Jae Kim

AI Research Engineer | Healthcare ML Specialist | Published Researcher | Cornell MS '25

Proven track record of deploying AI solutions that improve patient outcomes by 25%+ and reduce healthcare costs. 2 peer-reviewed publications with expertise in clinical ML, NLP, and real-time biosensor systems.

Featured Projects

Explore my portfolio of AI, machine learning, and software engineering projects.

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Key Achievements & Impact

92%

Sensitivity achieved in FDA-compliant cancer detection ML model

500+

Patients benefiting from deployed emotion tracking system

25%

Improvement in wearable-robotic system reliability

2

Peer-reviewed publications in top-tier journals (IF: 7.9, 9.4)

35%

Improvement in job placement rates through AI coaching

60%

Reduction in manual clinical assessment time

Labs & Work Experience

  • Developed FDA-compliant ML models achieving 92% sensitivity on 15K+ patient EHR data
  • Implemented SHAP explainability framework for clinical interpretability
  • Conducted external validation across 3 hospital systems with AUC 0.89
  • Reduced false positive rate by 15% compared to existing screening methods
  • Built iOS app with Arduino biosensor integration deployed to 500+ patients
  • Achieved 85% accuracy in real-time emotion classification using custom CNN
  • Reduced manual clinical assessment time by 60% enabling immediate interventions
  • Processed 1M+ biosensor data points with sub-100ms response time
  • Developed ACES: LLM-powered job coaching platform using GPT-4 and RLHF
  • Improved job placement rates by 35% across 200+ participants in pilot study
  • Implemented LoL-RL technique with personalized prompts and co-design studies
  • Reduced time-to-employment by average 3 weeks through AI-driven coaching
  • Developing NLP models for automated science fiction story structure analysis
  • Built generative models for narrative pattern recognition achieving 78% accuracy
  • Processing 10K+ sci-fi texts with transformer-based architecture
  • Cornell University research group focused on AI narrative analysis
  • Developed physics-informed ML models for yield strength prediction in metals
  • Achieved 15% improvement in prediction accuracy over traditional methods
  • Published in Acta Materialia (Impact Factor: 9.4) with 50+ citations
  • Physics-based modeling for material science applications
  • Developed real-time ML system for EMG-based wearable-robotic communication
  • Improved system reliability by 25% and reduced latency by 40%
  • Implemented custom signal processing pipeline handling 1000+ Hz data
  • Achieved sub-10ms response time for real-time robotic control
  • Developed full-body pose estimation using miniature wrist camera
  • Custom CNN architecture achieved 95% accuracy on pose keypoints
  • Published in ACM IMWUT (Impact Factor: 7.9) for privacy-preserving tracking
  • Real-time optimization for egocentric vision applications

Interactive Skills Showcase

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Python
JavaScript
TypeScript
React
Next.js
Node.js
TensorFlow
PyTorch
scikit-learn
SQL
MongoDB
AWS
Docker
Git
Machine Learning
Deep Learning

Education

CU

Cornell University

Master of Science in Information Science

Health Tech Concentration

New York, NY

CU

Cornell University

Bachelor of Arts in Information Science

Minor in Business and Computer Science

Ithaca, NY

Thoughts & Insights

Exploring the intersection of AI, technology, and human experience through writing.

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Publications

  • Acta Materialia, 2024
  • 15% improvement in prediction accuracy
  • Physics-informed ML for material science
  • ACM IMWUT, 2022
  • Real-time pose estimation from wrist camera
  • Privacy-preserving body tracking

Awards & Honors

  • Winner, Cornell Hackathon (2021)
  • Cornell Merit Scholarship (2020–2022)
  • Health Technology Representative, Cornell Tech Student Government (2023–2025)
  • Founder & President, Korean Graduate Club @ Cornell Tech (2023–Present)
  • 1st Clarinetist, National Youth Orchestra (2019)

Skills

PythonC++JavaJavaScriptSwiftTensorFlowPyTorchscikit-learnHugging FaceAWSGoogle CloudMATLABROpenCVGit

Let's Create SomethingAmazing Together

Whether you're interested in AI research collaboration, discussing innovative health tech solutions, or exploring new opportunities in machine learning, I'd love to hear from you.

Email Me

Best for detailed discussions and formal inquiries

jk2765@cornell.edu

Call Me

Quick conversations and immediate discussions

(+1) 609-206-9989

View My Code

Explore my projects and contributions

GitHub Profile

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🏥
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