IEEE Toronto Section


Archive for the ‘Events’ Category

Low Power Digital Equalization for High-Speed SerDes

Sunday, August 6th, 2017

Tuesday July 25, 2017 at 4:10 p.m. Dr. Masum Hossain, Assistant Professor at the University of Alberta, will be presenting “Low Power Digital Equalization for High-Speed SerDes”.

Day & Time: Tuesday July 25, 2017
4:10 p.m. – 5:10 p.m.

Speaker: Dr. Masum Hossain
Assistant Professor
University of Alberta

Location: Bahen Centre, room BA1180
40 St George St, Toronto, ON M5S 2E4

Contact: Dustin Dunwell

Organizers: Solid-State Circuits Society

Slides from Event: Low Power Digital Equalization for High-Speed SerDes

Abstract: High-speed signaling and Serdes architecture are evolving rapidly to accommodate higher data rates and higher insertion loss. Digital equalization is a natural progression to that tend where traditional analog equalization techniques are falling short to meet the performance demand of multilevel signalling. But digital equalization is also power consuming and mostly dominated by the analog to digital converters. This talk explores different power reduction techniques as well as higher information efficiency data converters to enable low power digital equalization at 10+ Gb/s in 65nm CMOS.

Biography: Masum Hossain received his Ph.D. at the University of Toronto in 2010. From 2008 to 2010 he worked for Gennum (now Semtech). From 2010 to 2013 he worked for Rambus. Since 2013 he joined faculty of engineering at University of Alberta. Masum won the best student paper award at the 2008 IEEE Custom Integrated Circuits (CICC) Conference. He also won Analog Device’s outstanding student designer award in 2010.

Engineering Employment Events

Saturday, August 5th, 2017

OSPE will be running Engineering Employment Events on September 14, 2017 and September 30, 2017. The September 14th E3 is in partnership with OACETT and will focus on recent grads, associates and individuals with EIT, P.Eng., C.E.T. and C.Tech. designations. The September 30th E3 is in partnership with Transport Canada and will focus on recent grads, associates and individuals with EIT and P.Eng. designations.

Thursday September 14, 2017 Session

Registration Link:
Time: 11:00 A.M. – 5:00 P.M.
Location: Parkview Manor, 55 Barber Greene Rd, Toronto.

Saturday September 30, 2017 Session

Registration Link:
Time: 9:00 A.M. – 5:00 P.M.
Location: Corporate Event Centre at CHSI – 5110 Creekbank Road, Mississauga, Ontario

Design Considerations for Power Efficient Continuous-Time Delta Sigma ADCs

Friday, August 4th, 2017

Tuesday August 8, 2017 at 4:10 p.m. Dr. Shanthi Pavan, Professor of Electrical Engineering at the Indian Institute of Technology, will be presenting “Design Considerations for Power Efficient Continuous-Time Delta Sigma ADCs”.

Recording of the Event:

Day & Time: Tuesday August 8, 2017
4:10 p.m. – 5:10 p.m.

Speaker: Dr. Shanthi Pavan
Professor of Electrical Engineering
Indian Institute of Technology, Madras

Location: Bahen Centre, room BA1230
40 St George St, Toronto, ON M5S 2E4

Contact: Dustin Dunwell

Organizers: Solid-State Circuits Society

Abstract: Continuous-time Delta-Sigma Modulators (CTDSMs) are a compelling choice for the design of high resolution analog-to-digital converters. Many delta-sigma architectures have been published (and continue to be invented). This leaves the designer with a bewildering array of choices, many of which seem to pull in opposite directions. Further, it is often difficult to make a clear comparison of various architectures, as they have been designed for dissimilar specifications, by different design groups, and in different technology nodes. This talk examines various design alternatives for the design of power efficient single-loop continuous-time delta sigma converters.

Biography: Shanthi Pavan obtained the B.Tech degree in Electronics and Communication Engineering from the Indian Institute of Technology, Madras in 1995 and the Masters and Doctoral degrees from Columbia University, New York in 1997 and 1999 respectively. He is now with the Indian Institute of Technology-Madras, where he is a Professor of Electrical Engineering. His research interests are in the areas of high-speed analog circuit design and signal processing. Dr.Pavan is the recipient of many awards for teaching and research, including the IEEE Circuits and Systems Society Darlington Best Paper Award and the Shanti Swarup Bhatnagar Award (from the Government of India). He has served as the Editor-in-Chief of the IEEE Transactions on Circuits and Systems: Part I – Regular Papers. He is a Fellow of the Indian National Academy of Engineering.

