Youngho Seo, Professor

Open (1) Computational Imaging Research (e.g., machine learning, artificial intelligence, high performance computing)

Open. Apprentices needed for the fall semester. Please do NOT contact faculty before September 11th (the start of the 4th week of classes)! Enter your application on the web beginning August 16th. The deadline to apply is Tuesday, August 29th at 8 AM.

UCSF Department of Radiology and Biomedical Imaging collects over 300,000 studies every year with approximately 10 million total studies, but most data remain unorganized and untapped. Our laboratory specializes in quantitative analysis of images from CT, PET/CT, and PET/MRI using image processing and machine learning. We work in close collaboration with UCSF clinical radiology section as well as UCSF Institute for Computational Health Sciences, to apply data science & deep learning techniques to uncover imaging biomarkers from clinical radiology studies. We wish to identify features that predict patient survival, therapeutic response, tumor characteristics, seriousness of a particular pathology, and others.

We are looking for enthusiastic undergraduate students with quantitative bent to join this exciting journey. We have state-of-art equipment with Pascal Titan X GPUs for deep learning. We also have cloud resources available for various tasks. You will work closely with a resident physician or post-doctoral research fellow mentor. Together, you will report to medical school faculty mentors. We will analyze large-scale imaging and associated text data from radiology. Deep learning and data science skills will be used throughout the research study.

Upon successful progress, student will be given opportunities to submit/present at medical school annual research symposium and national research meetings. Students are encouraged to seek out and apply for undergraduate research grants. We will provide our best support to help you obtain them. Pre-health students with interest in machine learning/data science or engineering students with interest in healthcare are especially encouraged to apply.

Time Commitment (~9-12 hours per week):
- Attend lab progress meeting at a UCSF site (every 2-4 weeks in early evening).
- 8-10 hours per week for the assigned tasks



Training Opportunity
-Collecting, Annotating, and Analyzing Big Data in Medicine.
-Applying Machine Learning, Statistical Methods, and Image Processing to tackle clinically important problems.
-Communicating research problems & results to a variety of audiences including clinicians, engineers, entrepreneurs, and general public.
-Writing clinical research papers & abstracts.

Qualifications: Requirement: - Working Knowledge of a programming language (Python or R preferred, but others acceptable). At least one college-level course in computer programming. - Working Knowledge of statistics (mean, variance, linear regression, t-test, chi-square, hypothesis testing). At least one college level coursework in statistics. - Working Knowledge of Microsoft Excel Recommended: - Working knowledge of basic machine learning (cost function, gradient descent / optimizer, back-propagation, cross-validation, bias-variance tradeoff, error analysis). This is not an initial requirement, but it will be used very often, so we expect you to read or take courses to familiarize yourself by end of first semester. CS 189/289A or equivalent. - Coursework in Multivariable Calculus, Linear Algebra, and some form of scientific computing. - Working knowledge of Tensorflow/Keras, Pytorch, or Caffe. Optional: - Working knowledge of AWS - Working knowledge of Version Control (such as Github). - Working knowledge of SQL, Microsoft Access - Working knowledge of Mendeley (or equivalent citation manager) - Coursework in linear programming and numerical analysis

Weekly Hours: 9-12 hrs

Off-Campus Research Site: UCSF Center for Molecular and Functional Imaging
185 Berry Street, Suite 350
San Francisco, CA 94107

Related website: http://www.radiology.ucsf.edu/physics
Related website: http://www.radiology.ucsf.edu/research/labs/quantitative-image-processing

Closed (2) Radionuclide molecular imaging using small laboratory animals

Closed. This professor is continuing with Spring 2017 apprentices on this project; no new apprentices needed for Fall 2017.

Our laboratory (UCSF Physics Research Laboratory), in collaboration with other colleagues and groups in UCSF Department of Radiology and Biomedical Imaging, performs research in small animal imaging using dedicated small animal PET/CT and SPECT/CT, important translational molecular imaging modalities. We currently perform oncologic, cardiovascular, and brain imaging research in collaboration with many UCSF and UC, Berkeley faculty members and their associated research groups. Students will have opportunities in assisting to acquire the imaging data and processing, and learning important biomedical research procedures.

Training opportunities:
Our group (UCSF Physics Research Laboratory) provides training to students at all levels, and in particular, clinical relevance of the research projects will be discussed at our regular group meetings for each project that is open for training opportunities. We have many collaborating research groups at UCSF, UC Berkeley, and Lawrence Berkeley National Laboratory. Hence, the training for the students could be tailored to each individual's research interest.

Data acquisition, quality control, data processing, experience with laboratory animals

Qualifications: Required: Basic knowledge of medical imaging and biology Required: Solid time commitment (1-2 afternoons per week minimum)

Weekly Hours: 9-12 hrs

Off-Campus Research Site: UCSF Physics Research Laboratory
185 Berry Street, Suite 350
San Francisco, CA 94107

Related website: http://www.radiology.ucsf.edu/physics
Related website: https://radiology.ucsf.edu/research/core-services/mspc