Graduate Alumni Talk About Our Program

Kianté Brantley ('22)

Adviser: Hal Daumé III
Website: https://www.cs.umd.edu/~kdbrant/

Kianté Brantley is a Postdoctoral scholar at Cornell working with Thorsten Joachims. He completed his Ph.D. in computer science at the University of Maryland College Park (UMD) advised by Professor Hal Daumé III. Brantley designs algorithms that efficiently integrate domain knowledge into sequential decision-making problems. He is most excited about imitation learning and interactive learning—or, more broadly, settings that involve a feedback loop between a machine learning agent and the input the machine learning agent sees.

Before coming to UMD in 2016, Brantley attended the University of Maryland, Baltimore County where he earned his bachelor’s degree and master's degree (advised by Tim Oates) in computer science. He also worked as a data scientist for the U.S. Department of Defense from 2010 to 2017. In his free time, Brantley enjoys playing sports; his favorite sport at the moment is powerlifting.

Why did you choose to study computer science at UMD? What did you enjoy about it?

I choose to study computer science at UMD for several reasons. The first reason is that I was born and raised in Baltimore, MD, and wanted to stay close to family. The second reason is that UMD has several professors I was excited to work with (spoiler, I was advised by one of them). The last reason was that when I started my Ph.D., I worked with the department of defense and was restricted to schools in the DMV (District of Columbia, Maryland, Virginia). These three main reasons and a few others factored into my decision to study computer science at UMD.

There are so many things that I enjoyed about my degree program. The first and foremost thing that I enjoyed was the people. Every environment has pros and cons, but the people you are around drastically affect your experience. From my degree program, not only did I obtain a solid education, but I also obtained a solid network of friends and colleagues. The other thing I enjoyed was the administrative support the program provided me through various stages of my degree program journey. I would not have made it through the program without that support.

What is your CS concentration and what have you been researching?

My computer science concentration is machine learning. In particular, I work on designing machine learning algorithms with theoretical guarantees and practical benefits to solve structured prediction problems. Structured prediction studies problems in which the input or output has structural interdependencies that are sequential, temporal, spatial, or combinator. For example, predicting a target-language sentence from a source-language sentence in machine translation or predicting the rankings in a recommendation system. Recently, I have been working on dealing with dynamic issues in recommendation systems. I have also been working on aligning Large Lange Models (LLM) with human preferences.

What are some resources provided by the department that you find valuable?

Some of the resources I found valuable provided by the department include but are not limited to: initial temporary advisor, large computational compute clusters, student department-wide slack channel, small/large fellowship notifications via email, and a modern building for working.

Rohan Chandra (‘22)

Adviser: Dinesh Manocha
Website: https://rohanchandra30.github.io

Rohan Chandra is a postdoctoral researcher in the AMRL hosted by Joydeep Biswas. I defended my PhD in April 2022 at the University of Maryland where I worked with Dr. Dinesh Manocha in autonomous driving. I completed my Masters in CS from UMD in 2018 under the guidance of Dr. Tom Goldstein and bachelors in ECE from DTU, India in 2016.

Why did you choose to study computer science at UMD? What did you enjoy about it?

The CS department at UMD is strong in research and teaching. It has a strong computer vision and robotics focus from both a classical and machine learning perspective. It also has wonderful faculty in theoretical machine learning, optimization, and natural language processing. Graduate students from UMD-CS go on to have successful academic and research industrial careers. I was also looking for a bigger-sized department with a large undergraduate student body since that comes with abundant TA opportunities and I enjoy teaching. I joined UMD-CS when it was in A.V. Wiliams, and the new building has only contributed to the strength of the program.

Before I received an RA, I enjoyed teaching. I have also enjoyed the classes and many of them have interesting course projects that help in research.

What are some resources provided by the department that you find valuable?

Several things. The Iribe building is probably the biggest resource we received last year. It has a good quality cafe to feed us with food and coffee. Generally, the dept. strives to help its graduate students be as research-productive as possible by simplifying a lot of technical and administrative issues by acting as the middle-man. For example, the advantages of having an in-staff IT cannot be overstated. Not all CS grad students are adept at handling technical IT problems and they can be mind-numbingly time-consuming to fix. The CS dept and UMIACS IT staff save the day in such cases and save us headaches so that we can focus on research. Seriously, it Our coordinator, Tom, liaisons with the Graduate school on our behalf and frees up more time for research for us. Another resource I find useful is that the CS website is updated regularly and contains useful information for grad students, for example, latest news and talks.

