Calculating the Perfect Algorithm: Alumni Spotlight on Sumanth Swaminathan

Sumanth was a Fellow in our Winter 2016 cohort who landed a job with one of our hiring partners, Revon.

Tell us about your background. How did it set you up to be a great Data Scientist?

I did my bachelors degree in Chemical Engineering at the University of Delaware and my PhD in Applied Mathematics at Northwestern University. After some postdoctoral work between Northwestern and Oxford University, I went into industry as a quantitative consultant for W.L. Gore & Associates. For the past 4 years, I have spent most of my time delivering technology solutions at W.L. Gore, teaching mathematics at the University of Delaware, and performing and teaching Indian Classical Music.

On the question of what makes a strong data scientist, I think that the better practitioners in the field tend to be hypothesis driven, strong critical thinkers with hard skills in statistics, programming, mathematics, and hardware. Hence, my background in engineering and mathematics, my consulting experience, and my years of teaching probably contributed the most to my success.

What do you think you got out of The Data Incubator?

1. I learned an incredible amount of new problem solving methods, concepts and technologies
2. I joined a large community of practicing and aspiring data scientists (the fellows admitted into this program were really accomplished and came from all educational backgrounds)
3. I was approached and interviewed by numerous companies of all sizes.
4. I received professional advice from hiring managers, computer scientists, and strong mathematical talent.

What advice would you give to someone who is applying for The Data Incubator, particularly someone with your background?

Definitely do your homework! Folks in applied mathematics generally know something about physics, numerical methods, and a broad range of mathematical concepts. These are all good things! Applied mathematicians might not, however, be algorithms experts. They may also be novices in statistics and data handling tools.

1) If you want to get through the incubator challenge test and interviews, it would be useful to brush up on efficient algorithms and writing clean code (project Euler is really helpful). You’ll also want to learn how to manipulate and query tables (sql or R)

2) Sharpening your professional skill set is really helpful for the program as well as for job interviews. Good writing and communication skills, strong critical thinking, an ability to work with different people in small and large teams, an understanding of deadlines and associated responsibilities, etc are all useful and marketable qualities.

What is your favorite thing you learned at The Data Incubator?

The sections on distributed computing (Hadoop, Map Reduce/Spark) were really interesting and useful. They were subjects that I had barely touched in the past. In general, the programming challenges in the mornings and the lectures were fun. Probably the best part was being around the most talented people.

Could you tell us about your Data Incubator Capstone project?

The goal of my project was to predict the probability that a technology patent would go through litigation using data that exists in freely available patent XMLs. The data sets were scraped mostly from google bulk patent.

I compared the success rates of a variety of machine learning classifiers in correctly identifying litigated and unlitigated patents. The features used in the classifiers included both intrinsic patent literature characteristics and post patent filing events. It was an interesting project. I didn’t expect the algorithm to be as successful as it was!

And lastly, tell us about your new job!

My company is called Revon Systems Inc. It is a technology startup that aims to help patients manage chronic illnesses. The specific technology that was designed by the company is a mobile app that patients use to enter daily health and symptom information related to the illness. The application collects data and shows the patient and his/her primary care physician trends.
The interesting differentiation in our app is that it includes a machine learning algorithm for triaging patients (predicting whether a patient is ok, should call the doctor, or should go to the ER based on the data that the patient enters). The app is currently triaging patients with COPD (chronic obstructive pulmonary disease) comparably or more accurately than physicians.
I am the lead in developing algorithms for a variety of chronic illnesses. As the company is a startup, I am also doing a variety of physician and patient interviews, research, and generation of new business cases.

Learn more about our offerings:

Related Blog Posts

Moving From Mechanical Engineering to Data Science

Moving From Mechanical Engineering to Data Science

Mechanical engineering and data science may appear vastly different on the surface. Mechanical engineers create physical machines, while data scientists deal with abstract concepts like algorithms and machine learning. Nonetheless, transitioning from mechanical engineering to data science is a feasible path, as explained in this blog.

Read More »
Data Engineering Project

What Does a Data Engineering Project Look Like?

It’s time to talk about the different data engineering projects you might work on as you enter the exciting world of data. You can add these projects to your portfolio and show the best ones to future employers. Remember, the world’s most successful engineers all started where you are now.

Read More »
open ai

AI Prompt Examples for Data Scientists to Use in 2023

Artificial intelligence (AI) isn’t going to steal your data scientist job! Instead, AI tools like ChatGPT can automate some of the more mundane tasks in your future career, saving you time and energy. To make life easier, here are some data science prompts to get you started.

Read More »