Shyam Rajagopalan

Postdoctoral researcher

My research applies tools from computer science, machine/deep learning and engineering towards tracing the biological underpinnings of Autism Spectrum Disorder (ASD).

About me

I received a master's degree in computer science from the Indian Institute of Technology, Madras (IIT), India and a PhD in Information Sciences & Engineering from the University of Canberra, Australia.  I worked in the software industry for 25+ yrs in leading multinational organizations, such as Citrix, Adobe & Microsoft Corp. I was working in senior leadership roles (Senior Director of Engineering) driving large teams and shipping successful software products in the cloud technology domain.

Research description

I am working on a large project that aims to build prediction models for neurodevelopmental disorders using clinical and genomic information. In addition, I am working on other projects analyzing genetic information in intervention outcomes, genomics studies in different neurodevelopmental disorder cohorts and analyzing functional genomics data generated from patient-derived neurons.

RESEARCH PROJECTS

Project 1: Multimodal machine learning models to identify presymptomatic markers and predict risk scores of Autism in infants (0 - 18 mo)

The characterizing behaviour symptoms of Autism Spectrum Disorder (ASD) - social impairments, language/communication deficits and restrictive/repetitive behaviours - are usually absent before 14 months of age. However, recent studies are pointing towards the presence of early traits common in the general population and start to diverge towards core ASD symptoms by 2yr for certain high-risk individuals. There is growing evidence of disruption in brain development resulting from a combination of genetic variations during this development phase. This warrants more focused longitudinal studies to identify combinations of presymptomatic markers in infants (< 18 mo) using cost-effective and efficient tools. The aim of this project is to develop multimodal machine learning approaches to identify presymptomatic markers and predict risk scores of ASD in Infants. Prediction models will be developed using genetic variants, populational level national registry data of children containing early medical records (prenatal, delivery, neonatal care, drug dosage details), cognitive and behavioural assessments, eye-tracking and EEG/MRI. 

Project 2:  Machine learning approaches to trace biological underpinnings of Autism from Twins study

It is widely known that ASD is heritable. However, prior studies have mostly identified genes with de novo variants (DNV) having a strong association with ASD. This study points to the association of most of these ASD genes with excitatory and inhibitory neurons and most affect synapses and regulate other genes. A study from NCBI reported that only 20% of individuals with ASD carry de novo variants. A more recent study [2] in Nature Genetics, 2022, studied a large number of individuals with ASD and identified a group of inherited risk genes including five new moderate risk genes carrying Loss-Of-Function (LOV) variants. While great progress has been made in identifying ASD-associated genes and their pathways, more studies are needed to identify all complex genetic and environmental factors that play a causal role in ASD. Twins can provide new knowledge on Autism, in particular, studying the genetic basis of monozygotic and dizygotic twins may provide us insights into the underlying biology of ASD.  The aim of this project is to exploit advancements in machine/deep learning to develop models using genotype, whole exome and whole genome sequencing data to characterize ASD in twins. 

[1] Satterstrom, F. K., Kosmicki, J. A., Wang, J., Breen, M. S., De Rubeis, S., An, J.-Y., Peng, M., Collins, R., Grove, J., Klei, L., Stevens, C., Reichert, J., Mulhern, M. S., Artomov, M., Gerges, S., Sheppard, B., Xu, X., Bhaduri, A., Norman, U.,…Buxbaum, J. D. (2020). Large-scale exome sequencing study implicates both developmental and functional changes in the neurobiology of autism. Cell, 180(3), 568-584.e23. doi:10.1016/j.cell.2019.12.036

[2] Zhou, X., Feliciano, P., Shu, C. et al. Integrating de novo and inherited variants in 42,607 autism cases identifies mutations in new moderate-risk genes. Nat Genet 54, 1305–1319 (2022). https://doi.org/10.1038/s41588-022-01148-2

 

SELECTED PUBLICATIONS

Peer-reviewed journal publications

1. V Kiran Raj, S.S. Rajagopalan, et al. – Machine learning detects EEG microstate alterations in patients living with temporal lobe epilepsy - Seizure: European Journal of Epilepsy, 2018.

Peer-reviewed conference publications

1. Abhijith Vasista, Sowmyashree Kaku,Anoop, Manjula James, GRK Sharma, Shyam Rajagopalan, Ashok Mysore - Evaluation of spectral EEG Correlation Dimension as an early-stage biomarker for Autism Spectrum Disorders in awake and sleep states - 32nd International Congress of Clinical Neurophysiology, Geneva, Switzerland, 4 - 8 Sep 2022.

2. Jeba Berlin S, Deepak Pandian, Shyam Sundar Rajagopalan, Dinesh Babu Jayagopi - Detecting a Child’s Stimming Behaviours for Autism Spectrum Disorder Diagnosis using RGBPose-SlowFast Network - IEEE International Conference in Image Processing, ICIP 2022 16-19 October 2022 Bordeaux, France

3. AbhijithVasista, Sowmyashree Mayur Kaku, GRK Sarma, Shyam Rajagopalan, Ashok Mysore - Investigating EEG Band differences in preschool children with Autism during awake and sleep stages and comparison with non-autistic controls - International Society for Autism Research (INSAR) Annual Meeting 2022, Austin, USA

4. S.S. Rajagopalan, et al. – Extending Long Short-Term Memory for Multi-View Structured Learning - The 14th European Conference on Computer Vision (ECCV 2016), Amsterdam, The Netherlands.

5. S.S. Rajagopalan, et al. Play with Me – Measuring a Child’s Engagement in a Social Interaction. IEEE Automatic Face and Gesture Recognition (FG 2015), Slovenia. (Oral)

6. S.S. Rajagopalan, et al. Detecting Self-Stimulatory Behaviours for Autism Diagnosis. Proceedings of the IEEE International Conference on Image Processing (ICIP 2014), Paris.

Education

Doctor of Philosophy (PhD) in Affective Computing, University of Canberra, Australia.

Master of Science by Research (MS) in Computer Science, Indian Institute of Technology, Madras, India.

Bachelor of Engineering (BE) in Computer Science, University of Madras, India

 

Professional Work Experience

Senior Director of Engineering, Citrix R&D, Bangalore, India. Feb 2021 - Apr 2022

Director of Engineering & Sr Research Scientist, Adobe Systems India Private Limited, Bangalore, India. Feb 2005 - Mar 2013, Jul 2016 - Feb 2021.

Senior Development Manager, Aditi Tech/Talisma Corp., Bangalore, India. Jul 1995 - Feb 2005

Scientific Officer, Bhabha Atomic Research Centre, Mumbai, India. Aug 1993 - Jun 1995.