Aarush
Agarwal

Researching dynamic Mixture-of-Experts architectures under Chenyan Xiong, developing adaptive strategies that expand model capacity on out-of-distribution data while mitigating reasoning degradation in continual pretraining.

Designing autonomous research agents that propose, execute, and evaluate model-adaptation experiments across tasks and modalities including vision, clinical, and financial time-series data, iteratively committing variants that improve reasoning and task-benchmark performance over dense backbones.

Search Relevance: Developing ranking systems for Shopify's commerce search stack. Fine-tuning and distilling LFM 2.5 models, then deploying them for low-latency DNN inference on custom Triton serving infrastructure.

Merchant-Aware Ranking: Designed and implemented a novel auxiliary merchant-aware training objective that teaches the ranker to prioritize a merchant's first-party catalog for merchant-intent queries. Improved a core merchant-search relevance metric by 10% while reducing the ranking prominence of third-party resellers.

Selected as a Venture Fellow in a highly competitive program focused on leveraging AI and technology for real-world impact.

Conducted startup diligence and market research across AI and emerging technology. Co-organized VentureHacks, a Felicis × CMU hackathon that attracted 500+ applicants and awarded $10K+ in prizes, with speakers including Felicis partners and a founding researcher at Skild AI.