معرفی
Dr. Sanjay Aneja is an Assistant Professor within the Department of Therapeutic Radiology at Yale School of Medicine, with a secondary appointment in Biomedical Informatics & Data Science. He serves as Director of Clinical Informatics and Director of Medical School Clerkship in Therapeutic Radiology, and is Assistant Cancer Center Director for Bioinformatics at Yale Cancer Center.
Dr. Aneja's educational background includes:
- MD from Yale School of Medicine (2013)
- BA in Applied Mathematics from Columbia University (2009)
- Residency in Radiation Oncology at Yale-New Haven Hospital
- Medicine Internship at Memorial Sloan Kettering Cancer Center
- Postdoctoral Research Fellowship in Machine Learning at the Center for Outcomes Research and Evaluation (CORE)
- Research Fellowship at the Department of Health and Human Services
Dr. Aneja is a physician-scientist whose research focuses on applying machine learning techniques to clinical oncology. His laboratory develops AI-driven solutions that leverage high-dimensional healthcare data to improve patient outcomes. The Aneja Lab's work spans multiple domains from "calculation to clinic," including machine learning algorithm development (metric learning, generalization theory, interpretability techniques, uncertainty quantification) and clinical applications (outcome prediction, disease classification, clinical workflow automation). His research group has established a national consortium of 7 institutions for developing imaging-based biomarkers of cancer outcomes.
Analysis of Dr. Aneja's recent publications reveals a strong focus on applying deep learning to medical imaging, particularly for cancer diagnosis and treatment planning. His work addresses critical challenges in medical AI including model robustness against adversarial attacks, interpretability of deep learning models, and generalization across heterogeneous healthcare data. His research spans multiple cancer types including brain metastases, lung cancer, and breast cancer, with growing interest in patient-reported outcomes and clinical trial matching.
Dr. Aneja has received significant research funding and recognition including:
- NIH Career Development Award
- NSF Research Grant
- American Cancer Society Research Award
- Radiation Society of North America (RSNA) Funding
- SWOG Hope Grant
- IBM Computing Research Grant
As a mentor, Dr. Aneja leads the Aneja Lab, which includes clinicians, computer scientists, mathematicians, and data scientists working collaboratively on interdisciplinary projects. His lab has established collaborations with Amazon for developing AI-driven patient-reported outcome collection and with SWOG for clinical trial matching research. The lab actively seeks postdoctoral associates and students with quantitative backgrounds to contribute to their innovative research at the intersection of machine learning and clinical medicine.
The Aneja Lab maintains active research programs in deep learning for imaging-based biomarkers, interpretability methods for deep learning models, metric learning for "digital twins," generalization of deep learning models across healthcare data streams, physician and patient perception of AI, deep learning methods for patient-reported outcome capture, and natural language processing for clinical trial matching. The lab's GitHub repository hosts several open-source projects related to adversarial imaging, capsule networks, and 3D/2D segmentation techniques.
