HDSI Undergrad Scholarship Program Mentorship Opportunities

HDSI Undergrad Scholar
Hi HDSI folks,
 
The HDSI Undergrad Scholarship Program is a fantastic opportunity for our students to design and carry out their own multidisciplinary research projects, under the supervision of a mentor. Many of you have already agreed to be mentors for some student projects, so thank you! For the 2022-23 program, we have 9 projects that we would like to support, but are in need of a mentor.
 
Mentors receive $500 in research funds for their service, and the duties are listed at the bottom of the info page. Mentors can be faculty, research staff, postdocs, grad students, or anyone with the expertise to mentor the project. The projects needing mentors are:
  • Applications of Neural Networks for Civil Design and Resource Management
  • ParkSmart: A robust and cost-effective computer-vision based solution to parking lot management
  • Resolving Major City Traffic Using Automated Train Route Optimization
  • Rough Rice Prediction (predicting price of rice)
  • SpamBack: Spam detection for social media
  • Fixing Form: Using pose estimation to minimize injury from the big three movements
  • House Price Prediction in Python Using Machine Learning
  • Race and Recidivism: Can Racial Bias Be Removed From Criminal Justice Risk Assessment Tools?
  • How to prevent people from over-spending using a machine learning model
If you or anyone you know is interested in mentoring one or more of these projects, please get in touch with me directly and I can share their more detailed proposals and put you in contact with the students. Ideally, you would have a chat with the team before agreeing to mentor them.
 
Thanks!
Stuart
 

graphic logo stating HDSI Scholarship Program
 
R. Stuart Geiger, Ph.D
Assistant Professor, Dept of Communication and the Halıcıoğlu Data Science Institute
Affiliate Faculty, Institute for Practical Ethics, Computer Science & Engineering, and Computational Social Science

University of California, San Diego
http://stuartgeiger.com | he/him or they/their

CZI Funding Opportunity for Data Science

Data Insights cycle 2

https://chanzuckerberg.com/rfa/single-cell-data-insights/

Deadline: Aug 25

Funding: $200,000 for grants that primarily support the effort of one to two full-time employees (FTEs) working on a given project. $400,000 for networked grants that will require the participation of two to four FTEs.

The goal of this opportunity is to create a network of projects that address broad computational challenges and needs within single-cell biology at a variety of scales. Applications are encouraged from computational experts outside the field of single-cell biology but with expertise relevant to overcoming current bottlenecks. Projects may include dedicated efforts to refine existing computational tools, benchmark classes of tools, improve standards, integrate available data that enables greater biological insight, develop new features that support interoperability of data or tools, and other major challenges brought forward. Projects must propose and rely on existing data that is openly and freely available (count matrices at minimum) at the time of application.

HDSI Seminar Series | Spurious or Causal? Studying Failure Modes of Deep Learning and Ways to Fix Them

Title: Spurious or Causal? Studying Failure Modes of Deep Learning and Ways to Fix Them

Abstract: Over the last decade, deep models have enjoyed wide empirical success. However, in practice, these models are not reliable due to their sensitivity against adversarial or natural input distributional shifts as well as a lack of meaningful reasoning behind their predictions. In this talk, I will show that a root cause of these issues is the heavy reliance of deep models on non-causal and spurious features in their inferences. I will then explain our progress in understanding failure modes of deep learning and outline a roadmap towards developing trustworthy learning paradigms.

Bio: Soheil Feizi is an assistant professor in the Computer Science Department at University of Maryland, College Park. Before joining UMD, he was a post-doctoral research scholar at Stanford University. He received his Ph.D. from Massachusetts Institute of Technology (MIT) in EECS with a minor degree in mathematics. He has received an NSF CAREER award in 2020 and is the recipient of several other awards including two best paper awards, a teaching award and multiple faculty awards from industry such as IBM, AWS and Qualcomm. He received a Simons-Berkeley Research Fellowship on deep learning foundations in 2019. He is the recipient of the Ernst Guillemin award for his M.Sc. thesis, as well as the Jacobs Presidential Fellowship and the EECS Great Educators Fellowship at MIT.