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Robust Beamforming Design: A New Approach

Room BA 2145. 40 St. George Street. Toronto, ON M5S 2E4

Wednesday June 7, 2017 at 2:00 p.m. Mostafa Medra, PhD. Candidate, will be presenting “Robust Beamforming Design: A New Approach”. Day & Time: Wednesday June 7, 2017 2:00 p.m. – 3:00 p.m. Speaker: Mostafa Medra, PhD. Candidate Dept. of Electrical & Computer Engineering McMaster University Location: Room BA 2145 40 St. George Street Toronto, ON M5S 2E4 Contact: Eman Hammad Event Link: https://events.vtools.ieee.org/m/45778 Abstract: Due to the increasing demand for higher data rates, spatial multiplexing received a lot of attention. The ability of a base station to do beamforming so that it can serve multiple users at the same time slot and frequency can provide significantly higher rates. When the channel state information is assumed to be perfectly known at the transmitter, designs as the zeroforcing, regularized zero-forcing and maximum ratio transmission can be applied. Those conventional methods are typically of low complexity. In reality the channel state information is estimated and estimation errors are inevitable. Many beamforming designs tried to incorporate the channel uncertainty model into the design problem. While those robust designs normally work better than the conventional designs, their computational complexity is usually much higher. Today we will provide a new approach to dealing with robust beamforming design that is of low- complexity and performs significantly better than both conventional and current robust methods. Biography: Mostafa Medra (S’06-M’16) received the B.Sc. and M.Sc. degrees, both in Electrical Engineering, from Alexandria University, Alexandria, Egypt in 2009 and 2013, respectively. Since the fall of 2013, he has been working towards his Ph.D. degree at McMaster University, Hamilton, Ontario, Canada. He held a research position with the Spirtonic research team in 2012-2013, working on digital signal processing for non-destructive testing using ultrasonic waves. His current research interests include MIMO communications, optimization, wireless communications and signal processing.

Women in Robotics: Building Smart Robots with AI

To be Announced

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/

InAs Quantum Dot Micro-disk Lasers Grown on Exact (001) Si Emitting at Communication Wavelengths

Room BA 1220 40 St. George Street, Toronto, ON M5S 2E4

Wednesday May 31, 2017 at 2:10 p.m. Kei May Lau, Fang Professor of Engineering and Chair Professor at the Hong Kong University of Science and Technology will be presenting “InAs Quantum Dot Micro-disk Lasers Grown on Exact (001) Si Emitting at Communication Wavelengths”. Day & Time: Wednesday May 31, 2017 2:10 p.m. – 3:00 p.m. Speaker: Kei May Lau Fang Professor of Engineering and Chair Professor Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Location: Room BA 1220 40 St. George Street Toronto, ON M5S 2E4 Contact: Junho Jeong Organizers: IEEE Toronto Photonics Society Abstract: To support an energy-efficient optical interconnect technology enabled by silicon photonics, development of low-energy-consumption active devices and the corresponding integration technology is needed. Most communication wavelength lasers with excellent device performance have been grown on III-V substrates and bonded to silicon. For integration, there are considerable advantages in a technology that allow growth and fabrication of such lasers on III-V/ Si compliant substrates. Quantum dot (QD) active layers grown on lattice-matched substrates have already shown their capability for lasers with low-threshold densities and temperature-independent operation. In addition, the reduced sensitivity of QD to defects and their unique capability of filtering dislocations make them an ideal candidate as the gain medium of hetero-integrated III-V on Si optical sources. In this talk, I will discuss the growth of multi-stack QDs on compliant substrates by MOCVD. Fabrication and laser characteristics of whispering-gallery-mode (WGM) micro-disk lasers using the grown epitaxial structures will also be discussed. Initial demonstration was achieved using simple a colloidal lithography process in combination with dry and wet-etching. The micro-disk lasers were one to four microns in diameter, with single mode lasing at either 1.3 or 1.55 μm, depending on the barrier/cladding system. With smooth sidewalls and sufficient undercut by wet etching of the pedestal, the air-cladded MDs exhibit ultra-low thresholds of a few mW by optical pumping. Preliminary results of electrically-pumped micro-lasers will also be presented. These energy-efficient microlasers are excellent candidates for on-chip integration with silicon photonics. Biography: Professor Kei May Lau is Fang Professor of Engineering at the Hong Kong University of Science and Technology (HKUST). She received the B.S. and M.S. degrees in physics from the University of Minnesota, Minneapolis, and the Ph.D. degree in Electrical Engineering from Rice University, Houston, Texas. She was on the ECE faculty at the University of Massachusetts/Amherst and initiated MOCVD, compound semiconductor materials and devices programs. Since the fall of 2000, she has been with the ECE Department at HKUST. She established the Photonics Technology Center for R&D effort in III-V materials, optoelectronic, high power, and high-speed devices. Professor Lau is a Fellow of the IEEE, and a recipient of the US National Science Foundation (NSF) Faculty Awards for Women (FAW) Scientists and Engineers (1991) and Croucher Senior Research Fellowship (2008). She is an Editor of the IEEE EDL and Associate Editor of Applied Physics Letters.