Building web applications with Django. In many of these projects, working as a lead designer and lead developer.

Written web scraper for categorically scraping school curriculum of various provinces, grades, and subjects into a relational database for a large-scale e-learning platform.

Currently supporting a React-based ERP application with a Symfony backend, incorporating RabbitMQ, Elasticsearch, and CI/CD pipelines using GitLab.

Designed and developed Dilex. An angular app with Node.js API hosted on the AWS platform.

Course Instructor of an undergrad course (CS215) for three semesters. I taught web designing, web development basics (HTML, CSS, JavaScript), and database (SQL) programming.

Worked as a full-time research assistant. My research topics included: Information seeking behaviour, Interactive Information Retrieval, UX design, and Information Visualization

Collected user interaction data of an angular app (Dilex) with JS, stored in MongoDB by web socket channel through a node API. This data was preprocessed with Python and statistical analysis was conducted using R.

Built multiple web applications with Angular

Worked on an ASP.NET web application which is used by a property management company for managing inventory and tenants

My thesis was about learning academic search behaviour and helping searchers to improve their search experience.

In this study, we propose a new method that aggregates keywords across a search engine results page (SERP), linking them visually to their source results. We created interactive and static interfaces, conducting a lab study to gauge their impact on utility and perceived value. The results highlight the effectiveness of using interactive keywords and visualization to summarize and link search results. Interestingly, whether keywords are shown alongside each result or aggregated over the entire SERP has minimal impact, emphasizing the importance of their interactive use in revealing relationships among search results.

We've built an academic digital library search interface called Dilex, aiding users in managing search tasks across mobile and desktop devices. In a controlled study, we compared Dilex to a standard academic search interface, finding increased user engagement and interaction with search results and personalization features. Participants spent more time on search result pages and showed enhanced engagement during resumed tasks. These results demonstrate the effectiveness of Dilex's visualization features in supporting cross-session search tasks and user engagement. This study showcases how semi-automatic task management and visualization can facilitate seamless cross-device search experiences.

Complex academic search tasks often span multiple sessions and devices, posing challenges for task resumption. Effective support for resuming tasks on mobile devices can utilize downtime effectively, while desktop/laptop support enables seamless access to mobile work. Our paper introduces an academic search interface facilitating cross-session, cross-device search using visualizations for browsing past topics and faceted navigation for examining saved documents. This approach optimizes search continuity and accessibility across varied environments and devices.

This research investigates a comparison between two different approaches for classifying emails based on their categories. Naive Bayes and Hidden Markov Model (HMM), two machine learning algorithms, have been used to detect whether an email is important or spam. Various combinations of NLP techniques- stopwords removing, stemming, and lemmatizing have been tried on both algorithms to inspect the differences in accuracy and find the best method.

Along with classifying emails, this paper also describes the methodologies used for automatic meeting scheduling by an intelligent email assistant. Users who regularly send or receive messages for setting up meetings will greatly benefit from this system as it will classify their emails and schedule their meetings automatically.