A bit of a delayed posting due to pre-spring break crunch time, but my inimitable collaborator and ex-colleague Kamalika Chaudhuri passed along the following announcement.
I write with the exciting news that UCSD has up to four postdoctoral fellowship openings in data science and machine learning.
The fellowships will prepare outstanding researchers for academic careers. The fellows will be affiliated with the CSE or ECE Departments, will enjoy broad freedom to work with any of or faculty, they will be allocated a research budget, and will teach one class per year.
If you know anyone who might be interested, please encourage them to apply!
The program is co-sponsored by UCSD’s CSE and ECE departments, the Interdisciplinary Qualcomm Institute, and the Information Theory and Applications Center.
More information is available at the UCSD Data Science site. Review begins March 21, so get your applications in!
This announcement came via Kush Varshney. IBM is launching a new fellowship program. This came out of his work on DataKind and Saška Mojsilović’s work on Ebola. It’s open to students and postdocs!
I am pleased to let you know that Saška Mojsilović and I are launching a new fellowship program at IBM Research related to data science for social good. We are offering both 3-month summer fellowships for PhD students and full-year fellowships for postdocs. The fellowship webpage and link to apply may be found here.
Fellows will come work with research staff members at our Yorktown Heights laboratory to complete projects in partnership with NGOs, social enterprises, government agencies, or other mission-driven organizations that have large social impact. We are currently in the process of scoping projects across various areas, such as health, sustainability, poverty, hunger, equality, and disaster management. The program is intended to allow students to develop their technical skills and produce publishable work while making a positive impact on the world.
I request that you spread the word to students in your respective departments and the broader community.
So Waheed Bajwa and I have been teaching this Byrne Seminar on “data science.” At Allerton some people asked me how it was going and what we were covering in the class. These seminars are meant to be more discussion-based. This is a bit tough for us in particular:
- engineering classes are generally NOT discussion-based, neither in the US nor in Pakistan
- it’s been more than a decade since we were undergraduates, let alone 18
- the students in our class are fresh out of high school and also haven’t had discussion-based classes
My one experience in leading discussion was covering for a theater class approximately 10 years ago, but that was junior-level elective as I recall, and the dynamics were quite a bit different. So getting a discussion going and getting all of the students to participate is, on top of being tough in general, particularly challenging for us. What has helped is that a number of the students in the class are pretty engaged with the ideas and material, and we do in the end get to collectively think about the technologies around us and the role that data plays a bit differently.
What I wanted to talk about in this post was what we’ve covered in the first few weeks. If we offer this class again it would be good to revisit some of the decisions we’ve made along the way, as this is as much a learning process for us as it is for them. A Byrne Seminar meets for 10 times during the semester, so that it will end well before finals. We had some overflow from one topic to the next, but roughly speaking the class went in the following order:
- Introduction: what is data?
- Potentials and perils of data science
- The importance of modeling
- Statistical considerations
- Machine learning and algorithms
- Data and society: ethics and privacy
- Data visualizaion
- Project Presentations
I’ll talk a bit more on the blog about this class, what we covered, what readings/videos we ended up choosing, and how it went. I think it would be fun to offer this course again, assuming our evaluations pass muster. But in the meantime, the class is still on, so it’s a bit hard to pass retrospective judgement.