2018 IEEE WIE TechW Speakers
Jenn Lin lives to experiment and build to find unique ways to delight customers. She is a Principal Engineer within Amazon’s retail organization and the sole principal for the Fashion team. Jenn is focused on using machine learning and optimization to continually refine and improve the customer experience. An experienced developer and engineering leader, Jenn also worked at Panopto, Get Satisfaction, Microsoft, and IBM. This gives her a broad set of experiences both with different sized companies and also up and down the software engineering stack. Working backwards from customer happiness, Jenn continually explores inclusive, ethical, and beautiful digital experiences.
Alex Zeltov is a Big Data Solutions Architect / Software Engineer / Programmer Analyst / Data Scientist with over 19 years of industry experience in Information Technology and most recently in Big Data and Predictive Analytics. He currently works as Global black belt Technical Specialist in Microsoft where he concentrates on Big Data and Advanced Analytics use cases. Previous to joining Microsoft he works as a Sr. Solutions Engineer at Hortonworks where he specialized in HDP and HDF platforms.
Jayne Landry, Global Vice President, Product Management, SAP Leonardo and Analytics, SAP In her role, Jayne Landry drives product portfolio planning, new product introduction, pricing and packaging, messaging, and go-to-market programs for the SAP Leonardo digital innovation system, as well as SAP’s hybrid analytics products, including SAP Business Objects solutions, SAP Lumira software, and the SAP Analytics Hub solution.
Dr. Angelica Lim, Asst. Professor of Prof. Practice in Computing Science, AI and Robotics, Simon Fraser University Angelica is Assistant Professor of Professional Practice in Computing Science at Simon Fraser University, building artificial intelligence software for robots to interact in a smart, but fun, way. She has a Ph.D. and M.Sc. in Computer Science from Kyoto University, Japan, specializing in AI applied to Robotics, and a B.Sc. in Computing Science (specializing in Artificial Intelligence) from Simon Fraser University in Canada. She is also a writer for the robotics online magazine IEEE Spectrum Automaton. Her work includes: a robot that plays music in a human ensemble, a robot that speaks and gestures with emotion, a robot that recognizes emotion in dynamics, and some exploratory robots for land and sea. She is currently Principal Investigator for the SFU Rosie Lab (www.rosielab.ca)
Dr. Palangi is a member of MSR AI at Microsoft Research. His research interests are mainly in the areas of Machine Learning [focusing on Deep Learning], Natural Language Processing, Machine Reading Comprehension [focusing on (Visual) Question Answering] and Linear Inverse Problems [focusing on Compressive Sensing].
Before joining MSR AI, Dr. Palangi worked at MSR on deep learning methods for Speech Recognition (2013), Sentence Modeling for Web Search and Information Retrieval (2014), and Image Captioning (2016). He did my Ph.D. at the University of British Columbia, where he mainly focused on two directions in my Ph.D. thesis: (a) Deep Learning Methods for Sequence Modeling and (b) Bridging the Gap between Compressive Sensing and Deep Learning.
Dr. Palangi also works as a mentor at the Microsoft AI School advanced projects class (AI-611) [currently only available for Microsoft FTEs].
Zahra is a senior data scientist and tech lead with Boeing Vancouver Digital Aviation Labs, advanced products team. She has received her PhD and master’s degrees in wireless communication at UBC’s electrical and computer engineering department and is currently applying her technical skills to solving analytical problems at aviation industry. Zahra is a former Google Anita Borg scholar and a passionate advocate for women in engineering. She has served in numerous volunteer and leadership positions including chair of IEEE Canada women in engineering committee.
Dr. FATOURECHI is the VP Engineering at BroadbandTV Corp (BBTV), a digital entertainment company which exists to empower creators and inspire audiences. Today, BBTV generates over 33 billion monthly impressions and is the 3rd largest video property in the world in terms of unique viewers, following only Google and Facebook.
Vicky Fu is Worldwide Tech Lead for Machine Learning and AI in Microsoft Intelligent Cloud. Vicky has more than 10 years industry experience in Machine Learning. She was previously a Cloud Solution Architect for Azure and a Data Scientist in Bellevue working on Bing. Vicky studied in the PhD program in Data Mining at the University of Maryland Baltimore County and has several academic publications. Before she joined Microsoft, Vicky was a data engineer at AOL and HP
Conati is a Professor of Computer Science at the University of British Columbia, Vancouver, Canada. . Her research is at the intersection of Artificial Intelligence (AI), Human Computer Interaction (HCI) and Cognitive Science, with the goal to create intelligent interactive systems that can capture relevant user’s properties (states, skills, needs) and personalize the interaction accordingly. Conati has over 100 peer-reviewed publications in these fields, and her research has received awards from a variety of venues, including UMUAI, the Journal of User Modeling and User Adapted Interaction (2002), the ACM International Conference on Intelligent User Interfaces (IUI 2007), the International Conference of User Modeling, Adaptation and Personalization (UMAP 2013, 2014), TiiS, ACM Transactions on Intelligent Interactive Systems (2014), and the International Conference on Intelligent Virtual Agents (IVA 2016). She is an associate editor for UMUAI, ACM TiiS, IEEE Transactions on Affective Computing, and the Journal of Artificial Intelligence in Education. She served as President of Association for the Advancement of Affective Computing, as well as Program or Conference Chair for several international conferences including UMAP, ACM IUI, and AI in Education.
