About Me ✨
I am a Ph.D. student at Carnegie Mellon University working on outlier detection, foundation models, LLM evaluation, and information retrieval. I study how to build reliable learning systems and how to evaluate them carefully across real-world settings.
My work focuses on robust outlier detection, foundation models for structured data, evaluation of LLM behavior and consistency, and retrieval methods that improve information access in long-context and open-domain settings.
I am advised by Leman Akoglu and Pradeep Ravikumar .
- Outlier detection
- Foundation models
- LLM evaluation
- Information retrieval
Recent News
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May 2026
🎉 Our papers"Threshold Differential Attention for Sink-Free, Ultra-Sparse, and Non-Dispersive Language Modeling" and "Hierarchical token prepending: Enhancing information flow in decoder-based llm embeddings" are accepted to ACL 2026 main track, with oral presentations!
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May 2026
🎉 Our paper"MacrOData: New Benchmarks of Thousands of Datasets for Tabular Outlier Detection" is accepted to KDD 2026 Dataset and Benchmark Track!
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Apr 2026
🎉 Our paper"From Zero to Hero: Advancing Zero-Shot Foundation Models for Tabular Outlier Detection" is accepted to ICML 2026 as a poster!
Experience 💼
- Research Intern, UMAP, Snap Research (May 2025 - Present)
- Quant Research Intern, Bloomberg (May 2024 - Aug 2024)
- Research Intern, Capital One (Jun 2022 - Aug 2022)