CV

Education

Boston University — M.Sc. in Computer Science (GPA 3.83)
09/2023 - 05/2025

New York University — B.Sc. in Mathematics
09/2019 - 05/2023

Coursework

Mathematics: Honors Linear Algebra, Multivariable Calculus, Discrete Mathematics, Honors Analysis I & II, ODEs, PDEs, Theory of Probability, Stochastic Processes, Abstract Algebra, Number Theory
Computer Science: Operating Systems, Algorithms, Theory of Computation, Software Engineering, Quantum Information Theory, Machine Learning, Generative AI
Data Science: Statistical Machine Learning, Advanced Machine Learning, Data Visualization, Database Management

Publication

Supervisors and Collaborators: Prof. Reza Rawassizadeh, Tahereh Javaheri, Eugene Pinsky

• Wen, Q., Zeng, X., Zhou, Z., Liu, S., Hosseinzadeh, M., & Rawassizadeh, R. (2025). GradES: Significantly Faster Training in Transformers with Gradient-Based Early Stopping. Submitted to ICLR 2026.
• Wen, Q., Kochhar, P., Zeyada, S., Javaheri, T., & Rawassizadeh, R. (2025). From Clicks to Conversations: Evaluating the Effectiveness of Conversational Agents in Statistical Analysis. International Journal of Human-Computer Interaction (Accepted).
• Liu, S., Wen, Q., Chen, X., Hu, X., & Su, N. (2025). One Ocean, All Tasks: A Holistic Simulation Environment for Marine Robotics. Submitted to ICRA 2026 (Under Review).
• Pinsky, E. & Wen, Q. (2025). Estimation of Distribution Parameters by Mean Absolute Deviations of a Truncated Distribution Using Quantile Functions. Statistical Papers (Under Review).
• Khedri, K., Wen, Q., & Rawassizadeh, R. (2025). Pruning and Quantization Impact on Graph Neural Networks. (Manuscript).
• Pinsky, E. & Wen, Q. (2025). Simple Approximations and Interpretation of Pareto Index and Gini Coefficient Using Mean Absolute Deviations and Quantile Functions. Econometrics, 13(3), 30.
• Longhitano, G., Wen, Q., & Rawassizadeh, R. (2025). Tiny-HAR: Extremely Resource-efficient Human Activity Recognition Model Compression. (Manuscript).

Work Experience

Boston University, Department of Computer Science, Metropolitan College
Visiting Scholar — 01/2025 – Ongoing

• Led a team of 3 researchers to develop GradES, a novel gradient-based early stopping algorithm for transformers (1.57–7.22× faster training, +1.2% accuracy).
• Established performance benchmarks on resource-constrained wearable platforms (WearOS, iOS).
• Contributed to publications in IJHCI and Econometrics, demonstrating strong research and communication skills.

Graduate Research Assistant — 09/2023 – 01/2025

• Conducted large-scale user experiments (51+ participants) comparing conversational agents with traditional GUIs.
• Developed closed-form statistical approximations for Pareto index & Gini coefficient.
• Researched neural network compression (quantization, pruning) across MLP, CNN, RNN, Transformer architectures.
• Benchmarked models on constrained devices (WearOS, iOS) evaluating accuracy, runtime, and energy efficiency.

East China Normal University, The Extreme Optoelectromechanix Lab
Research Analyst, Physics Department — 07/2022 – 12/2022

• Designed 3-D waveguide models with >50 simulations in Lumerical software (20% performance improvement).
• Assisted in femtosecond laser mask etching for optical waveguide circuits.
• Operated silicon polishing equipment for Niobate-coated devices.

Quantium
Data Analysis Intern (Remote) — 12/2022 – 02/2023

• Processed and cleaned >200,000 transaction and customer records using RStudio.
• Identified demographic purchasing trends (35% sales difference between segments).
• Presented insights using pyramid principle and advanced data visualizations.

Skills

Mathematics & Statistics: Hypothesis Testing, A/B Testing, Regression, Time Series, Bayesian Inference, Monte Carlo Simulation, Experimental Design, Causal Inference, Cross-validation
Programming: Python, SQL, Git/GitHub, AWS (EC2, S3, Lambda), Docker, REST APIs, Pandas/NumPy, Linux/Unix, Tableau, Power BI
Machine Learning: Regression, Random Forest & Boosting (XGBoost, LightGBM), Deep Learning (CNN, RNN/LSTM, Transformers), NLP (BERT, GPT), Computer Vision, Hyperparameter Tuning, Transfer Learning, Deployment & Optimization
Languages: GRE 328

Awards & Funding

• Artificial Intelligence & Computer Vision Lab Student Research Grant, Boston University

Projects & Competitions

• Fine-Tuning Stable Diffusion for Personalized Image Generation — Enhanced personalization using DreamBooth & AUTOMATIC1111.
• 3D Model Creation with Gaussian Splatting — Produced high-resolution models with OpenSplat + Colmap.
• LLM-Assisted Data Analysis Tool — Integrated Phi-3 LLM into Python data analysis UI for real-time guidance.
• Grover’s Algorithm on IBM Q — Implemented a quantum search algorithm in a 3-qubit environment.
• Atomic Clocks Optimization via GLS — Proposed precision improvements using Python-based GLS.
• Blockchain for Smart Cities — Explored encryption techniques for urban data security.
• Airline Database Project with Flask — Developed full-stack web app with MySQL backend.