Hello! 👋

I'm Zixuan Song

Neuroscience, Computer Science, and Mathematics

About Me

I work across experimental and computational neuroscience, combining molecular and in vivo laboratory techniques with behavior analysis, machine learning, and data engineering. My work spans mouse colony management, genotyping, Western blotting, real-time PCR, and IVIS workflows, as well as behavior tracking with DeepLabCut and YOLO, large-scale model optimization, and quantum-classical workflows in TensorCircuit.

Python C++ R Java JavaScript PyTorch TensorFlow Flask React (Ant Design) Git Docker Linux DeepLabCut YOLO XGBoost Transformers Mixture-of-Experts Hyperopt t-SNE/UMAP Time-Series Analysis RAG Data Engineering MLOps Model Optimization LMM/ANOVA Tukey HSD IBM LSF Kubernetes AWS WebSocket Asynchronous Programming Distributed Systems Statistical Modeling Data Visualization TensorCircuit Quantum ML Metal API In Vivo Electrophysiology Behavioral Experiments Animal Handling Mouse Colony Management Tail-Tip Tissue Collection PCR Gel Electrophoresis Western Blotting Cell Culture Bacterial Culture Rodent Surgery FFPE Tissue Processing Mouse Tail Vein Injection Intraperitoneal Injection IVIS Imaging Real-Time PCR

Live Demos

Brain Criticality Demo

https://markso.ng/demo/criticality

Canvas Agent

https://markso.ng/demo/canvas

Alzheimer's EEG Demo

https://markso.ng/demo/eeg

Experience

Molecular and In Vivo Research in Developmental Neuroscience

The Cai Lab, University of Louisville

Aug 2026 - PresentAug 2026-Present

Managed a mouse colony, collected tail-tip tissue, and performed PCR-based genotyping using agarose gel electrophoresis.
Performed Western blot and real-time PCR analysis in support of ongoing laboratory studies.
Trained in FFPE tissue processing, intravenous injection, and IVIS imaging following intraperitoneal injection.

Data Analysis and Animal Behavioral Studies in Complex Settings

The Hengen Lab, Washington University in St. Louis

Jan 2024 - May 2026Jan 2024-May 2026

Developed and deployed an automated behavior-tracking pipeline using DeepLabCut and YOLO that integrated labeling, quality control, task assignment, new annotations, and model retraining, reducing processing time from weeks to 3 days.
Implemented and evaluated behavioral classification models, including XGBoost, Transformer, Mixture-of-Experts, Random Forest, and Logistic Regression, using Hyperopt for large-scale hyperparameter optimization and model selection.
Designed an unsupervised discovery pipeline using t-SNE, UMAP, and PCA to identify and characterize latent clusters of hunting strategies from high-dimensional behavioral features.
Validated behavioral findings using statistical methods including LMM, ANOVA, Tukey HSD, emmeans, and entropy-based analyses.
Built a simulation model of hunting behavior from observational data and validated it against experimental patterns.
Improved experimental hardware reliability and developed a new, robust software system for behavioral experiments.
Contributed to in vivo studies through tetrode construction, animal monitoring, and standardized pre- and post-experiment handling.
Built visualization and reporting tools, including an interactive website, for a conference presentation at NEXTEN 2024.

Algorithm Design and Application of the Brain Criticality Hypothesis

The Hengen Lab, Washington University in St. Louis

Jan 2024 - May 2026Jan 2024-May 2026

Analyzed neural recordings from Alzheimer’s disease mouse models through the Brain Criticality Hypothesis using DCC, d2, and d_beta metrics, and examined relationships with experimental covariates including sleep deprivation and age.
Contributed to the development and optimization of the lab’s d2 Python implementation, correcting numerical edge cases and improving reliability in previously untested regimes.
Designed and benchmarked a d_beta algorithm for criticality analysis and developed validation procedures for metric computation.
Achieved over 90% accuracy in early disease-onset prediction using criticality-derived features while reducing execution time by more than 99% through algorithmic and implementation-level optimization.
Built an interactive demonstration website for criticality analysis.

Grader and Help Room Assistant

SDS 2020, Washington University in St. Louis

Aug 21, 2024 - Dec 17, 2025Aug 21, 2024-Dec 17, 2025

Designed grading rubrics, evaluated student work, and provided clear, consistent feedback.
Helped students understand concepts, develop problem-solving approaches, and monitored exams.

