Brandy Freitas is a senior data scientist at Precisely (formerly Pitney Bowes Software and Data), where she works with clients in a wide variety of industries to develop analytical solutions for their business needs. The Computational Social Science Lab is an interdisciplinary research group that works at the intersection of AI, data, and society. Attribution models for site search engines are stuck at "last-action" and Google Analytics-style reporting: since A/B testing the search bar is impossible, it is really hard to make informed business decisions involving the search experience. Intro to Machine Learning. This generally takes the form of large call center and repair technician workforces that are waiting for an issue to happen, in order to help solve it. Our faculty and students do everything from creating low-cost digital x-ray imagers to combat tuberculosis in developing countries, to AI technology is redefining almost every industry by enabling transformation of established business models and products. Real world applications of ML & How we operationalize model findings quickly in an. 2. She is a Member of the IEEE and of the ACM. Over the past 12+ years, he has explored the computational limits and opportunities of quantum computing for real-world applications. This session shows you how to train a high quality model with Azure Machine Learning automated ML by supplying only a dataset and a few configuration parameters. The practice of apply machine learning technology in healthcare, especially to deal with corona virus pandemic. Process Mining. Ontario Institute for Cancer Research (OICR). His students have gone on to faculty positions at universities that include Columbia and Stanford, and to leading roles in industry that include managing the largest app store in the world. Matt Sheehan is a Fellow at the Paulson Institute’s think tank, MacroPolo, where he leads work on U.S.-China technology issues, with a specialization in artificial intelligence. We are part of the Department of Computer Science at the University of Toronto. She holds a Ph.D. in computer science from Johns Hopkins University, where she was a National Science Foundation Graduate Research Fellow. We will share results demonstrating generalizability towards existing emotion benchmarks from other domains. The failure modes of machine learning systems are also different from those of traditional software applications. We have also combined this prediction with an action layer driven Robotics Automation, which takes the actions required to correct the technical issue "before the Customer notices it". For data practitioners, you'll have an opportunity to fast-track your learning process with access to relevant use-cases, and top quality speakers and instructors that you'll make lasting connections with while building your network. He presents at major industry events and writes tech content for leading publications including TheNewStack, Hackernoon, DZone, Towards Data Science and more. Our approach provides novel insights to theportfolio similarity problem as well as a data-driven method to remove bias from qualitative categorizations available in the market. Furthermore, transitioning to a career in practicing AL/ML, or managing ML and AI-driven businesses, are less than straightforward. While these are questions universal to any industry, they are particularly challenging to answer in the insurance industry because of its highly regulated and risk-averse nature. Lastly, we will share how organizations could use this dataset to train custom models for their use cases. Data Analytics Product Management, Machine Learning/ AI Operations, and Digital Transformation are few key areas of his recent focus. Applied ML Case Study Talk - Deep Reinforcement Learning at Zynga, Overcoming the challenges of using RL in production Patrick Halina Software Architect/ML Engineering Manager, Zynga, Advanced Research Talk - Explain Yourself! The results are pervasive across technology subcategories within the field of natural language: parsing, natural language understanding, sentiment detection, entity linking, speech recognition, abstractive summarization, and so on. This often limits the accuracy of models that can safely be deployed in mission-critical applications such as healthcare where being able to understand, validate, edit, and ultimately trust a model is important. Degree(s): Honours Bachelor of Science. Franziska started life as a physicist, and completed her PhD in condensed matter physics at the University of Oxford. Participants should be familiar with Supervised Machine Learning. Taken from the real-life experiences of our community, the Steering Committee has selected the top applications, achievements and knowledge-areas to highlight across 2 days, and 2 nights. You can inquire at, Ari Kalfayan - Winning Your First 50 Enterprise Customers: Practical Strategies to Successfully Launch a ML Startup, Jaakko Lempinen - How Finnish Public Broadcaster Yle is the Only Streaming Service Beating Out Netflix, Cynthia Rudin - Stop Explaining Black Box ML Models for High Stakes Decisions and Use Interpretable Models, Chip Huyen - Principles of Good Machine Learning Systems Design. He is jointly appointed as a Professor of Chemistry and Computer Science at the University of Toronto. Dr. Mamdani obtained a Doctor of Pharmacy degree (PharmD) from the University of Michigan (Ann Arbor) in 1995 and subsequently completed a fellowship in pharmacoeconomics and outcomes research at the Detroit Medical Center in 1997. I would like to share how this ecosystem (ML, Robotics and process engineering) will result in significant benefits for the organization. In the age of big digital data transformation, his mission is to discover algorithms and techniques that make sense and work in real life. Automated ML is an emerging field that helps developers and new data scientists build ML models without understanding the complexity of algorithm selection and hyper parameter tuning. He has applied this on-the-ground knowledge of how AI is transforming organizations and the economy as an expert participant in many forums investigating the broader social impact of the technology, including the Brookfield Institute, the Federal Economic Strategy Table for Digital Industries, and