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Work anxiety is linked with decreased job commitment and satisfaction. Yet, work anxiety, like other mental health problems, is not physically diagnosable. The lack of diagnosis and cure of job anxiety leads to lower levels of economic productivity and adds to the mental health epidemic. This study proposes a machine learning model to predict a corporate professional’s level of work anxiety using their tweets by identifying linguistic markers associated with work anxiety. The Twitter API was used to create a dataset of over 15,000 corporate professionals. Thousands of tweets were collected from these users over 3 periods of time (May–August 2019, May–August 2020, and January-April 2021). After conducting sentiment and linguistic analysis, tweets from 90 random users (manually labelled for their job anxiety scores according to the job anxiety scale) were used to train/test an SVM regression model. The model achieved an RMSE of 0.2 and an accuracy of 83%. This approach has the potential to enable early detection of work anxiety and alert individuals about their mental health.

As programmers in the Wolfram Language, we’ve all occasionally spent hours looking through the Wolfram documentation to find functions that’d help achieve our purpose. We’ve all also hoped for an easier way to do this without having to read through all the functions that come up from “search” or aimlessly scrolling through the documentation. Well, we have just the solution for you! While there have been computational explorations of generating code from natural language input using GPT-3 and Python transformers, this has been slightly limited in Wolfram Language (to the best of our knowledge). With that in mind, over the past semester, we created multiple functions where one can input their code’s general requirements in English and output a list of functions that can potentially be used. Even though it doesn’t tell you exactly what to do, it can provide some direction to new programmers looking for inspiration.

In recent years, machine learning has helmed technological innovation with its powerful potential for applications in a wide range of fields. From image classification to natural language processing, machine learning has fundamentally changed our daily lives. Yet, the applications of machine learning in the study of human names (anthroponomastics) have been limited. This could be accredited to the complexity of human names given vast cultural differences and personal preferences, but is still interesting because the existence of personal names, as abstract linguistic symbols for individual people, dates back to 3000 BC and is pervasive as an indispensable part of human societies. In this project, I explore how names can be represented and categorized computationally to create an algorithm to separate human names into first and last by factoring in the cultural origin of the name, and consider its merits and demerits in the process.

This study explored whether a digital role-playing card game, incorporating some of the most common prejudices of body image (like size prejudice, prejudices from the media, etc.) as identified by a digital survey/questionnaire completed by Indian girls aged 11-21, could counter these issues and reduce personal body dissatisfaction.

This study evaluated social media interface design features in their potential to limit the spread of misinformation.

Native Android app to facilitate Giesecke+Devrient Competence Center India's fitness challenge

Native Android app serving as a one-stop for domestic migrant workers in India, with features like a job portal, resources repository, and easy document storing

Native Android app for Yoga practice; second place at Comfest 2020