Suryansh completed his undergraduate degree in Biomedical Engineering from Shri. G.S. Institute of Technology and Science, Indore, M.P. India where he graduated with Bachelor of Technology (Honors). In 2021, he came to the United States for his master’s in biomedical engineering with focus area imaging and medical devices from Johns Hopkins University. During his master’s he worked on three different research projects. In his first project he developed a software to monitor adherence to immunosuppressive drug for adolescent and young adults undergone either kidney or liver transplant. In the second project he worked on enabling and verifying the interchangeability between mathematical model-based beamforming and deep learning-based beamforming of ultrasound images. In his third project, which was also his master’s thesis work, he published a thesis titled “Computational Analysis of 3D Cleared and Labeled Pancreatic Cancer Samples”, where he developed and utilized the image processing tools for quantification of invasion in three dimensional images of pancreatic ductal adenocarcinoma. At Einstein Suryansh is currently a Principal Staff Scientist in the department of pathology. He is working on developing machine learning and deep learning tools for digital pathology to evaluate and validate biomarkers.

1.       Semi-automated TMEM Assessment Algorithm and ML Model for NSCLC Metastatic Risk Prognostication
Shukla, S., Jindani R., Kurt S., Whitney K., Condeelis J.S., Oktay M, Stiles B., Entenberg D. (Submitted August 9, 2024)
Annual Academic Surgical Congress, Las Vegas. NV

2.       Unraveling Gene Expression Changes During Tumor Cell Extravasation in Breast Cancer Metastasis
Parmar P., Traub B., Shukla, S., Condeelis J.S., Oktay M, Entenberg D. (Submitted August 9, 2024)
Annual Academic Surgical Congress, Las Vegas. NV

3.       Racial Disparities in the Pro-Metastatic Tumor Microenvironment of Treatment-Naïve Breast Cancer
Karadal-Ferrena, B., Miller A., Shukla, S., Parmar P., Huang C., Zhang C., D’Alfonso T., Han R., Adler E., Ladak N., Ginter P., Ye X., Felder M., Ginsberg M., Rosenbaum C., Rohan T.E., Condeelis J.S., Anampa J.D., Entenberg D., Xue X., Sparano J.A., Oktay M.H. (submitted July 10, 2024)
San Antonio Breast Cancer Symposium, San Antonio, TX

4.       Spatial distribution and abundance of tumor cells, T cells and macrophages correlate with recurrence of stage II and III melanoma
Shukla, S., Zhang, C., Espinoza, G., Wang, S., Bracero, Y., Kovrizhkin, K., Horst, B., Leung, L., Nastiuk, K., Moon, J, Entenberg, D., Saenger, Y. (Accepted August 09, 2024)
Society for Immunotherapy of Cancer

5.       Understanding the Molecular Mechanisms of Breast Cancer Extravasation
Parmar, P., Shukla, S., Traub, B., Condeelis, J., Oktay, M., & Entenberg, D. (June 6, 2024).
Albert Einstein College of Medicine, Pathology Research Retreat.

6.       Comparison of De-Noising Methods Applied to Intravital Imaging
Shukla, S., Jung, Y., Entenberg, D. (April 07-10, 2024)
Optica Biphotonics Congress: Biomedical Optics.

7.       Evaluating and Validating Biomarkers Using Digital Pathology, Computer Vision, and Deep Learning
Shukla, S., Entenberg, D. (October 10, 2023)
Montefiore Einstein Comprehensive Cancer Center, Annual Scientific Retreat.

8.      Interchangeability of USTB and PyTorch DAS Beamformers for Ultrasound Image Formation
Shukla, S.; Sharma, A.; Bell, M.A.L., (2022).
Submitted to IEEE Ultrasound Symposium.