Carnegie Mellon University

1. Machine Learning in Experimental Biomedical Engineering Research (prev. Clinical Translations of AI)

Graduate Students | Spring 2026 | Dr. Newell Washburn | Department of BiomedicL Engineering, College of Engineering
This course is designed to introduce students to applications of artificial intelligence and machine learning in experimental BME research. The course will be focused on the main data types that are generated: tabular data from sets of experiments, image data, spectral data, and time-series data. A diversity of regression and classification methods, including linear models, Gaussian processes, tree-based methods, and TabPFN, will be introduced with an emphasis on experimental design and modeling datasets based on small sample sizes. Important examples include quantitative analysis of cells in culture, in situ spectroscopic data, and omics data. Methods of statistical analysis, feature selection, and transfer learning will also be introduced.

2. Computer Vision for Engineers 🔗 🔗

Graduate Students | Fall 2025 | Dr. Kenji Shimada | Department of Mechanical Engineering, College of Engineering
The course provides a general introduction to the basics of computer vision and its modern engineering applications such as factory automation, infrastructure inspection, mobile-robot navigation, self-driving cars, and medical diagnosis. Students learn the theories, algorithms, and computational methods of computer vision, including (1) Sensor Selection, (2) Image Processing and Analysis, (3) Motion Analyses, (4) 3D Reconstruction, (5) Pointcloud Processing, (6) Feature Tracking, and (8) Object Detection. After learning theories and algorithms in lecture, students will have weekly problem sets to design computational methods for computer vision, write computer programs, and present their results.

3. Fundamentals of Computational Biomedical Engineering 🔗

Graduate Students | Fall 2025 | Dr. Jason Szafron | Department of Biomedical Engineering, College of Engineeering
The primary objective of the course is to explore coding for biomedical computing. It is intended for students without a strong background in coding to serve as a bridge to more advanced modeling and computing courses. This course will enable students to use computational tools for solving biomedical engineering problems, in preparation for other graduate courses and for their future career. Students will gain solid skills in programming MATLAB and Simulink to organize, analyze, model, and visualize biomedical problems. Areas to cover include linear algebra and principal component analysis, non-linear equations, calculus, ordinary differential equations, and machine learning, with examples drawn from cancer diagnosis, glucose monitoring, immunotherapy, bioelectrical activities, cardiac simulation, kidney dialysis, and infectious disease modeling, etc. The course will end by transitioning to Python programming, taking advantage of the similarities between Python and MATLAB.

4. Applied Deep Learning 🔗

Graduate Students | Spring 2025 | Dr. Clarence Worrell | Department of Software and Societal Systems, School of Computer Science
Deep neural networks have made in-roads in virtually every industry, propelled by exponential increases in compute power and fundamental progress in modeling. Knowledge of these models is fast becoming a key asset for software engineers, as current systems are quickly starting to include many neural components, and the practice of software engineering itself is starting to benefit from neural program assistance (incl. automated bug finding, translation between programming languages). This course equips the next generation of software engineers with knowledge of neural models, the software engineering challenges involved in using these, and hands-on experience with their applications. It teaches both a rich vocabulary of general, essential concepts (including architectures), and recent work on applications of these models, aimed primarily at applications for and in software engineering itself. The course includes a hands-on deep learning project aimed that will be used to teach the various stages (and their pitfalls) of building and deploying deep learners.

Manipal Institute of Technology

BioInnovate Technical Workshop Series | Conducted through IEEE Engineering in Medicine in Biology Society, Student Chapter Manipal
During my tenure as the Head of Research (and Chairperson) at the Manipal student chapter of IEEE Engineering in Medicine & Biology Society, the student organization teamed up with the Department of Biomedical Engineering in organizing several workshops over my junior and senior year. I conducted workshops to introduce freshers and sophomores to the ​fundamentals of signal and image processing, deep learning, microcontrollers and Linux. Additionally, I also ​organized and executed a half-year long learning a semester long junior members in electronics, fundamentals of programming, market analysis and signal processing. Exceptional students from this initiatives shadowed my lab work and undertook a study alongside me, the results of which we presented at a university symposium.