Bio

I am a CS PhD candidate at Cornell Tech and an Apple AI/ML Scholar, working on privacy and security in machine learning and advised by Vitaly Shmatikov and Deborah Estrin.

My research goal is to build ethical, safe, and private machine learning systems. In our work, we demonstrate security drawbacks of Federated Learning (AISTATS'20) and fairness implications of Differentially Private Deep Learning (NeurIPS'19). Recently, we proposed a framework for backdoor attacks and defenses (USENIX'21).

Earlier, I worked on Ancile – a framework for language-level control over data usage, and OpenRec – a modular library for deep recommender systems. Amazon, Apple, and Google hosted me for summer internships. Before starting my PhD, I received engineering degree from Baumanka and worked at Cisco on OpenStack networking.

In my free time I play water polo and (used to...) travel.

Research papers

• campaignSpinning Language Models for Propaganda-As-A-Service

  We discover new capabilities of large language models to express attacker-chosen opinions on certain topics while performing tasks like summarization, translation, and language generation.

  [Preprint, 2021], [Code].
• visibility_offBlind Backdoors in Deep Learning Models

  We propose a novel attack that injects complex and semantic backdoors without access to the training data or the model and evades all known defenses.

  [USENIX, 2021], [Code].
• smartphoneHow To Backdoor Federated Learning

  We introduce a constrain-and-scale attack, a form of data poisoning, that can stealthily inject a backdoor into one of the participating models during a single round of Federated Learning training. This attack can avoid proposed defenses and propagate the backdoor to a global server that will distribute the compromised model to other participants.

  [AISTATS, 2020], [Code]
• local_hospitalSalvaging Federated Learning by Local Adaptation

  Recovering participants' performance on their data when using federated learning with robustness and privacy techniques.

  [Paper], [Code],
• doneAncile: Enhancing Privacy for Ubiquitous Computing with Use-Based Privacy

  A novel platform that enables control over application's data usage with language level policies and implementing use-based privacy.

  [WPES'19], [Code], [Slides].
• faceDifferential Privacy Has Disparate Impact on Model Accuracy

  This project discusses a new trade off between privacy and fairness. We observe that training a Machine Learning model with Differential Privacy reduces accuracy on underrepresented groups.

  [NeurIPS, 2019], [Code]
• extensionOpenrec: A modular framework for extensible and adaptable recommendation algorithms

  An open and modular Python framework that supports extensible and adaptable research in recommender systems.

  [WSDM, 2018], [Code]