IEEE DL Seminar by Paris Smaragdis on "Machine Learning Approaches for Speech Enhancement"
Paris Smaragdis, UIUC & Adobe Research
Date: 25 October 2016
Time: 11:45 AM to 01:00 PM
Technology Square Research Building (TSRB) 125
During the last few years, machine learning has started to permeate the world of speech enhancement and has produced results that drastically improve over the state of the art. In this talk I’ll touch on some of the most recent approaches on both multichannel and single channel enhancement, and I will show how traditional signal processing approaches can be reimagined using machine learning tools such as mixture models, matrix factorizations, deep learning regressions, and more.
Biography. Paris Smaragdis is an associate professor at the Computer Science and the Electrical and Computer Engineering departments of the University of Illinois at Urbana-Champaign, as well as a senior research scientist at Adobe Research. He completed his masters, PhD, and postdoctoral studies at MIT, performing research on computational audition. In 2006 he was selected by MIT’s Technology Review as one of the year’s top young technology innovators for his work on machine listening, in 2015 he was elevated to an IEEE Fellow for contributions in audio source separation and audio processing, and during 2016-2017 he is an IEEE Signal Processing Society Distinguished Lecturer. He has authored more than 100 papers on various aspects of audio signal processing, holds more than 40 patents worldwide, and his research has been productized by multiple companies
IRIM Seminar by Byron Boots on "Closing the Gap Between Machine Learning and Robotics"
Wednesday, Oct. 5, 2016
Marcus Nano Bldg. • Rooms 1116-1118
Given a stream of multimodal sensory data, an autonomous robot must continuously refine its understanding of itself and its environment as it makes decisions on how to act to achieve a goal. These are difficult problems that roboticists have attacked using classical tools from mechanics and controls and, more recently, machine learning. However, classical methods and machine learning algorithms are often seen to be at odds, and researchers continue to debate the merits of engineering vs. learning.
A recurring theme in this talk will be that prior knowledge and domain insights can make learning and inference easier. I will discuss several fundamental robotics problems including continuous-time motion planning, localization, and mapping from a unified probabilistic inference perspective. I will show how models from statistical machine learning like Gaussian Processes can be tightly integrated with insights from engineering expressed as differential equations to solve these problems efficiently. Finally, I will demonstrate the effectiveness of these algorithms on several existent robotics platforms.
Byron Boots is an assistant professor in the School of Interactive Computing and the Institute for Robotics and Intelligent Machines at the Georgia Institute of Technology. Prior to joining Georgia Tech, Boots was a postdoctoral researcher working with Dieter Fox in the Robotics and State Estimation Lab at the University of Washington. He received his Ph.D. in Machine Learning from Carnegie Mellon in 2012, where he was advised by Geoff Gordon. Boot’s work on learning models of dynamical systems received the 2010 Best Paper award at ICML. His current research focuses on developing theory and systems that integrate perception, learning, and decision-making.
Welcome new faculty member Chethan Pandarinath in BME
Welcome new faculty member Chethan Pandarinath in Wallace H. Coulter Department of Biomedical Engineering
Please join us in welcoming Chethan Pandarinath in Wallace H. Coulter Department of Biomedical Engineering as an Assistant Professor starting in December 2016. His work centers on understanding how the brain represents information and intention, and using this knowledge to develop high-performance, robust, and practical assistive devices for people with disabilities and neurological disorders. He takes a dynamical systems approach to characterizing the activity of large populations of neurons, combined with rigorous systems engineering (signal processing, machine learning, and real-time systems) to advance the performance of brain-machine interfaces and neuromodulatory devices.
Welcome to Georgia Tech Chethan!
Welcome to new faculty Mayya Zhilova in Math
We would like to welcome Mayya Zhilova, who joined the School of Mathematics at the Georgia Institute of Technology in Fall 2016. Mayya's primary research interests lie in the areas of mathematical statistics and probability theory, particularly in statistical inference for complex high-dimensional data. Her current research is focused on probabilistic and statistical properties of resampling procedures for high-dimensional data in presence of modelling errors in statistical models
Mayya Zhilova received her Ph.D. from Humboldt University of Berlin in 2015. Before joining Georgia Tech, she worked at Weierstrass Institute in Berlin, in the research group Stochastic Algorithms and Nonparametric Statistics from 2011 till 2016. She did her undergraduate studies in Lomonosov Moscow State University. In her leisure time, Mayya enjoys listening to music, dancing, and hiking. For more details see her website.
Welcome to new faculty member Huan Xu in ISyE
Please join us in welcoming a new faculty member Huan Xu in ISyE. Huan Xu is an assistant professor at the Milton School of Industrial and Systems Engineering at Georgia Institute of Technology since 2016. Before that, he has been an assistant professor at National University of Singapore, first at the department of Mechanical Engineering, then at the department of Industrial and Systems Engineering, from 2011.
Huan graduated from Shanghai Jiaotong University with a Bachelor’s degree in Automation in 1997 and got his Master’s degree in ECE from the National University of Singapore in 2005. He was fortunate enough to obtain a Ph. D. degree in ECE from McGill University under the supervision of Shie Mannor, in 2009. He then spent two wonderful years in the WNCG group of UT-Austin, as a postdoctoral research fellow working with Constantine Caramanis and David Morton. Prior to starting his Ph.D. study, he has worked in Panasonic, HP, and Oracle for almost six years.
His current research interest focus is on learning and decision-making in large-scale complex systems. Specifically, He is interested in machine learning, high-dimensional statistics, robust and adaptable optimization, robust sequential decision making, and applications to large-scale systems. He is an associate editor of IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) and Computational Management Science and was an area chair for NIPS. For more details, see his website.