Hello! 👋

My name is Shuaidong Zhao, a Principal Software Architect in Digital Solutions at Abbott, focused on AI Product Development.

I am a seasoned technical leader with over 10 years of experience in AI/ML, data science, quantitative analytics, cloud computing, and software development. Proven expertise in life sciences, medical devices, energy markets, and intelligent transportation systems (ITS), delivering innovative, data-driven solutions that drive business growth and operational excellence.

Experience 📜

Principal AI/ML Software Architect

Abbott Digital Solutions AI Product Development, IL
SEP 2024 – PRESENT

Architect and deploy robust AI/ML infrastructure tailored for cardiological applications
Design and develop a comprehensive data lakehouse of device and procedure data, enabling advanced analytics and ML research
Implement platform-agnostic software solutions for cardiological disease prediction
Lead cross-functional teams in AI project planning and execution, while effectively engaging stakeholders

Staff Software Developer, AI Lead

Abbott Labs Advanced Tech Center, MN
APR 2021 – SEP 2024

Built Vision Language Model as a first-step diagnosis for arrhythmia using ECG/EGM data
AI/ML Lead, filed 4 patents and built Deep Learning based mapping techniques
Tech Lead / SCRUM Master, 3D mapping model enhancement project with $200M funding
Designed and patented catheter navigation system, contributing 14% revenue growth ($2.2B)
Developed high-performance data pooling platform to achieve real-time data transmission up to 2000 Hz
Designed data warehousing platform for real-time data storage

Sr. Quantitative Analyst, Data Scientist

National Grid USA, NY
JAN 2019 – APR 2021

Developed Deep Learning models to energy market demand planning, saving operation costs > $1M
Developed a large-scale agent-based vehicle routing simulator for energy forecasting, securing a 5-year $3.3M funding
Designed data warehousing based on AWS Cloud services for scalable simulations and data storage

Researcher, Software Developer

Michigan Technological University, MI
2014 – 2018

Developed a data-driven optimization framework for connected and automated vehicles, securing a $2.8M USDOE funding and a $0.5M NSF funding
Applied risk-management into vehicle fleet scheduling, presented to US Senator Gary Peters
Deployed Hadoop for high-volume traffic data storage at ITS lab, maintaining > 64 TB traffic monitoring data
Developed a Deep Learning model to identify moving vehicles from drone videos, presenting a traffic detection tool to MDOT
Collaborated with General Motors to deploy cloud communication and computation system
Designed an optimization model for placing traffic monitoring sensors, reducing traffic operation costs

Selected Skills ⚙️

Advanced Computation

3D Data Processing
3D Geographic Data, 3D Voxelization, 3D Mesh Processing

Data Structures & Parsing
Spatial Indexing, XML/JSON Parsing

High-Performance Computing
Concurrency Computation, Multithreading, GPU Programming

Mathematics & Algorithms
Quaternions, 3D Mathematics, Numerical Optimization, Graph Algorithms

Simulation & Modeling
Physics-Based Simulation, Digital Signal Processing, 3D Rendering, Animation

Applied Math

Control Systems & Network Theory
Control Theory, State-Space Modeling, Stability Analysis, Network Theory

Linear Algebra & Optimization
Linear Algebra, Linear and Nonlinear Programming, Convex Optimization

Numerical Methods
Matrix Factorization, Numerical Differentiation & Integration, path-Finding Algorithms

Stochastic Processes & Decision Making
Markov Decision Processes, Bayesian Inference, Agent-Based Simulation, Risk Analysis

Applied Math

Control Systems & Network Theory
Control Theory, State-Space Modeling, Stability Analysis, Network Theory

Linear Algebra & Optimization
Linear Algebra, Linear and Nonlinear Programming, Convex Optimization

Numerical Methods
Matrix Factorization, Numerical Differentiation & Integration, path-Finding Algorithms

Stochastic Processes & Decision Making
Markov Decision Processes, Bayesian Inference, Agent-Based Simulation, Risk Analysis

Data Engineering

Big Data & Distributed Systems
Apache Spark, Databricks (Delta Lake, Spark), Hadoop, Dask

