Yash Bhisikar

I am a final year undergraduate student pursuing a major in Computer Science with a Minor in Data Science at BITS Pilani. My research interests include Vision, Autonomous Systems, Reinforcement Learning and Causal ML. I am also interested in High Performance Computing and Parallel File Systems.

I'm currently working as a student researcher at APPCAIR under Prof. Snehanshu Saha and Prof. Aditya Challa on developing a non-chaotic pruning strategy for neural networks. I'm also working at the Data, Systems and High Performance Computing Lab under Prof. Arnab K. Paul on an intelligent file-volume mapping framework in BeeGFS. I have previously interned at North Eastern Space Applications Centre, where I worked on enhancing rainfall forecasting predictions using Genetic Algorithms and Deep Learning.

I am a member of the Society for Artificial Intelligence and Deep Learning, a group of undergraduate researchers based out of BITS Pilani. I'm a core member of the Autonomous Subsystem of Project Kratos where we are building a Mars rover capable of autonomous navigation. At the Electronics and Robotics Club, I worked on Trobot, an omnidirectional indoor delivery robot.

I'm actively looking for research internships and oppurtunities. Check out my CV or drop me an e-mail for a chat!

September '23	I'm selected as one of the instructors for the CTE course "Intro to ML and DL" conducted by SAiDL.
September '23	Our paper got accepted at REX-IO 2023!
August '23	I will be a Teaching Assistant for CS F222 : Discrete Structures in Computer Science.
August '23	Completed research internship at North Eastern Space Applications Centre under Prof. D. Sriram and Ms. Ritu Anilkumar.
June '23	I'll be working at APPCAIR Lab on causal neural network pruning as a student researcher!
June '23	I will be an Instructor for a student-led course on ROS and Robot Automation through the Quark Summer Technical Projects.
April '23	I will start working at the Data, Systems and High Performance Computing Lab at BITS Goa!
March '23	Project Kratos released the 2023 SAR for the University Rover Challenge Watch it here.

	Research Intern \| North Eastern Space Applications Centre May'23 - August '23 The project aimed to develop predictive models for rainfall forecasting, improving upon the prediction horizon of existing weather models. A data pipeline was created for pre-processing geospatial datasets and integrated with a UNet-based architecture. Experiments were conducted combining genetic algorithms and gradient-descent based optimizers to improve accuracy.
	Student Researcher \| APPCAIR June '23 - Present Designing a non-chaotic neural network pruning strategy that also preserves feature importances. Investigating Granger Causality and comparing against L0, L1, Rank-based and other state-of-the-art algorithms.
	Student Researcher \| Data, Systems and High Performance Computing Lab April '23 - Present Part of a team that is maintaining a 32-node cluster consisting of rack servers, desktop workstations and Raspberry Pi's. Designing an algorithm for optimal file-volume mapping in BeeGFS, a parallel file system. Incorporating adaptive striping, access pattern analysis and file-size awareness.

Does Varying BeeGFS Configuration Affect the I/O Performance of HPC Workloads?

Arnav Borkar, Joel Tony, Hari Vamsi K. N, Tushar Barman, Yash Bhisikar, Sreenath T. M. and Arnab K. Paul

Accepted at the 3rd workshop on Re-envisioning Extreme-Scale I/O for Emerging Hybrid HPC Workloads (REX-IO) in conjunction with IEEE Cluster, 2023.

	Project Kratos: A Mars Rover Autonomous Subsystem Core Member Websites: Project Kratos, Kratos Code Base Developing a mars rover as part of the University Rover Challenge. We're responsible for the rover's autonomous navigation. Worked on a real-time object detector for the rover using YOLOv3 trained on our custom dataset and ran it on a Jetson Nano. Achieved integration with ROS using darknet_ROS. Designed a PID-inspired mechanism to navigate directions based on arrow directions captured in a monocular camera feed. Integrated GPS-based navigation mechanism with the tech stack to traverse between local GPS coordinates.
	Reinforcement Learning Via Sequence Modelling Code: Implementation My implementation of the Decision Transformer. I modified the architecture to use an LSTM to compare training accuracies and performance in a non-Markovian setting. Used a sequence of Reward, State and Action tokens to condition the model and predict the next expected reward and optimal action. Validated results against model-free offline RL Baselines in Mujoco's Hopper environment.
	Segmentation on Arbitary Image-Text prompts using Clip Code: Implementation Used OpenAI's Clip to generate embeddings of the input image/text prompt and performed Featurewise-Linear Modulation. Inspired from the UNet, trained a light-weight transformer-based decoder on top of the embeddings and integrated encoder activations to generate the binary segmentation mask in a supervised setting.
	Data Prefetching for Edge Deep Learning Workloads Semester Project for the Course - Data Storage Technologies and Networks Desigining a comprehensive framework to optimize convergence time for Deep Learning training workloads on edge devices. We're implementing a prefetching mechanism accounting for spatial and temporal locality to reduce I/O overheads and overall training time. Apache Kafka is being used to stream data from a distributed file system to the compute nodes.

This template is a modification to Jon Barron's website and a fork of Hardik Shah's website. Find the source code to my website here.

Yash Bhisikar

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