Abhinav Gorantla

Abhinav Gorantla

CS @ ASU

Abhinav Gorantla

Abhinav Gorantla is a researcher and software developer with a deep interest in causality, machine learning, multi-objective optimization and backend web-development. He is currently pursuing a Master of Science degree in Computer Science at Arizona State University, where he works as a Graduate Research and Teaching Assistant in the EMIT Lab. His research focuses on utilizing causal analysis to optimize traditional algorithms.

Abhinav has contributed to award-winning projects like CausalBench, a flexible benchmarking solution for causal ML algorithms. His academic journey is complemented by extensive practical experience in building research tools and developing full-stack applications.

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Education

Experience

  • August 2024 - Now: Graduate Research Assistant at Emitlab, School of Computing and Augmented Intelligence, ASU
    • Developing an optimized algorithm for efficient Skyline retrieval in relational database systems.
    • Collaborating with researchers at CASCADE Lab to maintain and improve causalbench.org, a platform dedicated to causal discovery benchmarks.
  • August 2024 - Now: Graduate Teaching Assistant at Emitlab, School of Computing and Augmented Intelligence, ASU
    • Assisting in CSE515 - Multimedia and Web Databases, a graduate-level course taught by Prof. K. Selcuk Candan.
    • Conducting bi-weekly office hours to provide support on course material and address student questions.
    • Facilitating project demos and offering guidance to students on project development and best practices.
  • March 2024 – August 2024: Graduate Services Assistant at Emitlab, School of Computing and Augmented Intelligence, ASU
    • Supported CASCADE Lab researchers, under the mentorship of Dr. K. Selcuk Candan and Dr. Huan Liu, in developing the causalbench Python package and website, establishing an end-to-end benchmarking solution for the causal machine learning community.
    • Served as a full stack developer on the CausalBench project, contributing to a comprehensive framework for benchmarking causal machine learning algorithms.
    • Optimized backend architecture for the Skysong project, enhancing data flow efficiency and achieving an 80% improvement in server response time. Reduced deployment costs by 30% by integrating AWS SageMaker.
  • April 2022 – June 2023: SDE Intern at Webknot Technologies Pvt. Ltd.
    • Revamped API endpoints within the Palette project, achieving a notable 30% reduction in response times.
    • Engineered a custom plugin for Sisense BI software, enabling the seamless display of geojson data on a GeoJSON layer atop maps rendered via DeckGL.
    • Fine-tuned data flow for the DeckGL plugin within Sisense by elevating the efficiency of JAQL queries, ensuring a smoother and more responsive user experience.

Publications

  1. Introducing CausalBench: A Flexible Benchmark Framework for Causal Analysis and Machine Learning [website]

Projects

  1. Research Publications Analysis tool
    • Proposed an architecture and built a research publications analysis tool for ASU. This tool was built as a web application which could fetch research paper information affiliated with ASU using SCOPUS APIs and perform a text analysis on their abstracts.
    • Reduced the server response time by 80% and improved the user experience by integrating RabbitMQ message queues in the system.
    • Tech stack used: ReactJS, NodeJS, Python-FastAPI, RabbitMQ, MongoDB, AWS S3, AWS Sagemaker, OpenAI APIs.
  2. Multimodal Image Retrieval System using Advanced Feature Analysis and Search Techniques
    • Developed a Python-based image retrieval engine encompassing feature extraction from Caltech101 dataset images, latent semantics computation, clustering, and classification.
    • Employed Locality Sensitive Hashing to index image features, optimizing nearest neighbor searches and ensuring scalability for expansive image datasets.