I am a PhD student in the Institute for Computational and Mathematical Engineering. My research interests lie in Numerical Linear Algebra and Parallel Computing. I'm working with Prof. Eric Darve on developing fast algorithms for general linear systems. I obtained my B.Tech (Honors) in Chemical Engineering from Indian Institute of Technology Madras, India.

I was born and brought up in Neyveli, an industrial town in south India. I enjoy listening to Indian music and reading novels.

Education & Certifications

  • B.Tech (Honours), Indian Institute of Technology Madras, Chemical Engineering (2016)

2021-22 Courses

All Publications

  • Hierarchical Orthogonal Factorization: Sparse Least Squares Problems JOURNAL OF SCIENTIFIC COMPUTING Gnanasekaran, A., Darve, E. 2022; 91 (2)

    View details for DOI 10.1137/20M1373475

    View details for Web of Science ID 000759673100004

  • Disparity in the quality of COVID-19 data reporting across India. BMC public health Vasudevan, V., Gnanasekaran, A., Sankar, V., Vasudevan, S. A., Zou, J. 2021; 21 (1): 1211


    BACKGROUND: Transparent and accessible reporting of COVID-19 data is critical for public health efforts. Each Indian state has its own mechanism for reporting COVID-19 data, and the quality of their reporting has not been systematically evaluated. We present a comprehensive assessment of the quality of COVID-19 data reporting done by the Indian state governments between 19 May and 1 June, 2020.METHODS: We designed a semi-quantitative framework with 45 indicators to assess the quality of COVID-19 data reporting. The framework captures four key aspects of public health data reporting - availability, accessibility, granularity, and privacy. We used this framework to calculate a COVID-19 Data Reporting Score (CDRS, ranging from 0-1) for each state.RESULTS: Our results indicate a large disparity in the quality of COVID-19 data reporting across India. CDRS varies from 0.61 (good) in Karnataka to 0.0 (poor) in Bihar and Uttar Pradesh, with a median value of 0.26. Ten states do not report data stratified by age, gender, comorbidities or districts. Only ten states provide trend graphics for COVID-19 data. In addition, we identify that Punjab and Chandigarh compromised the privacy of individuals under quarantine by publicly releasing their personally identifiable information. The CDRS is positively associated with the state's sustainable development index for good health and well-being (Pearson correlation: r=0.630,p=0.0003).CONCLUSIONS: Our assessment informs the public health efforts in India and serves as a guideline for pandemic data reporting. The disparity in CDRS highlights three important findings at the national, state, and individual level. At the national level, it shows the lack of a unified framework for reporting COVID-19 data in India, and highlights the need for a central agency to monitor or audit the quality of data reporting done by the states. Without a unified framework, it is difficult to aggregate the data from different states, gain insights, and coordinate an effective nationwide response to the pandemic. Moreover, it reflects the inadequacy in coordination or sharing of resources among the states. The disparate reporting score also reflects inequality in individual access to public health information and privacy protection based on the state of residence.

    View details for DOI 10.1186/s12889-021-11054-7

    View details for PubMedID 34167499

  • Variation in COVID-19 Data Reporting Across India: 6Months into the Pandemic. Journal of the Indian Institute of Science Vasudevan, V., Gnanasekaran, A., Sankar, V., Vasudevan, S. A., Zou, J. 2020: 1–8


    India reported its first case of COVID-19 on January 30, 2020. Six months since then, COVID-19 continues to be a growing crisis in India with over 1.6 million reported cases. In this communication, we assess the quality of COVID-19 data reporting done by the state and union territory governments in India between July 12 and July 25, 2020. We compare our findings with those from an earlier assessment conducted in May 2020. We conclude that 6months into the pandemic, the quality of COVID-19 data reporting across India continues to be highly disparate, which could hinder public health efforts.

    View details for DOI 10.1007/s41745-020-00188-z

    View details for PubMedID 33078049

  • On the role of hydrodynamic interactions in the engineered-assembly of droplet ensembles SOFT MATTER Raj, M., Gnanasekaran, A., Rengaswamy, R. 2019; 15 (39): 7863–75


    Droplets, as they flow inside a microchannel, interact hydrodynamically to result in spatio-temporal patterns. The nature of the interaction decides the type of collective behaviour observed. In this context, we study the application of droplet microfluidics in the area of complex-shape particle synthesis. We show how the dynamics of droplet motion, the steady-state characteristics, the short and long-range hydrodynamics, the dependence on inlet conditions etc. are all related to the features that characterize a device like the functionality (producing many shapes) and robustness (insensitivity to fluctuations). Two primary operating regimes are identified, one where long-range interactions are dominant and the other where they are short-range. In the former, the shapes formed by droplets are steady-state solutions to the governing equations, while in the latter they are a function of how the droplets enter the channel (frequency of entry). We show that identifying the inlet conditions for producing a particle of the desired shape requires the use of a systematic approach to design which involves solving an optimization problem (using genetic algorithms) to identify the optimal operating strategy. With the knowledge of the hydrodynamics between the droplets, we demonstrate how one can reduce the complexity of the design process. We also discuss the control strategies required if one were to realize the identified operating strategy experimentally.

    View details for DOI 10.1039/c9sm01528k

    View details for Web of Science ID 000496486700013

    View details for PubMedID 31531495