Sourya Dey is currently working as a Research Engineer at Galois in the Washington DC metro area. He completed his PhD specializing in machine learning at the University of Southern California (USC) in 2020, where he was a recipient of the Viterbi Graduate School PhD Fellowship. Prior to that, he obtained his Bachelor of Technology (Honours) degree in Instrumentation Engineering from the Indian Institute of Technology (IIT) Kharagpur in 2014, along with the Best B. Tech Project award in his department.

My research interests are primarily machine learning and artificial intelligence (ML/AI), with recent focus on large language models (LLMs). I am interested in the applications of ML in various fields such as modeling systems, formal verification, and privacy and security. This is reflected by my work at Galois, which involves interdisciplinary projects at the intersection of machine learning, data science and software engineering. As one example, we have designed a Python package DLKoopman to use deep learning to model and predict the behavior of dynamical systems such as pressure on an aircraft.

Recent News

May '24: Our abstract on Analyzing Large Language Model Behavior via Embedding Analysis got accepted to be presented at AHFE 2024, Hawaii Edition.
Dec '23: The US Census Bureau collaborated with Galois to publish the paper The 2010 Census Confidentiality Protections Failed, Here's How and Why in the National Bureau of Economic Research (NBER). Check it out here.

My PhD research can be summarized by the title of my thesis — Exploring Complexity Reduction in Deep Learning. I developed a technique known as pre-defined sparsity to simplify deep learning architectures with minimal performance degradation. My latest project was on developing an open-source automated machine learning (AutoML) framework called Deep-n-Cheap to search for deep learning models and explore tradeoffs between performance and complexity. I also received an award for developing a family of open-source synthetic machine learning datasets.

My initial PhD research was on non-uniform sampling and mixed signal circuits. I then switched track and was involved in the inception and subsequent growth of a new machine learning group at USC in 2016. Some of my early work involved a hardware architecture, however, I have now completely moved over to the software side. My current and future research interests include applying interpretability of machine learning and its applications to real-world problems.

I grew up in the beautiful and very unique city of Kolkata -- the City of Joy. My undergrad days were in nearby Kharagpur, before moving to the entertainment capital of the world -- Los Angeles. I currently live in Arlington in the Washington DC metro area. The natural beauty here is amazing, lots of opportunities to bike and hike!

I like reading, particularly detective thrillers, science fiction and fantasy, and sports-related books. Here's my Goodreads profile, I'd love to connect with other bibliophiles! I am also a big fan of watching football. My ideal weekend is waking up in time to watch Chelsea play and (hopefully) win. Some other activities which I enjoy are swimming, coding, and experiencing new places and cuisines.