I am a fifth year Ph.D. student at Rice University in the department of Computer Science advised
by **Prof. Moshe Y. Vardi**.
I am interested in designing provably efficient algorithms using tools from **Formal Methods**, for problems arising from **Artifical Intelligence**, with an emphasis on good empirical performance.

Research in AI has traditionally favored 'quick and dirty' techniques that work reasonably well in practice, over techniques with rigorous proofs of correctness, accuracy and scalability. This was largely due to lack of applications where formal guarantees are indispensable. For example, the price of making a mistake in speech recognition is usually not too high. However, the booming success of Machine Learning has given rise to myriad endeavors where an error could mean the difference between life and death. For instance, automated tools for helping doctors diagnose diseases from symptoms, and for creating reports from medical scans have already been deployed in hospitals across the world. Autonomous vehicles are poised to change the way we commute in the next decade. In all such cases, policy-makers as well as end consumers can rest assured only if the underlying AI engine has been shown to be theoretically sound and mathematically precise. Wearing two hats as a theoretician and a computer engineer, I hope to achieve the holy grail of industrial-scale performance without safety compromises. ...

In my work on __ approximate model counting__ [Slides], the goal is to design highly scalable approximation algorithms which do not compromise on accuracy. For example, in my work on counting solutions to Boolean Formulas in Disjunctive Normal Form, I designed a hashing-based randomized approximation algorithm with PAC guarantees (a strong notion of accuracy) and linear running time, which is the gold standard from a theoretical perspective. What set us apart from the then state-of-the-art was robust empirical performance across different benchmarks ensuring wider applicability than the other approaches. This abstract problem of DNF-Counting has direct applications in the real world, such as showing that the electrical grid is resilient to powerlines failing. This work has resulted in two publications including an invited journal paper.

In my latest work, I tackle other related counting problems from the same perspective of ensuring high performance with strong guarantees. The graph theoretical problem of counting perfect-matchings (equivalently,

In line with the theme of 'Formal Methods for AI', I have also worked on

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Author names are sorted alphabetically except in ^{*}

**On Symbolic Approaches for Computing the Matrix Permanent**

Supratik Chakraborty,**Aditya A. Shrotri**, Moshe Y. Vardi

*International Conference on Principles and Practice of Constraint Programming (CP '19)*,

Stamford, USA Oct 2019

[PDF] [BibTex] [Slides]

**Assessing Heuristic Machine Learning Explanations with Model Counting**^{*}

N. Narodytska,**Aditya A. Shrotri**, K.S. Meel, A. Ignatiev, J. Marques-Silva

*International Conference on Theory and Applications of Satisfiability Testing (SAT '19)*,

Lisbon, Portugal July 2019

[PDF] [BibTex]

**Not All FPRASs are Equal: Demystifying FPRASs for DNF-Counting**

Kuldeep S. Meel,**Aditya A. Shrotri**, Moshe Y. Vardi

*Constraints (2018) 1-23*(__Invited Journal Publication from CP '18__)

(__Invited to Best Paper at Sister Conference Track at IJCAI '19__)

[PDF] [BibTex] [Code] [Slides]

**On Hashing-Based Approaches to Approximate DNF-Counting**

Kuldeep S. Meel,**Aditya A. Shrotri**, Moshe Y. Vardi

*37th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science.*, Kanpur, India December 2017

(FST & TCS '17)

[PDF] [BibTex] [Slides]

- Selected for Israel Institute for Advanced Studies (IIAS) Winter School in Quantum Computing, December 2019
- Received the American Institute for Indian Engineers (ASIE) Scholarship. This award is presented annually to students of Indian origin working in Engineering fields in the US. I was among 5 Ph.D. students selected in 2019.
- "Not all FPRASs are equal: Demystifying FPRASs for DNF-Counting" was amongst 3 best papers at CP 2018
- I was
**All-India Rank 1**among 150,000+ candidates in the prestigious "Graduate Aptitude Test in Engineering" (GATE) for Computer Science in 2012. - My undergrad project "Memory Leak Analysis Using Saturn" won Best Project Award at FinePro 2011

Before joining Rice, I worked for a year with Prof. S. Akshay on Model Checking of Markov chains as a Research Assistant in IIT Bombay, India. I did my Master's in Web and Data Mining from IIT Bombay advised by Prof. Soumen Chakrabarti.

Last Updated: Jan 06, 2020