Wednesday May 31, 2017 at 6:00 p.m. hear about the work of Dr. Sanja Fidler, Assistant Professor in Machine Learning and Computer Vision, University of Toronto and Dr. Inmar Givoni, Director of Machine Learning at Kindred Systems Inc., as part of “Women in Robotics: Building Smart Robots with AI”. Day & Time: Wednesday May 31, 2017 6:00 p.m. – 9:00 p.m. Speakers: Dr. Sanja Fidler, Assistant Professor, Department of Computer Science, University of Toronto Dr. Inmar Givoni, Director, Machine Learning, Kindred Systems Inc. Location: To be Announced Organizers: IEEE Toronto Engineering in Medicine and Biology Society (EBMS), IEEE Women in Engineering, Society of Women Engineers Toronto RVSP: https://www.meetup.com/Get-Your-Bot-On-Robotics-Hackathon/events/240003715/ Agenda: 6:00 pm – Networking 6:30 pm – Welcome 6:40 pm – Speakers 7:30 pm – Panel Discussion – Women in Robotics 8:00 pm – Networking 9:00 pm – Close Get Your Bot On!, its partners Society of Women Engineers Toronto, IEEE Toronto Engineering in Medicine and Biology Society (EBMS) and IEEE Women in Engineering are pleased to bring you the ‘Women in Robotics Speaker Series’. This series celebrates the work of women in the field of robotics and provides a forum for them to share their work and career with the community. We invite all community members to come and learn, participate in the discussion, and celebrate the contribution of women to this field. Biography: Dr. Sanja Fidler, Assistant Professor, Department of Computer Science, University of Toronto Dr. Sanja Fidler is an Assistant Professor at the Department of Computer Science, University of Toronto. She is the recipient of the Amazon Academic Research Award (2017) and the NVIDIA Pioneer of AI Award (2016). Previously she was a Research Assistant Professor at TTI-Chicago a philanthropically endowed academic institute located in the campus of the University of Chicago. She completed her PhD in computer science at University of Ljubljana in 2010, and was a postdoctoral fellow at University of Toronto during 2011-2012. In 2010 she visited UC Berkeley. She has served as a Program Chair of the 3DV conference, and as an Area Chair of CVPR, EMNLP, ICCV, ICLR, and NIPS. Together with Rich Zemel and Raquel Urtasun, she received the NVIDIA Pioneer of AI award. Her main research interests are object detection, 3D scene understanding, and the intersection of language and vision. You can find Dr. Fidler on the web at http://www.cs.toronto.edu/~fidler/ Dr. Inmar Givoni, Director, Machine Learning, Kindred Systems Inc. Dr. Inmar Givoni is the Director of Machine Learning at Kindred, where her team develops algorithms for machine intelligence, at the intersection of robotics and AI. Prior to that, she was the VP of Big Data at Kobo, where she led her team in applying machine learning and big data techniques to drive e-commerce, customer satisfaction, CRM, and personalization in the e-pubs and e-readers business. She first joined Kobo in 2013 as a senior research scientist working on content analysis, website optimization, and reading modelling among other things. Prior to that, Inmar was a member of technical staff at Altera (now Intel) where she worked on optimization algorithms for cutting-edge programmable logic devices. Inmar received her PhD (Computer Science) in 2011 from the University of Toronto, specializing in machine learning, and was a visiting scholar at the University of Cambridge. During her graduate studies, she worked at Microsoft Research, applying machine learning approaches for e-commerce optimization for Bing, and for pose-estimation in the Kinect gaming system. She holds a BSc in computer science and computational biology from the Hebrew University in Jerusalem. She is an inventor of several patents and has authored numerous top-tier academic publications in the areas of machine learning, computer vision, and computational biology. She is a regular speaker at big data, analytics, and machine learning events, and is particularly interested in outreach activities for young women, encouraging them to choose technical career paths. You can find Dr. Givoni on the web at http://www.inmarg.net/
Women in Engineering
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Wednesday June 28, 2017 at 5:00 p.m. Dr. Shiva Amiri, CEO of BioSymetrics Inc, will be presenting “Large-Scale Analytics and Machine Learning for Biomedical Data Types”. Day & Time: Wednesday June 28, 2017 5:00 p.m. – 6:00 p.m. Speaker: Dr. Shiva Amiri CEO of BioSymetrics Inc Toronto, Ontario, Canada Location: Room ENG288 Department of Computer Science Ryerson University 245 Church St, Toronto, M5B 1Z4 Contact: Alireza Sadeghian, Alex Dela Cruz Organizers: Signals & Computational Intelligence Chapter, WIE Abstract: The scale of data being generated in medicine and research can easily overwhelm typical analytic capabilities. This is particularly true with MRI/fMRI scanning, genomics data, streaming/wearables data in addition to other clinical data types, especially if in combination. Challenges include 1) large file sizes often in heterogeneous formats 2) currently no standard Protocol exists for extraction of standardized characteristics, and 3) traditional methods for group-wise comparison can often result in spurious findings. The talk will address these challenges by discussing customized processing pipelines built for multiple data types in biomedicine, which enable effective machine learning and other types of analytics on these datasets. This approach leverages the rapid model building capabilities of our real-time machine learning software to iterate through normalization parameters for each data type and disease class. In addition, this platform allows easy integration between the various medical data types (genome sequence, phenotypic, and metabolic data) allowing generation of more comprehensive disease classification models. The ability to standardize and pre-process multiple types of biomedical data for machine learning, no matter the source and type, and effectively combine it with other data types is a powerful capability and holds promise for the future of diagnostics and precision medicine. Biography: Shiva Amiri is the CEO of BioSymetrics Inc. where they are developing a unique real-time machine learning technology for the analysis of massive data in biomedicine. BioSymetrics specializes in providing optimized pipelines for complex data types and effective methods in the analytics of integrated data. Prior to BioSymetrics she was the Chief Product Officer at Real Time Data Solutions Inc., she has led the Informatics and Analytics team at the Ontario Brain Institute, where they developed Brain-CODE, a large-scale neuroinformatics platform across the province of Ontario. She was previously the head of the British High Commission’s Science and Innovation team in Canada. Shiva completed her Ph.D. in Computational Biochemistry at the University of Oxford and her undergraduate degree in Computer Science and Human Biology at the University of Toronto. Shiva is involved with several organisations including Let’s Talk Science and Shabeh Jomeh International. |
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