Asiful Arefeen
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  • Recent Talks
    • A decoder-only foundation model for time-series forecasting
    • A General Search-Based Framework for Generating Textual Counterfactual
    • LEAD: Localized Explanations with Adversarial Decision Boundary Characterization for Interpretable Disease Prediction
    • A General Search-Based Framework for Generating Textual Counterfactual
    • Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers
    • Towards Unifying Evaluation of Counterfactual Explanations: Leveraging Large Language Models for Human-Centric Assessments
    • TABCF: Counterfactual Explanations for Tabular Data Using a Transformer-Based VAE
    • Realistic Counterfactual Explanations with Learned Relations
    • LLM-Guided Counterfactual Data Generation for Fairer AI
    • 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
  • Publications
    • RealAC: A Domain-Agnostic Framework for Realistic and Actionable Counterfactual Explanations
    • GlyTwin: Enhancing Digital Twin for Glucose Control in Type 1 Diabetes using Patient-Centric Counterfactual Treatments
    • LEAD: Localized Explanations with Adversarial Decision Boundary Characterization for Interpretable Disease Prediction
    • MealMeter: Using Multimodal Sensing and Machine Learning for Automatically Estimating Nutrition Intake
    • Cost-Effective Multitask Active Learning in Wearable Sensor Systems
    • GlyMan: Glycemic Management using Patient-Centric Counterfactuals
    • 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
  • 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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GlyMan

May 10, 2024 · 1 min read
Image credit: UMass Chan Medical School

With the current insulin pump technology, patients experience lots of hyperglycemic events. Can we reduce this number and also make the technology patient centric?

Last updated on May 10, 2024
Automated Insulin Delivery (AID)
Asiful Arefeen
Authors
Asiful Arefeen
PhD Candidate
HydroGuard Apr 20, 2024 →

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