About Me

Welcome to my corner of the internet! I am a 5th year PhD student at the University of Pennsylvania advised by Prof. Mayur Naik.

My research interests span Programming Languages and Machine Learning. The overarching goal of my research is to enable machine learning practitioners to write neurosymbolic programs that can scale to highly complex tasks and datasets using intuitive and expressive interfaces. My work focuses on combining symbolic reasoning with deep learning to address tasks requiring both perception and logic-based reasoning, while ensuring scalability through techniques like vectorized computations and GPU-accelerated differentiable reasoning.

I am also interested in developing techniques to help machine learning practitioners effectively understand where their models fail and identify ways to fix them. My other research interests include designing program synthesis techniques to streamline software analysis, bug finding, and code generation, as well as exploring compiler-based optimization strategies for large-scale machine learning frameworks in challenging domains such as image processing, text understanding, and multimodal reasoning.


News

  • I presented our paper "TorchQL: A Programming Framework for Integrity Constraints in Machine Learning" in the proceedings of OOPSLA 2024. Try out TorchQL here, and read our paper here.
  • Our work "LLM Based Test-Driven Interactive Code Generation: User Study and Empirical Evaluation" is published in the IEEE Transactions on Software Engineering. Read the article here.
  • I am very grateful to be awarded the 2023 Google PhD Fellowship in Programming Technology and Software Engineering.

Publications

Recent Manuscripts

Dolphin: A Programmable Framework for Scalable Neurosymbolic Learning
Aaditya Naik*, Jason Liu, Claire Wang, Saikat Dutta, Mayur Naik, Eric Wong

Conference Papers

TorchQL: A Programming Framework for Integrity Constraints in Machine Learning
Aaditya Naik, Adam Stein, Yinjun Wu, Mayur Naik, Eric Wong
Relational Query Synthesis ⨝ Decision Tree Learning
Aaditya Naik, Aalok Thakkar, Adam Stein, Mayur Naik, Rajeev Alur
Do Machine Learning Models Learn Statistical Rules Inferred from Data?
Aaditya Naik, Yinjun Wu, Mayur Naik, Eric Wong
CodeTrek: Flexible Modeling of Code using an Extensible Relational Representation
Pardis Pashakhanloo, Aaditya Naik, Yuepeng Wang, Hanjun Dai, Petros Maniatis, Mayur Naik
Sporq: An Interactive Environment for Exploring Code Using Query-by-Example
Aaditya Naik, Jonathan Mendelson, Nathaniel Sands, Yuepeng Wang, Mayur Naik, Mukund Raghothaman
Example-Guided Synthesis of Relational Queries
Aalok Thakkar, Aaditya Naik, Nate Sands, Mukund Raghothaman, Mayur Naik, Rajeev Alur
GenSynth: Synthesizing Datalog Programs without Language Bias
Jonathan Mendelson*, Aaditya Naik*, Mukund Ragothaman, Mayur Naik
Code2Inv: A Deep Learning Framework for Program Verification
Xujie Si*, Aaditya Naik*, Hanjun Dai, Mayur Naik, Le Song

Journal Publications

LLM-Based Test-Driven Interactive Code Generation: User Study and Empirical Evaluation
Sarah Fakhoury, Aaditya Naik, Georgios Sakkas, Saikat Chakraborty, Shuvendu Lahiri

Workshop Papers

Learning to Walk over Relational Graphs of Source Code
Pardis Pashakhanloo, Aaditya Naik, Hanjun Dai, Petros Maniatis, Mayur Naik