Recent Advances In Direct Torque and Flux Control of IPMSM Drives

Wednesday, July 26th, 2017

Friday August 11, 2017 at 10:00 a.m. IEEE Toronto’s Power & Energy Chapter is honoured to invite you to a seminar by professor M. Nasir Uddin, Senior IEEE member and Professor at Lakehead University, “Recent Advances In Direct Torque and Flux Control of IPMSM Drives”.

Day & Time: Friday August 11, 2017
10:00 a.m. – 11:30 a.m.

Speaker: M. Nasir Uddin
Senior IEEE member
Professor at Lakehead University

Location: Conference Room
147 Dalhousie St, Toronto, M5B 2R2

Contact: Omid Alizadeh

Organizers: IEEE Toronto Power & Energy Chapter

Abstract: With the advancements in magnetic materials and semiconductor technology, interior permanent magnet synchronous motor (IPMSM) is becoming more and more popular in industrial applications due to its high energy density, high power factor, low noise and high efficiency as compared to conventional AC motors. Conventional field oriented vector control (VC) techniques have been widely used for high performance motor drives for many years. As an alternative to VC scheme recently direct torque and flux control (DTFC) technique is developed which is faster and simpler than that of the VC scheme as DTFC doesn’t need any coordinate transformation, pulse width modulation and current regulators. The DTFC scheme utilizes hysteresis band comparators for both torque and flux controls. Both torque and flux are controlled simultaneously by the selection of appropriate voltage vectors from the inverter. However, conventional six-sector based DTFC suffers from high torque ripples due to discrete nature of control system and limited voltage vector selection from the inverter. Control techniques have been developed for hysteresis controllers to minimize the torque ripples but the six sectors still limits that improvement. Furthermore, in a conventional DTFC the reference air-gap flux is assumed constant at the rated value to make the control task easier. This produces erroneous results for high performance drives as the air-gap flux changes with the operating conditions and system disturbances. Moreover, if the reference air-gap flux is maintained constant, it is not possible to optimize the efficiency of the drive.

Therefore, this talk presents a novel eighteen-sector based DTFC scheme to achieve high dynamic performance with reduced torque ripples as compared to the conventional 6-sector based DTFC. In addition, a model based loss minimization algorithm is integrated with the proposed DTFC scheme in order to optimize the efficiency along with high dynamic performance. Eighteen sectors are developed to overcome the unbalanced voltage vector selection of conventional six-sector based system that minimizes the torque ripples. Further, a nonlinear controller with virtual torque and flux controls is also developed for IPMSM drive to minimize the drive torque ripples. The complete IPMSM drives incorporating the developed control techniques are successfully implemented in real-time using digital signal processor (DSP) board-DS1104 for laboratory 5-hp motor. The effectiveness of the proposed control techniques are verified in both simulation and experiment at different operating conditions. It is found that the nonlinear controller based IPMSM drive provides the best performance in terms of torque ripples among all the DTFC schemes. The results show that the proposed nonlinear/18-sector based DTFC scheme would have the potentiality to apply for real-time industrial drives.

Biography: M. Nasir Uddin received the B.Sc. and M. Sc. degrees both in electrical & electronic engineering from Bangladesh University of Engineering and Technology (BUET), Dhaka, Bangladesh, and the Ph.D. degree in electrical engineering from Memorial University of Newfoundland (MUN), Canada in 1993, 1996, and 2000, respectively.

He has been serving as a Professor in the Department of Electrical Engineering, Lakehead University (LU), Thunder Bay, ON, Canada since August 2001. He also served as a visiting Prof. at Univ. of Malaya (2013, 2012, 2011), Tokyo University of Science (2010), Japan and North South University (2006), Dhaka, Bangladesh. Previously, he was an Assistant Professor in the Department of Electrical and Computer Engineering, University of South Alabama, USA from January 2001 to May 2001, an Assistant Professor from 1996 to 1997 and a lecturer from 1994 to 1996 at BUET. He possesses more than 21 years of teaching experience and has authored/coauthored over 200 papers in international journals (39 in IEEE Transactions) and conferences.

Prof. Uddin is a registered professional engineer in the province of Ontario, Canada. Currently, he is serving as an Executive Board Member of IEEE Industry Applications Society (IAS) and Chair of IEEE-IAS-Manufacturing Systems Development and Applications Department. He also served as one of the Technical Program Committee Chairs for IEEE Energy Conversion Congress and Expo (ECCE) 2015 at Montreal, Canada. He was the Technical Committee Chair for the IEEE-IAS [Industrial Automation and Control Committee (IACC)] Annual Meetings in 2011 (Orlando) and 2012 (Las Vegas). He served as Papers Review Chair (2009–2010 and 2013–2014) of the IEEE Transactions on Industry Applications (IACC). Earlier he served IEEE IAS IACC for 9 years in different capacities. Due to his outstanding contributions IEEE IAS IACC recognized him with IEEE IAS Service Award 2015. He also received LU Distinguished Researcher Award 2010. He was the recipient of several Prize Paper Awards from IEEE IAS IACC and both 2004 Contributions to Research and Contributions to Teaching Awards from LU. His research interests include power electronics, renewable energy, motor drives, and intelligent controller applications.

