Sicong Huang

Sicong Huang

PhD Student at TAMU, ML for healthcare researcher

About Me

I’m Sicong Huang, a proud member of STMI lab, advised by Dr. Bobak Mortazavi.

Table of Contents

About Me

I’m currently a pursuing PhD in Computer Science at Texas A&M University, a member of STMI lab, advised by Dr. Bobak Mortazavi.

I am a passionate and dedicated researcher specializing machine learning for clinical and remote health applications, with a primary focus on enhancing remote-sensing data quality, accurately monitoring cardiovascular diseases remotely, and tackling data heterogenity in clinical settings. I am driven by the potential of leveraging cutting-edge technology to make a positive impact on patient outcomes and revolutionize the way healthcare is delivered.

Specifically, I am deeply interested in time-series analysis and its application to cardiovascular data. By harnessing the power of machine learning algorithms, I aim to extract meaningful insights from complex pulsatile physiological signals, such as Photoplethysmography (PPG), bio impedance, and electrocardiograms (ECGs) to accurately monitor hemodynamic parameters and detect cardiovascular diseases. I am also interested in developing both data-driven and method(knowledge)-driven novel signal processing techniques to enhance the quality of remote-sensing data, to improve prediction of any downstream ML-infused remote health monitoring.

Contact

Education

  • Doctor of Philosophy (PhD) in Computer Science: 2021-2025 (expected)
    • Texas A&M University
    • Advisor: Dr. Bobak Mortazavi
  • Bachelor of Science (BS) in Computer Science: 2017-2021
    • Texas A&M University
    • Magna Cum Laude
    • Minor in Cybersecurity

Research Experience

  • Research Assistant: June 2021 - present

    • STMI Lab, Texas A&M University
    • Advisor: Dr. Bobak Mortazavi
    • Cuffless Blood Pressure Monitoring with wearables.
    • Remote Cardiac Rehabilitation with wearables.

  • Undergraduate Researcher: August 2020 - May 2021

    • STMI Lab, Texas A&M University
    • Advisor: Dr. Bobak Mortazavi
    • Inverse Metabolic Monitoring (IMM) from Continuous Glucose Monitoring (CGM).
    • Twitter Sentiment Analysis with ML and natural language processing (NLP).

  • Undergraduate Researcher: August 2019 - June 2020

    • Information Innovation Lab, Texas A&M University
    • Advisor: Dr. Anxiao Jiang
    • “Looking down at phone” action Recognition with ML and computer vision (CV).

  • Team Member: November 2017 - May 2019

    • Smart City Lab, Texas A&M University
    • Mentor: Dr. Alireza Talebpour
    • Member of Texas A&M AutoDrive team (12th Unmanned) to the SAE/GM AutoDrive Challenge
      • Wrote an API to establish synchronization between LiDAR and GPS for sensor fusion in year 1
      • Led the UIUX team, developed a SaaS GUI using JavaScript and C++ on Unix platform in year 2

Work Experience

  • Database Administrator and Software Engineer: May 2020 - August 2020

    • Nuvenu LLC (Tech Startup), Fort Worth, TX
    • A social media platform that connects customers and local businesses across restaurants, bars, theatres, etc.
    • Architected and Implemented a cloud graph database to handle relationships and connections among business, user, post, etc.
    • Wrote RESTful APIs using .NET Core to enable the website to perform CRUD via HTTP requests to the database with MVC pattern and Agile development practice
    • Managed the project with Azure DevOps and deployed the website and database on Azure

  • Peer Teacher: August 2019 - May 2021

    • CSE Peer Teaching, Texas A&M University
    • CSCE 315: Programming Studio
    • CSCE 314: Programming Languages
    • CSCE 313: Introduction to Computer Systems
    • CSCE 222: Discrete Structures for Computing
    • CSCE 221: Data Structures and Algorithms
    • CSCE 121: Introduction to Program Design and Concepts

  • Student Assistant: May 2019 - October 2019

    • Health Promotion and Genomics Lab, Texas A&M University
    • Designed promotional documents to recruit Community Health Workers
    • Translated and modified recruitment letters and application forms for minority population
    • Created and pilot tested the training program

