Publications

Reddit language indicates changes associated with diet, physical activity, substance use, and smoking during COVID-19

Published in PLOS One, 2023

We leverage the popular social media platform Reddit to analyze 1 million posts between January 6, 2019, to January 5, 2021, from 22 different communities (i.e., subreddits) that belong to four broader groups—diet, physical activity, substance use, and smoking. We identified that before the COVID-19 pandemic, posts involved sharing information about vacation, international travel, work, family, consumption of illicit substances, vaping, and alcohol, whereas during the pandemic, posts contained emotional content associated with quarantine, withdrawal symptoms, anxiety, attempts to quit smoking, cravings, weight loss, and physical fitness.

Cross-modal Map Learning for Vision and Language Navigation

Published in CVPR, 2022

This paper proposes a cross-modal map learning model for vision-and-language navigation that first learns to predict the top-down semantics on an egocentric map for both observed and unobserved regions, and then predicts a path towards the goal as a set of way-points.

Retail demand forecasting: a comparison between Deep Neural Network and Gradient Boosting Method for univariate time series

Published in IEEE Conference on Convergence in Innovative Technology 2019, 2019

This paper aims at implementation and comparison of two popular and intriguing machine learning techniques, Deep Neural Network (DNN) and Gradient Boosting Method (GBM) for univariate time series sales data at store-day level of a German retail giant.

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Neural Networks and the Financial Forecasting: A Review

Published in IEEE Conference on Cognizance of Applied Engineering & Research , 2013

An overview of the artificial neural network basics and operation, architectures, and the major algorithms used for training the neural network models are presented in this paper.