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Researcher profile

Aman Singh

Aman Singh contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Artificial Intelligencecond-mat.mtrl-scics.CYMachine LearningQuantitative Methods

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Published work

4 published item(s)

preprint2026arXiv

Geometric dependence of exchange bias in tilted three-dimensional CoFe/IrMn microwires

The exchange bias (EB) effect, arising from interfacial coupling between ferromagnetic (FM) and antiferromagnetic (AF) layers, induces a unidirectional magnetic anisotropy and underpins a wide range of spintronic functionalities. Extending the EB effect to three-dimensional (3D) architectures enables investigation of interfacial coupling in non-planar structures, which is a key step toward realizing spintronic functionalities beyond planar systems. Achieving this requires the fabrication of FM/AF bilayers with smooth interfaces and well-defined thicknesses on non-planar scaffolds, together with suitable characterization methods. In this work, we realize exchange-biased 3D FM/AF microwires by combining two-photon lithography with magnetron sputtering. CoFe/IrMn bilayers are deposited on microwire scaffolds with inclination angles of 0 deg, 30 deg, 45 deg relative to the substrate, and their magnetization reversal is probed using dark-field magneto-optical Kerr effect (DF-MOKE) magnetometry. We find that the EB and coercive fields vary in a characteristic way with the inclination angle, consistent with the systematic reduction in film thickness expected from inclined directional deposition. In addition, the EB magnitude is influenced by the combined effects of surface roughness of non-planar geometries and the directional growth of the bilayer, highlighting the importance of 3D scaffold surface quality for integrating magnetic multilayers. These results provide insight into the growth and magnetic behavior of sputter-deposited magnetic multilayers with functional interfaces on 3D geometries.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Yield Curve Forecasting using Machine Learning and Econometrics: A Comparative Analysis

While machine learning has revolutionized many fields such as natural language processing (NLP) and computer vision, its impact on time-series forecasting is still widely disputed, especially in the finance domain. This paper compares forecasting performance on U.S. Treasury yield curve data across econometrics/time-series analysis, classical machine learning, and deep learning methods, using daily data over 47 years. The Treasury yield curve is important because it is widely used by every participant in the bond markets, which are larger than equity markets. We examine a variety of methods that have not been tested on yield curve forecasting, especially deep learning algorithms. The algorithms include the Autoregressive Integrated Moving Average (ARIMA) model and its extensions, naive benchmarks, ensemble methods, Recurrent Neural Networks (RNNs), and multiple transformers built for forecasting. ARIMA and naive econometric models outperform other models overall, except in one time block. Of the machine learning methods, TimeGPT, LGBM and RNNs perform the best. Furthermore, the paper explores whether stationary or nonstationary data are more appropriate as input to deep learning models.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Machine-Learning Driven Drug Repurposing for COVID-19

The integration of machine learning methods into bioinformatics provides particular benefits in identifying how therapeutics effective in one context might have utility in an unknown clinical context or against a novel pathology. We aim to discover the underlying associations between viral proteins and antiviral therapeutics that are effective against them by employing neural network models. Using the National Center for Biotechnology Information virus protein database and the DrugVirus database, which provides a comprehensive report of broad-spectrum antiviral agents (BSAAs) and viruses they inhibit, we trained ANN models with virus protein sequences as inputs and antiviral agents deemed safe-in-humans as outputs. Model training excluded SARS-CoV-2 proteins and included only Phases II, III, IV and Approved level drugs. Using sequences for SARS-CoV-2 (the coronavirus that causes COVID-19) as inputs to the trained models produces outputs of tentative safe-in-human antiviral candidates for treating COVID-19. Our results suggest multiple drug candidates, some of which complement recent findings from noteworthy clinical studies. Our in-silico approach to drug repurposing has promise in identifying new drug candidates and treatments for other viruses.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Usage Analysis of Mobile Devices

Mobile devices have evolved from just communication devices into an indispensable part of people's lives in form of smartphones, tablets and smart watches. Devices are now more personal than ever and carry more information about a person than any other. Extracting user behaviour is rather difficult and time-consuming as most of the work previously has been manual or requires feature extraction. In this paper, a novel approach of user behavior detection is proposed with Deep Learning Network (DNN). Initial approach was to use recurrent neural network (RNN) along with LSTM for completely unsupervised analysis of mobile devices. Next approach is to extract features by using Long Short Term Memory (LSTM) to understand the user behaviour, which are then fed into the Convolution Neural Network (CNN). This work mainly concentrates on detection of user behaviour and anomaly detection for usage analysis of mobile devices. Both the approaches are compared against some baseline methods. Experiments are conducted on the publicly available dataset to show that these methods can successfully capture the user behaviors.

0 citations0 reviewsNo code linkedOpen access