Area of study: Bioengineering PhD

Faculty advisors: Dr. Jason Bates and Dr. Charles M. Kinsey

From: Indiana—the crossroads of America.

Why did you choose UVM? What do you like best about being at UVM?
I primarily chose UVM because of Dr. Jason Bates and Dr. Charles M. Kinsey. During my interview, I felt like I could easily work with both of them and that the opportunity they provided was right where I wanted to position myself for my career. At the moment, I love Vermont’s weather—but honestly, ask me that in a couple months and that answer might change.

Why did you choose this area of graduate study? What career will you pursue when you complete your degree?
I chose computational modeling and machine learning as I am fascinated by the potential these two areas have for the medical industry. I love the notion of personalized medicine but realize that as a society, we are currently very far from reaching that goal.

Tell us a little about the research or project you are working on.
I am working on using deep learning to be able to categorize lung nodules in CT images as being cancerous or non-cancerous. The project is still in its early phases and will evolve over time, so I recommend staying informed with all the great research going on at UVM’s Lung Center.

If you had a time machine and could go back to before you started your graduate program, what advice would you give yourself?
I’ve just recently started my PhD program, so I don’t have that much advice. However, one “insight” that I may have is that you shouldn’t be afraid to look like a fool in front of your advisors. Advisors are here to guide you through you graduate career and provide feedback when necessary. If you don’t know something, don’t be afraid to ask questions. You should be genuine and authentic, if you don’t know something learn from your colleagues and mentors. There is no point in wasting 5 hours of research if someone could have corrected you and expedited the process. But that doesn’t mean that you should immediately seek help at the slightest difficulty—find the balance that lets you apply yourself.

Have you had an internship? If yes, where was it? What were your responsibilities? What were the highlights?
I interned with Eli Lilly and Company during the summer of 2017, which led to my getting a contract position while I applied to graduate school for the fall of 2017 and summer of 2018. During my stay at Eli Lilly, I became one of the lead experts on Hamilton Robotics automation system and had the opportunity to provide my feedback on certain projects and how to best optimize certain procedures in order to automate the process. Overall, the most rewarding feeling was getting offers from various automation companies because of how closely I worked with their representatives.

Area of study: MS Electrical Engineering

Faculty advisor: Mads Almassalkhi

From: Pelham, Massachusetts

Why did you choose UVM? What do you like best about being at UVM?
I already had good connections from my undergraduate program and my advisor has multiple grants for exciting research in my area of interest. Burlington is also an amazing place to live.

Why did you choose this area of graduate study? What career will you pursue when you complete your degree?
The masters program in electrical engineering will allow me to continue to grow my knowledge and experience in power systems and obtain new skills in the area of optimization and control systems. I plan on continuing to work in the energy grid sector once I finish my degree.

Tell us a little about the research or project you are working on.
Thesis Working Title: Distributed Optimal Control of Electric Vehicle Charging under Dynamic Grid Constraints
Abstract: As changing economics and global climate change concerns increase the adoption of renewables and electric vehicles, it is vital to study how best to integrate these in our existing energy systems. Plug-in electric vehicles (PEVs) are on track to quickly become a large factor in the energy grid. If left uncoordinated, the charging of PEVs will become a burden on the grid by increasing peak demand and overloading transformers. However, if the proper strategies and coordination is implemented, the problems will be mitigated without the need for expensive investments. A distributed control algorithm will be used to compute optimized charging schedules of a fleet of electric vehicles under multiple scenarios. Further work will compare optimized schedules against low-information packetized EV charging control schemes and will consider hardware in the loop validation to ensure practicality.

Can you share a time when a faculty advisor or mentor connected you with either new insights or a valuable opportunity (conference, publication etc.)?
My advisor met a colleague at a conference who works at a university lab in Germany (Karlsruhe Institute of Technology, or KIT). After discussing overlapping research interests, I was able to do a 6-week research internship at KIT this summer and apply one of the optimization algorithms they are working on to the problem I am studying. It was a great experience to work collaboratively with researchers in another part of the world.

Have you had an internship? If yes, where was it? What were your responsibilities? What were the highlights?
Yes, my most recent internship was with the Electric Power Research Institute in Palo Alto, California. My main task was to co-lead the development of a python tool called StorageVET, which is an open source energy storage evaluation tool. It was exciting to take a principle role in building an application that will have real-world implementations.