hi! I'm Parva

A researcher and engineer with a focus on artificial intelligence, data systems, and applied machine learning. I hold a master's degree in Data Architecture from Northeastern University, where my academic work centered on large-scale data pipelines, database design, and the integration of AI into analytical systems.

My current work involves architecting scalable AI and data engineering solutions, with particular interest in topics such as natural language processing, generative AI, and agentic workflows. This blog serves as a platform where I review research papers, highlight key contributions, and explore how theoretical advances connect to real-world applications.

Through my writing, I aim to critically analyze methodologies, discuss emerging trends, and foster dialogue that bridges academic research and industry practice.

Featured

• Clearly Explaining: Pangram’s approach to AI text detection

  6 Feb, 2026 · 4 min read

  Explaining Pangram’s approach to AI text detection

• Clearly Explaining: Long Short-Term Memory (LSTM) [1/16]

  6 Feb, 2026 · 8 min read

  How Hochreiter & Schmidhuber’s 1997 LSTM introduced gated memory cells that keep gradients alive, making long-range sequence learning practical.

• Clearly Explaining: Playing Atari with Deep Reinforcement Learning [1/35]

  30 Jan, 2026 · 8 min read

  How DeepMind combined Deep Learning with Q-Learning to master Atari games directly from pixels, kickstarting the Deep RL revolution.

• Clearly Explaining: The First Law of Complextropy [1/30]

  20 Jan, 2026 · 8 min read

  Why does the universe get more interesting before it gets boring? Explaining Scott Aaronson's quest to mathematically define 'Complexity' vs Entropy.