Industrial Relations and Toronto ComSoc Chapter: Site Visit G&W/Survalent

Monday, July 24th, 2017

Note: This event has been rescheduled from the original date. The new day and time is to be determined.

IEEE Toronto is thrilled to present a tour of the Manufacturing Facility of G&W Canada and Survalent in Brampton. This event is a joint event between IEEE Toronto Industrial Relations and Toronto ComSoc Chapter.

Day & Time: To Be Determined

Location: 7965 Heritage Rd, Brampton, ON L6Y 0B3

Contact: Maryam Alsomahi

Organizers: Industrial Relations, Communication Society Chapter


Abstract: G&W Electric has been a global supplier of electric power equipment since 1905. Our product offerings include overhead and underground distribution switches, Lazer® Automation solutions, reclosers, distribution and transmission cable accessories, and current limiting system protection devices. Combining cutting-edge design and manufacturing technology with world-class ISO certified quality systems; G&W specializes in custom solutions to meet specific customer requirements.

So whether you are searching for cable terminations and joints, simple manual switching, automation for smart grid applications, or the latest in renewable energy solutions, join G&W for a tour of their SF6 and Solid Dielectric manufacturing process.

Fees & Notes:
$10 for non-IEEE members and free for IEEE members.
1. Attendees are required to bring their own safety shoes and glasses. However, G&W can loan glasses and toe caps for those who don’t have them. For safety purposes, attendees are not allowed to wear shorts or open shoes.
2. Please add a note if you are able to drive/carpool or if you need a ride.

A framework for general purpose digital pathology image analysis, using machine learning methods to identify cancer subsets and immunotherapy biomarkers

Friday, July 7th, 2017

Monday July 17, 2017 at 4:00 p.m. Dr. Trevor McKee, STTARR Innovation Research Centre for Cancer Research, will be presenting “A framework for general purpose digital pathology image analysis, using machine learning methods to identify cancer subsets and immunotherapy biomarkers”.

Day & Time: Monday July 17, 2017
4:00 p.m. – 5:00 p.m.

Speaker: Dr. Trevor McKee
STTARR – Innovation Research Centre for Cancer Research
Toronto, Ontario, Canada

Location: Room ENG101
George Vari Engineering Building (intersection of Church & Gould)
Ryerson University
245 Church St, Toronto, M5B 1Z4

Contact: Alireza Sadeghian, Alex Dela Cruz

Organizers: Signals & Computational Intelligence Chapter

Abstract: Histological staining, interpreted by a pathologist, has remained the gold standard for cancer diagnosis and staging for over 100 years. There is a growing need for better – and more personalized – cancer treatments, to provide oncologists with the tools they need to best treat their patients. The advent of “molecular medicine”, or targeted therapeutic strategies that rely on knowledge of particular mutations in a cancer in order to tailor treatment, has improved cancer therapy for many patients. This has led to the use of companion diagnostics, in which tumor biopsies are stained for a specific marker or set of markers, using immunohistochemical approaches. The information obtained from the degree of staining or spatial arrangement of stained cells within the tumor helps to identify tumor molecular subclasses that may benefit from such tailored therapeutic approaches.

The increase in the number of slides being stained for specific markers and used in diagnosis, along with the increased need for quantitative assessment of the degree of staining, number of cells, or spatial arrangement of cells within the tumor, has increased the volume and type of work that pathologists encounter in their diagnostic workflow. Our team works on the development of tools for quantitative digital pathology analysis that can benefit pathologists, by building and validating semi-automated algorithms for cellular quantification and intensity scoring of stained slides. We use machine learning methods to learn features that distinguish different morphological regions from pathologist annotations. These are then fed into a tissue segmentation and classification framework to break the tissue down into its components, either on the individual cell level, or the glandular level. Staining intensity is quantified following colour deconvolution of the individual stain components, and reporting metrics are designed, in close collaboration with pathologists and biological scientists, to identify the appropriate outputs for comparing between treatment groups or different cancer types.

