Lucas Bandarkar
Tell me about yourself: your year, major, hometown, and a couple fun facts and hobbies!
I'm Lucas, I use he/him pronouns. I'm a fourth year, majoring in statistics and data science. I grew up in Los Altos, California. Currently I'm here in Berkeley this semester. Hobbies that I enjoy outside of my academics include tennis; I really enjoy playing tennis on the club tennis team here. I also enjoy cycling, hiking, and skiing. I'm really into maps, and geography in general as well.
What are your professional interests and why are you interested in those fields? What kinds of experiences, professional or otherwise, have contributed to these interests?
Coming into college I didn't know much about what I was going to discover by myself, but I thought that I probably wanted to do data science, maybe software engineering. I did one software engineering internship after my freshman year, and soon afterwards I realized, "Okay, we can rule that out, I don't really want to do that." So that also factored into my decision to not major in computer science, and rather I wanted to use the CS classes that I took and apply them to my Statistics and Data Science majors, and also so I wouldn't have to do more software engineering classes like CS 61C, for example.
Then my sophomore year summer I worked on an NLP project, which helped me to get into NLP along with the SAAS projects in the following semesters. At a certain point, I came to the realization that although these data science jobs are interesting, I didn't know for certain if they were exactly what would stimulate me the most. I was also thinking about grad school and had a lot of conversations about Master's or PhD opportunities, and then thought to myself that the types of problems that excite me the most are the ones related to research, which inspired me to pursue research. I've been really enjoying it and I also plan on doing a PhD.
Also, last summer I had a data science internship at Facebook AI, which wasn't necessarily research in the academic sense, but it was surrounded by a lot of research scientists in a research-type setting. That experience furthered the idea that I would want to do a PhD and if not that, be a research scientist in the future doing data science work very closely related to research. Speaking of the research I'm specifically interested in and tying it all together, I'm drawn to machine learning research and natural language processing, deep learning applications to language, and studying how deep learning models can improve the ways machines understand and generate text and language. Altogether I think my interest in NLP is somewhat situational; I had an internship that involved NLP which led me to SAAS projects about NLP, which led me to pursue research about NLP, and I like NLP enough and have enough experience in it where I can see myself continuing this for the long term.
You're a senior who has been in SAAS for quite a while now. How did you find out about SAAS originally, what inspired you to join SAAS in the first place, and what has motivated you to stay in SAAS since then?
When I first joined SAAS it was called SUSA at the time. I don't particularly remember how I was introduced to the club, although I'm sure it was flyering of some sort. I was a first semester freshman back then, so I was just trying to join whatever club I could. I applied to a lot of coding clubs and some statistics or data science clubs, but there really weren't a lot of those at the time, although there's a lot more now. Regardless, I got into CX which was my first committee in SAAS, and I learned so much and had a great time. I met a lot of really interesting personalities who were really passionate about data science, so that played a big role in my dedication and investment in the club. The next semester I was the CX Committee Director, and I learned probably as much as I did in that second semester as I did in my first, just because we changed CX quite a bit that semester and chose to emphasize different things in the curriculum.
In that first year I think I grew tremendously as a "data scientist", and then that also gave me access to Data Consulting. Although I definitely didn't feel qualified as a sophomore for Data Consulting, I had been in a leadership position for CX, and I think that definitely helped me to get onto the Data Consulting team. I spent two semesters on the Taco Bell project, which involved a lot of time series analysis that I didn't understand too much of at the time, but I also generally grew in terms of understanding of how data scientists think, how they work, and how they make conclusions. After that year, as a junior I then led two projects in two consecutive semesters, the first one being Data Secrets, which was an amazing experience. Iit was a super cool project, and it was really rewarding for me too, because at first I was not confident that I had the necessary skillset to lead a project team. But I really was able to see my growth as a data scientist, and I had a great team with a lot of really passionate and smart people. Altogether it was a cool project and it really all came together, we all worked really well together, and I felt really confident in my abilities to lead that team. The next data consulting project I did was with a legal startup called AiLanthus, which was very NLP-focused.
And so semester after semester, I was doing something that was somewhat different from what I did last. I spent four semesters in Data Consulting, but every project I did was so different and every role I played in my teams was so different. As the semesters progressed, I was getting so much out of this club and was growing so much more as a data scientist, which is why I wanted to stay in SAAS and why I kept coming back. Now as an advisor, looking back at all those semesters I definitely feel like I'm indebted to this club in a way and love giving back as a part of the advising process.
