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Eli Shlizerman

  • Assistant Professor

Appointments

Assistant Professor, Electrical Engineering
Assistant Professor, Department of Applied Mathematics
Data Science Fellow, eScience Institute

Biography

Eli Shlizerman is Washington Research Foundation Assistant Professor and holds a joint appointment in the Departments of Electrical Engineering and Applied Mathematics. He is also a Data Science Fellow at the eScience Institute.

His research focuses on classification and modeling of dynamics of complex systems. For this purpose, he develops methods that combine data analysis and dynamical systems theory. Complex systems that are being studied are neuronal networks. Shlizerman’s group is working on developing methods to analyze data recorded from a large number of cells in the nervous system in order to better understand how the system operates. Among the methods that the group develops are tools for derivation of reduced models, inference of connectivity in networks, classification and recognition of dynamics. The approaches are applied to modeling of functional connectomics, neurobiological networks that underlie insects’ sensory systems and to neural dynamics of organisms such as C. elegans.

In collaboration with experimental collaborators, the methods are being applied to various neurobiological systems, such as the olfactory system, recently published in Science, the nervous system in the C. elegans worm and sun-compass navigation in Monarch butterflies recently published in Cell Reports. Beyond the methodology for reading from and modeling neuronal networks, Shlizerman’s group is developing approaches for closing the loop between sensory and motor neurons to identify stimuli that trigger desired behaviors, and transferring the design of neural circuits to development of neuromorphic circuits.

Research Interests

Neural circuits, data-driven dynamical systems, inference of network architecture; modeling neuronal dynamics, neuromorphic computing, neural system control.

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                    [post_content] => [caption id="attachment_8534" align="alignleft" width="405"]EE Assistant Professor Eli Shlizerman EE Assistant Professor Eli Shlizerman[/caption]

Facebook Research recently recognized the pioneering potential of Electrical Engineering and Applied Mathematics Assistant Professor Eli Shlizerman’s company, Dreambit. Facebook acquired the company to apply the advanced image software to 1.7 billion Facebook users. Shlizerman will split his time between the University of Washington Department of Electrical Engineering (UW EE) and Facebook Research.

Shlizerman joins Facebook with UW Assistant Professor of Computer Science and Engineering Ira Kemelmacher-Shlizerman, world expert in faces and 3D modeling. Together they co-founded Dreambit, which is a personalized image search engine. Given one or more photos and a text query, it outputs versions of the input person in the query appearance.

Shlizerman brings expertise on neural networks and optimization of dynamical systems to Facebook. His expertise will be applied to applications of neural networks for computational photography, face processing, vision and graphics. The experience he gains at the social networking hub will inform future research developments at UW EE.

While at Facebook, Shlizerman will remain as Assistant Professor and keep an active research program and advise students at UW. Shlizerman is an expert in dynamical networks. He studies dynamic mechanisms in neurobiological networks and concepts we can learn from them to design better artificial neural networks.
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                    [post_content] => [caption id="attachment_7918" align="alignleft" width="425"]Assistant Professor Eli Shlizerman and Assistant Professor of Biology Jeff Riffell received the ASOFR Grant. Photo Credit: Kelly Riffell Photography Assistant Professor Eli Shlizerman and Assistant Professor of Biology Jeff Riffell receive the AFOSR Grant. Photo Credit: Kelly Riffell Photography[/caption]

Assistant Professor of Electrical Engineering and Applied Mathematics Eli Shlizerman, along with Associate Professor of Biology Jeff Riffell, will receive an award from the Air Force Office of Scientific Research (AFOSR) to study the mosquito olfactory system. This research will unearth the odor fingerprints of humans to mosquitoes, allowing for the development of bio-inspired detection systems.

The four-year project will investigate the mosquito olfactory system – a complex structure that is critically important, but not well understood. Through this in-depth investigation, the researchers will gain insight into how the mosquito’s olfactory neuronal network is able to identify and target humans.

Mosquitoes, which are considered the most dangerous animal in the world, kill over 700,000 people each year, mostly due to diseases like malaria and dengue. Understanding the sensory details of these insects carries significant societal impact. Shlizerman and Riffell’s approach investigates human scent “fingerprints.”

The human biomarkers – or odor fingerprints – are very complex, containing over 350 volatiles (or scents sensed by the olfactory system). Understanding how mosquitoes’ nervous systems processes these odors may lead to resolving the neural coding that identifies humans and distinguishes them from other species.

Resolution of chemi-signatures can also assist in understanding how neural network should be designed to process complex chemical environments, for disease detection through classification of abnormalities, and to aid in the development of new mosquito repellents. This is particularly important in regions of the world where mosquito-borne infectious diseases, like malaria or dengue, are endemic.

The researchers will employ a three-pronged approach for piecing together the neural coding of the mosquito olfactory system: they will first characterize human scent volatiles; then explore the possible neural network building blocks that provide detection and discrimination of human scent through development of machine learning methods to parse neural activity; and finally build an artificial, in-silico antennal lobe that detects volatiles at trace levels.

“After the four-year grant, the researchers plan to transform the unique functionalities of the mosquitos’ olfaction toward the design of novel sensors,” Shlizerman said.
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Facebook Research recently recognized the pioneering potential of Electrical Engineering and Applied Mathematics Assistant Professor Eli Shlizerman’s company, Dreambit. Facebook acquired the company to apply the advanced image software to 1.7 billion Facebook users. Shlizerman will split his time between the University of Washington Department of Electrical Engineering (UW EE) and Facebook Research.

