Scholar
Alif Munim
Google Scholar ID: 3XRvwJwAAAAJ
Ryerson University
Citations & Impact
Citations
34
H-index
3
i10-index
2
Publications
3
Co-authors
5
list available
Publications
Resume (English only)
Academic Achievements
- - Participated in the research on 'Classical vs Deep Learning Methods for Image Segmentation of Small Brain MRI Datasets', which reviewed approaches to overcome challenges in biomedical image processing.
- - As a software engineer intern on the Watson AIOps team, took on a lead role in building efficient data pipelines for data cleaning, feature extraction, and AI training.
Research Experience
- - Worked with Dr. Dafna Sussman, Daniel Nussey, and Rachita Singh at Ryerson's Maternal-Fetal Imaging Laboratory to compare u-nets, fully convolutional networks, and gradient-boosted ensemble models for the segmentation of brain metastases in <100 3D MRI scans.
- - Collaborated with the University of Toronto and IBM's Center for Advanced Studies (CAS) to extend prior work in speech-act theory and bring it into the realm of deep learning. Contributed to the development of IBM Watson Orchestrate, which won a CES 2022 Innovation Award.
Education
- Pursuing an undergraduate degree in Computer Science and Psychology at Ryerson University, supervised by Dr. Dafna Sussman as a computer vision research assistant at the Maternal-Fetal Imaging Lab located at the iBEST institute. Previously, supervised by Dr. Eric Yu at the University of Toronto as a research assistant in natural language understanding and business automation.
Background
- A fourth-year undergraduate co-op student studying Computer Science and Psychology at Ryerson University. My aim is to achieve a deeper understanding of human perception and reasoning using machine learning, with a primary interest in its applications in mitigating media bias and aiding biomedical professionals.