Databases
SQL (PostgreSQL, MySQL), NoSQL (CosmosDB, MongoDB, Cassandra), Snowflake, BigQuery

Data Pipelines & Orchestration
Airflow, Kafka, Pub/Sub, Azure Data Factory, AWS Glue

Feature Stores
Feast, Databricks Feature Store

Data Science

Big Data Tools
Apache Spark, Hadoop, PySpark, Dask

Data Analytics & Visualization
Pandas, NumPy, Matplotlib, Seaborn, Plotly

Geospatial Analysis
Spatial Analysis, GIS Tools

Signal Processing
FFT, Digital Signal Filters, Wavelet Transforms, Time-Frequency Analysis

Statistical & Probabilistic Modeling
Bayesian Networks, Monte Carlo Simulation, Hypothesis Testing

Developer Tool

AWS
SageMaker, S3, Lambda, EKS)

Azure
ML Studio, Cognitive Services, AKS, Databricks

Databricks
Delta Lake, MLflow, Spark

GCP
Vertex AI, BigQuery, GKE, Databricks

Others
Docker, Kubernetes, Jenkins, Git, Perforce, Confluence, MLflow, CMake, VTune, Qt.

ML Libraries

Deep Learning Frameworks
TensorFlow, PyTorch, Keras, JAX, DeepSpeed

ML Frameworks
Scikit-learn, XGBoost, LightGBM

NLP Libraries
Hugging Face Transformers, Gensim

Optimization Tools
Optuna, Ray Tune, Hyperopt

Visualization Tools
Matplotlib, Seaborn, Plotly, Bokeh

ML Models

Deep Learning Architectures
CNN, RNN, Transformers, Encoder-decoder

Generative Models
Autoencoders, Variational Autoencoders (VAE), GANs

Multimodal Models
Multimodal Language Models, Vision-Language Models

Traditional ML Models
Random Forests, Gradient Boosting (XGBoost, LightGBM)

Optimization Modeling

Optimization Techniques
Linear Programming, Nonlinear Programming, Convex Optimization, Mixed-Integer Programming (MIP), Quadratic Programming

Stochastic Optimization
Robust Optimization, Stochastic Programming, Monte Carlo Simulation for Uncertainty Modeling

Dynamic & Sequential Decision Making
Markov Decision Processes, Dynamic Programming, Agent-Based Optimization

Modeling Frameworks & Solvers
CPLEX, Gurobi, MOSEK, KNITRO, GLPK, COIN-OR, SDPT3, SEDUMI, GAMS, YALMIP, CVXPY, Pyomo

Applications
Network Flow Optimization, Scheduling & Routing, Supply Chain Modeling, Resource Allocation

Programming Languages

High-Performance & System Programming
C++/C, Java, Python

Statistical & Analytical Programming
SAS, Julia, MATLAB, R

Database Querying & Management
SQL (PostgreSQL, MySQL), NoSQL (MongoDB, Cassandra)

Scripting & Automation
Bash, PowerShell

Web Development & Frameworks
JavaScript, TypeScript, HTML, CSS

Awards 🏆

2024 Abbott AI representative in EHRA congress, Berlin

2024 Abbott AI Innovation Summit Best Presentation Award

2022 Abbott Labs Software Annual Release Gold Award

2020 National Grid Gold Contribution Award

2018 Outstanding Scholar Award at Michigan Tech (1/2)

2018 Finishing Scholar Fellowship (1/3)

2018 Wilbur Haas Excellence Scholarship (1/2)

2014–18 Full Scholarship (NSF & US DOT), Michigan Tech

Education 🎓

2014 – 2018 Doctor of Philosophy (Ph.D.)

