Hello everyone
I have some very exciting news to share for the Julia Community! After discussion with Northeastern University, I am pleased to share that we have the opportunity to bring in Julia-related projects into the graduate mathematics curriculum in Northeastern University’s Mathematics Department!
What this means is that folks within the Julia community can develop research projects that can be mentored by you and explored by student teams. Here are some of the classes offered at Northeastern University where these projects could take place:
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MATH7243: Machine Learning and Statistical Learning Theory 1 - Introduces both the mathematical and statistical theory of learning and the implementation of modern machinelearning algorithms appropriate for data science.
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MATH 7339: Machine Learning and Statistical Learning Theory 2 - Continues Math 7243. The course further covers theory and methods for regression and classification, along with other advanced topics in machine learning, statistics and deep learning.
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MATH 5110: Applied Linear Algebra and Matrix Analysis - This course provides a rigorous treatment of the concepts and computational techniques of linear algebra.
Any potential projects will mostly be brought in through Northeastern University’s Experiential Network Program. In general, the projects we are looking for fit the following description:
These short-term projects help students apply their skills to solve problems with real parameters and constraints that deliver real value to project sponsors. Students complete projects remotely and typically work 30-40 hours on a project over approximately six weeks. Projects are scoped and evaluated by sponsors with assistance from the XN staff, who provide the administrative and operational support for the academic programs and the student participants.
To see an example of two project proposals and what is needed, click below:
Project 1: Brain image segmentation
Title: XN Project Title: Brain CT image hemorrhage segmentation by artificial neural network
Description: Based in Kenilworth, New Jersey, a global healthcare leader is working to help the world be well. Through prescription medicines, vaccines, biologic therapies, and consumer care and animal health products, the company works with customers and operate in more than 140 countries to deliver innovative health solutions. The company demonstrates a commitment to increasing access to healthcare through far-reaching policies, programs and partnerships. The company is interested in mining publicly available genomic data sets to identify cancer vulnerabilities, differential cancer gene dependencies and classify predictive models that share common genetic determinants using CRISPR KO gene dependencies, mutational and copy number profiles and RNA expression data obtained from over 1000 cancer cell lines. This task requires application of methods that can integrate diverse data types and machine learning algorithms to classify the lineages that are dependent on essential genes for tumorigenesis. Broadly the main questions are, which algorithms are suitable for mixed data distribution types for classification and scalable for effective computing.
Deliverables: Students will 1) Identify a machine learning algorithm that is best suitable for data and classifies lineages into significant clusters. 2) Test if the classification schemes derived by associating gene dependencies with other data sets such as RNA expression, pathway signatures, DNA mutation and copy number data are unique. 3) Determine whether the machine learning algorithm can be extended to identification of predictors of general viability loss rather than specific priors lineage-specific genes only.
Project 2: Oncology Targets and Biology
Title: Machine Learning -Find Novel Intrinsic Oncology Targets and Biology
Description: The Zeta Surgical company was started by a team of Harvard graduates and academics. The mission is to democratize the access to accurate, safe and fast image-guidance, to unlock the use of image guidance directly at the point-of-care, and to enable new treatments in cases such as emergencies and bedside procedures. In this project, the company provides students a dataset consists of various brain CT scan slices, each of which has a hemorrhage (bleeding) within it. The hemorrhages have been labeled in some of the images. These hemorrhages are divided into different types: intraparenchymal, intraventricular, subarachnoid, subdural, epidural, and category for images with multiple sources of bleeding. Students will use techniques in machine learning, computer vision, user interface, and data analytics to do the classification, regression and the segmentation of these CT images.
Deliverable: Students will develop mathematical models, apply machine learning and artificial neural network techniques, and program using Python and TensorFlow to investigate the labeled dataset. The goal of the project are complete the machine learning model with python scripts for classification, regression and more importantly the segmentation of the brain hemorrhage in CT images.
At this time, I am soliciting potential interest in projects for the Fall 2024 semester. We are particularly interested in potential projects related to health, biology, and climate research and a priority on projects that use Python and/or Julia! Any of these projects could be used as the foundation for future research, publication, or demonstration purposes.
Feel free to reach out to me here in the comments, at my email at jacobszelko@gmail.com, or on Slack @TheCedarPrince.
Thanks everyone!
~ tcp
P.S. We will be discussing additional details and questions about this at the upcoming monthly JuliaHealth meeting – I would highly advise coming through if you are interested as I will be discussing additional potential project ideas and available to answer any questions!