BIOGRAPHY

Miguel Á. Carreira-Perpiñán is a professor in the Department of Computer
Science and Engineering at the University of California, Merced. He received
the degree of "licenciado en informática" (MSc in computer science) from the
Technical University of Madrid in 1995 and a PhD in computer science from the
University of Sheffield in 2001. Prior to joining UC Merced, he did
postdoctoral work at Georgetown University (in computational neuroscience) and
the University of Toronto (in machine learning), and was an assistant
professor at the Oregon Graduate Institute (Oregon Health & Science
University). He is the recipient of an NSF CAREER award, a Google Faculty
Research Award and best (student) paper awards at AISTATS and Interspeech. He
is an action editor for the Journal of Machine Learning Research, a past
associate editor for the IEEE Transactions on Pattern Analysis and Machine
Intelligence, and has been an area chair or senior area chair for several
machine learning, computer vision and data mining conferences (NIPS, ICML,
AISTATS, AAAI, ECCV, SDM). His research interests lie in machine learning and
optimization. Most recently, he has been interested in nonconvex optimization
problems that involve nested functions (such as deep neural nets) and possibly
a mixture of discrete and continuous parameters; in neural net compression,
formulated as a constrained optimization; and in decision tree optimization. 
Other interests are in unsupervised learning problems such as dimensionality
reduction (including spectral methods, nonlinear embedding methods and
autoencoders), clustering and denoising, mean-shift algorithms, learning
binary hash functions, and applications to speech processing (e.g. 
articulatory inversion and model adaptation), computer vision, sensor
networks, information retrieval and other areas.