Curriculum Vitae

Education

Ph.D. in Biomedical Engineering, Northwestern University, Chicago, USA
Expected February 2026
M.S.E. in Electrical Engineering (Machine Learning), University of Pennsylvania, Philadelphia, USA
August 2017 – May 2019
B.Tech. in Electrical and Electronics Engineering, Vellore Institute of Technology, India
June 2013 – May 2017

Relevant coursework: Signal Processing, Machine Learning, Data Mining, Deep Learning for Biomedical Imaging, Computational Neuroscience, Brain–Machine Interfaces, Dynamical Systems

Research Experience

Decoding speech from neural signals (PhD thesis)
Northwestern University
2020 – Present
- Led collection of human intracortical neural recordings (ECoG and microelectrode arrays) during speech production tasks.
- Designed synchronization pipelines across neural, audio, and behavioral acquisition systems.
- Developed signal processing and machine learning pipelines to decode speech intent from neural activity.
- Integrated language-model-based approaches to capture phoneme sequence dynamics during word production.
Classification of imagined movements from neural signals
- Applied feature engineering, signal processing, and machine learning methods to classify imagined limb movements.
- Achieved significant accuracy using ensemble classifiers including XGBoost and Random Forest.
Reinforcement-learning-based optimization of neural networks
- Contributed to the design of reinforcement-learning-based training rules for neural networks.
- Evaluated ANN and CNN architectures using TensorFlow.
Wheelchair control via visually evoked potentials (EEG)
- Acquired EEG during visual stimulation to elicit steady-state visual evoked potentials.
- Implemented correlation-based decoding to generate wheelchair control signals.

Work Experience

Research Engineer, Feinberg School of Medicine, Northwestern University
May 2019 – June 2020
- Developed a multilayer perceptron classifier to infer sleep stages from heart-rate data in stroke survivors.
- Built a real-time EMG acquisition interface and a custom rehabilitation game in Python.
- Applied deep-learning-based pose estimation (OpenPose) to quantify motor recovery outcomes.