Austin Senna Wijaya

Computer Science @ Columbia University

prof_pic.png

New York, NY

asw2215@columbia.edu

I’m an undergraduate at Columbia University studying computer science. Most of my work is on agentic systems and retrieval, right now through agent-based retrieval and question-answering research at the Columbia Data, Agents, and Processes Lab (DAPLab). I’m also interested in machine learning, data infrastructure, and software observability.

experience

news

Sep, 2026 I presented SANA: What Matters for QA Agents over Massive Data Lakes? at VLDB DASHSys 2026! Annotated slides are here, and I wrote the work up on the DAPLab blog.
Aug, 2026 MINA: Linear Probes Reveal Coding-Sequence Family Signal in Frozen DNA Encoders was accepted at MLCB 2026!
Jul, 2026 SANA: What Matters for QA Agents over Massive Data Lakes? was accepted at VLDB DASHSys 2026!
Jun, 2026 I joined Garner Health as a Software Engineer Intern!
May, 2026 LakeQA: A Benchmark for Complex Exploratory QA over a Million-Scale Data Lake was accepted at ICML 2026!
Apr, 2026 ResearcherX was a finalist at the Columbia Millard Chan Technology Startup Competition and took 2nd place at the Columbia Lion Cage Startup Competition!
Dec, 2025 I joined the Columbia Data, Agents, and Processes Lab (DAPLab) as an AI Research Assistant!

selected publications

  1. LakeQA: An Exploratory QA Benchmark over a Million-Scale Data Lake
    Haonan Wang, Jiaxiang Liu, Yurong Liu, Austin Senna Wijaya, Tianle Zhou, Eden Wu, Yijia Chen, Wanting You, Reya Vir, Daniela Pinto, Grace Fan, Yusen Zhang, Juliana Freire, and Eugene Wu
  2. SANA: What Matters for QA Agents over Massive Data Lakes?
    Austin Senna Wijaya, Jiaxiang Liu, Haonan Wang, and Eugene Wu
  3. MINA: linear probes reveal coding-sequence family signal in frozen DNA encoders
    Austin Senna Wijaya and Hayden Leung

selected projects

  • ResearcherX: local-first AI research IDE that answers questions against a bounded source library with graph-based RAG, flagging unsupported claims, contradictions, and citation drift in drafts. Finalist, Millard Chan Technology ‘99 Startup Competition; 2nd place, Columbia Lion Cage Startup Competition.
  • LakeAgent: deep-research system for answering analytical questions over structured and unstructured data lakes with provenance-backed outputs.