Tushar Nayak

Lab Space B15

4805 Frew St

Pittsburgh PA 15213

Hey there! I am a graduate student at Carnegie Mellon University, pursuing a research oriented masters in the Biomedical Engineering department interested in the intersection of computer vision, and physics-based learning models in the context of medical (and more specifically, surgical) robots informed and guided by medical imaging.

I’m currently part of Professor Kenji Shimada’s Computational Engineering & Robotics Lab, where I work on the vision sub-system of a haptically enabled endovascular robotic tele-surgerical platform as part of my masters thesis with doctoral candidate Rishi Basdeo. My main area of focus is investigating physics informed neural networks and neural ordinary differential equations to quantify deformation from 2D fluroscopy angiograms and register the deformation to the pre-operative computer tomography angiographs.

Always happy to connect with curious minds! Feel free to reach out on email: tusharn [at] andrew [dot] cmu [dot] edu or at the Steffey Robotics Lab space at Scaife Hall.

Before Pittsburgh, I completed my undergrad at Manipal Institute of Technology with a major in Biomedical Engineering, focusing on pattern recognition and image processing, and a minor in Data Science. Over the second half of my undergrad, I worked under Professor Niranjana S and Dr. Krishnaraj Chadaga at the Biomedical Computing Lab on projects ranging from skin lesion image-based viral infection detection to multi-stage and multi-modal cancer detection.

Between my bachelors and masters, I also spent a year as a researcher at IIT Hyderabad working on motion-capture & electromyography analysis for exercise and the Indian Council of Medical Research where I worked on an ultrasound-based fetal anomaly system.

Besides lab-work, I'm also usually giving/boring people with my usual spiel as one of the BME department's ambassador, going trailbiking, playing the piano or continuing work on this website!

news

Aug 22, 2025	Starting off the semester as a Teaching Assistant for Dr. Jason Szafron’s graduate course Fundamentals of Computational Biomedical Engineering and my thesis-guide Dr. Kenji Shimada’s Computer Vision!
Feb 01, 2025	Embarking on my first TA position, for Applied Deep Learning at Carnegie Mellon University

selected publications

lung-attention

Automated histopathological detection and classification of lung cancer with an image pre-processing pipeline and spatial attention with deep neural networks

Tushar Nayak, Nitila Gokulkrishnan, Krishnaraj Chadaga, and 3 more authors

Cogent Engineering, 2024

Abs DOI PDF

Lung Cancer is a major cancer in the world and specifically India. Histopathological examination of tumorous tissue biopsy is the gold standard method used to clinically identify the type, sub-type, and stage of cancer. Two of the most prevalent forms of lung cancer: Adenocarcinoma & Squamous Cell Carcinoma account for nearly 80% of all lung cancer cases, which makes classifying the two subtypes of high importance. Proposed in this study is a data pre-processing pipeline for the H&E-stained lung biopsy images along with a customized EfficientNetB3-based Convolutional Neural Network employing spatial attention, trained on a public three-class lung cancer histopathological image dataset. The pre-processing pipeline employed before training, validation and testing helps enhance features of the histopathological images and removes biases due to stain variations for increased model robustness. The usage of a pre-trained CNN helps the deep learning model generalize better with the pre-trained weights, while the attention mechanism On three-fold validation, the classifier bagged accuracies of 0.9943 ± 0.0012 and 0.9947 ± 0.0018 and combined F1-Scores of 0.9942 ± 0.0042 and 0.9833 ± 0.0216 over the validation and testing data respectively. The high performance of the model combined with its computational efficiency could enable easy deployment of our model without necessitating infrastructure overhaul.