Research
My research spans a wide range of topics around generative AI (particularly with Large Language Models and Diffusion Models), multimodality and applied research.
I am interested in (1) leveraging large language models (LLMs) and other foundational models to tackle both fundamental challenges and practical, real-world problems, (2) exploring modality alignment and the seamless integration of diverse data modalities, (3) developing more robust training methodologies, (4) improving model inference efficiency, and (5) model alignment. My work is dedicated to creating innovative solutions that not only enhance the performance of AI systems but also contribute meaningfully to scientific discovery.
Publications
Below is a list of my publications in reversed chronological order.
2024
2023
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Annals of OncologyMulti-site validation of a deep learning solution for HER2 profiling of breast cancer from H&E-stained pathology slidesAnnals of Oncology 2023
2022
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DatabaseHumanMine: advanced data searching, analysis and cross-species comparisonDatabase 2022
2021
2020
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Nature Scientific ReportsGenome-wide investigation of gene-cancer associations for the prediction of novel therapeutic targets in oncologyNature Scientific Reports 2020
2019
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WSOMNetwork Community Cluster-Based Analysis for the Identification of Potential Leukemia Drug TargetsIn International Workshop on Self-Organizing Maps 2019
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Neuromuscular DisordersAutomated diagnosis of collagen VI related muscular dystrophies using advanced image analysis and machine learningIn Neuromuscular Disorders 2019
2018
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arXivPerformance Evaluation of an Algorithm-based Asynchronous Checkpoint-Restart Fault Tolerant Application Using Mixed MPI/GPI-2arXiv preprint 2018
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BioinformaticsBIOLITMAP: a web-based geolocated, temporal and thematic visualization of the evolution of bioinformatics publicationsBioinformatics 2018