Dr. Danyal Bhutto is a patent agent in the Boston office of Wilson Sonsini Goodrich & Rosati and a member of the firm’s patents and innovations practice group. His work focuses on patent prosecution across the fields of artificial intelligence, medical devices, medical imaging, diagnostics, software, and digital health.
Prior to joining the firm, Danyal was a postdoctoral research fellow at Harvard Medical School, where he developed machine learning-driven tools for real-time neuro-navigation and functional mapping using transcranial magnetic stimulation, with clinical applications in stroke and bipolar disorder, under the supervision of Aapo Nummenmaa. Danyal earned his Ph.D. in biomedical engineering from Boston University and was awarded the National Science Foundation Graduate Research Fellowship. He completed his doctoral work at Massachusetts General Hospital and the Athinoula A. Martinos Center for Biomedical Imaging under the supervision of Matthew S. Rosen. His research focused on deployable AI for solving inverse problems in physics and biomedical imaging applications, including the development of uncertainty estimation methods for deep learning-based MRI and CT image reconstruction. Earlier in his career, Danyal was a research scientist at Johns Hopkins University Applied Physics Laboratory, where he conducted artificial intelligence and imaging research.
Dr. Danyal Bhutto is a patent agent in the Boston office of Wilson Sonsini Goodrich & Rosati and a member of the firm’s patents and innovations practice group. His work focuses on patent prosecution across the fields of artificial intelligence, medical devices, medical imaging, diagnostics, software, and digital health.
Prior to joining the firm, Danyal was a postdoctoral research fellow at Harvard Medical School, where he developed machine learning-driven tools for real-time neuro-navigation and functional mapping using transcranial magnetic stimulation, with clinical applications in stroke and bipolar disorder, under the supervision of Aapo Nummenmaa. Danyal earned his Ph.D. in biomedical engineering from Boston University and was awarded the National Science Foundation Graduate Research Fellowship. He completed his doctoral work at Massachusetts General Hospital and the Athinoula A. Martinos Center for Biomedical Imaging under the supervision of Matthew S. Rosen. His research focused on deployable AI for solving inverse problems in physics and biomedical imaging applications, including the development of uncertainty estimation methods for deep learning-based MRI and CT image reconstruction. Earlier in his career, Danyal was a research scientist at Johns Hopkins University Applied Physics Laboratory, where he conducted artificial intelligence and imaging research.
- Postdoctoral Research Fellow, Harvard Medical School, 2026
- Ph.D., Biomedical Engineering, Boston University, 2024
- B.S., Bioengineering, University of Louisville, 2018
- U.S. Patent and Trademark Office
- Postdoctoral Research Fellow, Harvard Medical School, 2026
- Ph.D., Biomedical Engineering, Boston University, 2024
- B.S., Bioengineering, University of Louisville, 2018
- U.S. Patent and Trademark Office
Select Publications
- Co-author with N.R. Reed, M.J. Turner, D.M. Daly, S.M. Oliver, J. Tang, K.S. Olsson, N. Langellier, M.J.H. Ku, M.S. Rosen, and R.L. Walsworth, “Machine learning for improved current-density reconstruction from two-dimensional vector magnetic images,” 23(3) Physical Review Applied 034035, 2025
- Co-author with B. Zhu, J.Z. Liu, N. Koonjoo, H.B. Li, B.R. Rosen, and M.S. Rosen, “Uncertainty estimation and out-of-distribution detection for deep learning-based image reconstruction using the local Lipschitz,” 28(9) IEEE Journal of Biomedical and Health Informatics 5422-5434, 2024
- Co-author with D.E.J Waddington, N. Hindley, N. Koonjoo, C. Chiu, T. Reynolds, P.Z.Y. Liu, B. Zhu, C. Paganelli, P.J. Keall, and M.S. Rosen, “Real‐time radial reconstruction with domain transform manifold learning for MRI‐guided radiotherapy,” 50(4) Medical Physics 1962-1974, 2023
- Co-author with N. Koonjoo, B. Zhu, G.C. Bagnall, and M.S. Rosen, “Boosting the signal-to-noise of low-field MRI with deep learning image reconstruction,” 11(1) Scientific Reports 8248, 2021
- Co-author with E.M. Murphy, M.C. Priddy, C.C. Centner, J.B. Moore IV, R. Bolli, and J.A. Kopechek, “Effect of molecular weight on sonoporation-mediated uptake in human cells,” 44(12) Ultrasound in Medicine & Biology 2662-2672, 2018
Select Publications
- Co-author with N.R. Reed, M.J. Turner, D.M. Daly, S.M. Oliver, J. Tang, K.S. Olsson, N. Langellier, M.J.H. Ku, M.S. Rosen, and R.L. Walsworth, “Machine learning for improved current-density reconstruction from two-dimensional vector magnetic images,” 23(3) Physical Review Applied 034035, 2025
- Co-author with B. Zhu, J.Z. Liu, N. Koonjoo, H.B. Li, B.R. Rosen, and M.S. Rosen, “Uncertainty estimation and out-of-distribution detection for deep learning-based image reconstruction using the local Lipschitz,” 28(9) IEEE Journal of Biomedical and Health Informatics 5422-5434, 2024
- Co-author with D.E.J Waddington, N. Hindley, N. Koonjoo, C. Chiu, T. Reynolds, P.Z.Y. Liu, B. Zhu, C. Paganelli, P.J. Keall, and M.S. Rosen, “Real‐time radial reconstruction with domain transform manifold learning for MRI‐guided radiotherapy,” 50(4) Medical Physics 1962-1974, 2023
- Co-author with N. Koonjoo, B. Zhu, G.C. Bagnall, and M.S. Rosen, “Boosting the signal-to-noise of low-field MRI with deep learning image reconstruction,” 11(1) Scientific Reports 8248, 2021
- Co-author with E.M. Murphy, M.C. Priddy, C.C. Centner, J.B. Moore IV, R. Bolli, and J.A. Kopechek, “Effect of molecular weight on sonoporation-mediated uptake in human cells,” 44(12) Ultrasound in Medicine & Biology 2662-2672, 2018
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