Jiale Zhang (张嘉乐)

Jiale Zhang (张嘉乐)

Biography

Hello, I am Jiale Zhang, an Applied Scientist with experience in healthcare-focused AI. Currently, I work at Dami and Xiaomi in Shenzhen, where I develop AI tools to support autism intervention. Previously, I worked at Mindray Bio-Medical Electronics, where I applied machine learning to improve blood testing for early sepsis detection. I hold an M.S. in Computer Science from the University of Leeds (2020) and a B.S. in Information and Computing Sciences from Shenzhen University (2019).

I am passionate about advancing research in AI Agents, Large Language Models, Medical Image Analysis and Human-Computer Interaction and am eager to contribute to impactful innovations in these fields through a Ph.D.

Interests
  • AI Agents
  • Large Language Models
  • Medical Image Analysis
  • Human-Computer Interaction
Education
  • MSc in Advanced Computer Science (Artificial Intelligence), GPA 3.55/4.0, Dissertation with Distinction

    University of Leeds, 2020

  • BSc in Information and Computing Sciences

    Shenzhen University, 2019

Patents

(2023). A Blood Analysis Device and Method for Species. WIPO Patent Application PCT/CN2023/094636, filed on May 3, 2023 (Pending).

(2022). Method and apparatus for Soft Tissue Motion Prediction. Patent No. ZL202110345245.8[P]., China.

Experience

 
 
 
 
 
Dami & Xiaomi
Applied Scientist
May 2024 – August 2024 Shenzhen, China

AI for autism intervention: Developing an integrated AI system for childhood autism that generate evaluation reports, personalized intervention strategies and post-intervention plans based on patient data analysis.

Project 1:LLM-based Clinical Decision Support System (LLM-CDSS) for Autism Spectrum Disorder

  • Designed and implemented an LLM-based multi-agent system where specialized agents simulate key clinical roles in ASD intervention through two main functions: conducting assessments and developing intervention plans.
  • Designed and conducted a large-scale clinical validation study (n=70) to evaluate the impact of LLM-CDSS on standardizing autism intervention decisions and reducing clinician-dependent variations in treatment planning.
  • Implemented a controlled pre-post study with 4,200 randomized cases and expert-validated gold standards (n=5 senior clinicians), demonstrating significant improvements in clinical decision accuracy (17-19%), consistency (25%), and efficiency (54% time reduction, p<0.001).

Project 2:AI-Powered Interactive Story Generation Platform for Children with Autism

  • Developed a Stable Diffusion-based model fine-tuned with LoRA, ensuring consistent style and therapeutic value in narratives.
  • Implemented a dynamic prompt engineering system with context-aware templates and few-shot examples, integrating Chain-of-Thought reasoning to enhance coherence and educational impact.
  • Built a full-stack platform for real-time story generation and feedback, now used exclusively by therapists with over 1,000 interactions, providing engaging, adaptive stories for autistic children.
 
 
 
 
 
Mindray
Applied Scientist
July 2021 – November 2023 Shenzhen, China

AI for hematology analyzer: Leveraging machine learning to enhance hematology analyzers, enabling cost-effective and rapid early diagnosis of sepsis through advanced blood cell counting and classification techniques.

Project 1: Advancements in AI-Based Hematological Analysis

  • Developed a Class Activation Mapping (CAM)-based feature selection method for blood biomarkers, which streamlined algorithm design and optimization by reducing feature space 80%.

Project 2: Innovative Sepsis Diagnosis Research

  • Led research project on novel hematological parameters for early sepsis diagnosis.
  • Implemented a targeted genetic algorithm, enabling precise identification of top N blood cell biomarkers.
  • Developed a rule-based algorithm for optimal usage of biomarker panels in varied clinical scenarios, surpassing traditional single biomarker limitations.
  • Outperformed current diagnostic methods (CRP\PCT) with superior accuracy (Acc 21%↑& AUC 14%↑), faster results, and cost-efficiency.
 
 
 
 
 
Chinese Academy of Sciences | Shenzhen Institute of Advanced Technology
Research Assistant
August 2020 – July 2021 Shenzhen, China

AI in Surgical Enhancement: Enhancing laparoscopic surgery by mitigating visibility issues due to smoke, and enabling precise tissue targeting in dynamic environments for ultrasound-guided HIFU therapy through advanced predictive modeling.

Project 1: Efficient Model for Medical Image Enhancement

  • Led the development of the PFAN model, a lightweight GAN framework integrating CNN and Transformer based on frequency domain for desmoking laparoscopic images.
  • Outperformed existing benchmarks in PSNR, SSIM, CIEDE2000, and visual effects.
  • Research accepted by PRCV 2023.

Project 2: Spatiotemporal Model for Soft Tissue Motion Prediction

  • Developed STU-Net, an Encoder-Decoder framework utilizing spatial and temporal information in HIFU image sequences for improved motion prediction and segmentation.
  • Achieved high performance metrics (98.65% BA, 94.32% Dice, 89.26% mIoU) with STU-Net and a prediction speed of 0.23 seconds, enabling real-time treatment adjustments.
  • Culminated in a clinically applicable method, recognized with CN Patent ZL202110345245.8.
 
 
 
 
 
Dubit
Data Analyst
February 2020 – September 2021 Leeds, UK

Data Mining in Social Media: Utilizing machine learning and feature engineering to extract valuable information from social media data, enabling more effective ad targeting by identifying user preferences and trending topics.

Project: Innovative Advertisement Video Recognition Method

  • Extracted audiovisual features like spectogram and duration using FFmpeg instead of visual features for advertisement video identification, achieving 92% accuracy and 98% AUC through LightGBM model.
  • Scripts still run in production today.
 
 
 
 
 
Co-Founder
Fastkey
March 2017 – February 2019 Shenzhen, China
  • Built and managed social media platforms catering specifically to college students, amassing over 60,000 active users.
  • Built a toolbox that included useful tools like credit calculators and course reminders, helping students with their studies.
 
 
 
 
 
Research Assistant
October 2016 – November 2017 Shenzhen, China

Machine Translation: Addressing the complexity of translating concise and allusion-rich ancient Chinese into understandable modern language through advanced neural network technology.

Project: Neural Network for Machine Translation

  • Proposed a convolution-based fixed-pattern noise removal method to optimize text recognition of OCR. Method accepted by ICAIBD 2018.
  • Build the largest parallel corpus of ancient Chinese and modern Chinese (78,000+ pairs) at that time.
  • Proposed a Character-based DBRNN Model for Ancient-Modern Chinese Neural Machine Translation which outperforms state-of-the-art models in terms of BLEU.
  • Rated as National Excellent Innovation and Entrepreneurship Project.

Skills

Programming Languages
Python
C++
Java
MATLAB
SQL
Frameworks and Tools
PyTorch
TensorFlow
Large Language Models
Prompt Engineering
Hobbies
Photography
Hiking
Cats