Rachneet Kaur

I am a PhD student in the Department of Industrial and Enterprise Systems Engineering at UIUC working with Richard Sowers. I am named the William A. Chittenden II graduate fellow.

I did my bachelors from the University of Delhi and masters from the Indian Institute of Technology (IIT), Delhi in Mathematics.

My research interests lie at the intersection of Computer Vision, Deep Learning and Healthcare.

I am currently a Ph.D. student in the Department of Industrial Engineering at the University of Illinois at Urbana-Champaign. I am advised by Richard Sowers and also collaborated closely with Manuel Hernandez. My current research is focused on applications of machine learning for unfolding problems in health and finance domain.

Prior to joining PhD, I have earned a B.S. (with honours) in Mathematics from the University of Delhi and an M.S. in Mathematics from the Indian Institute of Technology (IIT), Delhi. During my masters, my thesis was advised by Aparna Mehra.

We demonstrate the potential of deep learning with a multi-view digital camera-based gait analysis framework for neurological gait dysfunction prediction. This study suggests the viability of inexpensive vision-based systems for diagnosing certain neurological disorders.

The machine learning techniques presented in this study may assist providers in identifying different progression rates and trajectories in the early stages of the disease, hence allowing for more efficient and personalized healthcare deliveries.

We pose and study a scheduling problem for an electric load to develop an Internet of Things (IoT) control system for power appliances, which takes advantage of real-time dynamic energy pricing.

Evaluating the effectiveness of a spatiotemporal and kinetic gait data-based machine learning framework for Multiple Sclerosis prediction.

The machine learning techniques presented in this study may assist providers in identifying different progression rates and trajectories in the early stages of the disease, hence allowing for more efficient and personalized healthcare deliveries.

Examining the feasibility of using wearable sensors, when walking, to identify older adults who have trouble with balance at an early stage.

Automated removal of unwanted artifacts on noisy and visually engaging upright stance Electroencephalography (EEG) data.

Letting NeRF reason about occluders and appearance variation produces photorealistic view synthesis using only unstructured internet photos.

Examining the interaction between balance function and anxiety via a VR-based experimental setup, designed to simulate stressful environments involving postural threats.

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This chapter is primarily aimed at establishing and understanding underlying fluctuations relative to anxiety in human postural control using immersive virtual conditions.

At UIUC, I have had a chance to work with and mentor some excellent students:

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