About
Hi, I’m Siddhant Tandon, a Data Scientist based in Italy. I was born on the 20th of August 1994 in the liveliest city ever — Delhi, India. I pursued my engineering in Electronics & Communication in India from 2012 to 2016, a grueling set of four long years. I repeat nothing but gruel. Right after my engineering, I left home and moved to the historic city of Rome, Italy in 2016 to pursue Laurea Magistrale in Data Science from La Sapienza. I graduated in Jan, 2019 after two demanding but enjoyable years.
So far my data science journey has been shaped by very diverse & impactful experiences. My first job as a Research Intern at Nokia Bell Labs in Paris remains one of my fondest memories. I worked on Deep Reinforcement Learning for cloud resource management which also became my master’s thesis. Incredible work and an even more phenomenal environment.
Currently I am at Scratch&Screen developing an end-to-end document intelligence system that involves OCR, document parsing, regex, computer vision, NER and building databases.
Previously, I was part of Cgnal a small consulting firm specialized in data sciences where I built machine learning applications for clients. It was here where my love for software engineering truly began.
I'd like to mention some random miscellaneous topics I came across in life that continue to spark my curiosity to this day and leave me eager to keep learning more.
- Function Spaces — Sobolev spaces, Mercer’s Theorem
- Empirical Risk Minimization and Structural Risk Minimization
- Non parametric Bayesian Modeling
- PAC Learning — What sorcery are these weak estimators doing to model bias?
- Random Forests - Sorcery again, isn't it? but this time on model variance!
- Reinforcement Learning
- Search Engines
- Asynchronous Programming - The easiest way to scale apps on a budget? Really?
- Semaphores & Mutex
- Computational Graphs - How are gradients so cheaply accessible on this data structure?
- Python Generators - Every time you think you know them, you don’t :(
- Linguistics - Dr Rajpopat's work on Pāṇini’s grammar proving grammar is an aritifical construct teachable to machines & it's not NLP
- Laurie Spiegel's pioneering work on electronic music - decades before "generative AI" was a buzzword