ABCDEFGHIJKLMNOPQRSTUVWXYZ
1
(Last updated: Aug 2019)
2
MLD Research/Reading Groups
3
Group Name (Alphabetized by Topic)Example faculty from MLDContactWebsiteDescription
4
Machine learning in causal inference (Causal)Ed Kennedy (organizer, StatDS), Sivaraman Balakrishnan, Aaditya Ramdas, Larry WassermanMatteo BonviniRead and discuss recent papers or our own current work relating to causal inference using machine learning
5
DELPHI (Epidemiology)Ryan Tibshirani, Roni Rosenfeld, Larry WassermanMaria Jahjahttp://delphi.midas.cs.cmu.edu/Epidemiological forecasting is critically needed for decision making by public health officials, commercial and non-commercial institutions, and the general public. The Delphi group at Carnegie Mellon University focuses on developing the technological capability of epi-forecasting, and its role in decision making, both public and private. Our long term vision is to make epidemiological forecasting as universally accepted and useful as weather forecasting is today.
6
Statistics and Machine Learning Research Group (Machine Learning)Aaditya Ramdas, Pradeep Ravikumar, Aarti Singh, Sivaraman Balakrishnan, Cosma Shalizi, Ryan Tibshirani, Larry Wasserman, Nihar Shah, Alessandro RinaldoBeomjo Park, Sasha Podkopaevhttp://statml.cs.cmu.edu/We are group of faculty and students in Statistics and Machine Learning broadly interested in research at the intersection of these two disciplines.
7
Computer Music Reading Group (Computer Music)
Roger Dannenberg (CMU faculty), Gus Xia
(NYU-Shanghai faculty) and Shuqi Dai (PhD candidate and
organizer)
http://www.cs.cmu.edu/~music/reading-group/We are interested in topics not only related to Music Information
Retrieval and Music Computational Creativity, but also the state-of-art
results in Machine Learning and Deep Learning, including papers from
NLP, CV, audio, and data mining area. The computer music reading group discusses papers on music
learning, understanding, representations, generation, and signal
processing, with an emphasis on machine learning.
8
Fairness, Ethics, Accountability and Transparency in Machine LearningZachary Lipton, Alex ChouldechovaAmanda Coston
9
Reinforcement LearningBen EysenbachThe RL reading group is a weekly meeting of PhD and MS students (and,
occasionally, faculty). Last semester we read papers on off-policy RL
and model-based RL; potential topics for this semester include POMDPs,
Bayesian methods for RL, and exploration.
10
Andrej's Reading GroupAndrej Risteski (aristesk@andrew.cmu.edu)* The group is open to join *so long as they are willing to
contribute* -- this means presenting once every ~2 months roughly. I
help students choose paper or cluster of papers so it's something of
interest to the others. * The topics are related to things I
and others in the group work on: theory of unsupervised,
self-supervised, transfer learning (mostly in NLP and vision), theory of
deep learning more generally, provable algorithms for
learning/inference in latent-variable models.
11
Tom Mitchell's research groupTom Mitchell (tom.mitchell@cmu.edu)Tom MitchellTom's research group weekly meetingWe are an informal, friendly, collection of Tom’s students, collaborators, and other people interested in machine learning, including problems like conversational machine learning, never ending learning, curriculum learning, etc. We typically ask each member to run at least one session during the semester -- they often present their own research, or have us read and discuss a paper they find interesting. Other students and researchers are welcome to join and contribute to the group-- please email Tom.Mitchell@cmu.edu if you would like to join the group.
12
Multimodal Machine Learning (MMML) reading groupNicki Siverling (nickisiverling@cmu.edu)We will be discussing recent papers on machine learning methods to represent and integrate multimodal data.We
meet every other week starting Monday September 7th @ 5:00 pm. We will share details about future dates and topics to the MMML reading group list.
13
brAIn Reading GroupMike Tarr, Leila WehbeAria Wang (yuanw3@andrew.cmu.edu), Mariya TonevaA new reading group at the interface of machine learning, AI and neuroscience. We will meet at 2pm on Fridays starting next Friday (9/11).
14
STAMPS (Statistical Methods for the Physical Sciences) Research GroupAnn Lee, Mikael Kuusela and Larry WassermanNic Dalmasso (ndalmass@andrew.cmu.edu)http://stat.cmu.edu/stamps/Weekly group meetings (Fridays 3-4 pm) http://stat.cmu.edu/stamps/group-meetings/fall-2020-talks/
and monthly colloquia-style public webinars (2nd Friday of each month at 1:30-2:30 pm) http://stat.cmu.edu/stamps/webinar/summer-fall-2020/
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100