Emily Hand ('18)

Adviser: Rama Chellappa
Website: https://www.unr.edu/cse/people/emily-hand

Emily Hand is an assistant professor in the Department of Computer Science and Engineering at the University of Nevada, Reno. Dr. Hand received her Ph.D. in computer science from the University of Maryland, College Park under the supervision of Professor Rama Chellappa. She has held research positions at NASA Ames, NASA JPL and the Naval Research Laboratory.

Dr. Hand's primary research interest lies in the intersection of computer vision and machine learning, with an emphasis on understanding human perception. She has performed on several IARPA projects related to face recognition and action recognition from images and video. Her research focuses on bridging the gap between human and computer vision using research in human perception and machine learning. Dr. Hand is the director of the Machine Perception Lab at UNR.

Why did you choose to study computer science at UMD? What did you enjoy about it?

UMD has an excellent computer vision program, with a wide variety of faculty working on a wide range of problems. I knew I would find a good advisor in an area I was interested in.

I really enjoyed how big the program was. There were so many students studying computer vision. My group had around 20 PhD students, but the overall vision program had even more. It was nice going through grad school with a large cohort of students.

What are some resources provided by the department that you find valuable?

I found that running the Graduate Student Club was very easy with resources from the department. They provided food for all our events and were very helpful with the logistics of running the club. We also had a lot of interest from faculty to socialize with students and a lot of engagement overall.

Tongyang Li ('20)

Adviser: Andrew Childs
Website: https://www.tongyangli.com/

Tongyang Li is an assistant professor at the Center on Frontiers of Computing Studies, Peking University. Previously I was a postdoctoral associate at the Center for Theoretical Physics, Massachusetts Institute of Technology during 2020-2021. I received my Ph.D. degree from the Department of Computer Science, University of Maryland in 2020, and I received Bachelor of Engineering from the Institute for Interdisciplinary Information Sciences, Tsinghua University and Bachelor of Science from the Department of Mathematical Sciences, Tsinghua University, both in 2015.

My research investigates interdisciplinary subjects among quantum computing, machine learning, and theoretical computer science, with the focus on designing quantum algorithms for machine learning and optimization. I am also interested in quantum simulation, quantum query complexity, and quantum walks.

Why did you choose to study computer science at UMD? What did you enjoy about it?

When I applied for CS graduate programs, I was seeking positions in quantum computing. Back in 2015, UMD was probably one of only a few places that have this research direction in the CS department. In addition, Prof. Andrew M. Childs at UMD is a leading expert in this field and I really enjoy reading his papers. When he gave me the offer, I felt very honored to work with him and come to UMD.

I enjoyed many things about the program. First, the professors here at UMD are very nice. For me, not only my advisor Andrew is super helpful in my research, but also many other professors in the CS department, such as Furong Huang, Aravind Srinivasan, Xiaodi Wu, etc. Second, the quality of the students is very good, and it is very easy to find someone to discuss research and collaborate with. In addition, UMD is at a great location -- on the one hand, College Park is a nice small town, and also on the other hand it's very close to Washington DC with so many nice events to explore.

What are some resources provided by the department that you find valuable?

I particularly feel that UMD CS is very well-organized and all kinds of research information are very approachable. For instance, in my research area there is a specific center, the Joint Center for Quantum Information and Computer Science (QuICS); all people in quantum computing belong to this center and we have regular group meetings, talks, etc. I think there are ~15 centers in total spanning through different subjects in CS, which are great resources and make research at UMD CS very efficient in general.

Elissa Redmiles (B.S., '13, M.S., '19, Ph.D. '19)

Adviser: Michelle Mazurek
Website: https://elissaredmiles.com/

Elissa Redmiles is a faculty member and research group leader at the Max Planck Institute for Software Systems. She has additionally served as a consultant and researcher at multiple institutions, including Microsoft Research, Facebook, the World Bank, the Center for Democracy and Technology, and the University of Zurich. Dr. Redmiles uses computational, economic, and social science methods to understand users’ security, privacy, and online safety-related decision-making processes. Dr. Redmiles' work has been recognized with multiple paper awards at USENIX Security, ACM CCS and ACM CHI as well as research awards from Facebook and Google. As a graduate student, she was supported by a NSF Graduate Research Fellowship, a National Defense Science and Engineering Graduate Fellowship, and a Facebook Fellowship.

