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IIT Madras
Software Engineer
June 19, 2026 – Present
open-sim
May 17, 2026 – Present
A Tool to convert human videos doing actions (eg : cooking) to realistic robotic videos + actions for training/fine tuning Foundation VLM/VAM models
View Projectscene-graph-vit
September 5, 2024 – December 20, 2024
Implementation of the Paper Scene-Graph ViT
View Projectattention-models
October 16, 2023 – August 8, 2025
Simplified Implementation of SOTA Deep Learning Papers in Pytorch
View Projectdynamic-Pix2Pix
March 28, 2023 – March 31, 2023
Dynamic-Pix2Pix: Noise Injected cGAN for Modeling Input and Target Domain Joint Distributions with Limited Training Data
View ProjectEnd-to-End-Trainable-Multi-Instance-Pose-Estimation-with-Transformers
December 21, 2021 – August 17, 2022
pose-detection-transformer
View Projectlarge-scale-visual-relationship-understanding
October 4, 2020 – October 2, 2021
Visual Relationship Understanding
View Projectcnn-lstm
June 7, 2019 – November 2, 2022
CNN LSTM architecture implemented in Pytorch for Video Classification
View ProjectCultural Fit Analysis
The candidate's projects are heavily concentrated in academic/research-oriented machine learning and deep learning, primarily using Python. While this demonstrates deep technical interest in a specific domain, the lack of diversity in project types (e.g., web development, backend services, mobile) and technologies outside of Python/ML frameworks suggests a potentially narrow focus. The target role is 'Software Engineer,' which is broad, but the candidate's profile leans heavily towards ML research rather than general software engineering practices. This might indicate a specific cultural fit for ML-focused teams but less so for generalist software engineering roles without further evidence.
Soft Skills & Operational Fit
Insufficient data to assess soft skills and operational fit. The candidate's experience level is listed as 0, and the only listed employment is a future start date, suggesting a lack of professional work history. Project descriptions are brief, limiting insight into collaboration or problem-solving approaches.