Talengine
AI Engineer InternBuilt a LangGraph multi-agent interview system connecting resume-based questions, LLM answers, and adaptive follow-ups in structured JSON dialogue trees. Deployed MinerU parsing and candidate-matching workflows on AWS.
Research & Engineering Portfolio
I build and evaluate machine learning models, then connect them to practical systems—from robotics perception to multi-agent LLM applications.
PhD candidate, Statistics & Data Science
University of Delaware · Expected May 2029
Advisor: Wei Qian · MS in Biostatistics, NYU

What I built, how I evaluated it, and where to explore it.
Contribution. Built a ROS 2 pipeline connecting YOLOv8n detections, camera–LiDAR fusion, vehicle-relative localization, and RViz visualization.
Evidence. Demo, technical report, and result bags with 1,305 timestamped output arrays at 0.25× replay.
Demo & reproduction guide ↗Contribution. Contributed to a team retrieval solution combining Swin Transformer, NFNet, ArcFace, TF-IDF, and perceptual hashes.
Team result. Silver medal, top 4%. Includes the archived inference notebook and dependency notes.
Notebook & pipeline ↗Contribution. Applied the existing PatchTST model to Microsoft stock prices and AEP electricity demand using supervised training and self-supervised pretraining with fine-tuning.
Evaluation. Two univariate datasets, a seven-model baseline comparison, and accuracy/runtime summaries.
Code & recorded results ↗Contribution. Compared PPO, A2C, and DDPG in an NYU team course project using rolling training and validation windows.
Evaluation. Selected models by validation Sharpe ratio before evaluating the next trading period.
Experiment & report ↗Contribution. Collaborated on missing-data handling, exploratory analysis, and LASSO feature selection in R.
Evaluation. Compared models across sampling ratios using 858 patient records.
Analysis & report ↗Methods. Text preprocessing and BERT representations for hate speech classification.
Evaluation. Baseline model comparisons and ROC analysis.
Explore the project ↗More work: Graph Learning Prototypes · SQL Portfolio
Doctoral Research
My doctoral research focuses on supervised dimension reduction, sparse modeling, and variational autoencoders, using simulation, ablation studies, and Python/PyTorch pipelines on HPC infrastructure.
STAT 802 · Technical Review
A technical review connecting latent-variable precision matrix decomposition, convex optimization, geometric identifiability, and statistical consistency. Includes an attributed discussion of the reference paper’s experiments.
Read the report & source ↗Built a LangGraph multi-agent interview system connecting resume-based questions, LLM answers, and adaptive follow-ups in structured JSON dialogue trees. Deployed MinerU parsing and candidate-matching workflows on AWS.
Integrated OpenAI APIs for conversational English practice, deployed a serverless text-to-speech workflow with Azure Functions, and supported Supabase databases and APIs.
PyTorch, transformers, VAEs, regression, classification, feature selection
LangGraph, multi-agent orchestration, OpenAI APIs, structured JSON outputs
Python, R, SQL, SAS, pandas, NumPy
AWS EC2/S3/Lambda, Azure Functions, PostgreSQL/Supabase, Linux, Shell
Controlled simulations, ablation studies, chronological validation, model benchmarking
For internship opportunities and research conversations.