A big challenge and opportunity of our time is to make use of the vast majority of data that is being collected every day.
As a consequence, Rice university has undertaken a university-wide initiative to increase its activities in data-driven knowledge discovery, both in education as well as in research.
An important component of data science is to design systems that learn automatically from data, a field known as machine learning.
Academically, machine learning lies in the intersection of Electrical and Computer Engineering (ECE), Computer Science (CS), Statistics, and Computational and Applied Mathematics (CAM).
Rice is in an excellent position to make fundamental contributions in this area since several groups at Rice in the ECE, CS, Statistics, and CAM departments—lead both by senior faculty as well as newly hired junior faculty members—focus on applied and theoretical machine learning.
Moreover, Rice lies in the heart of Houston, right next to the largest medical center and the energy sector, both industries where machine learning plays an increasingly important role.
What makes Rice a unique place for machine learning research is that all the corresponding faculty and students from ECE, CS, Statistics, and CAM are located in the same building (Duncan Hall), which fosters collaborations.
To leverage this structural advantage, we, the machine learning faculty at ECE, CS, Statistics, and CAM (see \href{http://machinelearning.rice.edu/}{http://machinelearning.rice.edu/} for a list of the people involved) are starting two interdepartmental and complementary machine learning seminars: a seminar featuring invited speakers as well as a weekly lunch seminar.
The goal of the former is to invite distinguished guests for a talk and discussion to Rice, both to present Rice to the larger machine learning community, as well as to learn from our guests.
The goal of the later is to encourage collaborations within Rice.
Each weak, a faculty member, an advanced PhD student or postdoctoral scholar from Rice will present his or her work or an open problem he or she is working on to all other machine learning research groups during the lunch seminar.
While the majority of the talks will be from machine learning researchers to machine learning researchers, we will also have speakers from other departments at Rice and from the industry presenting their open machine learning problems to our community.
Besides the talk, there will be a few minutes at each seminar reserved for discussion and announcements on machine learning specific activities within Rice.
This will also give the (industry) sponsors of our series a change to reach our community.
We are organizing this as a lunch seminar since it is impossible to schedule a interdepartmental seminar at any other time without clashing with classes or departmental meetings.