Transportation Engineering (Intelligent Transportation System) Michigan Technological University, MI

- Dissertation: Data-Driven Optimization-Based Transportation Models for Connected and Automated Vehicles
- Teaching Assistant: CEE5404 Transportation Planning; CEE5760 Optimization Methods; CEE5490 Traffic Flow Theory
- Advisor: Prof. Kuilin Zhang

2012 – 2014 Master of Urban Planning

Transportation Planning Renmin University of China, Beijing, China

Publications ✒️

Patent

Automatic Detection of Conduction Block using AI-based 3D Gaussian Edge Detector
CNN-based Automatic Detection for Termination Area of Tachycardia, 2024
Entropy-based Chaotic Zone Detection using Cardiac Voltage Data, 2024
Deep Learning-based Mapping Segmentation for Cardiac Anomaly Detection, 2024
Integrated Navigation System for Catheters in Cardiac Mapping, 2022

Journal Paper

Zhao, S., Zhang, K., 2021. Online predictive connected and automated eco-driving on signalized arterials considering traffic control devices and road geometry constraints under uncertain traffic conditions. Transportation Research Part B: Methodological, 145, pp.80-117. (Top journal in Transportation Modeling)
Zhao, S., Zhang, K., 2020. A Distributionally Robust Stochastic Optimization-based Model Predictive Control with Distributionally Robust Chance Constraints for Cooperative Adaptive Cruise Control under Uncertain Traffic Conditions. Transportation Research Part B: Methodological. 138, pp.144-178. (Top journal in Transportation Modeling)
Zhao, S. and Zhang, K., 2019. A distributionally robust optimization approach to reconstructing missing locations and paths using high-frequency trajectory data. Transportation Research Part C: Emerging Technologies, 102, pp.316-335.
Tang, Y., Zhao, S., Ten, C.W., Zhang, K., Thillainathan, L., 2019. Establishment of Enhanced Load Modeling by Correlating with Occupancy Information. IEEE Transactions on Smart Grid.
Jafari, M., Gauchia, A., Zhao, S., Zhang, K., Gauchia, L., 2018. Electric Vehicle Battery Cycle Aging Evaluation in Real-World Daily Driving and Vehicle-to-Grid Services. IEEE Transactions on Transportation Electrification, 4(1), pp.122-134.
Zhao, S., Zhang, K., 2017. Observing Individual Dynamic Choices of Activity Chains from Location-Based Crowdsourced Data. Transportation Research Part C: Emerging Technologies, 85, pp.1-22.

Conference Paper

Zhao, S., Zhang, K., 2019. A Data-Driven Optimization based Model Predictive Control for Real-Time Connected and Automated Driving on Arterials under Uncertain Traffic Conditions. The 98th Transportation Research Board Annual Meeting, Washington, D.C.
Zhao, S., Zhang, K., 2018. A Data-Driven Model Predictive Control Framework For Robust Cooperative Adaptive Cruise Control Using Mobile Sensing Data. The 97th Annual Meeting of Transportation Research Board, Washington D.C.
Zhao, S., Zhang, K., 2018. A Data-Driven Dynamic Route Choice Model Under Uncertainty Using Connected Vehicle Trajectory Data. The 97th Annual Meeting of Transportation Research Board, Washington D.C.
Zhao, S., Zhang, K., 2018. A Comprehensive Overview to Improve Traffic Flow Observability Using UAVs As Mobile Sensors. The 97th Annual Meeting of Transportation Research Board, Washington D.C.
Tang, Y., Zhao, S., Ten, C.W. Zhang, K., 2016. Enhancement of Distribution Load Modeling Using Statistical Hybrid Regression. Accepted for 2017 IEEE PES Innovative Smart Grid Technologies Conference, Washington, D.C.
Zhao, S., Zhang, K., 2017. A Data-Driven Optimization Model to Observe Individual Dynamic Choices of Activity-Travel-Path using Connected Vehicles as Mobile Sensors. The 96th Annual Meeting of Transportation Research Board, Washington D.C.
Zhao, S., Zhang, K., 2017. Observing Space-Time Queuing Dynamics at a Signalized Intersection using Connected Vehicles as Mobile Sensors. The 96th Annual Meeting of Transportation Research Board, Washington D.C.
Zhao, S., Zhang, K., 2016. Observing Individual Dynamic Choices of Activity Chains from Location-based Crowdsourced Data. The 95th Annual Meeting of Transportation Research Board, Washington D.C.

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