Profile

Krishna Praneet Gudipaty

pronounced: /ˈkrɪʃ.nə/ (krish-nuh)

About me


I love building things—and there’s something fascinating about how lines of code can transform into meaningful systems that power real-world applications. Currently, I am pursuing a Ph.D. in Computer Science at the University of Massachusetts Amherst, broadly working on distributed systems and networking, optimization and machine learning. I hold an M.S. degree in Computer Science from UMass Amherst and B.Tech. degree from the Indian Institute of Technology Madras. I also have 2+ years of industry experience in developing and maintaining webapps for large-scale, distributed processing of data. Check out - Biography.

Beyond research, I developed a strong interest in financial markets, particularly in how data and technology drive modelling for real-time decisions and pricing. What captivated me is the union of my love for mathematics and programming. This is how I spend rest of my time - check out Quant.

What I've been working on recently:


MEL: Multi-level Ensemble Learning - paper

A novel Machine Learning framework for resilient AI/ML inference in heterogeneous edge environments.


LDPCDecoders.jl - v0.3.3

A software library of fast and efficient implementations of Quantum LDPC (qLDPC) decoders in Julia.


Outside of work:

I’m a regularly active individual who works out daily and loves to play a lot of sports - it’s a great way to unwind and helps me stay sharp and build resilience both physically and mentally. What if I told you I have played field hockey, badminton, and table tennis competitively? However, more recently, I’ve been enjoying playing basketball in the evenings at the gym.

In addition, I have a lifelong passion for music. Music has always been a creative outlet for me, offering both a meditative focus and a powerful way to express emotion and rhythm. I have dedicated over six years to earning a diploma in Hindustani music and can play several instruments- piano/keyboard, guitar, sitar, tabla, and drums are a notable few.