Hi, I am Mriganka! I am a 3rd year Doctoral Candidate at the Department of Statistics, UC Berkeley, supervised by Shirshendu Ganguly. I am broadly interested in various topics in probability, including spin systems, spectra of matrices, random graphs, Gibbs measures, and mixing times of Markov chains. More recently, I have been involved in some projects on more statistical topics such as nonparametric estimation, and variational inference.
Industrial resume: link
Academic resume: link
mriganka_brc at berkeley dot edu (UC Berkeley),
mbrc12 at gmail dot com (personal).
Probability: My primary research area is probability, and as a result I have significant exposure to modern ideas and techniques involved in the probabilistic analysis of complex systems. A broad goal of my research is to understand complex Gibbs measures arising out of various phenomena in random graphs and statistical physics. You may find some details in the papers above.
Statistics: I have taken the 210 sequence at UC Berkeley, which prepares the student for theoretical research in statistics (including topics such as concentration inequalities, minimax theory, PCA etc.). Currently, I am also taking 241B, which is a topics course in statistical learning. A key focus of my near-term research efforts include some problems I find interesting in these domains, specifically in variational inference, and nonparametric estimation.
I have also taught STAT 154/254 at UC Berkeley, which is a more ''implementation focused'' take on the traditional topics in ML theory.
Programming: Apart from my undergraduate degree, a key source of my training in programming has been my long involvement with the competitive programming scene. I also interned for software development positions twice during my undergrad, developing JVM bytecode analysis tools, and a bespoke and efficient autocomplete engine. You may find details in my CV. Creating software is also a hobby of mine, so I have picked up many different skills over the years (this website is written in Rust!) I am also very interested in functional programming and enjoy ideas coming from the "Haskell" circle.
Mathematical Finance: A key component of my undergraduate degree was a sequence of courses on mathematical finance (using Shreve as the textbook). As a result, I have a fairly rigorous understanding of (option) pricing theory, as well as general financial ideas. This knowledge is supplemented by the reasonably complete set of simulation and numerical analysis classes I took during my undergrad, including numerical PDEs, numerical matrix algorithms, and Monte-Carlo simulation.
I am currently a GSI for STAT 205B (Graduate Probability Theory Part 2) taught by Prof. Shirshendu Ganguly, and STAT 206 (Advanced Topics in Probability and Stochastic Processes) taught by Prof. Steve Evans.
Prior to that, I have been a GSI for STAT 205A (Graduate Probability Theory Part 1), STAT 134 (which is an upper-division probability class for undergraduates), and STAT 88 (which is a more introductory variant of STAT 134 combined with some emphasis on statistics).
Apart from research, I spend a significant amount of time coding, making simulations (often probabilistic in nature!) and (simple/incomplete) games. Although I am no longer active in the scene, I used to participate quite frequently in competitive programming competitions, for example, on Codeforces (mbrc) and Codechef (mbrc). Once a year, I would also participate in the now-defunct but celebrated Google Code Jam. In 2021, I (with my team of 3) qualified for the ICPC World Finals, held in Moscow, Russia.
These more "scientific" pursuits aside, I also enjoy gaming, listening to music, reading manga, and watching anime/TV shows. I participated (as a team of one) in one of the biggest game jams, the GMTK Game Jam 2023 - a 48-hour game jam based around a theme. My entry, A Conscious Virus, can be found here.