Arun Zachariah

Arun Zachariah

Senior Systems Software Engineer - Data Services

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Posts

Gratitude Chronicles

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Last month, I graduated with a with a Ph.D. in Computer Science from the University of Missouri-Columbia, bringing an end to what has been a challenging, fulfilling, and rewarding journey. This would not have been possible without the endlees support and prayers of many.

portfolio

publications

A Gossip-Based System for Fast Approximate Score Computation in Multinomial Bayesian Networks

Arun Zachariah, Praveen Rao, Anas Katib, Monica Senapati, Kobus Barnard - "A Gossip-Based System for Fast Approximate Score Computation in Multinomial Bayesian Networks." 35th IEEE International Conference on Data Engineering (ICDE)., Macau, China, 2019.

Large-Scale Storage of Whole Slide Images and Fast Retrieval of Tiles Using DRAM

Daniel E. Lopez Barron, Praveen Rao, Deepthi Rao, Ossama Tawfik, Arun Zachariah - "Large-Scale Storage of Whole Slide Images and Fast Retrieval of Tiles Using DRAM." 2020 SPIE Defense + Commercial Sensing: Big Data II: Learning, Analytics, and Applications Conference, Anaheim, CA.

FoodKG: A Tool to Enrich Knowledge Graphs Using Machine Learning Techniques

Mohamed Gharibi, Arun Zachariah, Praveen Rao - "FoodKG: A Tool to Enrich Knowledge Graphs Using Machine Learning Techniques." Frontiers in Big Data., Volume 3, 2020.

A System for Large-Scale Image Retrieval on Everyday Scenes With Common Objects

Arun Zachariah, Mohamed Gharibi, Praveen Rao - "QIK: A System for Large-Scale Image Retrieval on Everyday Scenes With Common Objects." Annual ACM International Conference on Multimedia Retrieval (ICMR 2020), Dublin, Ireland.

Deepfakes for Histopathology Images: Myth or Reality?

Nouf Alrasheed, Arun Zachariah, Shivika Prasanna, Deepthi Rao, and Praveen Rao - "Deepfakes for Histopathology Images: Myth or Reality?" 49th Annual IEEE Applied Imagery Pattern Recognition (AIPR) Workshop 2020: Trusted Computing, Privacy, and Securing Multimedia, Washington, D.C., 2020.

Evaluation of Internet-based Crowdsourced Fundraising to Cover Healthcare Costs in the United States

Suveen Angraal, Arun Zachariah, Raaisa Raaisa, Rohan Khera, Praveen Rao, Harlan M Krumholz, and John A Spertus - "Evaluation of Internet-based Crowdsourced Fundraising to Cover Healthcare Costs in the United States." JAMA Network Open,2021.

A Large-Scale Image Retrieval System for Everyday Scenes

Arun Zachariah, Mohamed Gharibi, Praveen Rao - "A Large-Scale Image Retrieval System for Everyday Scenes." 2nd ACM International Conference on Multimedia in Asia (MM Asia 2020), Singapore.

Accelerating Variant Calling on Human Genomes Using a Commodity Cluster

Praveen Rao, Arun Zachariah, Deepthi Rao, Peter Tonellato, Wesley Warren and Eduardo Simoes - "Accelerating Variant Calling on Human Genomes Using a Commodity Cluster." 30th ACM International Conference on Information and Knowledge Management (CIKM), Australia, 2021.

Private-Share: A Secure and Privacy-Preserving De- Centralized Framework for Large Scale Data Sharing

Arun Zachariah and Maha Alrasheed - "Private-Share: A Secure and Privacy-Preserving De- Centralized Framework for Large Scale Data Sharing." 3rd ACM International Conference on Multimedia in Asia (ACM MM Asia 2021), Australia, 2021.

Zero Shot Learning for Predicting Energy Usage of Buildings in Sustainable Design

Arun Zachariah, Praveen Rao, Brian Corn, and Dominique Davison - "Zero Shot Learning for Predicting Energy Usage of Buildings in Sustainable Design." AAAI Workshop on AI to Accelerate Science and Engineering (AI2ASE), Canada, 2022.

