513-527.

  • Anurag Shukla, Urvashi Arora and N.Sukavanam, Approximate controllability of semilinear stochastic system with multiple delays in control, Cogent Mathematics, 2016.

  • Urvashi Arora and N.Sukavanam, Approximate controllability of semilinear fractional stochastic system with nonlocal conditions, Dynamic Systems and Applications, 27, No. 1 (2018), 45-62.

  • Urvashi Arora, V. Vijayakumar, Anurag Shukla, Kottakkaran Sooppy Nisar, Shahram Rezapour, Wasim Jamshed, Results on Exact Controllability of Second order Semilinear Control System in Hilbert Spaces, Advances in Difference Equations, 2021.

  • Urvashi Arora, M Singh, S Dabade, A. Karim, Analyzing the efficacy of different machine learning models for property prediction of solid polymer electrolytes. ChemRxiv. 2024; doi:10.26434/chemrxiv-2024-0t7mw.

CONFRENCE PRESENTATIONS

  • Urvashi Arora and N. Sukavanam, Complete Controllability of a Delayed Semilinear Stochastic Control System, International Conference on Recent Trends in Mathematics and Applications (ICRTMAA 2014), IIT Roorkee, Roorkee, India, Dec.21- Dec. 23, 2014.

  • Urvashi Arora and N.Sukavanam, Approximate controllability of second order semilinear stochastic system with variable delay in control and with nonlocal conditions, SIAM Conference on Control and its Applications(SIAMCT15), Paris, France, July. 08- 10, 2015.

  • Urvashi Arora and N.Sukavanam, Approximate controllability of a second order delayed semilinear stochastic system with nonlocal conditions, IEEE international Conference on signal processing, computing and control(ISPCC 2015), Jaypee University, Solan, Himachal Pradesh, India, Sep. 24-26, 2015. 

  • Urvashi Arora and N.Sukavanam, Approximate Controllability of semilinear fractional stochastic system of order (1, 2] with nonlocal conditions, International Conference on Mathematical Analysis and its Applications(ICMAA 2016), IIT Roorkee, Roorkee, India, Nov.28- Dec.02, 2016.

Professional Experience & Memberships

Data Science Research Associate, University of Houston (Aug 2023 – July 2024)

  • Developed a predictive model for polymer electrolytes' ionic conductivity, leveraging a suite of machine learning techniques including Random Forest, XGBoost, KNN, Linear Regression, and the Chemprop model, with a focus on correlating chemical composition to conductivity.

  • Curated a robust training dataset by meticulously extracting and synthesizing data from a range of experimental publications that reported on the ionic conductivity of various polymer electrolytes.

  • Demonstrated the superior predictive power of the XGBoost algorithm through comparative analysis, which consistently surpassed competing models in precision, highlighting my adeptness in both experimental data interpretation and the application of sophisticated machine learning methodologies.

Assistant Professor, Bennett University, India (Oct 2017- Feb 2021)

  •  Crafted and delivered curriculum blending mathematical theories with data science practices.

  • Achieved outstanding student evaluations, reflecting effective teaching strategies.

  •  Engaged in scholarly research and provided academic mentorship to students.