Machine Learning and Modern AI: Strengths, Weaknesses, Opportunities, and Threats (SWOT)
Event details
| Date | 21.02.2020 |
| Hour | 13:15 › 14:15 |
| Speaker | Volkan Cevher received the B.Sc. (valedictorian) in electrical engineering from Bilkent University in Ankara, Turkey, in 1999 and the Ph.D. in electrical and computer engineering from the Georgia Institute of Technology in Atlanta, GA in 2005. He was a Research Scientist with the University of Maryland, College Park from 2006-2007 and also with Rice University in Houston, TX, from 2008-2009. Currently, he is an Associate Professor at the Swiss Federal Institute of Technology Lausanne and a Faculty Fellow in the Electrical and Computer Engineering Department at Rice University. His research interests include machine learning, signal processing theory, optimization, and information theory. Dr. Cevher is an ELLIS fellow and was the recipient of the Google Faculty Research Award on Machine Learning in 2018, IEEE Signal Processing Society Best Paper Award in 2016, a Best Paper Award at CAMSAP in 2015, a Best Paper Award at SPARS in 2009, and an ERC CG in 2016 as well as an ERC StG in 2011. |
| Location | |
| Category | Conferences - Seminars |
Abstract: Machine Learning (ML) is an interdisciplinary study of algorithms, statistical models, and error functions jointly with computer systems to perform specific tasks. The ML community that we can see at its premiere venues, such as NeurIPS, ICML, AISTATS, COLT, and ALT, is really a union of many distinct sub-communities, from pure optimization and efficient inference to deep learning and vision, and from traditional high dimensional statistics and computer science theory to control and reinforcement learning, reflecting this definition perfectly.
This research diversity in ML is really critical in moving forward towards the grand goal of achieving artificial intelligence (AI). To this end, my talk describes the key directions and developments in ML and AI via a running SWOT analysis, supposed by our recent research results at the LIONS laboratory (https://lions.epfl.ch).
Practical information
- General public
- Free
Organizer
- Prof. Elison Matioli
Contact
- [email protected] / +4121 69 33721