During your time in SAAS, what have been some developments or transformations that you really liked, and what is your personal vision for SAAS and how you would want the club to keep changing for the better?
One big change for me was the changing of CX from the semester when I was in it to the semester when I was leading it, which I guess I was somewhat involved with at the time, being a sort of cog in the brainchild of Arun and Patrick that really recreated like the entire curriculum of CX and made it into something that's super practical for a first-year student coming into Berkeley who's trying to see whether or not they would want to do data science, whereas before it was very speaker-based. So that change was really great for CX, and I'm obviously biased because I started my SAAS journey in CX for two semesters. But to me CX really is the gateway to this club and really is one of the most important committees, if not the absolute most important committee in this club, because it is available to such a broad range of people in the Berkeley community.
Beyond that, I think a big change definitely came with everything going online with Zoom, where SAAS, especially for freshmen, was something that people really relied on much more heavily as a social outlet compared to previous semesters. I think in previous semesters, there was always a core of people that were very socially involved in SAAS, but the online transition made it a lot more important in many people's lives as a way to stay in touch socially with a community. I really see that people are investing a lot of time and energy socially in SAAS in a way that is very different from other semesters, which is really cool, and the leadership team this year has done a really great job in reimagining what SAAS can be as an online version.
Beyond that, as a soon-to-be graduate imaging what I would want SAAS to be going forward, I think that the greatest strength of SAAS is how well rounded it is, in terms of the type of people that it targets and helps. I'd like definitely for that to continue in terms of emerging or budding data scientists being the target audience for Career Exploration. As another example, Data Consulting can be great for people who are midway through college and are trying to develop their applied skills on a deeper level, and Insights & Analytics would be the bridge between CX and Data Consulting. Research and Publication could be somewhat of a different option for people who maybe aren't trying to pursue industry-type roles, but for people who just might be interested in working on their own independent project. That sort of diversity is something that makes this club really powerful, and definitely I always felt like I was always in the place that I wanted to be thanks to this club.
In terms of the three SAAS core values (community, exploration and mentorship), which one would you value the most and why?
I would say, for me, it's been mentorship. There's been a lot of direct and indirect mentorship that I have benefited from, throughout all these years. By direct mentorship, I mean literal workshops that people would host, or little conversations that I've had with certain individuals, or direct questions that I've been able to ask individuals on Slack for example. Indirect mentorship has involved, for instance, working on a Data Consulting project under people who were older than me or under a leader who was older and more experienced than I was. From those experiences I really learned a lot of things, both technical skills and soft skills. That has definitely been super valuable to me, being able to follow the ropes and understand what applied statistics can broadly mean for me.
Now as an advisor, something that I've been enjoying has been answering questions, providing both classwork help and professional help, and being able to have a lot of donutbot one-on-one's with new people, oftentimes freshmen that I've never met before, and being able to help them with anything they might want.
What has been your favorite class at Berkeley?
I'll give two answers for that one. One class is very related to the people in the club, which is CS 182, a class about deep neural networks and deep learning and overall a very well-designed class. I learned a lot through it, and it was really the culmination of semesters and semesters of classwork that I had done before that. I felt like I had taken nearly ten classes prior to CS 182, which all really built up in some way to this one course. As someone who wants to do machine learning research, that class was super informative, and I learned a lot through it.
Another one of my favorite classes is GEOG 140A and 140B, which were just classes that I took completely for fun. These classes are about physical landscapes, and they were classes I wanted to take for a while but didn't know if I was ever going to fit them into my schedule. It was a super fun class, everyone was super passionate about the class material, and it was a really cute setting to learn about stuff that I've loved for a long time.
Who has been your favorite professor at Berkeley?
I would say that my favorite professor was probably Alexander Paulin, who taught my Math 54 class about three or four years ago. He was just a great lecturer, and I feel like he taught linear algebra in a way that was really understandable and relatable, which was important to me because linear algebra is just so useful, so I'm very glad that I learned linear algebra from him.
What is your favorite place on the Berkeley campus that you think more people should know about?
One of my favorite study spots is the Philosophy Library in Moses Hall. If I ever had the chance to study back on campus, I probably wouldn't reveal this information publicly because the Philosophy Library in Moses Hall is a very small and secret spot, but a super cute spot, like an old wooden old library, completely silent. Beautiful, beautiful place to study.