23 citations · 46 across the 32 of their papers we have counts for
33 papers
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…
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…
A Fast Algorithm for Finding Minimum Weight Cycles in Mining Cyclic Graph Topologies
Heman Shakeri, Torben Amtoft, Behnaz Moradi-Jamei +2
Cyclic structures are fundamental topological features in graphs, playing critical roles in network robustness, information flow, community structure, and various dynamic processes…
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…
Fusing Biomechanical and Spatio-Temporal Features for Fall Prediction: Characterizing and Mitigating the Simulation-to-Reality Gap
Md Fokhrul Islam, Sajeda Al-Hammouri, Christopher J. Arellano +2
Falls are a leading cause of injury and loss of independence among older adults. Vision-based fall prediction systems offer a non-invasive solution to anticipate falls seconds befo…
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…
Operator-Based Detecting, Learning, and Stabilizing Unstable Periodic Orbits of Chaotic Attractors
Ali Tavasoli, Heman Shakeri
This paper examines the use of operator-theoretic approaches to the analysis of chaotic systems through the lens of their unstable periodic orbits (UPOs). Our approach involves thr…
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…
MAD-FC: A Fold Change Visualization with Readability, Proportionality, and Symmetry
Bruce A. Corliss, Yaotian Wang, Francis P. Driscoll +2
We propose a fold change visualization that demonstrates a combination of properties from log and linear plots of fold change. A useful fold change visualization can exhibit: (1) r…
Contra-Analysis for Determining Negligible Effect Size in Scientific Research
Bruce A. Corliss, Yaotian Wang, Heman Shakeri +1
Scientific experiments study interventions that show evidence of an effect size that is meaningfully large, negligibly small, or inconclusively broad. Previously, we proposed contr…
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…
Contra-Analysis: Prioritizing Meaningful Effect Size in Scientific Research
Bruce A. Corliss, Yaotian Wang, Heman Shakeri +1
At every phase of scientific research, scientists must decide how to allocate limited resources to pursue the research inquiries with the greatest potential. This prioritization di…
The Least Difference in Means: A Statistic for Effect Size Strength and Practical Significance
Bruce A. Corliss, Yaotian Wang, Heman Shakeri +1
With limited resources, scientific inquiries must be prioritized for further study, funding, and translation based on their practical significance: whether the effect size is large…
GeoTyper: Automated Pipeline from Raw scRNA-Seq Data to Cell Type Identification
Cecily Wolfe, Yayi Feng, David Chen +4
The cellular composition of the tumor microenvironment can directly impact cancer progression and the efficacy of therapeutics. Understanding immune cell activity, the body's natur…
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…
The Most Difference in Means: A Statistic for the Strength of Null and Near-Zero Results
Bruce A. Corliss, Taylor R. Brown, Tingting Zhang +3
Statistical insignificance does not suggest the absence of effect, yet scientists must often use null results as evidence of negligible (near-zero) effect size to falsify scientifi…