Education.
A slightly unusual route into computer science, and the people who made each step of it work.
- 2025–26GPA 3.93
Master of Computer Science (MCS)
University of Illinois Urbana-Champaign
Completed while working full time, through the Chicago-based MCS program. Distributed systems, applied machine learning, and AI agents, with a research semester in human-computer interaction. Some of my iCAN coursework counted toward the degree, so the two run together more than the dates suggest.
Professor Indranil Gupta made distributed systems the most fun I had in the program. Professor Daniel Kang brought industry practice into a class about agents as the field was changing quickly. Professor Ranjitha Kumar taught me to think about the person on the other end of a model, and picked the best reading list of my degree. Professor Abdu Alawini made databases genuinely enjoyable. Professor Brian Bailey advised my first research work.
Selected coursework
- CS 411 Database Systems
- CS 425 Distributed Systems
- CS 427 Software Engineering I
- CS 441 Applied Machine Learning
- CS 498 AI Agents in the Wild
- CS 568 User-Centered Machine Learning
- CS 597 Individual Study (HCI research)
- CS 598 Machine Learning for Software Engineering
- 2024–25GPA 4.0
iCAN Graduate Certificate, Computing Fundamentals
University of Illinois Urbana-Champaign
iCAN is the Illinois Computing Accelerator for Non-Specialists: a one-year program built for people whose bachelor’s degree is in something other than computer science, and it assumes you have never programmed. I’d taken programming courses and written code at work, but I wanted a more structured foundation in computer science. It is also the pathway most of its graduates take into the MCS, including me.
Dr. Tiffani Williams and Dr. Yael Gertner built this program, and it deserves to be better known than it is. Dr. Williams started us on algorithmic thinking with sudoku puzzles before we touched an IDE, which surprised me at first but was exactly what we needed.
Selected coursework
- CS 400 Accelerated Fundamentals of Computing I
- CS 401 Accelerated Fundamentals of Algorithms I
- CS 402 Accelerated Fundamentals of Computing II
- CS 403 Accelerated Fundamentals of Algorithms II
- 2018–21GPA 3.73
B.S. in Actuarial Science, with Distinction
University of Illinois Urbana-Champaign
I arrived as a pre-engineering student aiming at computer science and took CS courses from my first semester. Plans changed, so I pivoted to actuarial science, finished in three years, and passed three actuarial exams before starting full-time work: 1/P in September 2020, 2/FM that December, and 3/IFM in July 2021.
Professor Geoffrey Challen taught the intro course that kept me interested. Professor David Dalpiaz taught ASRM 451, where supervised and unsupervised learning finally clicked as something I could actually do rather than read about, and I still reach for the habits that course built.
Selected coursework
- ASRM 450 Methods of Applied Statistics
- ASRM 451 Basics of Statistical Learning
- ASRM 461 Loss Models
- ASRM 499 Predictive Analytics
- CS 125 Intro to Computer Science
- CS 173 Discrete Structures
- MATH 415 Applied Linear Algebra
- STAT 400 Statistics and Probability I
- STAT 410 Statistics and Probability II
- 2014–18GPA 3.36
Schaumburg High School
Schaumburg, IL
Where the whole thing started. Computer Programming as a sophomore, AP Computer Science A as a junior, then an honors Mobile Application Development course as a senior. Also three years of band, three years of Chinese, AP Calculus BC, and AP Physics C.
Jeffrey O’Brien taught those CS courses, and he is one of the most impactful teachers I have ever had. He is the reason I found this field at all.