The use of multiplexed digital pathology stains allows us to build a generalized analytical framework to perform “tissue cytometry”. This new technology can extract quantitative image-derived features in a reproducible and robust fashion, providing clinicians and biological scientists with tools to measure previously inaccessible phenomena, like measuring the hypoxic gradient directly within tumor sections, or comparing glucose uptake to lactic acid production in the same tumor sample. This approach establish the foundation for a bridge between traditional morphometric assessment of tumor biopsies, and the detailed spatially resolved chemical and molecular content maps of each tumor, providing an invaluable toolkit for the discovery of cancer molecular subtypes, and development of therapeutic interventions.

Biography: Dr. Trevor McKee received his Ph.D. in Biological Engineering from the Massachusetts Institute of Technology in 2005, in the laboratory of Dr. Rakesh Jain of Harvard Medical School. During his graduate work, he pioneered the application of new imaging and analysis technologies to studying drug transport within tumors, and on developing methods to improve drug delivery. He also holds a Bachelors of Science in Chemical Engineering with a Biotechnology minor from the University at Buffalo. He moved to Toronto to continue postdoctoral work at the Ontario Cancer Institute, applying multi-modality imaging and quantitative image analysis methods to study preclinical cancer models. He has a successful track record of high-impact publications with a number of clinical and basic science collaborators, and has also collaborated with pharmaceutical companies on imaging-based preclinical testing of new compounds. He is currently Image Analysis Core Manager of the STTARR Innovation Centre, and manages a team of analysts to develop new algorithms for machine-learning powered image segmentation and quantification across a number of disease sites. His research interests lie in studying the tumor microenvironment, drug and oxygen delivery, and the development of tools for “tissue cytometry” – deriving complex biological and spatial relationships from tissue sections via computational image analysis methods.

Large-Scale Analytics and Machine Learning for Biomedical Data Types

Wednesday, June 21st, 2017

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.

IEEE ComSoc Distinguished Lecture: Topology Preserving Maps: A Localization-Free Approach for 2-D and 3-D IoT Subnets

Saturday, June 3rd, 2017

Tuesday June 13, 2017 at 3:00 p.m. Prof. Anura Jayasumana, Distinguished Lecturer of the IEEE Communications Society, will be presenting a distinguished lecture “Topology Preserving Maps: A Localization-Free Approach for 2-D and 3-D IoT Subnets”. Note refreshments begin at 2:00 p.m.

Day & Time: Tuesday June 13, 2017
2:00 p.m. – 3:00 p.m. Refreshments
3:00 p.m. – 4:00 p.m. Lecture

Speaker: Prof. Anura Jayasumana
Dept. of Electrical & Computer Engineering
Colorado State University, Ft. Collins, CO 80523 USA

Location: Room BA 2135
40 St. George Street
Toronto, ON M5S 2E4

Contact: Eman Hammad

Event Link:

Abstract: Driven by higher potency and lower cost/size of devices capable of sensing, actuating, processing and communicating, the Internet of Things and of Everything promises to dramatically increase our ability to embed intelligence in the surroundings. Subnets of simple devices such as RFIDs and tiny sensors/actuators deployed in massive numbers in 2D and complex 3D spaces will be a key aspect of this emerging infrastructure. Most techniques for self-organization, routing and tracking in such networks rely on distances and localization in the physical domain. While geographic coordinates fit well with our intuitions into physical spaces, their use is not feasible in complex environments. Protocols based on geographical coordinates do not scale well to 3D either. We present a novel localization-free coordinate system, the Topology Coordinates (TC). Interestingly, geographic features such as voids and shapes are preserved in the resulting Topology-Preserving Maps (TPMs) of 2-D and 3-D networks. Ability to specify virtual cardinal directions and angles in networks is a radical change from the traditional approaches. A novel self-learning algorithm is presented to provide network awareness to individual nodes, a step toward large-scale evolving sensor networks. Application of TCs to social networking will be illustrated.

Biography: Anura Jayasumana is a Professor of Electrical and Computer Engineering at Colorado State University, where he also holds a joint appointment in Computer Science. He is the Associate Director of Information Sciences & Technology Center at Colorado State. He is a Distinguished Lecturer of the IEEE Communications Society. His research interests span high-speed networking to wireless sensor networking, and anomaly detection to DDoS defense. He has served extensively as a consultant to industry ranging from startups to Fortune 100 companies. He received the B.Sc. degree from the University of Moratuwa, Sri Lanka and M.S. and Ph.D. degrees in Electrical Engineering from the Michigan State University. Prof. Jayasumana has supervised 20+ Ph.D. and 50+ M.S. students, holds two patents, and is the co-author over 250 papers. He is the recipient of the Outstanding Faculty Award from the Mountain States Council of the American Electronics Association.

Robust Beamforming Design: A New Approach

Saturday, June 3rd, 2017

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:

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.

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

Monday, May 29th, 2017

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.