Vignesh Gokul

Hi! I am a Ph.D. candidate in Computer Science at UC San Diego advised by Prof. Shlomo Dubnov. My research interests includes Creative Interaction/Improvisation, Video/Audio Understanding and Federated Learning.

I am on the job market for research roles in the industry (exp. graduation by June 2023)

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• Co-creativity/Improvisation: My research goal is to enable creative interactions between humans and visual assistants. These interactions can be either visual (dance) or audio (music improvisation) .

• Distributed Co-creativity: I also focus on building federated/distributed systems that can learn such interactions in the absence of large datasets.

We introduce Symmetric Transfer Entropy (SymTE) as a quantitative metric to switch between generative models based on a control signal.

In this paper we survey the methods for control and cre-ative interaction with pre-trained generative models for audio and music.

In this paper, we propose FedLTN, a novel approach motivated by the well-known Lottery Ticket Hypothesis to learn sparse and personalized lottery ticket networks (LTNs) for communication-efficient and person- alized FL under non-identically and independently distributed (non-IID) data settings.

Federated GANs propagate data biases onto the global model. We propose Bias-Free FedGAN, an approach to generate bias-free synthetic datasets using FedGAN.

We propose a differentially private version of InfoGAN (DP-InfoGAN). We also extend our framework to a distributed setting (DPD-InfoGAN) to allow clients to learn different attributes present in other clients' datasets in a privacy-preserving manner.

In this paper we describe a gestural motif extraction system that combines deep feature learning with structural similarity analysis to allow such query based human-computer motion interaction.