Peter Howard Gann

    Email Address:
    College: Medicine Department: Pathology
    Secondary Department: Epidemiology and Biostatistics
    Title: Professor
    Office: COMRB 6133 Phone: 312-355-3723
    Participating in the Chancellor’s Undergraduate Research Awards program: Yes

    Research Interest:
    Our lab is generally engaged in developing and validating new biomarkers in human tissue samples for predicting cancer outcome and response to treatment. Our current projects focus on creating computer vision approaches to detect clinically significant patterns in whole slide digital images. Specifically, we are using deep learning and other machine learning techniques to identify the histological signatures of multiple breast cancer subtypes in a single patient. This involves working with genomic databases and training machine learning classifiers to label different patches within a microscopy image, based on “pure” genomic subtypes. We are testing the general hypothesis that patients whose cancers are admixed will respond less favorably to therapy that is targeted (typically) to a single subtype. We are looking for students with a background in computer engineering, programming and big-data analysis who are excited about working in medical research.

    Minimum time commitment in hours per week: 10

    Qualifications of a Student:
    Prefer: freshman, soph or junior students; Honors students; previous experience in medical research.

    Must have: GPA at least B+; background in computer engineering, computer science, statistics, or bioengineering.

    Brief Summary of what is expected from the student:
    The student will be assigned duties as a regular member of the research team (commensurate with academic schedule). Depending upon the student's skills and trainability they can learn a variety of techniques either at the computer or at the lab bench (or both!). To build confidence in making presentations, the student will be invited to discuss their work in lab meetings and possibly Division seminars. We expect students to contribute to publishable work and earn co-authorship.

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