Christopher Pal

During his PhD studies, he was also a research assistant at the University of Toronto in the Department of Electrical and Computer Engineering. He closely collaborated with the Banting and Best Department of Medical Research. He researched on image processing and statistical methods for the analysis of large scale genomics and computational molecular biology experiments using DNA microarrays. Before his graduate studies, he was with the multimedia research company Interval in Silicon Valley, California. He was awarded a patent on audio signal processingaudio signal processing as a result of his research at Interval.

Christopher Pal is an Associate Professor at the École Polytechnique of Montreal. He is currently teaching Multimedia data processing and analysis in the Department of Computer and Software Engineering. He was a former professor at the University of Rochester, Department of Computer Science before arriving in Montreal. He was a research scientist in the University of Massachusetts and affiliated with the Interactive Visual Media Group and the Machine Learning and Applied Statistics groups at Microsoft Research. His research led to three patents on image processingimage processing, computer vision and interactive multimedia.

During his PhD studies, he was also a research assistantresearch assistant at the University of Toronto in the Department of Electrical and Computer Engineering. He closely collaborated with the Banting and Best Department of Medical Research. He researched on image processing and statistical methods for the analysis of large scale genomics and computational molecular biology experiments using DNA microarrays. Before his graduate studies, he was with the multimedia research company Interval in Silicon Valley, California. He was awarded a patent on audio signal processing as a result of his research at Interval.

Christopher Pal is a faculty member at the Montreal Institute for Learning Algorithms (MILA), working alongside with leading world experts in machine learning and deep learningdeep learning Prof. Yoshua Bengio, Prof. Aaron Courville, Prof. Pascal Vincent, Prof. Simon Lacoste-Julien, Prof. Laurent Charlin and Prof. Roland Memisevic. He is also a member of the Institute for Data Valorization (IVADO).

He earned his Master of Science in Math and PhD from the University of Waterloo, CanadaCanada. He developed methods for automated cartography and the analysis of high-resolution digital aerial photography during his masters research. He was also involved with a group of software engineering projects developing spatial databases for managing environmental information. His PhD research led to contributions applying probability models and optimization techniques to image, video and signal processing.

Christopher Pal is an Associate Professor at the École Polytechnique of Montreal. He is currently teaching Multimedia data processing and analysis in the Department of Computer and Software Engineering. He was a former professor at the University of Rochester, Department of Computer Science before arriving in Montreal. He was a research scientist in the University of Massachusetts and affiliated with the Interactive Visual Media Group and the Machine Learning and Applied Statistics groups at Microsoft Research. His research led to three patents on image processing, computer visioncomputer vision and interactive multimedia.

During his PhD studies, he was also a research assistant at the University of Toronto in the Department of Electrical and Computer Engineering. He closely collaborated with the Banting and Best Department of Medical Research. He researched on image processing and statistical methods for the analysis of large scale genomics and computational molecular biology experiments using DNA microarrays. Before his graduate studies, he was with the multimedia research company Interval in Silicon Valley, CaliforniaCalifornia. He was awarded a patent on audio signal processing as a result of his research at Interval.

During his PhD studies, he was also a research assistant at the University of Toronto in the Department of Electrical and Computer Engineering. He closely collaborated with the Banting and Best Department of Medical Research. He researched on image processing and statistical methods for the analysis of large scale genomics and computational molecular biology experiments using DNA microarrays. Before his graduate studies, he was with the multimedia research company Interval in Silicon ValleySilicon Valley, California. He was awarded a patent on audio signal processing as a result of his research at Interval.

During his PhD studies, he was also a research assistant at the University of Toronto in the Department of Electrical and Computer EngineeringComputer Engineering. He closely collaborated with the Banting and Best Department of Medical Research. He researched on image processing and statistical methods for the analysis of large scale genomics and computational molecular biology experiments using DNA microarrays. Before his graduate studies, he was with the multimedia research company Interval in Silicon Valley, California. He was awarded a patent on audio signal processing as a result of his research at Interval.

During his PhD studies, he was also a research assistant at the University of Toronto in the Department of Electrical and Computer Engineering. He closely collaborated with the Banting and Best Department of Medical Research. He researched on image processing and statistical methods for the analysis of large scale genomics and computational molecular biology experiments using DNA microarraysDNA microarrays. Before his graduate studies, he was with the multimedia research company Interval in Silicon Valley, California. He was awarded a patent on audio signal processing as a result of his research at Interval.

During his PhD studies, he was also a research assistant at the University of Toronto in the Department of Electrical and Computer Engineering. He closely collaborated with the Banting and Best Department of Medical Research. He researched on image processing and statistical methods for the analysis of large scale genomics and computational molecular biology experiments using DNADNA microarrays. Before his graduate studies, he was with the multimedia research company Interval in Silicon Valley, California. He was awarded a patent on audio signal processing as a result of his research at Interval.

