PhD Student, Statistics and Data Science, Northwestern University
I am a third-year Ph.D. student in Statistics and Data Science at Northwestern University, working with Prof. Bradly C. Stadie. My research sits at the intersection of statistics, machine learning, and large language models.
I study how to make LLMs more trustworthy and reliable when they are used for prediction and reasoning—especially failure modes such as hallucination, temporal contamination in evaluation, and explanations that look fluent but may not track the model’s actual decisions. I am particularly interested in interpretation of LLMs and in methods that help us audit, constrain, and improve model behavior under realistic deployment conditions.
| May 25, 2026 | Our new preprint on catching models that “peek” at the future is live on arXiv—with code to reproduce the claim-level leakage audit and TimeSPEC mitigation. |
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| May 13, 2026 | TEMPO is out: a training approach that teaches LLMs temporal discipline for trustworthy backtesting. Code released. |
| Jan 22, 2026 | Thrilled that LAMP was selected as a Spotlight at AISTATS 2026—a lightweight way to audit whether a model’s explanations behave like they matter. |
A central challenge in evaluating an LLM’s prediction ability is that testing on the true future is often impractical. The standard alternative is backtesting: ask the model historical questions under a cutoff date, as if it were forecasting from that earlier time. The hard problem is temporal leakage—a special form of data contamination in which post-cutoff knowledge from pretraining seeps into the model’s reasoning and inflates backtest scores. My work treats this problem as a whole. On the evaluation side, a statistical theory shows the inflation is systematic rather than random, proves it cannot be separated from genuine skill using backtest scores alone, and restores a trustworthy, leakage-adjusted score through practical external references. On the mitigation side, an inference-time architecture blocks leaked claims from the model’s output without retraining—paired with an interpretable claim-level metric that quantifies how much of the decision-driving reasoning is contaminated—while an RL post-training method teaches the model temporal discipline itself.
LLMs often give fluent explanations for their predictions, but fluency is not faithfulness. The hard question is whether the factors a model cites actually behave like they drive the decision—and how to check that without gradients or internal access. That makes interpretation both practically needed for trust and scientifically interesting as a window into model behavior.
