Publications

2026

1. 

   Align-RAG: Alignment Is All You Need for TSFM In-Context Learning

   Mohammad Asadi, Soheil Hor, Bardiya Akhbari, Jack W. O’Sullivan, Tahoura Nedaee, Layne C. Price, Raviteja Anantha, Euan A. Ashley, and Ehsan Adeli

   arXiv preprint, 2026

   Abs arXiv Code

   Retrieval-augmented forecasting promises to adapt frozen Time Series Foundation Models (TSFMs) to new domains without fine-tuning, but recent methods rely on learned fusion modules. We show this is unnecessary. Align-RAG is a training-free method that applies a closed-form per-pair amplitude rescaling and integer-lag phase shift to retrieved past-future windows before they enter a frozen backbone’s context. With no learned parameters, it outperforms the state-of-the-art trained retrieval adapter on a frozen Chronos-Bolt across all seven datasets of the standard benchmark, and improves zero-shot accuracy on four additional frozen TSFMs. A collaboration with Amazon.

2. 

   MIRAGE: The Illusion of Visual Understanding

   Mohammad Asadi^*, Jack W. O’Sullivan^*, Fang Cao, Tahoura Nedaee, Kamyar Rajabalifardi, Fei-Fei Li^†, Ehsan Adeli^†, and Euan A. Ashley^†

   arXiv preprint, 2026

   Abs arXiv Code Website

   Frontier multimodal models readily generate detailed image descriptions and elaborate reasoning traces (including pathology-biased clinical findings) for images they were never shown, a phenomenon we term "mirage reasoning." In the most extreme case, a small text-only model reached the top of a chest X-ray question-answering benchmark without access to any images. We introduce the Mirage Score, the Phantom-0 benchmark, and the B-Clean decontamination framework to measure and contain the effect.

3. 

   MARCUS: An Agentic, Multimodal Vision-Language Model for Cardiac Diagnosis and Management

   Mohammad Asadi^*, Jack W. O’Sullivan^*, Lennart Elbe, Akshay Chaudhari, Tahoura Nedaee, Francois Haddad, Michael Salerno, Fei-Fei Li, Ehsan Adeli, Rima Arnaout, and Euan A. Ashley^†

   arXiv preprint, 2026

   Abs arXiv Code Website

   MARCUS (Multimodal Autonomous Reasoning and Chat for Ultrasound and Signals) is an agentic vision-language assistant that reads and reasons over ECG, echocardiography, and cardiac MRI, individually or jointly. Modality-specific expert models are coordinated by a multimodal orchestrator that, by design, resists the "mirage reasoning" failure mode.

4. MICCAI 2026

   

   Deterministic Hallucination Detection in Medical VQA via Confidence-Evidence Bayesian Gain

   Mohammad Asadi, Tahoura Nedaee, Jack W. O’Sullivan, Euan A. Ashley, and Ehsan Adeli

   In Medical Image Computing and Computer Assisted Intervention (MICCAI), 2026

   arXiv

5. 

   OpenMHC: Accelerating the Science of Wearable Foundation Models

   Narayan Schuetz, Yuze Bai, Lianggang Pan, Edgar Eggert, Favour Nerrise, Juan Delgado-SanMartin, Max Rosenblattl, Milana Gurbanova, Mohammad Asadi, Anders Johnson, Paul Schmiedmayer, Dennis Wang, Allan Lawrie, Daniel Seung Kim, Xin Liu, Akshay Paruchuri, Ehsan Adeli, Euan A. Ashley, and Kelly W. Zhang

   arXiv preprint, 2026

   Abs arXiv Code Website

   OpenMyHeartCounts (OpenMHC) is the largest and most comprehensive open-access wearable health dataset released to date, published alongside open-source implementations of recent wearable foundation models. Derived from over a decade of data collected through the My Heart Counts study app, it includes more than 60 million hours of wearable data across 19 sensor channels and 169 linked variables from 11,894 consenting participants. A unified open benchmark enables standardized comparison of wearable health models across downstream prediction, multivariate data imputation, and time-series forecasting.

6. 

   NeuroQA: A Large-Scale Image-Grounded Benchmark for 3D Brain MRI Understanding

   Mohammad H. Abbasi^*, Favour Nerrise^*, Shaurnav Ghosh, Ridvan Yesiloglu, Yuncong Mao, Bailey Trang, Mohammad Asadi, Merryn Daniel, Gustavo Chau Loo Kung, Ken Chang, Pavan Pinkesh Shah, Adam Turnbull, Kyan Younes, Seena Dehkharghani, and Ehsan Adeli

   arXiv preprint, 2026

   Abs arXiv Website

   NeuroQA is a large-scale benchmark for visual question answering in 3D brain MRI, with 56,953 question-answer pairs from 12,977 subjects across 12 datasets. It spans ages 5 to 104 and five clinical domains: Alzheimer’s disease, Parkinson’s disease, tumors, white matter disease, and neurodevelopment. Unlike prior medical VQA efforts that operate on 2D slices or rely on narrow diagnostic labels, NeuroQA pairs every item with a full 3D volume and evaluates 11 clinically grounded reasoning skills across yes/no, multiple-choice, and open-ended formats.

7. 

   EchoAtlas: A Conversational, Multi-View Vision-Language Foundation Model for Echocardiography Interpretation and Clinical Reasoning

   Chieh-Ju Chao, Mohammad Asadi, Lavonda Li, Gokul Ramasamy, Nicolo Pecco, Yu-Chiang Wang, Timothy Poterucha, Reza Arsanjani, Garvan Kane, Jae K. Oh, Imon Banerjee, Curtis Langlotz, Fei-Fei Li, Ehsan Adeli, and Bradley J. Erickson

   medRxiv preprint, 2026

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2025

1. 

   EchoGraph: Automated Quality Assessment of Echocardiography Reports

   Chieh-Ju Chao, Jean-Benoit Delbrouck, Mohammad Asadi, Imon Banerjee, Juan M. Farina, Francesca Galasso, Ahmed K. Mahmoud, Mohammed Tiseer Abbas, Yu-Chiang Wang, Reza Arsanjani, Garvan C. Kane, Jae K. Oh, Bradley J. Erickson, Fei-Fei Li, Ehsan Adeli, and Curtis Langlotz

   npj Digital Medicine, 2025

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2. 

   Synthetic Hands Meet Legacy Data: A Synthetic Dataset for Structured, Controllable, and Multimodal Evaluation

   Menghe Zhang, Haley M. So, Mohammad Asadi, Dongfang Zhao, Yangwen Liang, Shuangquan Wang, Gordon Wetzstein, Kee-Bong Song, and Donghoon Kim

   In ICCV 2025 Workshops (DataCV), 2025

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2023

1. 

   Ripple: Concept-Based Interpretation for Raw Time Series Models in Education

   Mohammad Asadi, Vinitra Swamy, Jibril Frej, Julien Vignoud, Mirko Marras, and Tanja Käser

   In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2023

   arXiv Code

2021

1. Graphene-Based Nanomaterials in Fighting the Most Challenging Viruses and Immunogenic Disorders

   Mahsa Ebrahimi, Mohammad Asadi, and Omid Akhavan

   ACS Biomaterials Science & Engineering, 2021

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