Hello! My name is Karan and I joined TRG as an engineer to research into the applications of machine learning in the development of biological tools that can ultimately assist in solving critical problems in wider society. My current interest is focused on contributing to the development of machine learning models geared towards genomic data.

I grew up in Singapore, spent my last four years studying Computer Science at the University of Pennsylvania and recently moved to Boston (where I’m slowly adapting to ‘real’ winters).

What am I doing today?

I recently began work as an AI Engineer in the world of consulting, so my day-to-day job involves developing software solutions for clients across a range of industries.

Outside of this work, I’m focused on expanding my understanding of and experience in the world of computational biology through research with the TRG. This work is currently focused on enhancing existing DNA sequencing tools applied to fungal data, as well as creating an evaluative tool for the purpose of baselining machine learning models trained on the task of generating novel DNA sequences.

What I was up to in the past?

During my time at university, I engaged in work as a Machine Learning Engineer with two research groups that were focused on the application of artificial intelligence techniques to the world of biology. My work with the Davis Lab in the Penn Medicine system involved leveraging deep learning tools to predict seizure activity from raw electroencephalogram readings, while my work with a group in the Department of Chemical & Biomolecular Engineering was focused on developing a programming library to provide automated voltage readings from input video data of electrodes tracking neural activity.

Prior to my current full-time role, I’ve also worked/interned as an engineer at a couple of software firms (Roblox, barePack) as well as on the data analytics side at organizations in various industries (Golden Gate Ventures, New York Mets).

In the past, I was also a regular contributor to the educational side of machine learning. These contributions have varied from publishing guides (like this) directed at introducing high school students to the topic, to leading a two-month bootcamp in college on applying deep learning models in the healthcare space, to summarizing newly-published and state-of-the-art research papers for the Virgillio project.

How about a little fun?

When I have luxury of free time on my side, I enjoy spending my days exploring new places. More recent excursions have ranged from domestic ones, such as visiting the mountains of Maine and the coast of California, to an international trip spanning various cities across South-East Asia. If I’m constrained to a more local scale, I’m constantly on the lookout for new parks, museums and restaurants too.

Outside of this, I’m slowly reviving my career as a part-time DJ that was a staple of my college years, while also continuing my long-standing interest in sports and concert photography. Any additional free time I have is usually spent either playing squash or combing through any form of media to learn more about topics relating to the past, present and future of our society.