33 citations · 91 across the 17 of their papers we have counts for
17 papers
Position: Evaluation Scores Are Perishable Knowledge Claims
Sankalp Gilda, Shlok Gilda
Evaluation methodologies for language models increasingly combine multiple signals, from automated metrics and LLM-as-judge ratings to human assessments and benchmark suite results…
tsbootstrap: Distribution-Free Uncertainty Quantification and Conformal Prediction for Time Series
Sankalp Gilda
Finance, sensing, and demand streams violate the exchangeability that IID conformal prediction and the IID bootstrap assume, and existing libraries implement either a general resam…
Structured Abductive-Deductive-Inductive Reasoning for LLMs via Algebraic Invariants
Sankalp Gilda, Shlok Gilda
Large language models exhibit systematic limitations in structured logical reasoning: they conflate hypothesis generation with verification, cannot distinguish conjecture from vali…
AI-Assisted Engineering Should Track the Epistemic Status and Temporal Validity of Architectural Decisions
Sankalp Gilda, Shlok Gilda
This position paper argues that AI-assisted software engineering requires explicit mechanisms for tracking the epistemic status and temporal validity of architectural decisions. LL…
deep-REMAP: Probabilistic Parameterization of Stellar Spectra Using Regularized Multi-Task Learning
Sankalp Gilda
In the era of exploding survey volumes, traditional methods of spectroscopic analysis are being pushed to their limits. In response, we develop deep-REMAP, a novel deep learning fr…
tsbootstrap: Enhancing Time Series Analysis with Advanced Bootstrapping Techniques
Sankalp Gilda, Benedikt Heidrich, Franz Kiraly
In time series analysis, traditional bootstrapping methods often fall short due to their assumption of data independence, a condition rarely met in time-dependent data. This paper…
Robust Calibration For Improved Weather Prediction Under Distributional Shift
Sankalp Gilda, Neel Bhandari, Wendy Mak +1
In this paper, we present results on improving out-of-domain weather prediction and uncertainty estimation as part of the \texttt{Shifts Challenge on Robustness and Uncertainty und…
Beyond mirkwood: Enhancing SED Modeling with Conformal Predictions
Sankalp Gilda
Traditional spectral energy distribution (SED) fitting techniques face uncertainties due to assumptions in star formation histories and dust attenuation curves. We propose an advan…
deep-REMAP: Parameterization of Stellar Spectra Using Regularized Multi-Task Learning
Sankalp Gilda
Traditional spectral analysis methods are increasingly challenged by the exploding volumes of data produced by contemporary astronomical surveys. In response, we develop deep-Regul…
Unsupervised Domain Adaptation for Constraining Star Formation Histories
Sankalp Gilda, Antoine de Mathelin, Sabine Bellstedt +1
The prevalent paradigm of machine learning today is to use past observations to predict future ones. What if, however, we are interested in knowing the past given the present? This…
Uncertainty-Aware Learning for Improvements in Image Quality of the Canada-France-Hawaii Telescope
Sankalp Gilda, Stark C. Draper, Sebastien Fabbro +6
We leverage state-of-the-art machine learning methods and a decade's worth of archival data from CFHT to predict observatory image quality (IQ) from environmental conditions and ob…
{\sc mirkwood:} Fast and Accurate SED Modeling Using Machine Learning
Sankalp Gilda, Sidney Lower, Desika Narayanan
Traditional spectral energy distribution (SED) fitting codes used to derive galaxy physical properties are often uncertain at the factor of a few level owing to uncertainties in ga…
Astronomical Image Quality Prediction based on Environmental and Telescope Operating Conditions
Sankalp Gilda, Yuan-Sen Ting, Kanoa Withington +6
Intelligent scheduling of the sequence of scientific exposures taken at ground-based astronomical observatories is massively challenging. Observing time is over-subscribed and atmo…
Gamma-ray Bursts as distance indicators through a machine learning approach
Maria Dainotti, Vahé Petrosian, Malgorzata Bogdan +7
Gamma-ray bursts (GRBs) are spectacularly energetic events, with the potential to inform on the early universe and its evolution, once their redshifts are known. Unfortunately, det…
Automatic Kalman-Filter-based Wavelet Shrinkage Denoising of 1D Stellar Spectra
Sankalp Gilda, Zachary Slepian
We propose a non-parametric method to denoise 1D stellar spectra based on wavelet shrinkage followed by adaptive Kalman thresholding. Wavelet shrinkage denoising involves applying…
Feature Selection for Better Spectral Characterization or: How I Learned to Start Worrying and Love Ensembles
Sankalp Gilda
An ever-looming threat to astronomical applications of machine learning is the danger of over-fitting data, also known as the `curse of dimensionality.' This occurs when there are…
The first super-Earth Detection from the High Cadence and High Radial Velocity Precision Dharma Planet Survey
Bo Ma, Jian Ge, Matthew Muterspaugh +23
The Dharma Planet Survey (DPS) aims to monitor about 150 nearby very bright FGKM dwarfs (within 50 pc) during 20162020 for low-mass planet detection and characterization using t…