Civil Engineering Python

LIMON BARUA

Contact: +1-312-***-****, Email: *******@***.***

EDUCATION

Ph.D. Civil Engineering

University of Illinois at Chicago

Expected May 2022

M.Sc. Civil Engineering

University of Illinois at Chicago

May 2020

B.Sc. Civil Engineering

BUET, Dhaka, Bangladesh

March 2016

SKILLS

Machine learning

Scikit-learn, mlr

Deep learning

Keras, TensorFlow

Reinforcement learning

OpenAI Gym

Programming

• Object oriented: C++,

Python, MATLAB

• Script: R, Python, SQL

• IDE: Visual Studio, Jupyter

Notebook, RStudio

Others

ArcGIS, CPLEX, Microsoft office

COURSEWORK

• Intro to Data Science

• Data Mining for Business

• Computer Algorithms

• Game Theory

• Combinatorial Optimization

• Stochastic Process and

Queuing

• Operations Research I

• Operations Research II

AWARDS

• Graduate Research Award

on Public-Sector Aviation

Issues for 2019-2020

• Dean list award in B.Sc.

EXPERIENCE

UIC Research Assistant Spring 2018 - present

Prediction of airport pavement condition

• Developed different ML models (ANN, GB, RF, SVM) to predict the pavement condition of an airport using Python scikit-learn library

• Utilized relative importance and partial dependence plot to interpret the relation of input variables with the output Urban online shopping demand

• Applied ML models on NHTS 2017 and 2019 datasets to predict the online shopping demand of households

• Incorporated SHAP to explain the output of the ML models using Python shap library

Airport pavement management

• Integrated supervised ML and RL modeling for airport pavement asset management using OpenAI Gym

Stable truck platoon formation

• Developed a framework for user preference-based truck platooning system using Python pulp library and CPLEX

• Yen’s k-shortest path and longest common subsequence algorithm is used to solve the routing part of the problem

• Irving’s algorithm and Morill’s algorithm is used to solve the matching part

ACADEMIC PROJECT EXPERIENCE

Predicting the surge price of ridesharing service

• Determined surge factor from Ride Austin dataset using different types of ML (ANN, GB, RF, SVM)

Divvy bike demand prediction

• Developed ML models to estimate Divvy bike demand from each census tract

Net promote score prediction

• Predicting net promote score to improve patient experience at hospital from a dataset published in Harvard business publishing website using R mlr library

PUBLICATIONS

• Predicting Airport Runway and Taxiway Pavement

Conditions: A Gradient Boosting Approach. [TRB, 2019]

• A Gradient Boosting Approach to Understanding Airport Runway and Taxiway Pavement Deterioration. [IJPE, 2020]

• Machine Learning for International Freight Transportation Management: A Comprehensive Review. [RTBM, 2020]

• Planning Maintenance and Rehabilitation Actions for Airport Pavements: A Combined Supervised Machine Learning and Reinforcement Learning Approach. [Accepted for TRB, 2021]

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