Asiful Arefeen
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  • Recent Talks
    • Counterfactual Explainable Recommendation
    • Adversarial Counterfactual Visual Explanations
    • CounterNet: End-to-End Training of Prediction Aware Counterfactual Explanations
    • Generating Interpretable Counterfactual Explanations By Implicit Minimization of Epistemic and Aleatoric Uncertainties
    • Counterfactual Explanations for Multivariate Time Series
    • Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations
    • Forecasting the future clinical events of a patient through contrastive learning
    • Feature Importance Explanations for Temporal Black-Box Models
    • Entropy-based Logic Explanations of Neural Networks
    • Rep-Net: Efficient On-Device Learning via Feature Reprogramming
    • Training Generative Adversarial Networks with Limited Data
    • Domain Knowledge Aided Explainable Artificial Intelligence for Intrusion Detection and Response
    • Augmented Experiment in Material Engineering Using Machine Learning
    • Computational Framework for Sequential Diet Recommendation: Integrating Linear Optimization and Clinical Domain Knowledge
    • Local Interpretable Model-Agnostic Explanations
    • Characterizing Decision Boundary for DNN on High Dimensional Data
    • Grad-CAM for Interpreting DNN Model Decisions
    • Boosting Lying Posture Classification with Transfer Learning
    • Optimizations in Sparse Coding
    • Recent Techniques in Semi-Supervised Learning
    • SNR- Sub-Network Routing for Flexible Parameter Sharing in Multi-Task Learning
    • Which Tasks Should Be Learned Together in Multi-task Learning?
    • Anywidth Network
    • Paper Review: Attention is All You Need.
    • Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis
    • MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
    • U-Net: Convolutional Networks for Biomedical Image Segmentation
    • Social and competition stress detection with wristband physiological signals
  • Projects
  • Projects
    • GlyMan
    • HydroGuard
    • Smart Home
    • ExAct
  • Teaching
    • BMI 310: App Development for Population Health
    • BMI 502: Foundations of BMI Methods
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  • Awards
  • Publications
    • Designing User-Centric Behavioral Interventions to Prevent Dysglycemia with Novel Counterfactual Explanations
    • GlucoseAssist: Personalized Blood Glucose Level Predictions and Early Dysglycemia Detection
    • GlySim: Modeling and Simulating Glycemic Response for Behavioral Lifestyle Interventions
    • Inter-Beat Interval Estimation with Tiramisu Model: A Novel Approach with Reduced Error
    • Use of Machine Learning to Predict Medication Adherence in Individuals at Risk for Atherosclerotic Cardiovascular Disease
    • Computational Framework for Sequential Diet Recommendation: Integrating Linear Optimization and Clinical Domain Knowledge
    • Forewarning Postprandial Hyperglycemia with Interpretations using Machine Learning
  • Recent news
  • BMI 310: App Development for Population Health
  • BMI 502: Foundations of BMI Methods
Teaching
BMI 310: App Development for Population Health

BMI 310: App Development for Population Health

We are adding more contents to this page. Until then, you are welcome to visit the course syllabus on ASU website. If you have specific questions, please reach out to me via email.

Last updated on Jan 10, 2024
BMI 502: Foundations of BMI Methods Aug 19, 2023 →

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