the Partnership on AI. This talk is designed to help you land your first 50 enterprise machine learning customers. Deadline to submit a talk is Sept 16th, however, we will continue to review submissions. Also, he was an advisor to the International Monetary Fund and led missions to several Latin American countries. He is also working towards his PhD in the same discipline, focused on scaling and accelerating algorithms for exploratory data analysis. She has developed multiple algorithms and use-cases for the financial institutes like boutique firms. The objective of this tutorial is to give the audience hands-on experience to work through the basics about knowledge graph and recommender technology, and how to use them for building an article recommender for COVID-19 research. Our approach enables efficient ML to solve complex prediction tasks for such applications both on-device and on Cloud, keeping model size, compute and power usage low while simultaneously optimizing for accuracy. Talk: Scaling Global Models with Regional Data Strategies and Model Governance. Build career skills in data science, computer science, business, and more. Talk: Graphical Models For Financial Time Series and Portfolio Selection, EVP, Head of Corporate Model Risk, Wells Fargo. His work focuses on applied Machine Learning in Portfolio Construction and Risk Management, with a particular interest in AI systems and Natural Language. He is a co-founder of Zapata Computing and Kebotix, two early-stage ventures in quantum computing and self-driving laboratories respectively. He holds masters and doctorate degrees in engineering and management from Wayne State University and the Massachusetts Institute of Technology. Bing Han. The IEEE Communications Society is a global community of engineers, practitioners and academics working together to advance communications technology for the betterment of humanity. Program(s): Computer Science (Major, Minor, Specialist) Another is financial security. Q: What are the transportation/parking options for getting to and from the event?There are multiple parking options around College and Yonge, as well as the College Subway station and both the Yonge St Bus and College Streetcar. She is currently working at Data Cognition Team in Global Market Engineering Group. Another family of models discussed are adaptive computational time that remedy some of the challenges related to time series data. Jaakko Lempinen works as a Head of Customer Experience at Yle – Finnish public broadcaster. The traditional methods are either qualitative, and hence prune to biases and often not reproducible,or, are known not to capture all the nuances (non-linearities) among the portfolios from the raw data. Neural machine translation, applications of machine learning to Indigenous languages, challenges of domain adaptation in low-resource settings. Workshop: Docker Based Workflow for Deploying a Machine Learning Model. Azin holds a Master of Science in Computer Science from University of Toronto and a Bachelor of Computer Science from University of Tehran. What would you do if you knew causation, not correlation, in the search behavior of your shoppers? I will discuss how to make sophisticated machine learning models such as Neural networks (Deep Learning) as self-explanatory models. Before coming to Treasury she studied backer behavior and what makes projects successful at Kickstarter. His research interests spans computer vision, machine learning and autonomous robotics, with a focus on real-time computation, safety and adaptability. The quality of online comments is critical to the Washington Post. Rich's Ph.D. is from CMU. Deploy machine learning algorithms to mine your data. As one of Canada’s preeminent centres of artificial intelligence, we thrive in our unique role bridging world-leading research and industry adoption. Join a group and attend online or in person events. Prior to that, she represented Treasury as its expert on financial applications of artificial intelligence and machine learning to the international Financial Stability Board in Switzerland and briefly served as Program Manager for OFR’s grant programs on the study of financial computation. Her latest mission is accelerating and democratizing Artificial Intelligence via Automated Machine Learning. He was also the Co-Founder of Qubitera LLC, a consulting company acquired by Rigetti Computing where he worked after NASA and before his current appointment with Zapata Computing. So, the question is how to enable the machine learning algorithm to access the inherent structure of the graph itself. As the field continues to advance, responsibility is becoming increasingly important to meet expectations of all stakeholders. She is working on variational quantum algorithms and computational tools for quantum simulation. We will present them in context of sequence-to-sequence with attention. For more information please review our cookie policy. Yet, it takes a lot of processing before we can build predictive models or perform analysis on them. Her background includes particle physics phenomenology, multipartitie entanglement and quantum information. The discussion will cover a broad-spectrum of considerations on moving Analytics journey to cloud. Nathan holds a PhD in Physics from the University of Waterloo, with expertise in quantum computing, deep learning, and quantum optics. Machine Learning/Data Mining. Machine learning for comparative analysis of mutual funds using machine learning. Making it possible for medical experts to understand and repair a model is critical because most clinical data has unexpected problems. We highlight top thought leaders globally, and our group consists on people from around the world. In 2007 he became an associate professor at Northwestern and in 2012 he was promoted to a full professor. Ilnaz is VP, Data Science at BMO Capital Markets. This virtual summit will examine how academia, government and industry can align to support all facets of society… Biases may arise at different stages in machine learning systems, from existing societal biases in the data, to biases introduced by the data collection or modeling processes. It helped Scotiabank to capture international banking customer behaviour and their price sensitivity more promptly .