I am a postdoc at the Institute of Science and Technology (IST) Austria in the Henzinger Group. In fall 2022 I will be joining the Department of Computer Science and Engineering at the Pennsylvania State University as a Hartz Family Career Development Assistant Professor. Before joining IST, I completed my PhD from the University of Texas at Austin advised by Prof. Swarat Chaudhuri. My research lies at the intersection of machine learning and formal methods, with a focus on building intelligent systems that are reliable, transparent, and secure. This work builds connections between the symbolic reasoning and inductive learning paradigms of artificial intelligence.

# Research Overview

My research combines ideas from formal methods and machine learning to efficiently build models that are reliable, transparent, and secure. This means that such a system can be expected to learn desirable behaviors with limited data, while provably maintaining some essential correctness invariant and generating models whose decisions can be understood by humans. I believe that we can achieve these goals via Neurosymbolic learning.

## [Read More]

Current machine learning models are dominated by Deep Neural Networks, because they are capable of leveraging gradient-based algorithms to optimize a specific objective. However, neural models are considered “black-boxes” and are often considered untrustworthy due to the following drawbacks:- Hard to interpret: this makes these models hard to audit and debug.
- Hard to formally verify: due to the lack of abstractions in neural models they are often too large to verify for desirable behavior using automated reasoning tools.
- Unreliable: neural models have notoriously high levels of variability, to the extent that the random initialization of the weights can determine whether the learner finds a useful model.
- Lack of domain awareness: neural models lack the ability to bias the learner with commonsense knowledge about the task or environment.

# Publications

**Neurosymbolic Reinforcement Learning with Formally Verified Exploration**

Greg Anderson, Abhinav Verma, Isil Dillig, Swarat Chaudhuri

Conference on Neural Information Processing Systems (NeurIPS), 2020.

ArXiv Code**Learning Differentiable Programs with Admissible Neural Heuristics**

Ameesh Shah, Eric Zhan, Jennifer J Sun, Abhinav Verma, Yisong Yue, Swarat Chaudhuri

Conference on Neural Information Processing Systems (NeurIPS), 2020.

ArXiv Code Video

**Imitation-Projected Programmatic Reinforcement Learning**

Abhinav Verma, Hoang M. Le, Yisong Yue, Swarat Chaudhuri

Conference on Neural Information Processing Systems (NeurIPS), 2019.

ArXiv Code Video

**Control Regularization for Reduced Variance Reinforcement Learning**

Richard Cheng, Abhinav Verma, Gabor Orosz, Swarat Chaudhuri, Yisong Yue, Joel W. Burdick

International Conference on Machine Learning (ICML), 2019.

ArXiv Code Video

**Representing Formal Languages: A Comparison Between Finite Automata and Recurrent Neural Networks**

Joshua J. Michalenko, Ameesh Shah, Abhinav Verma, Richard G. Baraniuk, Swarat Chaudhuri, Ankit B. Patel

International Conference on Learning Representations(ICLR), 2019.

ArXiv**Programmatically Interpretable Reinforcement Learning**

Abhinav Verma, Vijayaraghavan Murali, Rishabh Singh, Pushmeet Kohli, Swarat Chaudhuri

International Conference on Machine Learning (ICML), 2018.

ArXiv Video

# Teaching

### Standalone Instructor

Math 105: University Mathematics.

Introduction to logic, combinatorics, and probability. Core requirement for BS degree.Math 111: College Algebra.

Foundational course in algebra, functions, and mathematical modeling. Calculus preparation course, prerequisite for higher-level math courses.Math 112: Elementary Functions.

Focus on mathematical induction and trigonometric functions. Precalculus designed for math, biology, physiology, and CS majors.

### Teaching Assistant

COMP 539: Software Engineering Methodology.

Project based graduate course on software engineering.COMP 503: Reasoning About Software.

Graduate course on formal methods and automated reasoning.COMP 310: Advanced Object-Oriented Programming and Design.

Senior undergraduate course on OOP.Math 243: Introduction to Probability and Statistics.

Undergraduate course on statistical reasoning.M01: Introduction to Mathematical Thinking.

First course on abstract mathematics.