Dani operates at the intersection of artificial intelligence (AI) and healthcare. He is currently on the research faculty at Cedars-Sinai. He was a postdoctoral fellow at Caltech, jointly advised by Anima Anandkumar and Andrew Hung, where he developed AI systems to decode the activity of surgeons through videos. He completed his DPhil at the University of Oxford, under the guidance of David Clifton and Tingting Zhu, designing deep learning algorithms that overcome the challenges posed by limited labelled physiological time-series data, such as the electrocardiogram. He earned his BS in biomedical engineering from The Johns Hopkins University. Dani has broad interests in applied machine learning for healthcare, and focuses on developing resource-efficient and trustworthy clinical machine learning systems. He has spent time conducting machine learning research at Ford Motor Company, Mayo Clinic, Merck & Co., Flatiron Health, and Vicarious Surgical. His research has been published in ICML, NeurIPS, Nature Biomedical Engineering, and Nature Communications.
