Abhinav Gorantla

Abhinav Gorantla

Ph.D. student in Computer Science, ASU

Abhinav Gorantla

I am a Ph.D. student in Computer Science at Arizona State University, working with Prof. K. Selçuk Candan in EMIT Lab. Broadly, I study how ideas from causality can improve machine learning. During my M.S. at ASU (completed with distinction), I contributed to CausalBench, an open benchmarking platform for causal ML (Best Demo Award, CIKM 2024; tutorial at KDD 2025), and co-authored work on causally-informed data retrieval at SIGMOD 2026. I’m currently investigating how causal structure can be leveraged to improve the robustness and generalization of ML models.

Research

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

  • Causal structure for ML generalization. Investigating how causal priors can improve the robustness and generalization of ML models.
  • 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, Jan 2026 - Present
    • 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. P. Mandal, A. Gorantla, K. S. Candan, and M. L. Sapino.
    “Causal Search for Skylines (CSS): Causally-Informed Selective Data De-Correlation.”
    Accepted to the ACM SIGMOD/PODS International Conference on Management of Data (SIGMOD 2026).

Proposes a selective de-correlation method to optimize time complexity of Skyline search algorithms. [preprint]

  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.

    Extends CausalBench with causally-informed explanations and recommendations to help researchers better understand and reproduce their results. [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.

    Overview of causal machine learning, challenges in benchmarking, and hands-on use of the CausalBench platform.
    [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)

    Introduces an open platform that standardizes how causal learning algorithms are evaluated and compared across the research community. [paper] [website]

Community Service & Outreach

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

Awards

  • University Graduate Fellowship, School of Computing and Augmented Intelligence, Arizona State University