Research

I am broadly interested in the field of Trustworthy Machine Learning and Cryptography. In the past, I have worked on various projects across Machine Learning, Reinforcement Learning, Deep Learning, Differential Privacy, and Adversarial Attacks. 

Link to my Google Scholar page: https://scholar.google.com/citations?user=JC0iWhMAAAAJ 

PUBLICATIONS

Thesis:

1. Privacy Attacks in Reinforcement Learning with Sensitive Rewards
   July 2022, IIIT Hyderabad

Kritika Prakash
Thesis for MS by Research in Computer Science & Engineering

Work done as a graduate student in the Machine Learning Lab, IIIT Hyderabad. Co-advised by Dr. Praveen Paruchuri and Dr. Sujit Gujar.

Conference Publications:

1. How Private Is Your RL Policy? An Inverse RL Based Analysis Framework
   AAAI 2022
   Selected for Oral Presentation
   Kritika Prakash, Fiza Husain, Praveen Paruchuri, Sujit Gujar
   Work done as a graduate student in the Machine Learning Lab, IIIT Hyderabad

2. ADVISER: AI-Driven Vaccination Intervention Optimiser for Increasing Vaccine Uptake
   IJCAI-ECAI 2022 AI for Good track
   🏆 **Best Paper Award**
   Vineet Nair, Kritika Prakash, Michael Wilbur, Aparna Taneja, Corrine Namblard, Oyindamola Adeyemo, Abhishek Dubey, Abiodun Adereni, Milind Tambe, Ayan Mukhopadhyay
   Work done as a research associate in the AI for Social Good team at Google Research

3. PredictRV: Prediction Based Strategies for Negotiations with Dynamically Changing Reservation Value
   GDN 2020
   🏆 **Best Student Paper Runner-Up Award**
   Aditya Gear, Kritika Prakash, Nonidh Singh, Praveen Paruchuri
   Work done as a graduate student in the Machine Learning Lab, IIIT Hyderabad

Workshop Publications:

1. Towards General Purpose Infrastructure for Protecting Scientific Data under Study
   PPML Workshop NeurIPS 2020
   ​​Andrew Trask, Kritika Prakash
   Work done as a contributor in the Differential Privacy team at OpenMined

2. Sensitivity Analysis in Differentially Private ML using Hybrid Automatic Differentiation
   TPDP Workshop, ICML 2021, July 24, 2021

Alexander Ziller, Dmitrii Usynin, Moritz Knolle, Kritika Prakash, Andrew Trask, Rickmer Braren, Marcus Makowski, Daniel Rueckert, Georgios Kaissis
Work done as a contributor in the Differential Privacy team at OpenMined

3. Syft 0.5: A Platform for Universally Deployable Structured Transparency

DPML Workshop, ICLR 2021, May 7, 2021

Adam Hall, Madhava Jay, Tudor Cebere, Bogdan Cebere, Koen van der Veen, George Muraru, Tongye Xu, Patrick Cason, William Abramson, Ayoub Benaissa, Chinmay Shah, Alan Aboudib, Théo Ryffel, Kritika Prakash, Tom Titcombe, Varun Kumar Khare, Maddie Shang, Ionesio Junior, Animesh Gupta, Jason Paumier, Nahua Kang, Vova Manannikov, Andrew Trask

Work done as a contributor in the Differential Privacy team at OpenMined

4. VidyutVanika: An Autonomous Broker Agent for Smart Grid Environment

PASS Workshop 2019 

Susobhan Ghosh, Kritika Prakash, Sanjay Chandlekar, Easwar Subramanian, Sanjay Bhat, Sujit Gujar, Praveen Paruchuri

Work done as a graduate student in the Machine Learning Lab, IIIT Hyderabad

Book Chapters:

1. PySyft: A Library for Easy Federated Learning

Chapter from the book, "Federated Learning Systems: Towards Next-Generation AI", 2021

Alexander Ziller, Andrew Trask, Antonio Lopardo, Benjamin Szymkow, Bobby Wagner, Emma Bluemke, Jean-Mickael Nounahon, Jonathan Passerat-Palmbach, Kritika Prakash, Nick Rose, Théo Ryffel, Zarreen Naowal Reza, Georgios Kaissis

EXPERIENCE

ACHIEVEMENTS