📍 MS in AI Engineering | Carnegie Mellon University
📧 jintongh@andrew.cmu.edu
🔗 LinkedIn Profile
I am a highly motivated AI engineering student seeking internship and full-time opportunities in machine learning engineering and data science. Currently pursuing a Master’s degree in AI Engineering – Mechanical Engineering at Carnegie Mellon University, I bring a strong foundation in machine learning, computer vision, and data analytics.
As a Machine Learning Engineer at Zhongke Xingqi Technology Co., Ltd., I led a team to develop high-precision object detection models for satellite imagery, achieving over 90% precision and recall. I also optimized GPU-based model deployment, significantly reducing inference time through parallelization strategies.
Beyond technical skills, I value mentorship and team collaboration, having trained new engineers in deployment and optimization best practices. I am passionate about delivering robust AI solutions and fostering continuous learning environments.
📍 Zhongke Xingqi Technology Co., Ltd. | Aug 2023 – Jun 2024
Objective:
Automate large-scale remote sensing tasks for environmental monitoring and urban planning using deep learning.
Technologies: PyTorch · YOLOv8 · Vision Transformers (ViT) · Flask · GPU Optimization · Multi-threading
Key Contributions:
- Model Development: Designed and fine-tuned YOLOv8 and ViT-based models, achieving 90% precision and recall on diverse terrains.
- Deployment Optimization: Built Flask APIs and applied multi-threading and batching, reducing inference latency by 30%.
- Team Leadership: Directed a 4-member team, applying Agile practices for integration with downstream analytics pipelines.
📍 University of Wollongong | Jul 2022 – Jul 2023
Objective:
Enhance the safety and efficiency of metal 3D printing through real-time defect detection.
Technologies: Python · PyTorch · CNNs · Computer Vision
Key Contributions:
- Signal-Based Feature Engineering: Extracted voltage and current features, boosting model F1 score by 5%.
- Model Development: Built a CNN classifier achieving 99.71% F1 score on the test set.
- Research Communication: Created technical visualizations and delivered a conference-level poster presentation.
📍 University of Wollongong | Feb 2022 – Nov 2022
Objective:
Develop a low-cost, real-time system to improve driver awareness and road safety.
Technologies: Jetson Nano · Python · TensorFlow · OpenCV
Key Contributions:
- Efficient Object Detection: Developed a lightweight MobileNetV2-based model integrated with IR-switching cameras.
- Embedded System Optimization: Achieved 18 FPS real-time inference on Jetson Nano.
- Full System Integration: Tested robustness under diverse urban and lighting conditions.
📍 Feng Bian Technology Co., Ltd. | Oct 2021 – Jan 2022
Objective:
Refine advertising strategies for beginner Python courses to improve marketing ROI.
Technologies: Pandas · SQL · Seaborn · Matplotlib
Key Contributions:
- Data Cleaning and Analysis: Processed and analyzed survey data from over 10,000 customers.
- Insightful Visualization: Created compelling visuals that influenced business strategy across departments.
- Business Impact: Recommended optimizations leading to an 8% increase in profitability.
To: Dr. Alan Thomas Kohler
From: Jonathan He
Date: April 25, 2025
Subject: Application of Communication Skills Learned in Class to Other Coursework
- Assertion-Evidence Slide Design
- IMRD (Introduction, Methods, Results, Discussion) Format
- Effective Use of Hand Gestures
- Known/New Information Structuring
In course 24-665 (Wearable Health), I applied the assertion-evidence approach to all presentation slides. Instead of using vague titles, each slide began with a clear, declarative statement summarizing the key takeaway. This significantly improved audience comprehension and engagement. As a result, I earned a perfect score (10/10) on the presentation.
During the Wearable Health presentation, I deliberately used hand gestures to support my verbal communication:
- Topic Introduction Gesture: Used a broad open-hand motion to introduce new topics.
- Bullet Emphasis Gesture: Used finger-pointing motions to guide the audience through key bullet points.
- Summary Sweep Gesture: Used a sweeping motion to summarize major takeaways.
These gestures helped provide visual structure for the audience and emphasized key transitions during the talk.
In course 24-782 (AI Engineering Project), our final report strictly followed the IMRD structure:
- Introduction: Introduced the background of imitation learning and its relevance to humanoid locomotion.
- Methods: Described our approach to applying imitation learning algorithms to train a walking agent.
- Results: Presented experimental outcomes, performance metrics, and visual results.
- Discussion: Analyzed the strengths, limitations, and proposed future work.
This structure improved readability and made our logical progression clear to readers.
Throughout the AI Engineering report, I applied the known/new strategy to improve coherence. For example, after explaining what imitation learning is (known), the next section naturally transitioned into how our team applied it (new). This technique made the report smoother and easier to follow.
Attached materials: