Akshay Sujatha Ravindran

Biosketch

I am a Data Scientist II at Alto Neuroscience. Here at Alto, I use my expertise on biomedical signal processing and machine learning to help individuals suffering from different mental health disorders by helping them find the right treatment. Specifically, I use machine learning and signal processing tools to predict clinical response and identify patients who are most likely to benefit from our drug candidates using their brain activity measured using electroencephalography (EEG). I use different data analysis tools to identify pharmacodynamic profile of drug candidate as well as neurophysiological signature of psychiatric diseases.
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. 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.
h) Point process characterization of heartbeat dynamics measured using Photoplethysmography


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 TKM College of Engineering, University of Kerala, India in 2015.

What are my values and hobbies

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, play and watch soccer (huge arsenal fan since 2006), do yoga, go for casual walks/biking and most importantly I am a huge foodie! I love to engage in conversations and activities related to spirituality, interoception for health and wellbeing, developing a growth mindset, and overcoming failures/challenges.