Her work focuses on three main thrusts: 1) security economics (understanding how people value their accounts and perceive risk to those accounts, and how those perceptions drive behavior); 2) aligning technical privacy protections with users' concerns (e.g., how do we explain the guarantees offered by trusted execution environments, differential privacy, etc. to end users and once we explain these guarantees, do people care about them?); 3) safety for marginalized & vulnerable groups (e.g., what does it mean to be safe in a post-digital world for example when things like dating or engaging in gig work that requires you to interact with clients in-person is increasingly digitally mediated).

Why did you choose to study computer science at UMD? What did you enjoy about it?

I went to UMD for my BS in CS and found that the department had wonderful mentorship! My undergraduate research mentor, Samir Khuller, stayed in touch with me after I graduated with my BS and kept encouraging me to pursue a PhD, and eventually I came back.

I really enjoyed how big the program was. There were so many students studying computer vision. My group had around 20 PhD students, but the overall vision program had even more. It was nice going through grad school with a large cohort of students.

What are some resources provided by the department that you find valuable?

I appreciated the open door environment. I had the opportunity to collaborate with several UMD faculty throughout my PhD and I always felt that I could reach out to anyone to chat about a research problem or career question.

Welles Robinson ('21)

Adviser: Eytan Ruppin

Welles is currently a post-doc researcher in the Surgery Branch in the National Cancer Institute (NIH). He graduated with a PhD in computer science from University of Maryland where he was co-advised by Max Leiserson and Eytan Ruppin. Before graduate school, Welles worked on a DARPA program.

Why did you choose to study computer science at UMD? What did you enjoy about it?

I chose to study computer science at UMD because it is the best school for computer science in the DC area (where I was working) and I initially started as a part-time master's student (before switching to the PhD program full-time). My concentration was computational biology. I had never heard of computational biology before I attended UMD. During orientation, my future adviser (who I'd never met or heard of) talked for fifteen minutes about how he was using computer science to try to cure cancer and I was hooked. My dissertation was on computational methods for analyzing large cancer datasets and now I research cancer immunotherapy using both computational and wet-lab methods at the National Cancer Institute.

I enjoyed the opportunity to explore new areas of research by taking classes in areas of computer science that weren't available at my undergraduate university. It also gave me the opportunity to reinvent myself as a computational biologist (I had no prior experience with biology), including allowing me to count a cellular biology class as one of my electives.

What are some resources provided by the department that you find valuable?

I was able to obtain an internal fellowship from an NSF funded grant obtained by professors in the department, which was very helpful. I was also able to present one of my papers at a conference in Switzerland thanks in part to department funding. I also really appreciated the administrative staff, who helped me navigate any bureaucratic issues that I ran into.

Nitin J. Sanket (‘21)

Adviser: Yiannis Aloimonos
Website: https://nitinjsanket.github.io

Dr. Nitin J. Sanket is currently an Assistant Professor in Robotics Engineering with affiliations to Computer Science and Electrical and Computer Engineering at Worcester Polytechnic Institute (pronounced as Wuster). He received his M.S. in Robotics from the University of Pennsylvania's GRASP lab, where he worked with Prof. Kostas Daniildis on developing a benchmark for indoor to outdoor visual-inertial odometry systems. He was an Assistant Clinical Professor in the First-Year Innovation and Research Experience and a Postdoctoral fellow in the Perception and Robotics Group at the University of Maryland, College Park. During this time, he worked with Prof. Yiannis Aloimonos and Dr. Cornelia Fermuller on developing Bio-inspired AI frameworks using the Action-Perception Synergy for resource-constrained tiny mobile robots. His doctoral thesis won the Larry S. Davis award and the MDPI Drones Ph.D. Thesis award. His work has been covered by BBC Earth, IEEE Spectrum, and UMD Media among various other online articles. He is also a recipient of the Dean's fellowship, Future Faculty Fellowship, Ann G. Wylie fellowship and was the Maryland Robotics center student ambassador. He has also taught courses, including hands-on aerial robotics and vision, planning and control in aerial robotics. Nitin is currently an Associate Editor for the Nature npj Robotics and IEEE Robotics and Automation Letters Journal. He is also a reviewer for RA-L, T-ASE, IMAVIS, CVPR, ICRA, RSS, IROS, SIGGRAPH and many other top journals and conferences.

