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about_me
Dr. Akshay Sujatha Ravindran
I am a Data Scientist II at Alto Neuroscience. I use machine learning and signal processing tool to to predict clinical response and identify patients who are most likely to benefit from our drug candidates using their brain activity meassured using electroencephalography (EEG). I obtained my doctoral degree from the Department of Electrical and Computer Engineering at the University of Houston in 2021. Under the supervision of Dr. Jose Contreras Vidal, I worked in the Non-Invasive Brain-Machine Interface Systems Lab as part of the BRAIN Center (NSF IUCRC). My thesis was on Developing Explainable Deep Learning Models Using EEG for Brain Machine Interface Systems.
My broader research focused on developing different engineering tools for analyzing non-invasive electrophysiological data, mainly involving EEG. My expertise lies in developing machine learning models and signal processing tools to study EEG. Even though my thesis is centered around using deep learning models, I believe that deep learning is not the answer to all questions and many questions are best answered by classical signal processing and machine learning approaches. Finding appropriate use-case for classical vs more advanced modeling algorithms will be key in deploying sucesssful products.
I have a diverse portfolio of projects covering areas such as: a) Spearheaded the design and implementation of cutting‐edge machine learning pipelines tailored specifi‐ cally for precision medicine in psychiatry. Achieved measurable success in accurately stratifying individuals with depression and predicting their response to various medications, thereby optimizing treatment outcomes and patient care b) Expertly integrated and optimized diverse EEG features to enhance the predictive power of machine learn‐ ing models. This meticulous optimization process resulted in the development of highly resilient and reli‐ able models, capable of discerning subtle patterns crucial for personalized treatment approaches in psychiatry. c) Identified pharmacodynamic markers for multiple psychiatric drugs, facilitating development of drugs from phase 1 to later stages d) Brain-computer interface systems: predicting balance perturbation, lower limb kinematics, and decoding hand motor imagery from EEG e) Exploring the feasibility of studying the brain in real-world settings (using museums and public venues as a laboratory) f) Changes in EEG associated meditation g) Developing synergistic activities between arts and science to promote interdisciplinary research opportunities while also serving as outreach activities in STEM. Before joining the University of Houston for my doctoral studies, I worked at Health Technology Innovation Center at the IIT Madras, India as a Research Intern for a year. During that time I worked on developing wearable vital signal monitoring devices. I earned my Bachelor’s Degree in Electrical and Electronics Engineering from the University of Kerala, India in 2015
Outside of research, I value teaching and mentoring significantly and I find satisfaction the most when I get to help someone advance their career and dreams. I feel very blessed and happy to be in a profession that provides incentives for lifelong learning and offers great flexibility to pursue things that spark your curiosity and interest. Outside of work, I love to travel, spend quality time in nature, meditate, yoga, go for walks, play and watch soccer and most importantly am a huge foodie! I love to engage in conversations and activities related to spirituality, developing a growth mindset, and overcoming failures/challenges.
portfolio
EEG based Gait Decoding
An empirical comparison of neural networks and machine learning algorithms for EEG gait decoding 
Minecraft: Museum as a laboratory
Assaying neural activity of children during video game play in public spaces: a deep learning approach to uncover neural patterns in exploratory studies 
Point Process Characterization of HR dynamics
Emotion Recognition by Point Process Characterization of Heartbeat Dynamics 
VitalSens
Wearable devices for physiological vital signal measurement 
publications
Book Chapter: Deep learning methods for EEG neural classification
Published in Springer Publication, 2010
Recommended citation: Nakagome S, Craik A, Ravindran AS, He Y, Cruz‐Garza JG, and Contreras‐Vidal JL. Springer Handbook of Neuroengineering. In: ed. by Thakor NV. Springer Nature. Chap. Deep learning methods for EEG neural classification. In Press
A Wrist Worn SpO2 Monitor with Custom Finger Probe for Motion Artifact Removal
Published in Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2016
Recommended citation: Preejith, S. P., Ravindran, A. S., Hajare, R., Joseph, J., & Sivaprakasam, M. (2016, August). A wrist worn SpO 2 monitor with custom finger probe for motion artifact removal. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 5777-5780). IEEE.
Assaying neural activity of children during video game play in public spaces: a deep learning approach
Published in Journal of Neural Engineering, 2019
Recommended citation: Ravindran, A. S., Mobiny, A., Cruz-Garza, J. G., Paek, A., Kopteva, A., & Vidal, J. L. C. (2019). Assaying neural activity of children during video game play in public spaces: a deep learning approach. Journal of neural engineering, 16(3), 036028.
Emotion Recognition by Point Process Characterization of Heartbeat Dynamics
Published in 2019 IEEE Healthcare Innovations and Point of Care Technologies, (HI-POCT), 2019
Recommended citation: Ravindran, A. S., Nakagome, S., Wickramasuriya, D. S., Contreras-Vidal, J. L., & Faghih, R. T. (2019, November). Emotion recognition by point process characterization of heartbeat dynamics. In 2019 IEEE Healthcare Innovations and Point of Care Technologies,(HI-POCT) (pp. 13-16). IEEE.
An empirical comparison of neural networks and machine learning algorithms for EEG gait decoding
Published in Scientific reports, 2020
Recommended citation: Nakagome, S., Luu, T. P., He, Y., Ravindran, A. S., & Contreras-Vidal, J. L. (2020). An empirical comparison of neural networks and machine learning algorithms for EEG gait decoding. Scientific reports, 10(1), 1-17.
Interpretable Deep Learning Models for Single Trial Prediction of Balance Loss
Published in 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2020
Recommended citation: Ravindran, A. S., Cestari, M., Malaya, C., John, I., Francisco, G. E., Layne, C., & Vidal, J. L. C. (2020, October). Interpretable Deep Learning Models for Single Trial Prediction of Balance Loss. In 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 268-273). IEEE.
Characterization of the Stages of Creative Writing With Mobile EEG Using Generalized Partial Directed Coherence
Published in Frontiers in human neuroscience, 2020
Recommended citation: Cruz-Garza, J. G., Sujatha Ravindran, A., Kopteva, A. E., Rivera Garza, C., & Contreras-Vidal, J. L. (2020). Characterization of the Stages of Creative Writing With Mobile EEG Using Generalized Partial Directed Coherence. Frontiers in human neuroscience, 14, 533.
A Roadmap towards Standards for Neurally Controlled End Effectors
Published in IEEE open journal of engineering in medicine and biology, 2021
Recommended citation: Paek, A. Y., Brantley, J. A., Ravindran, A. S., Nathan, K., He, Y., Eguren, D., ... & Contreras-Vidal, J. L. (2021). A Roadmap towards Standards for Neurally Controlled End Effectors. IEEE open journal of engineering in medicine and biology, 2.
