Saransh Chopra

selected publications

1. liionpack: A Python package for simulating packs of batteries with PyBaMM

   Tranter, Thomas G., Timms, Robert, Sulzer, Valentin, Planella, Ferran Brosa, Wiggins, Gavin M., Karra, Suryanarayana V., Agarwal, Priyanshu, Chopra, Saransh, Allu, Srikanth, Shearing, Paul R., and Brett, Dan J.

   Journal of Open Source Software 2022

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   Electrification of transport and other energy intensive activities is of growing importance as it provides an underpinning method to reduce carbon emissions. With an increase in reliance on renewable sources of energy and a reduction in the use of more predictable fossil fuels in both stationary and mobile applications, energy storage will play a pivotal role and batteries are currently the most widely adopted and versatile form. Therefore, understanding how batteries work, how they degrade, and how to optimize and manage their operation at large scales is critical to achieving emission reduction targets. The electric vehicle (EV) industry requires a considerable number of batteries even for a single vehicle, sometimes numbering in the thousands if smaller cells are used, and the dynamics and degradation of these systems, as well as large stationary power systems, is not that well understood. As increases in the efficiency of a single battery become diminishing for standard commercially available chemistries, gains made at the system level become more important and can potentially be realised more quickly compared with developing new chemistries. Mathematical models and simulations provide a way to address these challenging questions and can aid the engineer and designers of batteries and battery management systems to provide longer lasting and more efficient energy storage systems.

2. vector: vector utilities and classes

   Schreiner, Henry, Pivarski, Jim, and Chopra, Saransh

   Sep 2022

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   Vector is a Python 3.7+ library for 2D, 3D, and Lorentz vectors, especially arrays of vectors, to solve common physics problems in a NumPy-like way. Main features of Vector: 1. Pure Python with NumPy as its only dependency. This makes it easier to install. 2. Vectors may be represented in a variety of coordinate systems: Cartesian, cylindrical, pseudorapidity, and any combination of these with time or proper time for Lorentz vectors. In all, there are 12 coordinate systems: x-y vs ρ-φ in the azimuthal plane × z vs θ vs η longitudinally × t vs τ temporally. 3. Uses names and conventions set by ROOT’s TLorentzVector and Math::LorentzVector, as well as scikit-hep/math, uproot-methods TLorentzVector, henryiii/hepvector, and coffea.nanoevents.methods.vector. Implemented on a variety of backends: pure Python objects, NumPy arrays of vectors (as a structured array subclass), Awkward Arrays of vectors, potential for more: CuPy, TensorFlow, Torch, JAX... 4. NumPy/Awkward backends also implemented in Numba for JIT-compiled calculations on vectors. 5. Distinction between geometrical vectors, which have a minimum of attribute and method names, and vectors representing momentum, which have synonyms like pt = rho, energy = t, mass = tau.

3. Predicting efficacy of antiseizure medication treatment with machine learning algorithms in North Indian population

   Kaushik, Mahima, Mahajan, Siddhartha, Machahary, Nitin, Thakran, Sarita, Chopra, Saransh, Tomar, Raj Vardhan, Kushwaha, Suman S., Agarwal, Rachna, Sharma, Sangeeta, Kukreti, Ritushree, and Biswal, Bibhu

   Epilepsy Research Sep 2024

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   Purpose This study aimed to develop a classifier using supervised machine learning to effectively assess the impact of clinical, demographical, and biochemical factors in accurately predicting the antiseizure medications (ASMs) treatment response in people with epilepsy (PWE). Methods Data was collected from 786 PWE at the Outpatient Department of Neurology, Institute of Human Behavior and Allied Sciences (IHBAS), New Delhi, India from 2005 to 2015. Patients were followed up at the 2nd, 4th, 8th, and 12th month over the span of 1 year for the drugs being administered and their dosage, the serum drug levels, the frequency of seizure control, drug efficacy, the adverse drug reactions (ADRs), and their compliance to ASMs. Several features, including demographic details, medical history, and auxiliary examinations electroencephalogram (EEG) or Computed Tomography (CT) were chosen to discern between patients with distinct remission outcomes. Remission outcomes were categorized into ‘good responder (GR)’ and ‘poor responder (PR)’ based on the number of seizures experienced by the patients over the study duration. Our dataset was utilized to train seven classical machine learning algorithms i.e Extreme Gradient Boost (XGB), K-Nearest Neighbor (KNN), Support Vector Classifier (SVC), Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB) and Logistic Regression (LR) to construct classification models. Results Our research findings indicate that 1) among the seven algorithms examined, XGB and SVC demonstrated superior predictive performances of ASM treatment outcomes with an accuracy of 0.66 each and ROC-AUC scores of 0.67 (XGB) and 0.66 (SVC) in distinguishing between PR and GR patients. 2) The most influential factor in discerning PR to GR patients is a family history of seizures (no), education (literate) and multitherapy with Chi-square (χ2) values of 12.1539, 8.7232 and 13.620 respectively and odds ratio (OR) of 2.2671, 0.4467, and 1.9453 each. 3). Furthermore, our surrogate analysis revealed that the null hypothesis for both XGB and SVC was rejected at a 100 % confidence level, underscoring the significance of their predictive performance. These findings underscore the robustness and reliability of XGB and SVC in our predictive modelling framework. Significance Utilizing XG Boost and SVC-based machine learning classifier, we successfully forecasted the likelihood of a patient’s response to ASM treatment, categorizing them as either PR or GR, post-completion of standard epilepsy examinations. The classifier’s predictions were found to be statistically significant, suggesting their potential utility in improving treatment strategies, particularly in the personalized selection of ASM regimens for individual epilepsy patients.