March 4, 2025

Speaker: David Hogg (NYU/MPIA/Flatiron)

Contact Information: 

Title:  Is machine learning good or bad for astrophysics?

Abstract:       Machine learning methods are having a huge impact in astrophysics. However, ML has a strong ontology — in which only the data exist — and a strong epistemology — in which a model is considered good if and only if it performs well on held-out training data. These philosophies are in strong conflict with our goals, practices, and philosophy in astrophysics. I identify locations in astrophysics practice at which the philosophy of ML is valuable (for example: in causal separations of foregrounds). I also show that there are contexts in which the use of ML methods introduce strong, unwanted, and un-correctable biases. The answer to the question posed in my title is “both.” Work in collaboration with Soledad Villar (JHU).

Visitor's room: 129

Visit Schedule:

Tuesday

• 9:30 a.m.: Andrew Saydjari (Peyton 109A)

• 10:00 a.m:  

• 10:30 a.m.:  Coffee

• 11:00 a.m.: Colloquium

• 11:30 a.m.: Colloquium 

• 12:00 p.m.: Bahcall lunch

• 12:30 p.m.: Bahcall lunch

• 1:00 p.m.: Bahcall lunch

• 1:30 p.m.:  Jim Peebles

• 2:00 p.m.:  Jim Peebles

• 2:30 p.m.: Matt Sampson (022)

• 3:00 p.m.:  Jake Nibauer (129)

• 3:30 p.m.: Yan Liang (020)

• 4:00 p.m.: Peter Melchior (131)

• 4:30 p.m.:  Tea Time with Grad Students in Grand Central 

• 5:00 p.m.: Tea Time with Grad Students in Grand Central 

Dinner Sign-up - add your name here:

Tuesday (March 4th, 2025)

1. Faculty Host: Peter Melchior

2. Postdoc Host:  Andrew Saydjari

3.  Christian Kragh Jespersen

4. Matt Sampson

5. Jasmine Parsons

6. Yan Liang

7.  Jiaxuan Li

8.  Speaker

9. WAITING LIST: Josh Winn

STUDENTS HAVE PRIORITY.

PLEASE NO MORE THAN 8 PEOPLE (it is more difficult to properly interact with the speaker in larger groups).