• Designed and deployed a probabilistic ETA prediction model using DoorDash’s NextGen architecture, replacing single-point baselines with non-parametric distribution-based forecasts to capture uncertainty.

• Led multiple iterations of Base Layer model experiments, improving MAE by 3% and CRPS by 7% over production benchmarks.

• Explored and evaluated 20+ modeling strategies and distance-aware loss functions for predicting non-parametric distributions to balance accuracy, long-tail performance, and reliability at scale.

• Optimized Decision Layer model parameters through grid search, raising On Time Accuracy by 0.41% and reducing 20-min Lateness by 3.1% without degrading other business metrics.

• Delivered two superior model candidates launched as online experiments serving 5M+ daily orders, now progressing toward full production rollout.

• Led full-stack development for an RAG-based conversational search engine and an LLM-driven Slack Bot, enabling real-time intelligent query handling for enterprise users.

• Developed an LLM-based Slack Bot, delivering context-aware responses (drawn from search results, chat history, and file attachments).

• Enhanced Lepton Search by integrating SearXNG as search backend and incorporating WizardLM and Llama3.

• Optimized query concurrency and data pipelining, reducing average LLM response times

while supporting concurrent user queries.

• Containerized Slack Bot server with Docker and deployed both SearXNG backend and Slack App on Kubernetes.

• Pioneered a real-time BCI signal classification pipeline, enabling rapid detection and visualization of neural responses with minimal latency.

• Extended OpenBCI codebase to handle real-time signal data ingestion and filtering from OpenBCI

headsets.

• Crafted a CNN-based model in ONNX to classify P300 wave signals at 82% accuracy.

• Incorporated classifier into OpenBCI GUI, ensuring live feedback within 100ms latency.

• Spearheaded development and integration of advanced Computer Vision pipelines (YOLOv7) and Neural Rendering (Nerfstudio) into full-stack solutions, boosting detection and interactive 3D capabilities.

• Adapted Nerfstudio into a Flask–React web interface.

• Developed and fine-tuned a YOLOv7-based custom hand detector for motion-blurred videos.

• Integrated legacy PyTorch code with MindSpore.

• Led data collection, hyperparameter tuning, and environment setup for projects using YOLO, MANO, ViT, and NeRF.

• Contributed to a distributed inference framework for deep learning models using PyTorch and Node.js, focusing on partition algorithms, performance optimization, and scalability.

• Designed and implemented a partition algorithm for convolution layers in distributed YOLOv5 inference.

• Applied linear scheduling to distributed inference tasks, cutting overall latency by 41% on various models.

• Collaborated on a Node.js-based orchestration module with Prof. Li and Ph.D. students, ensuring seamless scaling and communication for large-scale AI workloads.