The Machine Learning Lunch Seminar is a weekly series, covering all areas of machine learning theory, methods, and applications. Each week, over 70 students and faculty from across Rice gather for a catered lunch, ML-related conversation, and a 45-minute research presentation. If you’re a member of the Rice community and interested in machine learning, please join us! Unless otherwise announced, the ML lunch occurs every Wednesday of the academic year at 12:00pm in Duncan Hall 3092 (the large room at the top of the stairs on the third floor).

The student coordinators are Michael Weylandt, Tan Nguyen, Chen Luo, Lorenzo Luzi, and Cannon Lewis, and the faculty coordinator is Reinhard Heckel. The ML Lunch Seminar is sponsored by EOG Resources.

Announcements about the ML Lunch Seminars and other ML events on campus are sent to the mailing list. Click here to join.

Data-Driven Energy Informatics

Jan. 23, 12:00 pm- 1:00 pm in DCH 3092
Speaker: Cristina Heghedus
Please indicate interest, especially if you want lunch, here.
Abstract:  The large automation of appliances and adoption of high energy consuming utilities in homes has the potential to exert a huge constraint on the current energy infrastructure. Majority of the energy supply chain infrastructure across the world are incapable to handle large and concentrated energy demands. Therefore, electric energy suppliers are challenged to make accurate and granular forecasting of the future electricity demand, ensuring efficiency and preventing energy waste and theft. Within Data Analysis, Machine Learning and Deep Learning models are efficiently used for electricity demand forecasting, price forecasting, peak prediction and so on. Data analysis has brought already numerous advantages to the energy field, started to evolve in the oil and gas industry as well. The oil and gas industry operates now with huge number of sensors installed in different facilities, particularly in production and injections wells. These sensors provide millions measurements like pressure, temperature and rate every year for every well. We apply DL models, that already produced remarkable results in Energy Informatics, to such measurements. The reasonable performance of these models on given data sets (electricity, oil and gas, transportation) brings new benefits to the energy field, facilitating decision making and efficient resource management.


Image data and analysis for tropical conservation

Jan. 16, 12:00 pm- 1:00 pm in DCH 3092
Speaker: Lydia Beaudrot
Please indicate interest, especially if you want lunch, here.
Abstract:  Over the past two decades, trail cameras that photograph animals as they pass by have become a primary method for collecting data on wildlife. These images have enabled ecological and
conservation research that was not previously possible. In this talk, I will 1) provide examples of how we have employed statistical models to address conservation questions, 2) describe limitations in current
statistical approaches and 3) discuss future directions for applications of machine learning.


Locality Sensitive Hashing for Large Scale Machine Learning

Jan. 9, 12:00 pm- 1:00 pm in DCH 3092
Speaker: Anshumali Shrivastava
Please indicate interest, especially if you want lunch, here.
Abstract:  Anshu will introduce some recent works in Rush Lab. The mission of Rush Lab is to push machine learning to the extreme-scale. They design and implement exponentially resource-frugal and scalable machine learning (ML) algorithms by using randomized hashing and sketching algorithms, suited for modern big-data constraints. Apart from being exponentially cheap, the algorithms are embarrassingly parallelizable. The extremely low resource requirements make our techniques ideal for IoT devices as well. Furthermore, the algorithms are naturally privacy-preserving as they do not work directly with data attributes and instead only operate on secure hashes or sketches.


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