Robustness of Information Aggregation in LMSR-Based Prediction Markets

CSE 5106 Multi-Agent Systems, Washington University in St. Louis · Supervised by Prof. Yevgeniy Vorobeychik

Jan 2026 - May 2026Jan 2026-May 2026

Implemented a lightweight LMSR market maker and end-to-end prediction market simulator in Python using NumPy, producing reproducible CSV/JSON outputs and Matplotlib visualizations.
Built a heterogeneous set of trading agents, including informed (Bayesian), risk-averse, noise, momentum/mean-reversion, and adversarial strategies.
Defined robustness and convergence metrics for aggregation quality, including KL divergence, total variation distance, convergence time, and post-attack recovery time.
Added market defenses such as transaction fees, order-size and position caps, cooldowns, and adversary active windows, along with solvency-style safeguards.
Developed a YAML-driven experiment framework with parameter sweeps, multi-seed evaluation, and suite-level summary tables in CSV and Markdown.
Wrote pytest sanity tests and structured the codebase for fast iteration and scalable batch execution.

WashU AI Hackathon Agent Project

WashU AI Hackathon

Oct 2025

Placed among the top eight of more than 350 teams; awarded a $500 prize.
Built the ReAct AI Agent for reasoning-action workflows in real-time academic assistance.
Developed model routing for OpenAI and implemented an asynchronous backend integrating 22 Canvas LMS tools and 4 vector store tools for RAG uploads.
Built UI/UX in React (Ant Design) for faster navigation and improved usability over Canvas.

Surgery, In Vivo Neural Recording, Experimental Design, Data Analysis, and Scientific Writing

Biol 404 Neurophysiology Lab, Washington University in St. Louis · Supervised by Dr. Mitchell Kundel

Aug 2024 - Dec 2024Aug 2024-Dec 2024

Designed experiments, hypotheses, and evaluation criteria, and analyzed electrophysiological measurements.
Performed mouse tracheostomy and cochlear electrode implantation and recorded neural activity under anesthesia.
Conducted crayfish tail ablation, recorded neuromuscular activity, and analyzed the resulting traces.
Generated waveform visualizations, formulated quantitative summaries, and co-authored three manuscripts.
Peer-reviewed manuscripts, refining experimental interpretation and scientific communication.
Developed surgical proficiency through repeated procedures and strengthened scientific writing through literature-based investigation.

Research Intern and Teaching Assistant

Tencent Quantum Lab

Sep 2022 - Sep 2023Sep 2022-Sep 2023

Collaborated with an international team of PhD-level researchers, contributing to TensorCircuit through feature development, documentation, and code review.
Developed and validated new TensorCircuit functionality with pytest-based unit tests to strengthen reliability and integration coverage.
Assisted release engineering for macOS Apple Silicon builds by resolving packaging dependencies and implementing a Metal API backend.
Optimized tensor contraction performance in C++ and Python bindings, improving throughput by over 30%.
Implemented error-mitigation algorithms (HAMMER, zero-noise extrapolation) on NISQ hardware for improved stability and fidelity.
Led a 12-member training initiative on quantum computing and quantum-enhanced ML, including seminar and onboarding design.

Research Intern

Tsinglan Bio-Adv Lab

Oct 2022 - Aug 2023Oct 2022-Aug 2023

Researched and synthesized melanin-combined photosensitizers.
Evaluated antibacterial effects with SOSG test analysis and wet-lab workflows.
Applied DNA sequencing, PCR, and cell culture techniques in experimental studies.

Publications

Information preference and information supply efficiency evaluation before, during, and after an earthquake: Evidence from Songyuan, China

2021

Li, S., Peng, X., Pang, R., Li, L., Song, Z., & Ye, H.

International Journal of Environmental Research and Public Health 18, 13070 (2021)

Hunting in the Urban Jungle: Unveiling Complex Predatory Behaviors in Mice Through Ecologically-Inspired Environments

Sep 2024

Schneider, A.*, Chitalia, J.*, Song, Z. (M.)*, & Hengen, K.

Poster presented at NEXTEN 2024, St. Louis, MO (September 16, 2024)

*Denotes co-first authorship

TensorCircuit-NG: A Universal, Composable, and Scalable Platform for Quantum Computing and Quantum Simulation

Feb 2026

Zhang, S.-X., et al. (including Song, Z.)

arXiv preprint arXiv:2602.14167 [quant-ph] (2026)

Environmental complexity reveals memory-guided search as the locus of learning in prey capture

Jul 2026

Schneider, A. M., McGregor, J. N., Song, M., Amme, J. L., Zheng, S., Wu, D., Tu, J., Yao, G., Eslinger, E., Chitalia, J., Powers, J., Sinha, V., Dyer, E. L., Levenstein, D., & Hengen, K. B.

bioRxiv preprint 2026.06.28.735138 (2026)