arXiv subjects

Shraddha Rajpal

Publications and source records attributed to Shraddha Rajpal.

3 recordsLinked to original sources

arXiv2026

Gen AI in Proof-based Math Courses: A Pilot Study

With the rapid rise of generative AI in higher education, understanding how students use AI is increasingly important. This exploratory study examines student use and perceptions of generative AI across three proof-based undergraduate mathematics courses: a first-semester abstract algebra course, a topology course, and a second-semester abstract algebra course. In each case, course policy permitted some use of generative AI. Drawing on survey responses and student interviews, we analyze how students engaged with AI tools as well as their perceptions of generative AI's usefulness, accuracy and limitations.

cs.AI↗

arXiv2026

A Kalman Filter Based Approach to NV Diamond Data Fusion For Improved Temperature Sensing

Nitrogen-vacancy (NV) centers in diamond have been demonstrated to enable highly sensitive temperature measurements using multiple modalities. Standalone optically detected magnetic resonance (ODMR) provides robust temperature estimates, albeit with high latency, whereas all-optical measurements provide millisecond resolution but suffer from poorer long-term accuracy. In this work, we demonstrate a hot-start Kalman filtering approach that fuses the two modalities, leading to a 57% improvement in accuracy. The fused estimate achieves higher long-term accuracy with lower latency, demonstrating a viable route toward implementing self-correcting, high-precision NV-diamond temperature sensing schemes.

physics.ins-det↗

arXiv2025

Evaluating probabilistic and data-driven inference models for fiber-coupled NV-diamond temperature sensors

We evaluate the impact of inference model on uncertainties when using continuous wave Optically Detected Magnetic Resonance (ODMR) measurements to infer temperature. Our approach leverages a probabilistic feedforward inference model designed to maximize the likelihood of observed ODMR spectra through automatic differentiation. This model effectively utilizes the temperature dependence of spin Hamiltonian parameters to infer temperature from spectral features in the ODMR data. We achieve prediction uncertainty of $\pm$ 1 K across a temperature range of 243 K to 323 K. To benchmark our probabilistic model, we compare it with a non-parametric peak-finding technique and data-driven methodologies such as Principal Component Regression (PCR) and a 1D Convolutional Neural Network (CNN). We find that when validated against out-of-sample dataset that encompasses the same temperature range as the training dataset, data driven methods can show uncertainties that are as much as 0.67 K lower without incorporating expert-level understanding of the spectroscopic-temperature relationship. However, our results show that the probabilistic model outperforms both PCR and CNN when tasked with extrapolating beyond the temperature range used in training set, indicating robustness and generalizability. In contrast, data-driven methods like PCR and CNN demonstrate up to ten times worse uncertainties when tasked with extrapolating outside their training data range.

physics.ins-det↗