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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
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Posts
New Product Launches: Diffusion Across Markets and Brands
Published:
This is the most salient and inexorable question every marketer asks himself before he creates the marketing strategy for his new product launch in existing or fresh untapped markets. As a marketing analyst, let me help the CMO with his diffusion strategy and respective sales forecast by introducing him to the concept of Diffusion Models.
Brand Loyalty – A Paramount Strategic Affair
Published:
Understanding Brand Perception and Brand Loyalty and explore a method to model 8 metrics to help you analyze your Brand Loyalty using the conventional and reliable Multiple Regression technique or with my favorite Markov Chain.
Exploring the Markovian Process for Equity Price Forecasting
Published:
Have you ever been an investor in the stock market? Have you been successful enough in predicting the correct bunch of equities to invest in? Well as a business analyst I have been pretty inquisitive about the behavior of the markets and fluctuations in equities. Thinking about capturing the trend of these stock prices might intrigue a statistician to drill into forecasting techniques and build a model to forecast the next equity price or even perhaps purchase the most stable equity in the market. This made me explore the stochastic processes domain and experiment with a probabilistic technique known as Markov Chain.
portfolio
Portfolio item number 1
Short description of portfolio item number 1
Portfolio item number 2
Short description of portfolio item number 2
publications
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.
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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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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.
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.
talks
Talk on Future of Data Science & Machine Learning in Industry and Academia
Published:
Future of Data Science & Machine Learning in Industry and Academia
teaching
Teaching Assistantship
Undergraduate course, Detkin Lab, Electrical Engineering Department, University of Pennsylvania, 2021
Teaching Assistant to Professor Marc Miskin at the Detkin Lab for the course ESE 112-001 2021A Eng Electromagnetics for Spring 2021.
Research Assistantship
Graduate Research, David Rittenhouse Laboratory, University of Pennsylvania, 2021
Research Assistant to Dr. A.T Charlie Johnson, Rebecca W. Bushnell Professor of Physics and Astronomy at the Penn School of Arts & Sciences, and Dr. Lyle Ungar, Professor in Computer and Information Science at Penn Engineering and Psychology at the School of Arts & Sciences.
Master’s Thesis & Research Assistantship
Thesis, School of Engineering and Applied Science, University of Pennsylvania, 2021
My research is being supervised by Dr. Lyle Ungar Professor in Computer and Information Science at Penn Engineering and Psychology at the School of Arts & Sciences and Dr. Sharath Chandra Guntuku Professor in Computer and Information Science. Professor Lyle Ungar’s research group (World Well-Being Project) develops explainable machine learning, deep learning, and natural language processing methods for psychology and medical research. The World Well-Being Project (WWBP) founded by the Father of Positive Psychology, Dr. Martin EP Seligman, pioneers scientific techniques for measuring psychological well-being and physical health based on the analysis of language in social media. The World Well-Being Project is based out of the University of Pennsylvania’s Positive Psychology Center and Stony Brook University’s Human Language Analysis Lab.
Research Assistantship
Graduate Research, GRASP Laboratory, University of Pennsylvania, 2021
Research Assistant to Dr. Kostas Daniilidis, Ruth Yalom Stone Professor at Department of Computer and Information Science, and Dr. Eleni Miltsakaki, Senior Researcher, Department of Computer and Information Science at GRASP Laboratory at University of Pennsylvania.