Sankalp Gambhir — Homepage

Hello! I'm a doctoral student at EPFL working in the Laboratory for Automated Reasoning and Analysis (LARA) with Prof. Viktor Kunčak , interested in automata theory, program analysis, algebra, and their applications in verifying and improving computing systems. Mostly fueled by mortal fear of a machine takeover.

My CV is probably completely outdated unless I'm looking for a job (which is not right now). This page tends to be updated more often. See also: my EPFL directory page.


I enjoy a myriad of topics within formal logic, computer science, and algebra. My research work currently revolves around theorem provers and foundations of mathematics, their applications to both automatic and human-assisted verification, and their use in education. I also study applications of formal methods to program synthesis, and to database systems, their testing and verification, and algebraic embeddings.

At a more fundamental level, I'm deeply interested in the utilisation of formal methods to understand structures in computer science on a more fundamental level to pave the way for more efficient computation, deeper insights into data analysis and testing, and using it to make tools that help people today.

previous work

During my undergraduate studies, my work revolved around robust temporal logics, their mathematical structure, and approaches to utilizing them in data mining, pattern detection, and causal inference.

I have briefly worked with partial-order reduction techniques for program analysis under weak memory models.

Alongside my work in logic, my Bachelor's Thesis was focused on information-theoretic error bounds for quantum machine learning systems (formally, VQAs), with Prof. Sai Vinjanampathy as my advisor. A copy can be found here.


Guilloud, Simon, Sankalp Gambhir, and Viktor Kuncak. "LISA–A Modern Proof System." (2023). Link

Afzal, Mohammad, Sankalp Gambhir, Ashutosh Gupta, Ashutosh Trivedi, and Alvaro Velasquez. "LTL-Based Non-Markovian Inverse Reinforcement Learning." In Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 2857-2859. 2023. Link

Afzal, Mohammad, Sankalp Gambhir, Ashutosh Gupta, and Shankaranarayanan Krishna. "Quantitative Learning of LTL from Finite Traces." arXiv preprint arXiv:2110.13616 (2021). arXiv:2110.13616 (2021).


My time outside work is spread across many hobbies with wildly varying skill levels. I love digital sketching and painting, cooking, random puzzles, and occasionally like to suck at embroidery and knife sharpening. I have recently found myself primarily distracted by handwired keyboards and the weird mess of USB-HID and Unicode input. Some of my not-work stuff can be found on my Behance page here.



office: BC 355, EPFL

github: sankalpgambhir

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