Enabling Large-Scale Human Genome Sequence Analysis on CloudLab

Praveen Rao, Arun Zachariah – "Enabling Large-Scale Human Genome Sequence Analysis on CloudLab." In the 9th International Workshop on Computer and Networking Experimental Research using Testbeds (Collocated with IEEE INFOCOM), 2022.

Video Retrieval for Everyday Scenes With Common Objects

Arun Zachariah, and Praveen Rao - "Video Retrieval for Everyday Scenes with Common Objects." Annual ACM International Conference on Multimedia Retrieval (ICMR 2023), Greece, 2023.

EVA-GAN: Enhanced Various Audio Generation via Scalable Generative Adversarial Networks

Shijia Liao, Shiyi Lan, and Arun George Zachariah - "EVA-GAN: Enhanced Various Audio Generation via Scalable Generative Adversarial Networks." arXiv preprint arXiv:2402.00892, 2024.
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Improving Hyperparameter Optimization with Checkpointed Model Weights

Nikhil Mehta, Jonathan Lorraine, Steve Masson, Ramanathan Arunachalam, Zaid Pervaiz Bhat, James Lucas, and Arun George Zachariah - "Improving Hyperparameter Optimization with Checkpointed Model Weights." arXiv preprint arXiv:2407.01526, 2024.
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talks

QIK: Querying Images via Knowledge Graphs

The global image recognition market is expected to reach $40 billion by 2021. There is a growing demand for intelligent image retrieval solutions for specialized domains. With this in mind, we design QIK, a system for large-scale image retrieval. As an update to the Spring 2019 Semiannual meeting, in this presentation, we describe the outcomes in terms of the system evaluation and a few search results from QIK.

Deep Learning for Energy Usage Prediction in Sustainable Building Design

As per the 2030 challenge, all new buildings, developments, and major renovations shall be carbon-neutral by 2030. In this talk, as a step forward towards achieving sustainable design of buildings we present our objective of developing a unified and accurate deep learning-based model for predicting energy usage of different building types. In addition to it, we also present our evaluation results compared against traditional machine learning techniques.

QIK: A System for Large-Scale Image Retrieval on Everyday Scenes With Common Objects

In this presentation, we propose a system for large-scale image retrieval on everyday scenes with common objects by leveraging advances in deep learning and natural language processing (NLP). Unlike recent state-of-the-art approaches that extract image features from a convolutional neural network (CNN), our system exploits the predictions made by deep neural networks for image understanding tasks. Our system aims to capture the relationships between objects in an everyday scene rather than just the individual objects in the scene. We also present the performance of our system on the Microsoft COCO dataset containing everyday scenes (with common objects) and prove that our system can outperform state-of-the-art techniques in terms of mean average precision for large-scale image retrieval. [Presentation Recording]

A Large-Scale Image Retrieval System for Everyday Scenes

In this presentation, we showcase a large-scale image retrieval system for everyday scenes. The system leverages advances in deep learning and natural language processing (NLP) for image understanding tasks. The system allows a user to retrieve highly relevant images by leveraging relationships between the objects within an image. We also demonstrate how the system can let a user to intuitively explore the repository and obtain suggestions for similar image queries to further explore the repository. [Presentation Recording]

Deepfakes for Histopathology Images: A Myth or Reality?

Deepfakes have become a major public concern on the Internet as fake images and videos could be used to spread misleading information about a person or an organization. The field of digital pathology is gaining a lot of momentum as the Food and Drug Administration (FDA) is approving new digital pathology systems for primary diagnosis and consultation in the US. Hence, in this presentation, we explore if deepfakes can be generated for histopathology images using advances in generative adversarial networks (GANs) and deep learning in general.

Private-Share: A Secure and Privacy-Preserving DeCentralized Framework for Large Scale Data Sharing

The various data and privacy regulations introduced around the globe, require data to be stored in a secure and privacy-preserving fashion. Non-compliance with these regulations come with major consequences. This has led to the formation of huge data silos within organizations leading to difficult data analysis along with an increased risk of a data breach. Isolating data also prevents collaborative research. In this presentation we show how we address this through Private-Share, a framework that would enable secure sharing of large scale data. [Presentation Recording]

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.