Srishti Yadav

Srishti Yadav

ML Researcher

About Me

I am a graduate from Simon Fraser University, Canada where was a Research Assistant at Networked Robotics and Sensing Laboratory under the supervision of Prof. Shahram Payandeh. My work revolved around intersection of computer vision and machine learning. I worked on problem of semi-supervised detection-based-tracking using kinect based RGB and depth images for a mobile robot.

In past, I also worked at UrtheCast where I was responsible for developing computationally intensive image processing algorithms for large scale satellite data within a cloud-based infrastructure using deep learning.

In (long) past, I was a part of Helicopter and VTOL Laboratory, IIT Kanpur (supervised by Prof. Abhishek), Samsung IoT Innovation Lab and Applied Cognitive Science Lab, IIT Mandi.

Over the course of my work experience, I have developed excellent skills in PyTorch, TensorFlow, Python, MATLAB, Numpy, Scipy, OpenCV, Matplotlib, Docker, GDAL, etc. scientific stack as well as cloud services like AWS.

I am an active member of developer community groups of Vancouver where I run Women in Machine Learning and Data Science meetup. As a strong proponent of tech and diversity, my involvement goes beyond local community work. In past, I have been one of the the chairs of Women in Computer Vision workshop co-hosted with CVPR, 2020 and was on the committe of the Women in Machine Learning workshop, 2019. I am currently an advisor for the Women in Computer Vision workshop, 2021 to be co-hosted with CVPR, 2021.

If you have questions about any of my work, feel free to reach out to me via email.

Note: If you are someone who wants to chat about Masters (especially research-track) in Canada, DO-NOT hesitate to email me. I’ll be more than happy to tell you about it!


  • Semi-supervised learning
  • Low shot Learning
  • Class imbalance problems
  • Hyperparameter tuning


  • Masters (Research-Track), 2021

    Simon Fraser University, Canada

Recent Posts

CLIP: Connecting Text and Images

Paper: Learning Transferable Visual Models From Natural Language Supervision PDF: Learning Transferable Visual Models From Natural Language Supervision Blog: CLIP: Connecting Text and Images General Terms:

Datasets for Fine-Grained Image Classification

iNat2017 Data: iNat2018 and iNat2019 Data: Data: Details: The dataset is similar to iNat2017 with small differences, which are mentioned in the website.

Hyperopt: A tool for parameter tuning

In deep learning, it is not easy to tune hyperparameters for optimal results. If we have 2 parameters (each with 3 prior desirable values), it is an easier problem. We will have possible combinations to try.


What is KL-Divergence KL Divergence is a measure of how one probabilty distributon is different from another. Some people also call it the distance between two distributions, however, strictly speaking it is not the distance.

Low/Few Shot Learning

Question: If a class has only two samples, can a computer make correct prediction? Note: Number of samples is too less for training. Approach: Few Shot Learning Few shot learning is a problem where we try to learn when the training data is very small.