Abhinav Gorantla

Abhinav Gorantla

Ph.D. student in Computer Science, ASU

Abhinav Gorantla

Abhinav Gorantla is a Ph.D student in Computer Science at Arizona State University, working with Prof. K. Selçuk Candan in the EMIT Lab. He recently completed his M.S. in Computer Science at ASU. His research focuses on algorithms and systems for causal and generative machine learning, with an emphasis on causal benchmarking frameworks, multi-objective optimization (Skyline/Pareto-style selection).

He also has experience as a full-stack web developer and applied ML engineer. He is a co-author of CausalBench, a flexible benchmarking platform for causal machine learning that received the Best Demo Award at ACM CIKM 2024.

Research

My research lies at the intersection of causal machine learning, multi-objective optimization, and generative models. I am especially interested in:

  • Causal benchmarking and systems. Designing and maintaining CausalBench, a benchmarking framework and service for causal learning algorithms (causal discovery, causal inference, and interpretability).
  • Multi-objective optimization & skyline queries. Studying efficient Skyline/Pareto-style retrieval in relational data, including how causal structure can impact efficiency of Pareto-style retrieval algorithms.

Education

  • Doctor of Philosophy in Computer Science, Arizona State University, starting Jan 2026 (incoming)
    • Advisor: Prof. K. Selçuk Candan
  • Master of Science in Computer Science, Arizona State University, Dec 2025
    • GPA: 4.00/4.00
    • Advisor: Prof. K. Selçuk Candan
  • Bachelor of Technology in Computer Science and Engineering, Vellore Institute of Technology, May 2023
    • GPA: 8.98/10.00

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.
    • Built a causal-analysis recommendation system; led end-to-end integration and serverless deployment on AWS Lambda; published at CIKM 2025.
  • August 2024 - May 2025: Graduate Teaching Assistant at School of Computing and Augmented Intelligence, ASU
    • Assisted in CSE515 and CSE510 graduate-level computer science courses.
  • March 2024 – August 2024: Graduate Services Assistant at Emitlab, School of Computing and Augmented Intelligence, ASU
    • Helped develop the causalbench Python package and causalbench.org website as an end-to-end benchmarking solution for the causal machine learning community.
    • Served as a full-stack developer on CausalBench, integrating datasets, models, and metrics into a unified evaluation workflow.
    • Redesigned the backend architecture for the Skysong project, enabling responsive causal analyses in production (≈80% faster response times, ≈30% lower deployment cost via 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.
    • 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.
    • Engineered a custom plugin for Sisense BI software, enabling the seamless display of geojson data on a GeoJSON layer atop maps rendered via DeckGL.

Publications

  1. A. Kapkıç, P. Mandal, A. Gorantla, S. Wan, E. Çoban, P. Sheth, H. Liu, and K. S. Candan.
    “CausalBench-ER: Causally-Informed Explanations and Recommendations for Reproducible Benchmarking.”
    Proceedings of the 34th ACM International Conference on Information and Knowledge Management (CIKM ’25),
    Seoul, Republic of Korea, Nov 10–14, 2025, pp. 6426–6431.
    [paper]
  2. A. Kapkıç, P. Mandal, A. Gorantla, S. Wan, E. Çoban, P. Sheth, H. Liu, and K. S. Candan.
    “CausalBench: Causal Learning Research Streamlined.”
    Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’25), Vol. 2,
    Toronto, Canada, Aug 3–7, 2025, pp. 6239–6240.
    [paper] [tutorial site] [slides] [video]
  3. A. Kapkıç, P. Mandal, S. Wan, P. Sheth, A. Gorantla, Y. Choi, H. Liu, and K. S. Candan.
    “Introducing CausalBench: A Flexible Benchmark Framework for Causal Analysis and Machine Learning.”
    Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM 2024), pp. 5220–5224, 2024. (🏆 Best Demo Paper Award)
    [paper] [website]

Tutorials

  • CausalBench: Causal Learning Research Streamlined – Tutorial at KDD 2025, Toronto, Canada.
    Overview of causal machine learning, challenges in benchmarking, and hands-on use of the CausalBench platform.
    [tutorial site] [slides] [video]

Community Service & Outreach

  • Student Volunteer, ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2025)

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.
  3. Enhancing Diversity in the LLM Modulo Framework through Multi-Response Generation
    • Developed the Diversified LLM Modulo framework to address looping and redundancy in the LLM Modulo framework.
    • Improved the performance of the LLM Modulo Framework on Planning tasks. Tested my framework on the Google Deepmind Natural Plan benchmark and achieved a performance improvement of 300% by increasing the diversity of LLM (Large Language Model) Responses.