23 citations · 46 across the 32 of their papers we have counts for
8 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…
Data-Driven Personalization of Automated Insulin Delivery
Ali Kashani, Ali Tavasoli, Heman Shakeri
Automated insulin delivery (AID) systems are often tuned for the population and offer limited online adaptation to the inter- and intrapatient variability in insulin needs caused b…
Optimal Scheduling of Dengue Vector Control
Aram Vajdi, Lee W. Cohnstaedt, Caterina M. Scoglio +1
Dengue transmission is shaped by the population dynamics of the Aedes aegypti mosquito, making vector control a central strategy for disease mitigation. The impact of interventions…
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…
FlashSpread: IO-Aware GPU Simulation of Non-Markovian Epidemic Dynamics via Kernel Fusion
Heman Shakeri, Behnaz Moradi-Jamei, Aram Vajdi +1
Non-Markovian (renewal) epidemic simulation on multi-million-node contact networks is essential for realistic forecasting under general age-dependent holding-time distributions (lo…
The Metaphysics We Train: A Heideggerian Reading of Machine Learning
Heman Shakeri
This paper offers a phenomenological reading of contemporary machine learning through Heideggerian concepts, aimed at enriching practitioners' reflexive understanding of their own…
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…