During his PhD studies, he was also a research assistant at the University of Toronto in the Department of Electrical and Computer Engineering. He closely collaborated with the Banting and Best Department of Medical Research. He researched on image processing and statistical methods for the analysis of large scale genomics and computational molecular biology experiments using DNADNA microarrays. Before his graduate studies, he was with the multimedia research company Interval in Silicon Valley, California. He was awarded a patent on audio signal processing as a result of his research at Interval.

During his PhD studies, he was also a research assistant at the University of Toronto in the Department of Electrical and Computer EngineeringComputer Engineering. He closely collaborated with the Banting and Best Department of Medical Research. He researched on image processing and statistical methods for the analysis of large scale genomics and computational molecular biology experiments using DNA microarrays. Before his graduate studies, he was with the multimedia research company Interval in Silicon Valley, California. He was awarded a patent on audio signal processing as a result of his research at Interval.

His publications include, Brain tumor segmentation with Deep Neural Networks; Deep Learning: A Primer for Radiologists; Improving probabilistic inference in graphical models with determinism and cycles; EmoNets: Multimodal deep learning approaches for emotion recognition in video; A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI; Metastatic liver tumour segmentation with a neural network-guided 3D deformable model; Semi-supervised Learning with Encoder-Decoder Recurrent Neural Networks: Experiments with Motion Capture Sequences; Improving facial analysis and performance driven animation through disentangling identity and expression; Learning from weakly labeled faces and video in the wild; 3D segmentation of abdominal CT imagery with graphical models, conditional random fields and learning; On consistent inter-view synthesis for autostereoscopic displays; and On Learning Conditional Random Fields for Stereo Exploring Model Structures and Approximate Inference.

Christopher Pal is an associateAssociate professorProfessor at the École Polytechnique of Montreal. He is currently teaching Multimedia data processing and analysis in the Department of Computer and Software Engineering. He was a former professor at the University of Rochester, Department of Computer Science before arriving in Montreal. He was a research scientist in the University of Massachusetts and affiliated with the Interactive Visual Media Group and the Machine Learning and Applied Statistics groups at Microsoft Research. His research led to three patents on image processing, computer vision and interactive multimedia.

Christopher Pal is an associate professor at the École Polytechnique of Montreal. He is currently teaching Multimedia data processing and analysis in the Department of Computer and Software Engineering. He was a former professor at the University of Rochester in the, Department of Computer Science before arriving in Montreal. He was a research scientist in the University of Massachusetts and affiliated with the Interactive Visual Media Group and the Machine Learning and Applied Statistics groups at Microsoft Research. His research led to three patents on image processing, computer vision and interactive multimedia.

Christopher Pal is an associate professor at the École Polytechnique of Montreal. He is currently teaching Multimedia data processing and analysis in the Department of Computer and Software Engineering. He was a former professor at the University of Rochester in the Department of Computer Science before arriving in Montreal. He was a research scientist in the University of Massachusetts and affiliated with the Interactive Visual Media Group and the Machine Learning and Applied Statistics groups at Microsoft Research. His research led to three patents on image processing, computer vision and interactive multimedia.

He earned his Master of Science in Math and PhD from the University of Waterloo, Canada. He developed methods for automated cartography and the analysis of high-resolution digital aerial photography during his masters research. He was also involved with a group of software engineering projects developing spatial databases for managing environmental information. His PhD research led to contributions applying probability models and optimization techniques to image, video and signal processing.

During his PhD studies, he was also a research assistant at the University of Toronto in the Department of Electrical and Computer Engineering. He closely collaborated with the Banting and Best Department of Medical Research. He researched on image processing and statistical methods for the analysis of large scale genomics and computational molecular biology experiments using DNA microarrays. Before his graduate studies, he was with the multimedia research company Interval in Silicon Valley, California. He was awarded a patent on audio signal processing as a result of his research at Interval.

Christopher Pal is a faculty member at the Montreal Institute for Learning Algorithms (MILA), working alongside with leading world experts in machine learning and deep learning Prof. Yoshua Bengio, Prof. Aaron Courville, Prof. Pascal Vincent, Prof. Simon Lacoste-Julien, Prof. Laurent Charlin and Prof. Roland Memisevic. He is also a member of the Institute for Data Valorization (IVADO).

His publications include, Brain tumor segmentation with Deep Neural Networks; Deep Learning: A Primer for Radiologists; Improving probabilistic inference in graphical models with determinism and cycles; EmoNets: Multimodal deep learning approaches for emotion recognition in video; A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI; Metastatic liver tumour segmentation with a neural network-guided 3D deformable model; Semi-supervised Learning with Encoder-Decoder Recurrent Neural Networks: Experiments with Motion Capture Sequences; Improving facial analysis and performance driven animation through disentangling identity and expression; Learning from weakly labeled faces and video in the wild; 3D segmentation of abdominal CT imagery with graphical models, conditional random fields and learning; On consistent inter-view synthesis for autostereoscopic displays; and On Learning Conditional Random Fields for Stereo Exploring Model Structures and Approximate Inference.