I currently work at Ederlabs on privacy preserving Machine learning (PPML).

My interests are in Machine Learning , Privacy Preserving Machine Learning (PPML) and Software Engineering. In PPML , I am specifically interested in Differential Privacy , Federated Learning and Trusted Execution Environments (TEE's).

Available on Twitter as kamathhrishi and on LinkedIn.

I would like to write Blogposts

Experience

Privacy AI Engineer , Ederlabs (October 2020-Present)

Worked on privacy preserving machine learning using Trusted Execution Environment(TEE) Intel SGX.

Intern , Ederlabs (January 2020-September 2020)

Worked on Differential Privacy and Intel SGX

Freelance Web Developer (July-October 2018)

Worked on two frontend projects for two clients using AngularJS and Bootstrap4.

Open Source Contributions

PySyft

“A library for answering questions using data you cannot see”
A framework for privacy-preserving deep learning using Pytorch and Tensorflow.
I worked on Pytorch development. Part of the Core team Contributed to Core PySyft Team

The experience helped me learn a lot of software engineering practices such as unit tests, code reviews, Git, writing clean and documented code.

• Wrote use cases of Federated Learning for developing Word Embeddings from Private Data and demonstration on CIFAR10
• Wrote an implementation of Differential Privacy method Private Aggregation of Teacher Ensembles (PATE)
• Tried working on Polynomial Tensor for non-linear computation in multiparty computation (MPC) setting. The method approximated non-linear computation using interpolation/Taylor series methods. Could not integrate it as part of Syft chain of tensors.
• Refactored data loaders and federated dataset and wrote a tutorial on developing custom federated datasets
• Type annotated the codebase and wrote documentation for parts of it

Deepgaze

Computer Vision library for human-computer interaction in Python

I have refactored certain parts of the library for improved usability and wrote test cases to ensure accurate functionality. Ported library to Python 3.0.

Some Projects I am proud of

For complete list of projects feel free to check my Github Profile.

Privacy Preserving Pandas
Coming Soon

Indoor Scene Recognition with Visual Attributes

• Trained a Residual Network to classify 67 different Indoor Scenes on MITIndoor 67, obtained 60% accuracy on the test dataset.
• MITIndoor67 is a small dataset consisting of 5400 images, training a network directly on the dataset led to substantial overfitting.
• In order to learn finer features, a separate attribute prediction network was trained on SUN attribute database.
• The predicted attributes are used to augment image features in the linear layers of the network scene recognition network.

Weakly Supervised Street View Text Detection

• Used Pytorch, OpenCV and Pillow in Python
• Trained a character agnostic text detector on Chars74K dataset along with images consisting of indoor/outdoor scenes without text. The character agnostic model is a alexnet network pretrained on imagenet.
• Used the classifier and sliding windows to annotate images in UCSD SVT and NEOCR dataset to derive bounding boxes
• Trained a street text localisation and detection Fully Convolutional Network(FCN) on the weakly supervised labelled dataset by using text detector’s starting CNN layers
• Currently working on character level segmentation and recognition

Animals with Attributes
Supervised Learning

• Trained a 30 layer Residual CNN from scratch which could predict 50 different species of animals with 72% accuracy. Trained it with probabilistic augmentations using Alubmentations library.
• Finetuned an ImageNet pre-trained 18 layer network to attain 89% accuracy
• Spent time understanding and analyzing top Imagenet CNN architectures

Zero Shot Learning

• Currently exploring Zero shot learning on the dataset. A naive implementation with Direct Attribute prediction attains 35% accuracy prediction unseen classes.

Contact

Contact me on kamathhrishi [at] gmail