23 citations · 46 across the 32 of their papers we have counts for
11 papers · 1 filter
Interventional Flow Matching: Prospective Dose-Response Forecasting with Velocity-Field Jacobian Regularization
Amirreza Dolatpour Fathkouhi, Justin Lee, Heman Shakeri
Predicting a patient's physiological trajectory under a planned treatment sequence is a prospective interventional problem, not standard time-series extrapolation. We study this pr…
Deep Kernel Learning for Stratifying Glaucoma Trajectories
Bruce Rushing, Angela Danquah, Alireza Namazi +2
Effectively stratifying patient risk in chronic diseases like glaucoma is a major clinical challenge. Clinicians need tools to identify patients at high risk of progression from sp…
From Prediction to Practice: A Task-Aware Evaluation Framework for Blood Glucose Forecasting
Alireza Namazi, Heman Shakeri
Clinical time-series forecasting is increasingly studied for decision support, yet standard aggregate metrics can obscure whether a model is actually useful for the task it is mean…
The Stationarity Bias: Stratified Stress-Testing for Time-Series Imputation in Regulated Dynamical Systems
Amirreza Dolatpour Fathkouhi, Alireza Namazi, Heman Shakeri
Time-series imputation benchmarks employ uniform random masking and shape-agnostic metrics (MSE, RMSE), implicitly weighting evaluation by regime prevalence. In systems with a domi…
Mitigating Exposure Bias in Risk-Aware Time Series Forecasting with Soft Tokens
Alireza Namazi, Amirreza Dolatpour Fathkouhi, Heman Shakeri
Autoregressive forecasting is central to predictive control in diabetes and hemodynamic management, where different operating zones carry different clinical risks. Standard models…
The Driver-Blindness Phenomenon: Why Deep Sequence Models Default to Autocorrelation in Blood Glucose Forecasting
Heman Shakeri
Deep sequence models for blood glucose forecasting consistently fail to leverage clinically informative drivers--insulin, meals, and activity--despite well-understood physiological…
Multi-Marginal Stochastic Flow Matching for High-Dimensional Snapshot Data at Irregular Time Points
Justin Lee, Behnaz Moradijamei, Heman Shakeri
Modeling the evolution of high-dimensional systems from limited snapshot observations at irregular time points poses a significant challenge in quantitative biology and related fie…
Online Meal Detection Based on CGM Data Dynamics
Ali Tavasoli, Heman Shakeri
We utilize dynamical modes as features derived from Continuous Glucose Monitoring (CGM) data to detect meal events. By leveraging the inherent properties of underlying dynamics, th…
An Interpretable Approach to Load Profile Forecasting in Power Grids using Galerkin-Approximated Koopman Pseudospectra
Ali Tavasoli, Behnaz Moradijamei, Heman Shakeri
This paper presents an interpretable machine learning approach that characterizes load dynamics within an operator-theoretic framework for electricity load forecasting in power gri…
Leveraging Wastewater Monitoring for COVID-19 Forecasting in the US: a Deep Learning study
Mehrdad Fazli, Heman Shakeri
The outburst of COVID-19 in late 2019 was the start of a health crisis that shook the world and took millions of lives in the ensuing years. Many governments and health officials f…
Using Machine Learning to Evaluate Real Estate Prices Using Location Big Data
Walter Coleman, Ben Johann, Nicholas Pasternak +3
With everyone trying to enter the real estate market nowadays, knowing the proper valuations for residential and commercial properties has become crucial. Past researchers have bee…