Dacheng Shen

Dacheng Shen

Computer Science Ph.D. Student

Passionate about AI, Machine Learning, and NLP. Currently pursuing my Ph.D. at Washington State University, building intelligent systems that solve real-world problems.

🔬 View Research 💡 My Projects

🎓 Education

Washington State University, Tri-Cities Jan 2026 - Present
Ph.D. in Computer Science
University of Southern California June 2024 - Dec 2025
M.S. in Computer Science
University of Connecticut Sept 2020 - May 2024
B.S. in Computer Science
Dean's List Honors

💼 Experience

Software Development Engineer Jul 2023 - Aug 2023
HICOCA Intelligent Equipment Technology
  • Maintained web server backends and optimized database operations, ensuring data integrity, security, and system stability.
  • Developed and integrated new client-side UI features into the existing framework, significantly improving overall system functionality and user experience.

📄 Publications, Preprints & Presentations

A Simplex-Inspired Architecture for Integrating Quantum Capabilities into Cyber-Physical Systems 🔗 Mar. 2026
Poster Presentation — 2nd HAIQ Workshop, Pittsburgh, PA
  • Tamim Ahmed, Dacheng Shen, Mengyu Liu, and Monowar Hasan. Poster presented at the 2nd HAIQ Workshop, Pittsburgh, PA, Mar. 2026.

🔬 Research

LLM-Guided Power Grid Contingency Mitigation Jan. 2026 - Apr. 2026
Research Assistant / Ph.D. Researcher
  • Designed an LLM-guided decision framework for post-contingency power grid restoration under the N-1 reliability criterion, targeting overload mitigation in emergency operating conditions.
  • Integrated large language models with AC-OPF-based verification and runtime assurance checks to generate, validate, and refine sparse generator redispatch actions.
  • Developed and evaluated the framework on IEEE benchmark power systems under both in-distribution and out-of-distribution contingency scenarios, comparing against OPF-based and rule-based baselines.
  • Demonstrated improved restoration success rate and reduced control cost under safety constraints, highlighting the feasibility of LLM-assisted decision support for cyber-physical power systems.
PyHazards: AI-Powered Hazard Prediction Framework 🔗 Sep. 2025 - Jan. 2026
Research Assistant, FORTIS Lab at USC (PI: Prof. Yue Zhao) 🔗
  • Engineered and integrated Transformer-based prediction learning algorithms into PyHazards, an open-source Python toolkit for natural hazard forecasting.
  • Developed scalable machine learning modules for complex data processing, evaluating the Transformer model's predictive performance against various public baselines.
  • Collaborated with the RAI Lab research team to standardize the AI prediction pipeline, significantly enhancing the framework's overall accuracy and scalability.
Comparative Narrative Analysis with Claude 3.7 Mar. 2025 - Jun. 2025
Research Assistant, FORTIS Lab at USC (PI: Prof. Yue Zhao) 🔗
  • Designed and automated a full Claude 3.7 Sonnet-based LLM evaluation pipeline for 208 narrative pairs under four comparison dimensions (conflict, unique, holistic, overlapping)
  • Engineered scalable prompt-based inference using anthropic API and batched output parsing, reducing manual judgment workload by over 90%
  • Structured model responses into JSON and human-readable formats to enable systematic cross-model analysis and future human annotation
  • Collaborated with faculty and research team on dataset preparation, narrative extraction, and designing metrics for minimizing bias in LLM comparison
OEM Controls Automated Angle Test for AS5 🔗 Aug. 2023 - May. 2024
Capstone Project Lead
  • Led a year-long capstone project to automate Quality Assurance testing for the AS5 sensor system, significantly improving testing efficiency and reliability
  • Developed a Python-based control system on Ubuntu to operate a 6-axis robot arm, automating sensor calibration and test procedures with precise "Pass/Fail" feedback and real-time result logging
  • Designed and implemented a seamless UI-Backend integration using APIs, replacing the previous client version with a fully automated calibration solution
  • Successfully delivered the project, reducing manual testing time by 67% and improving calibration accuracy by 26%, which enhanced overall efficiency and reliability

📚 Projects

Transfer Learning for Imbalanced Waste Classification 🔗 Jan. 2025 - May 2025
  • Conducted a controlled ablation of three transfer learning strategies on VGG16 over the 9-class RealWaste dataset, with stratified 64/16/20 splits and a custom classification head.
  • Compared frozen-backbone vs. block5 fine-tuning, and inverse-frequency class weights vs. RandomOverSampler for long-tailed correction; fine-tuning with cost-sensitive weights raised test accuracy from 60.6% to 69.9% and Macro F1 by +10.3 pp.
  • Performed confusion-matrix-based error analysis identifying persistent fine-grained boundaries (Plastic/Metal, Paper/Cardboard) and proposed a class-selective hybrid imbalance strategy as follow-up.
AI-Driven Travel Assistant Design – FlySmart Mar. 2025 - May. 2025
  • Led a requirements engineering project to design an AI-powered flight booking assistant
  • Conducted stakeholder analysis, surveys, and interviews, derived user personas, empathy maps, and categorized requirements
  • Created and validated a Figma prototype with features including flight search, visa management, smart alerts, and personalized AI chat interface
  • Applied agile methodology with story-driven backlog, sprint-based development roadmap, and formal validation via usability testing and prototyping
Semantic Retrieval and QA System with Weaviate & RAG Sep. 2024 - Dec 2024
  • Built a semantic search pipeline using Weaviate with text2vec-transformers for vector-based brand similarity via GraphQL nearText queries
  • Developed a PDF QA chatbot with Streamlit, leveraging PyPDFLoader, ChromaDB, and Hugging Face embeddings in a RAG framework for document-grounded response generation
Large-Scale Sentiment Analysis on Amazon Office Reviews 🔗 Aug. 2025 - Oct. 2025
  • Cleaned and normalized 200k+ review texts (regex HTML/URL removal, tokenization, stopword filtering, lemmatization)
  • Trained TF-IDF (uni + bi-gram) ML models including LinearSVC, Logistic Regression, and Perceptron
  • Reproduced and evaluated two GloVe-based representations: 100-d average pooling vs. 1000-d first-10-token concatenation
  • Demonstrated that 100-d average pooling generalizes more robustly across unseen reviews
Student Admin Design Sep. 2023 - Dec. 2024
  • Collaborated on a group project, using Figma to design the product model and present the plan to stakeholders
  • Developed and implemented the code to bring each Figma design feature to life
  • Completed the website development and thoroughly tested all functionalities
Food Ordering App Design Aug. 2021 - Nov. 2021
  • Used Java to build a food ordering app that could generate an invoice with the name and price of the dish and the total price after ordering
  • Group leader, used GitHub to share and merge information and parts of what each group member had finished
  • Completed a report and made a presentation

🔧 Skills

💻

Programming Languages

Python C# Java C/C++ SQL Bash/Shell HTML
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AI/ML & NLP

PyTorch TensorFlow/Keras scikit-learn Hugging Face Transformers Transfer Learning RAG Semantic Search Prompt Engineering NLTK Gensim
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Domain & Research

Cyber-Physical Systems Reinforcement Learning Time-Series Forecasting
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Data & Databases

NumPy Pandas MySQL MongoDB ChromaDB Weaviate GraphQL Data Preprocessing
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Software Engineering

REST APIs OOP Design Patterns Modular Architecture Agile (Scrum) Git
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Tools & Platforms

Linux/Ubuntu Docker AWS Streamlit Jupyter Figma