Email: mhnaik@cis.upenn.edu
Office Hours (Fall 2024): By appointment
[C.V.]
I'm a professor in the Department of Computer and Information Science at the University of Pennsylvania. My areas of specialization are programming languages and artificial intelligence.
In an earlier life, I was a faculty in Computer Science at the Georgia Institute of Technology and a researcher at Intel Labs, Berkeley. I obtained a Ph.D. in Computer Science from Stanford University in 2008, advised by Alex Aiken. Before that, I received a Masters from Purdue University in 2003, advised by Jens Palsberg, and a bachelors from BITS Pilani in 1999. And even before that, I spent many idyllic years in the beautiful state of Goa, India.
Recent News 
- My PhD students Ziyang Li and Jiani Huang are on the 2024-25 job market!
- In Fall 2024, I am teaching a new course CIS 7000 - Large Language Models.
- I am honored to be named the Misra Family Professor effective July 2024.
Research
I am broadly interested in topics related to programming languages and artificial intelligence.
Neurosymbolic Programming
Neurosymbolic programming is an emerging paradigm that combines the otherwise complementary worlds of symbolic reasoning and data-driven machine learning. In doing so, it enables more accurate, interpretable, and domain-aware solutions than the neural or symbolic counterparts alone.
My research group is investigating all aspects of neurosymbolic programming including language design, learning algorithms, and compiler optimizations. To validate these ideas, we build practical open-source systems and develop advanced applications using them in diverse domains such as computer vision, cyber-security, medicine, and bioinformatics.
Our most mature effort in this space is the Scallop neurosymbolic programming language and compiler toolchain. You can learn more about Scallop at scallop-lang.org or using the below resources:
- Read our invited monograph Neurosymbolic Programming In Scallop: Principles and Practice in the Foundations and Trends® in Programming Languages series by Now Publishers.
- Do a hands-on tutorial on programming with Scallop from the 2024 Summer School on Neurosymbolic Programming.
- Download and run Scallop from its source code or pre-built binaries.
- Read the PLDI 2023 paper for a technical overview of Scallop's core language and semantics.
- Read the AAAI 2024 paper for how to combine Scallop programs with foundation models such as GPT and SAM.
- Watch videos of talks presenting Scallop in a UC Berkeley Seminar (Oct 2022) and Simons Institute (Nov 2023).
Trustworthy AI for Healthcare
AI stands to significantly enhance healthcare by alleviating costs, reducing human errors, and improving patient outcomes. Important applications abound both in advancing the frontiers of healthcare (e.g., personalized medicine) and in delivering routine healthcare to the masses in emerging economies (e.g., testing and reporting).
The effective application of AI to healthcare is hindered by stringent requirements of safety, explainability, and ability to incorporate expert knowledge. Neurosymbolic programming offers these features and thereby stands to be an attractive fit for this domain. My research group is collaborating with bioinformatics researchers and clinicians to apply neurosymbolic programming to problems in healthcare that stand to benefit from AI.
AI-Enabled Programming Tools
Another topic at the intersection of programming languages and machine learning that I am interested in concerns improving programmer productivity and software quality through AI-enabled programming tools.
This research direction culminated in a Google Tech Talk I gave in the summer of 2022. It outlines the limitations of purely neural models for code and traditional program analysis systems, which I call System 1 and System 2 respectively, following Daniel Kahneman's terminology in his book Thinking, Fast and Slow. I also talk about how to overcome those limitations by combining the two approaches.
You can also check out our research on applying deep learning to program verification (Code2Inv, NeurIPS 2018), program repair (Hoppity, ICLR 2020), program analysis (CodeTrek, ICLR 2022), and program merging (DeepMerge, FSE 2022).
Teaching
I regularly teach the following courses:
CIS 5470: Software Analysis
This course covers the principles and practice of software analysis. A significant -- and fun! -- part of this course is a series of "labs" that involve implementing modern analysis tools in C++ atop the LLVM compiler framework.
All the material for this course is publicly available at https://software-analysis-class.org/.
The course caters to those who wish to become more effective software engineers or are embarking on research in topics related to software engineering or security. It is open to graduate and upper-level undergraduate students in computer science. Students from other disciplines who satisfy the prerequisites are also welcome. I teach this course every Fall.
The course is also offered in two online graduate degree programs: Penn's MCIT Online usually in the Spring semester, and Georgia Tech's OMSCS in Spring, Summer, and Fall semesters.
CIS 5500: Database Systems
This course covers topics in database systems including data modeling, logical foundations, popular languages, and implementation aspects. A significant component of the course is a group project that involves teams of 3-4 students building a full-fledged web-based database application using datasets, features, and frameworks of their choice.
The course caters to those who wish to pursue a career in data science or gain a broad yet rigorous understanding of database principles. It is open to students from all majors and departments across campus who satisfy the prerequisites. I teach this course every Spring, and it is also offered every Fall (and occasionally in Summer). The course is also offered in Penn's MCIT Online every semester (Spring, Summer, and Fall).