Sarah is a Machine Learning Engineer on the Commercial Software Engineering team at Microsoft, and has published work in the Machine Learning space. She holds a Master of Computer Science specializing in Machine Learning from the University of Ottawa. There, she and her colleagues developed a novel, unsupervised Machine Learning algorithm to rapidly detect evolving concepts in streaming data, and appropriately adapt the model. Professionally, Sarah has lead various projects including credit card fraud detection, churn prediction and user behavior pattern discovery. She is passionate about all things ML, but remains especially captivated by adaptive learning.
Shalaleh specializes in design processes and qualitative data analysis. She received her Master’s of Applied Science at the University of British Columbia (UBC) specializing in design theory and processes. She is an expert in Activity Theory, a well-known framework to map and analyze human relationships and social structures in a comprehensive and systematic manner. With her expertise, Shalaleh devised a new needs-finding technique based on the theory called the Activity Theory-based Needs-finding Technique to systematically evaluate and incorporate various stakeholder needs into engineering design processes.
Dr. Wang’s research interests are in the broad areas of statistical signal processing, with applications to information security, biomedical imaging, genomic, and wireless communications. She co-received the 2004 EURASIP Best Paper Award and 2005 Best Paper Award from IEEE Signal Processing Society, and a Junior Early Career Scholar Award from Peter Wall Institute for Advanced Studies at the University of British Columbia in 2005. She co-edited a book Genomic Signal Processing and Statistics (Hindawi Publishing Co., 2005) and co-authored a book Multimedia Fingerprinting Forensics for Traitor Tracing (Hindawi Publishing Co., 2005). She is the chair and founder of the IEEE Vancouver SP chapter.
Bozenna Pasik-Duncan received Master’s degree in Mathematics from the University of Warsaw in 1970, and Ph.D. and D.Sc. (Habilitation) degrees from the Warsaw School of Economics (SGH) in Poland in 1978 and 1986, respectively. Before moving to the University of Kansas (KU) in 1984, she was a faculty member of the Department of Mathematics at SGH. At KU she is a Professor of Mathematics, a Courtesy Professor of both Electrical Engineering and Computer Science (EECS) and Aerospace Engineering (AE), and an Information & Telecommunication Technology Center (ITTC) Investigator, a Chancellors Club Teaching Professor, and a member of the KU Women’s Hall of Fame. She is a Fellow of IEEE, a Fellow of IFAC, a recipient of the IEEE Third Millennium Medal, and IEEE Control Systems Society (CSS) Distinguished Member Award. She is founder of Women in Control (WIC), first chair of IEEE CSS Standing Committee on WIC and member of WIC Advisory Board, founder and faculty advisor of Association for Women in Mathematics (AWM) Student Chapter at KU, founder and coordinator of the MAM/Outreach Program at KU, and founder and chair of Stochastic Adaptive Control Seminar at KU. She is an Associate Editor of several Journals, and an author and co-author of over 200 technical papers and book chapters. Her research interests are primarily in stochastic systems and stochastic adaptive control, and in STEM education.. She is a recipient of many awards including IREX Fellow, NSF Career Advancement Award, KU Women of Distinction, Service to Kansas, H.O.P.E., Kemper, L. Hay, Morrison, Price, Polish Ministry of Higher Education Award, and recently received the 2016 IEEE EAB Meritorious Achievement Award in Continuing Education
Andréa’s research interests are in parallel and distributed computing encompassing cluster, grid, cloud and inter-cloud computing, information management, machine learning, and autonomic computing. Her current focus is on NoSQL stores, Hadoop-based solutions, and use of machine-learning for crowdsourcing.
Olivier Martin is a data science architect at Microsoft, with over 20 years of industry experience in technology. His previous roles within Microsoft included HPC architect and network engineer. In his current role, Olivier works with customers and partners to architect scalable solutions around machine learning and in the deep learning spectrum. Specifically, he’s helped customers using the cloud to run TensorFlow, PyTorch and Horovod to scale training to multi nodes on Azure, in fully automated fashion. Previous to joining Microsoft, Olivier was an entrepreneur and before that, a network engineering consultant for a few years.