Vinayak Sharma

Publications

LUCI: Lightweight UI Command Interface

Lagudu, Guna and Sharma, Vinayak and Shrivastava, Aviral ; 2025

[Machine Learning]

@inproceedings{LUCI2025,
  author = {Lagudu, Guna and Sharma, Vinayak and Shrivastava, Aviral},
  title = {LUCI: Lightweight UI Command Interface},
  year = {2025},
  isbn = {9798400719219},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3735452.3735536},
  doi = {10.1145/3735452.3735536},
  booktitle = {Proceedings of the 26th ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems},
  pages = {182-191},
  numpages = {10},
  keywords = {Embedded Systems, LLM, System Automation},
  location = {Seoul, Republic of Korea},
  tag = {Machine Learning},
  series = {LCTES '25}
}

Primer on Data in Quantum Machine Learning

Shrivastava, Aviral and Sharma, Vinayak ; 2024

[Quantum Machine Learning]

@inproceedings{QuantumPrimer2024,
  author = {Shrivastava, Aviral and Sharma, Vinayak},
  booktitle = {2024 International Conference on Compilers, Architecture, and Synthesis for Embedded Systems (CASES)},
  title = {Primer on Data in Quantum Machine Learning},
  year = {2024},
  volume = {},
  number = {},
  pages = {19-20},
  tag = {Quantum Machine Learning},
  keywords = {Performance evaluation;Quantum system;Machine learning algorithms;Quantum algorithm;Embedded systems;Computer aided software engineering;Machine learning;Computer architecture;Quantum state;Machine Learning;Quantum Information;Quantum Computing},
  doi = {10.1109/CASES60062.2024.00010}
}

Quantum Polar Metric Learning: Efficient Classically Learned Quantum Embeddings Pre-Print

Sharma, Vinayak and Shrivastava, Aviral ; 2023

[Quantum Machine Learning]

@misc{sharma2023quantum,
  title = {Quantum Polar Metric Learning: Efficient Classically Learned Quantum Embeddings},
  author = {Sharma, Vinayak and Shrivastava, Aviral},
  year = {2023},
  eprint = {2312.01655},
  archiveprefix = {arXiv},
  primaryclass = {quant-ph},
  url = {https://mpslab-asu.github.io/QPMeL/},
  tag = {Quantum Machine Learning},
  preprint = {true}
}

Projects

Face De-Identification

Scalable privacy preserving face de-identification

A scalable approach to preserving the privacy of bystanders in social media posts

Entirely ‘edge’ system tested on low end android phones-
Client side nature makes it scalable as no server compute required for new nodes,
Edge compute improves privacy as unfiltered data never leaves the device
Aesthetics are preserved via custom filter implementation based on human tendecies

Tensorflow OpenCV Android Image Filter

QML-Tutorials

Set of tutorials to learn teach Quantum Machine Learning

A set of tutorials in Pennylane to teach Quantum Machine Learning convering:

Toy dataset generation using random quantum circuits
Basic Variational or Explicit Models
Data ReUploading Models
Quantum Kernel Classifiers or Implicit Models

Pennylane Tensorflow Numpy

ARCNN-keras

TF-keras implementation of ARCNN which removes JPEG compression artifacts

A tf-keras implementation of ARCNN mentioned in :

Implemented ARCNN in tf.keras for easy of use
Created a novel “lite” version using dilated convolutions and reducing the number of parameters to half of that of Faster ARCNN (64k -> 32k)

Tensorflow Keras Docker tf.data PIL

vinayak19th.github.io

The code repo for this website and instructions

Website hosting my resume and profile. Notable Features include:

System persistent dark mode (Remembers your preference via cookies)
Live Github based Statistcs
- Profie Statistics on the about page
- Project statistics per repository in the project page
Reactive design for mobile friendly operation
Tabbed Page for certifications for easy of reading and management
Header section with live links such as :
- Direct whatsapp message
- Medium Profile
- Stack Overflow
- etc
Modular self generating based on yml datasheets

Jekyll BootStrap JQuery SCSS HTML5

LOW-RESOURCE ASR

A project based on the BABEL Dataset from the US government

An End-to-End Framework for performing automatic speech recognition on low-resource languages such as Urdu and Pashto
Created the framework based on ESPnet.

Kaldi ESPNet Bash PyTorch

Quantum Machine Learning

Implementations of code for the University of Toronto QML course

Implementing various Quantum Machine Learning algorithms from the University of Toronto Course
Implemented using the QISKit API from IBM

QisKit Numpy