Lakshmi Pillai
Research Associate
University of Arkansas for Medical Sciences
postdoc
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Biography and Research Information
OverviewAI-generated summary
Lakshmi Pillai's research focuses on the application of advanced computational methods, particularly machine learning and artificial intelligence, to address challenges in health sciences. Her work includes developing machine learning algorithms to process voice samples for the identification of Parkinson's disease, utilizing pre-trained convolutional neural networks to detect the disease from spectrogram images of voice samples. Pillai has also investigated the feasibility of telemedicine for research visits among individuals with Parkinson's disease residing in medically underserved areas, aiming to improve access to research participation.
Her research extends to understanding the nuances of Parkinson's disease, including the relationship between levodopa's ON/OFF states and freezing of gait, as well as gait declines and their predictive value in patients converting to a freezing of gait phenotype. Pillai also explores increased foot strike variability during turning in Parkinson's disease patients experiencing freezing of gait. Additionally, her work involves the semantic integration of multi-modal data and neuroimaging results through platforms like PRISM within the Arkansas Imaging Enterprise System (ARIES).
Pillai collaborates with a network of researchers at the University of Arkansas for Medical Sciences, including Tuhin Virmani, Aliyah Glover, Linda Larson‐Prior, and Aaron S. Kemp, with whom she has co-authored numerous publications. She maintains an active lab website and has a scholarly profile with an h-index of 12 and over 65 publications.
Metrics
- h-index: 12
- Publications: 65
- Citations: 633
Selected Publications
- Lessons Learned From a Delayed‐Start Trial of Modafinil for Freezing of Gait in Parkinson's Disease (2025) DOI
- Exploring the relationship between orthostatic hypotension and gait in people with Parkinson’s disease (2025) DOI
- Levodopa influence on turning dynamics in people with Parkinson’s disease (2025) DOI
- Longitudinal Monitoring of Digitized Cursive Writing in People with Parkinson’s Disease Shows Increased Variability in Absolute Jerk and Decreased Writing Duration in Those with Freezing of Gait (P9-5.019) (2025) DOI
- Pre-trained convolutional neural networks identify Parkinson’s disease from spectrogram images of voice samples (2025) DOI
- Pre-trained Convolutional Neural Networks Identify Parkinson’s Disease from Spectrogram Images of Voice Samples (2024) DOI
- Levodopa influence on turning dynamics in people with Parkinson’s disease (2024) DOI
- Tandem gait step-width increases more rapidly in more severely affected people with Parkinson's disease (2024) DOI
- Feasibility of regional center telehealth visits utilizing a rural research network in people with Parkinson’s disease (2024) DOI
- Development and implementation of the frog-in-maze game to study upper limb movement in people with Parkinson’s disease (2023) DOI
- A machine learning method to process voice samples for identification of Parkinson’s disease (2023) DOI
- A Machine Learning Method to Process Voice Samples for Identification of Parkinson’s Disease (2023) DOI
- Gait Declines Differentially in, and Improves Prediction of, People with Parkinson’s Disease Converting to a Freezing of Gait Phenotype (2023) DOI
- Levodopa responsive gait dynamics in OFF- and ONOFF-state freezing of gait in Parkinson’s disease (2023) DOI
- Objective quantification of responses to the clinical pull-test in people with Parkinson’s disease (2023) DOI
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