Gokul Monikantan Nair

Roberts Center for Pediatric Research
734 Schuylkill Ave
Philadelphia, PA 19146

Gokul Monikantan Nair

Data Scientist at the Center for Autism Research, Children's Hospital of Philadelphia. I work at the intersection of computer vision, behavioral analysis, and social signal processing to build computational tools that support clinical research for pediatric populations.


Latest Updates

Apr 2026

Micro-DualNet accepted to IEEE FG 2026 in Japan! 🇯🇵🌸

Mar 2026

Poster accepted to INSAR 2026 in Prague! 🇨🇿

Apr 2025

Joined Center for Autism Research at CHOP as Data Scientist I!

Oct 2024

Joined Chima Inc. as AI Engineer in San Francisco 👨🏻‍💻

May 2024

Graduated with MSE in Data Science from University of Pennsylvania! UPenn 🥳

July 2022

Presented Paper at 12th ICCNT, IIT Kharagpur 🥳


Academic Background

MS in Data Science GPA: 3.8 / 4.0

University of Pennsylvania

  • Big Data Analytics
  • Machine Learning
  • Deep Learning
  • Natural Language Processing
  • Computer Vision
  • Statistical Learning
  • Data Engineering
  • Databases & Information Systems
2022 – 2024

B.Tech in Electronics & Telecommunication GPA: 3.8 / 4.0

Sardar Patel Institute of Technology, Mumbai

2018 – 2022

Where I’ve Worked

Data Scientist I Apr 2025 – Present

Children's Hospital of Philadelphia — Center for Autism Research

Developing computational frameworks for quantifying social behavior in pediatric populations through video-based body and facial analysis. Building end-to-end pipelines for 2D-to-3D pose lifting, micro-expression recognition, and behavioral feature extraction on high-performance computing infrastructure. Focused on bridging machine learning research with clinical workflows to support autism diagnostics and intervention studies.

AI Engineer Oct 2024 – Apr 2025

Chima Inc., San Francisco

Designed and deployed multi-agent LLM pipelines for enterprise applications. Managed Kubernetes-based infrastructure for scalable model serving and orchestration.

Data Analyst Feb 2023 – Dec 2024

The Wharton School, University of Pennsylvania

Conducted large-scale ESG analytics over 1M+ news articles using AWS services (S3, Athena, Redshift). Built automated data pipelines and dashboards to support research in sustainable finance.


Selected Research

Micro-DualNet figure

Micro-DualNet: Dual-Path Spatio-Temporal Network for Micro-Action Recognition

N. V. S. Chappa, E. Sariyanidi, L. Yankowitz, G. Nair, C. J. Zampella, R. T. Schultz, B. Tunc

IEEE FG 2026 2026 Forthcoming Oral arXiv

Facial Expression Asymmetry as a Novel Nonverbal Marker of Autism in Naturalistic Social Interaction

M. McNealis, G. Nair, R. Chappa, C. Zampella, K. Campbell, E. Sariyanidi, L. Yankowitz, J. D. Herrington, R. T. Schultz, B. Tunc

INSAR 2026 2026 Poster
Bitbox figure 1 Bitbox figure 2

Bitbox: Behavioral Imaging Toolbox for Computational Analysis of Behavior from Videos

E. Sariyanidi, G. Nair, L. Yankowitz, C. J. Zampella, M. K. Pargi, A. Manakiwala, R. T. Schultz, B. Tunc

Preprint Journal arXiv
Copy-Move figure 1 Copy-Move figure 2

Identification of Multiple Copy-Move Attacks in Digital Images using FFT and CNN

G. Nair, K. Venkatesh, D. Sen, R. Sonkusare

IEEE ICCCNT 2021 2021 Oral DOI
Pneumonia figure 1 Pneumonia figure 2

Pneumonia Detection from Chest X-ray using Transfer Learning Techniques

S. Kalgutkar, V. Jain, G. Nair, K. Venkatesh, K. Parab, A. Deshpande

IEEE I2CT 2021 2021 Oral DOI

Research Projects

Disaster Tweet Classification with Multimodal Learning

Built a multimodal system fusing BERT text embeddings with CLIP/BLIP visual features and attention mechanisms to classify disaster-related tweets. Achieved 79% F1 on a benchmark dataset, demonstrating effective cross-modal information fusion for crisis informatics.

BERT CLIP BLIP Attention Multimodal

Depression Likelihood Prediction on Reddit

Developed a retrieval-augmented prediction system combining LSTM networks with MentalBERT to assess depression likelihood from Reddit posts. Leveraged domain-specific language models and retrieval-augmented inference to improve sensitivity on mental health discourse.

LSTM MentalBERT RAG NLP Mental Health

Technical Toolkit

Languages & Tools

Python SQL R Git Linux HPC / Slurm Docker Kubernetes Bash

ML & Frameworks

PyTorch TensorFlow OpenCV scikit-learn PySpark HuggingFace ONNX Dask MediaPipe

Data & Infrastructure

Snowflake Airflow AWS Singularity MLflow PostgreSQL

Research Areas

Computer Vision Behavioral Analysis ML Pipelines Social Signal Processing Pose Estimation Multimodal Learning