  • Student Worker, Dec 2017 – Dec 2018

    • Open Access Labs, Texas A&M University
    • Troubleshoot Win 10 and MacOS machines for customers
    • Refilled and troubleshooted varieties of printers

Publications

  • Sicong Huang, Roozbeh Jafari, and Bobak Mortazavi, ArterialNet: Arterial Blood Pressure Reconstruction, IEEE International Conference on Biomedical and Health Informatics (BHI), 2023 (Accepted with oral: 12%)
  • Lida Zhang, Sicong Huang, Anurag Das, Edmund Do, Namino Glantz, Wendy Bevier, Rony Santiago, David Kerr, Ricardo Gutierrez-Osuna, and Bobak Mortazavi, Joint Embedding of Food Photographs and Blood Glucose for Improved Calorie Estimation, IEEE International Conference on Biomedical and Health Informatics (BHI), 2023 (Accepted with oral: 12%)
  • Sicong Huang, Roozbeh Jafari, Bobak Mortazavi, Pulse2AI: An Adaptive Framework to Standardize and Process Pulsatile Wearable Sensor Data for Clinical Applications, Open Journal of Engineering in Medicine and Biology (OJEMB), 2023
  • Zhale Nowroozilarki, Sicong Huang, Rohan Khera, Bobak Mortazavi, ECG Abnormality Detection in MIMIC-IV-ECG Data Using Supervised Contrastive Learning, Conference of IEEE EMBS (EMBC), 2024

Contributions and Open-Source Projects

  • ArterialNet: We proposed a pre-training framework that can be combined with various seq2seq models to reconstruct arterial blood pressure using cuffless pulse signals (Accepted in BHI’23).
  • Calorie Prediction with Joint Embedding: We demonstrated that jointly embedding post prandial glucose response data and food image data will elevate food calorie estimation.(Accepted in BHI’23).

Workshops and Invited Talks

  • IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Oct 2023

    • Workshop on Unraveling Challenges in Time Series Analysis with Open Source Tools for Digital Health Applications
    • Judge for BHI 2023 Data Challenge Competition (Phase 2)

  • Research Experience for Undergraduates at Texas A&M University, Jul 2023

    • Towards automatic diet monitoring, Tutorial on Macronutrient Estimation with Machine Learning

Skills

  • Programming Languages: Python, MATLAB, C++, LaTeX, R, C#, Java, SQL, Cypher, JavaScript, JMP, TypeScript
  • Tools/Packages: Pytorch, Scikit-learn, Git, Weights&Bias (wandb), TF/Keras,Pandas, Numpy, Matplotlib, Seaborn, Plotly
  • Technologies/Frameworks: Linux, .NET, Apptainer/Singularity/Docker, Version Control, .NET, CI/CD, Scrum/Agile, Cloud Computing (AWS & Azure), Distributed Computing, Neo4j, Node.js, JDBC, MongoDB, PostgreSQL

Professional Service

  • Reviewer: ACM Transactions on Computing for Healthcare (ACM HEALTH), 2023, 2024
  • Reviewer: IEEE Journal of Biomedical and Healthinformatics (IEEE JBHI), 2023, 2024
  • Reviewer: NPJ Biosensing, 2023
  • Reviewer: International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2024
  • Reviewer: Conference on Health, Inference, and Learning (CHIL), 2024

  • Reviewer: IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), 2024

Awards

  • BHI NSF Student Travel Award, Oct 2023
    • IEEE International Conference on Biomedical and Health Informatics (BHI), 2023

  • BSN NSF Student Travel Award, Oct 2023

    • IEEE International Conference on Body Sensor Networks (BSN), 2023
  • Year 2 Competition, May 2019
    • SAE/GM AutoDrive Challenge, MCity, Ann Arbor, MI
    • Third Place in Overall Competition
  • Year 1 Competition, May 2018
    • SAE/GM AutoDrive Challenge, Yuma, AZ
    • First Place in Object Detection & Avoidance, Second Place in Overall Competition

Thank You

Please go to my GitHub profile to learn more about me.