Jasdeep Singh

Undergraduate Researcher

I am a fifth-year undergraduate at the University of Colorado Boulder, pursuing a B.S. in Computer Science and a B.S. in Applied Mathematics, along with a minor in Statistics and the Quantitative Finance Certificate. I will graduate in May 2026.

I am a researcher bridging the gap between optimization, machine learning, neural networks, and human-computer interaction. I am currently advised by Professor Sriram Sankaranarayanan and conduct research in the Programming Languages and Verification Laboratory. My current work focuses on providing effective feedback to users in a 2D drone simulation environment by computing optimal corrective trajectories and leveraging large language models for interactive feedback.

I intend to pursue a Ph.D. starting in Fall 2026, focusing on control systems, autonomous systems, and formal verification. My research goal is to develop rigorous, mathematically grounded methods to ensure the safety and reliability of intelligent systems, particularly where learning-based control intersects with formal safety guarantees.

Outside of academics, I enjoy playing basketball, tennis, and pickleball.

news

Jul 30, 2025	Presented at the Undergraduate Research Symposium
May 27, 2025	Started the REU program at the University of Utah
Mar 22, 2025	Accepted the Intelligent Systems in Electrical Engineering and Computer Engineering REU at the University of Utah.
Jun 04, 2024	Started the 10 week research program at Purdue University
May 13, 2024	Attended HSCC @ CPS IoT Week 2024 in Hong Kong, China

selected publications

Temporal Behavior Trees: Robustness and Segmentation

Sebastian Schirmer, Jasdeep Singh, Emily Jensen, Johann Dauer, Bernd Finkbeiner, and Sriram Sankaranarayanan

In Proceedings of the 27th ACM International Conference on Hybrid Systems: Computation and Control, Hong Kong SAR, China, 2024

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This paper presents temporal behavior trees (TBT), a specification formalism inspired by behavior trees that are commonly used to program robotic applications. We then introduce the concept of trace segmentation, wherein given a TBT specification and a trace, we split the trace optimally into sub-traces that are associated with various portions of the TBT specification. Segmentation of a trace then serves to explain precisely how a trace satisfies or violates a specification, and which portions of a specification are actually violated. We introduce the syntax and semantics of TBT and compare their expressiveness in relation to temporal logic. Next, we define robustness semantics for TBT specification with respect to a trace. Rather than a Boolean interpretation, the robustness provides a real-valued numerical outcome that quantifies how close or far away a trace is from satisfying or violating a TBT specification. We show that computing the robustness of a trace also segments it into subtraces.Finally, we provide efficient approximations for computing robustness and segmentation for long traces with guarantees on the result.We demonstrate how segmentations are useful through applications such as understanding how novice users pilot an aerial vehicle through a sequence of waypoints in desktop experiments and the offline monitoring of automated lander for a drone on a ship. Our case studies demonstrate how TBT specification and segmentation can be used to understand and interpret complex behaviors of humans and automation in cyber-physical systems.
Response Construct Tagging: NLP-Aided Assessment for Engineering Education

Ananya Ganesh, Hugh Scribner, Jasdeep Singh, Katherine Goodman, Jean Hertzberg, and Katharina Kann

In Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022), Jul 2022

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Recent advances in natural language processing (NLP) have greatly helped educational applications, for both teachers and students. In higher education, there is great potential to use NLP tools for advancing pedagogical research. In this paper, we focus on how NLP can help understand student experiences in engineering, thus facilitating engineering educators to carry out large scale analysis that is helpful for re-designing the curriculum. Here, we introduce a new task we call response construct tagging (RCT), in which student responses to tailored survey questions are automatically tagged for six constructs measuring transformative experiences and engineering identity of students. We experiment with state-of-the-art classification models for this task and investigate the effects of different sources of additional information. Our best model achieves an F1 score of 48. We further investigate multi-task training on the related task of sentiment classification, which improves our model’s performance to 55 F1. Finally, we provide a detailed qualitative analysis of model performance.