Portrait de Shayan Hajhashemi

Shayan Hajhashemi

shayan.hajhashemi@mila.quebec
Doctorat - McGill
Superviseur⋅e principal⋅e
Amin Emad
Sujets de recherche
Apprentissage par renforcement
Biologie computationnelle
Causalité
Modèles probabilistes
Optimisation
Réseaux de neurones en graphes
Systèmes dynamiques
Théorie de l'information
Site web
GitHub

Publications

DiffDose: Differentiable Programming for Personalized Dose-Regimen Optimal Control
Shayan Hajhashemi
Amin Emad
Morgan Craig
Dose-regimen design requires choosing how much drug to give, when to give it, and how treatment should vary across patients. Mechanistic pha… (voir plus)rmacokinetic-pharmacodynamic (PK/PD) and quantitative systems pharmacology (QSP) models can predict treatment responses, but optimizing dosing inputs depends on model-specific sensitivity derivations or derivative-free search. Here, we introduce DiffDose, a differentiable programming framework for mechanistic open-loop dose-regimen optimization that uses automatic differentiation (AD) to handle clinically interpretable dose amounts and administration times as differentiable controls. We evaluate our method in three settings: fixed-schedule dose-amplitude optimization in OptiDose PK/PD benchmarks; dose-timing optimization in a chemotherapy-induced neutropenia model with state-dependent delay; and individualized mosunetuzumab dosing in a QSP virtual population. Across these examples, AD produced gradients consistent with references, reduced model-specific derivative work, and shortened benchmark time to solution. DiffDose thereby turns mechanistic PK/PD and QSP models from tools that evaluate prespecified regimens into gradient-based engines for individualized regimen design.
2026-09-12
bioRxiv (accepté)
doi.org
A Latent Space Thermodynamic Model of Cell Differentiation
Ali Poursina
Shayan Hajhashemi
Arsham Mikaeili Namini
Alihossein Saberi
Amin Emad
Hamed S. Najafabadi
Abstract Inferring the governing dynamics of differentiation that capture cell state evolution remains a central challenge in single-cell bi… (voir plus)ology. We present Latent Space Dynamics (LSD), a thermodynamics-inspired framework that models cell differentiation as evolution on a learned Waddington landscape in latent space. LSD jointly infers a low-dimensional cell state, a differentiable potential function governing developmental flow, and a local entropy term that quantifies cellular plasticity. Using a neural ordinary differential equation, LSD reconstructs continuous differentiation trajectories from time-ordered single-cell data. Across diverse developmental systems, LSD accurately recovers lineage hierarchies, predicts fate commitment for unseen cell types, and outperforms existing trajectory inference approaches in directional accuracy. Moreover, in silico gene perturbations reveal how individual regulators reshape the landscape, and entropy provides a quantitative measure of plasticity in development and cancer.
2026-03-05
bioRxiv (prépublication)
doi.org