Why did you choose to study computer science at UMD?

What did you enjoy about it?

I wanted to work at the intersection of computer vision and NLP when I applied for a Ph.D. program. After interviewing with many great schools, I gravitated to Prof. Yiannis' personality which led me to choose UMD over other comparably ranked schools.

What is your CS concentration and what have you been researching?

I worked on robotics, specifically building autonomous tiny aerial robots -- the size of a hummingbird to autonomously pollinate flowers using only on-board sensing and computation.

What do you enjoy about your degree program?

I loved a lot of things about the program. First was the flexibility to transfer credits from another school to reduce course load. Secondly, there are so many professors across various disciplines, so there is always someone to talk to about any question(s) you may have. Thirdly, TA positions are always available, hence you do not really have to worry about funding so much as a Ph.D. student if you enjoy TAing. Fourth, you have high flexibility in choosing your advisor and changing them if needed. Fifth, I did like the average intellect level of the students. Finally, the staff and faculty are super awesome and friendly which makes the overall experience amazing even though UMD is a huge school. You really feel special!

What are some resources provided by the department that you find valuable?

I loved the kitchens and the building layout of Iribe - really helps to talk to other students as you are bound to run into them and learn something cool! The coffee hours help a lot in bonding, although I would wish there were more social events with free food for an even better experience. The seminars were great. I do like the new initiatives the department is taking with respect to helping students in their job hunt (academic or industry).

Daniel Votipka (‘20)

Adviser: Michelle Mazurek
Website: https://www.eecs.tufts.edu/~dvotipka/

Daniel Votipka is an assistant professor in the Computer Science Department at Tufts University. Votipka's research focuses on computer security, with an emphasis on the human factors affecting security professionals. Professor Votipka is interested in understanding the processes and mental models of professionals who perform security-related tasks such as vulnerability discovery, network defense, and malware analysis to provide research-based recommendations for education, policy, and automation changes to best leverage human intelligence against challenging computer security problems.

Why did you choose to study computer science at UMD? What did you enjoy about it?

Primarily, I chose UMD for the opportunity to work with some of the best researchers in my field.  Through the Maryland Cybersecurity Center and Human-Computer Interaction Lab, I have  had the opportunity to collaborate and learn from the top professors and students in several disciplines.

I've enjoyed the freedom to work on challenging problems that are of particular interest and the opportunity to work with, learn from, and be supported by so many amazing people. It is always a joy to come into work because it feels like an extended family.  I know they always care about me and are there for me.

What are some resources provided by the department that you find valuable?

I think the most valuable thing the department provides is the breadth of expertise in our research.  If I ever have questions about a topic I'm unfamiliar with, there's always someone at UMD I can turn to who is an expert in that field.  Also, the new Iribe Center building with so much natural light and available meeting space is pretty great!

Pan Xu ('19)

Adviser: John Dickerson
Website: https://people.njit.edu/faculty/pxu

Pan Xu is an Assistant Professor in the Department of Computer Science at the New Jersey Institute of Technology. His research area spans Algorithms, Operations Research, and Artificial Intelligence. He got his first Ph.D. in Operations Research at Iowa State University in 2012 and a second Ph.D. in Computer Science at the University of Maryland, College Park, in 2019.

Why did you choose to study computer science at UMD? What did you enjoy about it?

First, the CS program at UMD is quite reputable (at least at the time when I applied, and I believe it probably gets even better now). Second, I was recommended by one of my friends, whose Ph.D. advisor was an ECE alumnus at UMD.

Honestly, the CS Ph.D. program at UMD is quite challenging and tough; but that's the exact reason I chose it. I like challenging myself intellectually!

What are some resources provided by the department that you find valuable?

First of all, professors! They are surely the most valuable resource, especially for graduates planning academic careers. Secondly, peers. While pursuing the Ph.D. program, I was fortunate to make a few new friends that are pretty excellent (and I am now working with some of them, and they are surely my lifetime-long collaborators! :)