Meet new faculty member Sandeep Silwal, who works at the intersection of theoretical computer science and machine learning

Sandeep Silwal joins the Computer Sciences department after completing his PhD at the Massachusetts Institute of Technology (MIT). He works on algorithm design and first became interested in college when he “was hooked by how really cool and diverse mathematical ideas were used to tackle problems related to computing.” He hopes that for students in his classes, “the problem solving skills they will learn will be their lifelong friends.”

Hometown:

Kathmandu, Nepal and Morganton, North Carolina

Educational/professional background:

Bachelor’s degree in math at MIT (2019), PhD at MIT in computer science (2024)

How did you get into your field of research?  

I’ve loved math ever since high school when my teacher started giving me fun problems to work on. During my sophomore year in college, I took an introductory course in algorithms. I was hooked by how really cool and diverse mathematical ideas were used to tackle problems related to computing. For example, how does Google maps know it is giving you THE shortest route? Did it check all possible routes? How can your bank check that you entered your password correctly even though it did not store your passwords anywhere? Lastly, what is the nature of computing? Are there certain tasks that no computer can ever accomplish even if you gave it a million years?A quote attributed to Sandeep Silwal reads, “What is the nature of computing? Are there certain tasks that no computer can ever accomplish even if you gave it a million years?”

What are your areas of focus?

I work in the intersection of theoretical computer science and machine learning.

What main issue do you address or problem do you seek to solve in your work? 

We are in an era of “big data” where lots and lots of data is collected. Think of all the books and articles that are online. We need efficient algorithms to process them, especially since “Moore’s Law” is slowing down (aka the increase in our computing power is slowing down!). This is where my research come in. I design algorithms to compute on large datasets without “looking at” the entire data. For example, given a question, can we try to quickly find the relevant document which answers the question among a very large corpus? I also work on “beyond worst case analysis”, where we try to exploit patterns in real world data to obtain more efficient algorithms in theory and practice.

Tell us about something you’re working on in layperson’s terms:

Suppose a GPS service knows that on the weekend, 90% of the people traveling from Madison want to go to Devil’s Lake, which is to the north. We might hope to detect this pattern and then exploit it to find the shortest routes for users faster. For example, if you are from Madison and using a GPS on the weekend, we can bias our search for the shortest route in the north direction first since it is likely you are heading there. However, we must also be careful and also hedge our guess by searching in the other neglected direction in case you happen to be in the 10% that wants to go somewhere else. Thus, how can we effectively exploit this pattern so that we find the shortest routes faster for “typical” users, while ensuring that all users eventually get the right directions?

What’s one thing you hope students who take a class with you will come away with? 

I want my students to be challenged in the classroom so that they will find the “real world” much easier. While specific bits of knowledge might not be relevant in their careers afterwards, the problem solving skills they will learn will be their lifelong friends.

What attracted you to UW-Madison?  

My wife and I thought the quality of life in Madison was very high. I also loved the department and I am very excited for our new building next year!

What was your first visit to campus like?

It was a bit of a cold day but I enjoyed the lake view! To be honest, the interviews were taxing, but I met a lot of very nice folks in the department.

What are you looking forward to doing or experiencing in Madison?

Exploring the nature of the area and Wisconsin in general.

How does your work relate to the Wisconsin Idea

Computing is increasingly touching every aspect of our lives. While my work is on the more theoretical side, much of it touches on issues of broad interest such as data privacy: how can we compute on large data with respecting the privacy of users? As well as efficiency: faster algorithms are an integral part of reducing the (increasingly) enormous environmental impacts of computing.

Hobbies/other interests:

Playing soccer, eating spicy food, traveling to new countries.