M.S. in Applied Statistics (Data Science)
I hold a B.S. in Financial Mathematics and an M.S. in Applied Statistics (Data Science) from ZUEL. My research centers on Natural Language Processing, Affective Computing, and cognitive-inspired multi-agent reasoning. I have hands-on experience with open-source projects and am proficient in PyTorch, TensorFlow, LangChain, and vLLM.
I also have strong Linux-based full-stack development skills and programming experience in Python, R, and Java. I am passionate about building open-source research platforms and applying machine learning to solve real-world problems.
Developing techniques for text understanding, generation, and semantic analysis.
Research on emotion recognition, sentiment analysis, and human-computer emotional interaction.
Cognitive-inspired reasoning and communication optimization for multi-agent collaboration.
Chain-of-thought reasoning, prompt engineering, and LLM agent frameworks.
Advanced statistical methods, multivariate analysis, and time series modeling.
LLM cognition, mental health AI, and human-AI interaction.
Submitted to NeurIPS 2026, First Author
Read Paper →Submitted to NeurIPS 2026, First Author
Read Paper →Submitted to EMNLP 2026, Corresponding Author
Read Paper →Findings of ACL 2026, Third Author
Read Paper →Submitted to Nature Biomedical Engineering, Fourth Author
Read Paper →IEEE Transactions on Cybernetics (SCI-Q1, CCF-B), First Author
Read Paper →Proceedings of the Cognitive Science Society (CogSci) 2025 (Vol. 47), CCF-B, First Author
Read Paper →Developed an open-source agent-native database system for multi-agent applications, unifying cognitive object storage, event-driven materialization, and structured evidence retrieval within a single runnable Go server.
Developed an open-source research platform for topic analysis in social science, integrating domain-adaptive document embeddings from Qwen-3 models with generative topic modeling workflows. Built support for zero-shot, supervised, and unsupervised embedding modes.
Built a modular platform for LLM/VLM/Agent systems across text, multimodal, retrieval-augmented generation, and tool-use tasks. Designed core components including model adapters, reasoning strategies, RAG pipelines, and evaluation modules.
Developed an open-source automation system that allows users to submit ML/DL/statistical experiment requests via email, with Claude Code handling experiment planning, code generation, execution, and result packaging.
An online memory and communication-graph optimization framework for multi-agent systems using Swarm and LangGraph. Has earned over 500+ GitHub stars. Improves deployment accuracy by about 40% over existing solutions.