Shlizerman joins Facebook with UW Assistant Professor of Computer Science and Engineering Ira Kemelmacher-Shlizerman, world expert in faces and 3D modeling. Together they co-founded Dreambit, which is a personalized image search engine. Given one or more photos and a text query, it outputs versions of the input person in the query appearance.

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While at Facebook, Shlizerman will remain as Assistant Professor and keep an active research program and advise students at UW. Shlizerman is an expert in dynamical networks. He studies dynamic mechanisms in neurobiological networks and concepts we can learn from them to design better artificial neural networks.
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                            [post_content] => [caption id="attachment_7918" align="alignleft" width="425"]Assistant Professor Eli Shlizerman and Assistant Professor of Biology Jeff Riffell received the ASOFR Grant. Photo Credit: Kelly Riffell Photography Assistant Professor Eli Shlizerman and Assistant Professor of Biology Jeff Riffell receive the AFOSR Grant. Photo Credit: Kelly Riffell Photography[/caption]

Assistant Professor of Electrical Engineering and Applied Mathematics Eli Shlizerman, along with Associate Professor of Biology Jeff Riffell, will receive an award from the Air Force Office of Scientific Research (AFOSR) to study the mosquito olfactory system. This research will unearth the odor fingerprints of humans to mosquitoes, allowing for the development of bio-inspired detection systems.

The four-year project will investigate the mosquito olfactory system – a complex structure that is critically important, but not well understood. Through this in-depth investigation, the researchers will gain insight into how the mosquito’s olfactory neuronal network is able to identify and target humans.

Mosquitoes, which are considered the most dangerous animal in the world, kill over 700,000 people each year, mostly due to diseases like malaria and dengue. Understanding the sensory details of these insects carries significant societal impact. Shlizerman and Riffell’s approach investigates human scent “fingerprints.”

The human biomarkers – or odor fingerprints – are very complex, containing over 350 volatiles (or scents sensed by the olfactory system). Understanding how mosquitoes’ nervous systems processes these odors may lead to resolving the neural coding that identifies humans and distinguishes them from other species.

Resolution of chemi-signatures can also assist in understanding how neural network should be designed to process complex chemical environments, for disease detection through classification of abnormalities, and to aid in the development of new mosquito repellents. This is particularly important in regions of the world where mosquito-borne infectious diseases, like malaria or dengue, are endemic.

The researchers will employ a three-pronged approach for piecing together the neural coding of the mosquito olfactory system: they will first characterize human scent volatiles; then explore the possible neural network building blocks that provide detection and discrimination of human scent through development of machine learning methods to parse neural activity; and finally build an artificial, in-silico antennal lobe that detects volatiles at trace levels.

“After the four-year grant, the researchers plan to transform the unique functionalities of the mosquitos’ olfaction toward the design of novel sensors,” Shlizerman said.
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                    [post_content] => [caption id="attachment_8534" align="alignleft" width="405"]EE Assistant Professor Eli Shlizerman EE Assistant Professor Eli Shlizerman[/caption]

Facebook Research recently recognized the pioneering potential of Electrical Engineering and Applied Mathematics Assistant Professor Eli Shlizerman’s company, Dreambit. Facebook acquired the company to apply the advanced image software to 1.7 billion Facebook users. Shlizerman will split his time between the University of Washington Department of Electrical Engineering (UW EE) and Facebook Research.

Shlizerman joins Facebook with UW Assistant Professor of Computer Science and Engineering Ira Kemelmacher-Shlizerman, world expert in faces and 3D modeling. Together they co-founded Dreambit, which is a personalized image search engine. Given one or more photos and a text query, it outputs versions of the input person in the query appearance.

Shlizerman brings expertise on neural networks and optimization of dynamical systems to Facebook. His expertise will be applied to applications of neural networks for computational photography, face processing, vision and graphics. The experience he gains at the social networking hub will inform future research developments at UW EE.

While at Facebook, Shlizerman will remain as Assistant Professor and keep an active research program and advise students at UW. Shlizerman is an expert in dynamical networks. He studies dynamic mechanisms in neurobiological networks and concepts we can learn from them to design better artificial neural networks.
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Representative Publications

  • Liang, S., Shapiro, L. G., & Kemelmacher-Shlizerman, I., Head Reconstruction from Internet Photos, European Conference on Computer Vision, 2016.
  • Neural integration underlying a time-compensated sun compass in the migrating monarch butterfly, Eli Shlizerman, James Phillips-Portillo, Daniel Forger, Steven Reppert, Cell Reports (2016)
  • Closing the Loop: Optimal Stimulation of C. elegans Neuronal Network via Adaptive Control to Exhibit Full Body Movements, J. Santos, E. Shlizerman, BMC Neuroscience, OCNS Oral (2015)
  • Data-driven inference of network connectivity for modeling the dynamics of neural codes in the insect antennal lobe, Eli Shlizerman, Jeffrey Riffell and J. Nathan Kutz, Frontiers in Computational Neuroscience (2014)
  • Flower discrimination by pollinators in a dynamic chemical environment, Jeffrey Riffell, Eli Shlizerman, Elischa Sanders, Leif Abrell, Billy Medina, Armin Hinterwirth, and J. Nathan Kutz, Science (2014)
  • Low-dimensional functionality of complex network dynamics: Neuro-sensory integration in the C. elegans connectome, J. Kunert, E. Shlizerman, and J. N. Kutz, Physical Review E, (2014)

Associated Labs

Research Areas

Affiliations

Education

  • Ph.D. Computer Science and Applied Mathematics, 2009
    Weizmann Institute of Science
  • M.S.c Computer Science and Applied Mathematics, 2005
    Weizmann Institute of Science