works on

From the 1 of 7 linked papers with an AI index.

most citedA Machine Learning Benchmarking Framework for Lipid Nanoparticle Transfection Efficiency Prediction

1 citations · 1 across the 2 of their papers we have counts for

collaborators

8 papers

q-bio.QM20261 cited

A Machine Learning Benchmarking Framework for Lipid Nanoparticle Transfection Efficiency Prediction

Asal Mehradfar, Mohammad Shahab Sepehri, Jose Miguel Hernandez-Lobato +4

The paper introduces a standardized machine‑learning benchmarking framework for predicting lipid‑nanoparticle transfection efficiency from ionizable lipid structures, evaluating ma…

cs.CV2026

ATHENA: Adaptive Test-Time Steering for Improving Count Fidelity in Diffusion Models

Mohammad Shahab Sepehri, Asal Mehradfar, Berk Tinaz +2

Text-to-image diffusion models achieve high visual fidelity but surprisingly exhibit systematic failures in numerical control when prompts specify explicit object counts. To addres…

cs.AR2026

EM-Aware Physical Synthesis: Neural Inductor Modeling and Intelligent Placement & Routing for RF Circuits

Yilun Huang, Asal Mehradfar, Salman Avestimehr +1

This paper presents an ML-driven framework for automated RF physical synthesis that transforms circuit netlists into manufacturable GDSII layouts. While recent ML approaches demons…

cs.LG2025

FALCON: An ML Framework for Fully Automated Layout-Constrained Analog Circuit Design

Asal Mehradfar, Xuzhe Zhao, Yilun Huang +5

Designing analog circuits from performance specifications is a complex, multi-stage process encompassing topology selection, parameter inference, and layout feasibility. We introdu…

cs.LG2025

CryptoMamba: Leveraging State Space Models for Accurate Bitcoin Price Prediction

Mohammad Shahab Sepehri, Asal Mehradfar, Mahdi Soltanolkotabi +1

Predicting Bitcoin price remains a challenging problem due to the high volatility and complex non-linear dynamics of cryptocurrency markets. Traditional time-series models, such as…

cs.NI2025

Leveraging Uncertainty Estimation for Efficient LLM Routing

Tuo Zhang, Asal Mehradfar, Dimitrios Dimitriadis +1

Deploying large language models (LLMs) in edge-cloud environments requires an efficient routing strategy to balance cost and response quality. Traditional approaches prioritize eit…