Shaobo Han
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  • Selected Talks & Tutorials
    • Machine Learning for Optical Fiber Sensing: Generalization, Adaptation, and Real-World Deployment
    • Extreme Parameter-Efficient Fine-Tuning of Foundation Models
    • Generative AI for Distributed Acoustic Sensing over Telecom Networks
    • Automatic Differentiation and Differentiable Programming
  • Selected Publications
    • Uni-LoRA: One Vector is All You Need
    • VB-LoRA: Extreme Parameter Efficient Fine-Tuning with Vector Banks
    • Uncertainty-Aware Knowledge Distillation for Multimodal Large Language Models
    • CLAP-S: Support Set Based Adaptation for Downstream Fiber-Optic Acoustic Recognition
    • Learning Transferable Reward for Query Object Localization with Policy Adaptation
    • Variational Gaussian Copula Inference
    • Deep Learning-Based Intrusion Detection and Impulsive Event Classification for Distributed Acoustic Sensing Across Telecom Networks
    • Manhole Localization and Condition Diagnostics in Telecom Networks Using Distributed Acoustic and Temperature Sensing
    • Mix-CLAP: Adaptive Fusion of Knowledge-Distilled Audio Embeddings for Noise-Aware Audio-Language Models
    • Leveraging Deployed Telecom Cables for Distributed Fiber Sensing Topologies and Applications
    • Energy-Based Generative Models for Distributed Acoustic Sensing Event Classification in Telecom Networks
    • Provable Adaptation across Multiway Domains via Representation Learning
    • Dynamic Rank Factor Model for Text Streams
    • Integrated Non-Factorized Variational Inference
    • Cross-Domain Multitask Learning with Latent Probit Models
  • Research Programs
    • Generalization & Adaptation under Distribution Shift
    • Efficient Foundation-Model Adaptation & Post-Training
    • Small Multimodal Models: From Recognition to Reasoning
    • Sensing AI for Real-World Physical Systems
  • Courses
    • STA 111: Probability and Statistical Inference
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      • Labs
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      • Homework
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  • STA 111: Probability and Statistical Inference
    • Lectures
    • Labs
    • Homework
    • Resources

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  • STA 111: Probability and Statistical Inference
Courses
STA 111: Probability and Statistical Inference
Labs

Labs

  • Lab 1: Introduction to R, RStudio, and R Markdown
  • Lab 2: Probability: Hot Hand in Basketball
  • Lab 3: Visualizing Distribution Approximations
  • Lab 4: Sampling Distributions of Estimators
  • Lab 5: Confidence Interval
  • Lab 6: Bootstrap Method
  • Lab 7: Inference for Numerical Data
  • Lab 8: Inference for Categorical Data
docs
Last updated on Aug 24, 2018
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