At Rogers, we have used Machine Learning to develop such a model with precision rates of over 90%. - How AI/ML is being used by NASA to enable the next frontier in robotics space exploration. Engineers, Researchers, Data Practitioners: Will get a better understanding of the challenges, solutions, and ideas being offered via breakouts & workshops on Natural Language Processing, Neural Nets, Reinforcement Learning, Generative Adversarial Networks (GANs), Evolution Strategies, AutoML, and more. Differences in label confidence make model building challenging, as the optimization cannot be done while amalgamating all the data points in the training process. "Wenming Ye is an AI and ML Product Manager at Amazon Web Services, helping researchers and enterprise customers use cloud-based machine learning services to rapidly scale their innovations. Additionally, she is a co-organizer of Women in Data Science ATX and promotes diversity and fostering a welcoming space for newcomers to the field. As the field continues to advance, responsibility is becoming increasingly important to meet expectations of all stakeholders. Textbook deep learning models of course work but their performance can be improved with tailored approaches for data and problem in question. He is advised by Dr. Tom Goldstein on his work in AI security, relating to data security and model vulnerability. Next we will look at examples of machine learning technologies we are developing for autonomous robotic applications on Earth, Mars and beyond, and describe some of the grand challenges in AI for such safety-critical systems. Finally, we investigate the feasibility of realistic adversarial attacks in which an adversarial trader fools automated trading systems into making inaccurate predictions. Widespread increase in availability of connected “smart” appliances (e.g., conversational assistants) means that there is an ever-expanding surface area for mobile intelligence and ambient devices in homes. Talk: Machine Learning in Finance: Lessons Learned, Head, Personal and Commercial banking, BorealisAI, In recent years most companies are being forced to innovate and many are particularly excited about Machine learning applications. How should an applied ML project be directed and organized? Ari began his career in the AI/ML space in 2009, where he led sales at Figure Eight (sold for $300M to Appen) which pioneered data labeling technology. Talk: Quantum - Assisted Machine Learning with Near-Term Quantum Devices. Brad has been focusing on Data Analytics, Artificial Intelligence, and HPC for over 30 years. These technologies power conversational AI (e.g., Smart Reply), Web and Image Search; On-Device predictions in Android and Assistant; and ML platforms like Neural Structured Learning in TensorFlow, Learn2Compress as Google Cloud service, TensorFlow Lite for edge devices. Toronto Conferences Prior to his career in banking, he was a product design manager in the Powertrain Division of Ford Motor Company. Finally, I will offer best practices to guide future industry collaborative projects. Randi manages a team of data scientists at Dell Technologies within Support and Deployment Services who deliver data science solutions using telemetry data to proactively prevent customer issues and resolve them more quickly when they do happen. Machine learning, deep learning, and AI are some of the fastest growing and most exciting areas for knowledge workers - simultaneously, they are the key to untapped revenue sources and strategic insights for businesses. How does AI for good and sustainability play into your work? He also has decades of expertise applying AI to practical problems in areas ranging from natural language processing and data mining to robotics, video gaming, national security and bioinformatics. The Vector Institute’s project, Recreation of Large Scale Pre-Trained Language Models (the NLP Project), is an industry-academia collaboration that explores how state-of-the-art natural language processing (NLP) models could be applied in business and industry settings at scale. Attendees will gain an understanding of principles of knowledge translation in applied machine learning in healthcare and understand issues related to privacy and ethics as well as legal considerations. What we are looking for is finding what caused the client to leave the firm and what prior information led him/her to make that decision. Prior to working at NVIDIA, Corey spent over a decade building massive-scale exploratory data science & real-time analytics platforms for HPC environments in the defense industry. We will describe sources of bias in ML technology, why addressing bias matters, and techniques to mitigate bias, with examples from our work on inclusive AI at Pinterest. Jaakko Lempinen works as a Head of Customer Experience at Yle – Finnish public broadcaster. 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. It examines how ML applications differ from traditional software engineering applications, the scaling challenge, and the rise of MLOps. In this presentation, we study a specific synthetic data generation task called downscaling, a procedure to infer high-resolution information (e.g., individual level records) from low-resolution variables (e.g., average of many individual records), and propose a multi-stage framework. Advisor for Amplify Partners, IBM Data Science Community, Recognai, KUNGFU.AI, Primer. This presentation will be broken up into three parts: - Landing your first customer (0-1 customer), - Validating your business model (1-10 customers), - Scaling your business (10-50 customers). Research internships are open to candidates studying towards a PhD in machine learning, computer science, computational neuroscience, or a related field. Ron is responsible for leading engineering teams that apply Vector’s leading AI research to industry and health care problems for Canada, establishing and supporting world class scientific computing infrastructure to scale the adoption of beneficial AI, and ensuring that all Vector users, sponsor participants and partners are upskilled to use it effectively. Yaron is an active contributor to the CNCF Working Group and was one of the foundation’s first members. Things to do in Toronto, Canada She worked in data-intensive R&D project development and academic-industry partnerships in the area of AI/ML at SOSCIP, the Southern Ontario Smart Computing for Innovation Platform. Using node embedding to create a low dimension vector representation of the node and its structural components, you no longer need to compromise and query away important structural relationships. Deadline to submit a talk is Sept 15th, however, we will continue to review submissions. 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. Xunyu Zhou is the Liu Family Professor of Industrial Engineering and Operations Research at Columbia University in New York. Finally, I will explain the state of development of experimental quantum computers and future prospects. Her background includes particle physics phenomenology, multipartitie entanglement and quantum information. Arima aims to be a full-stack solution for data scientists to easily acquire individual-level consumer intelligence, connecting those who want better data to build more robust ML models and those who have data, without compromising data privacy. Jaya Kawale is the Director of Machine Learning at Tubi leading all of the machine learning efforts at Tubi encompassing homepage recommendations, content understanding and ads. This talk introduces relevant work on AI/ML at the United Nations to provide an overview of key developments and use cases and highlight opportunities for collaboration. With widespread use of machine learning, there have been serious societal consequences from using black box models for high-stakes decisions, including flawed bail and parole decisions in criminal justice. In a previous life, Mark produced documentary films on poverty and development, most notably the award-winning documentary Poverty, Inc., which received international acclaim and distribution on Netflix, Amazon, and television stations around the world. She is also a proud Action Canada Fellow alumni. Q: Will you focus on any industries in particular?Yes, we will have talks that cover Finance, Healthcare, Retail, Transportation and other key industries where applied ML has made an impact. Glass-Box vs. Black-Box ML and explanation methods. Neural machine translation, applications of machine learning to Indigenous languages, challenges of domain adaptation in low-resource settings. We highlight top thought leaders globally, and our group consists on people from around the world. The talk will provide an overview of data-driven approaches for financial time series modelling and different performance evaluation metrics that could be used. You will learn about various machine learning methods that can be used to address this problem. The Toronto Declaration: Protecting the right to equality and non-discrimination in machine learning systems Preamble 1. He received his PhD from Georgia Institute of Technology in 2019. Our faculty and students do everything from creating low-cost digital x-ray imagers to combat tuberculosis in developing countries, to He is also a researcher in the artificial intelligence and machine learning field. He is the author of XVA: Credit, Funding and Capital Valuation Adjustments which is published by Wiley, co-editor of Landmarks in XVA which is published by Risk Books and co-author of a number of technical articles on XVA in recent years. About Research People Blog Datasets. The audience will also get insights into how edge computing, edge analytics and fog computing can be leveraged by Intrusion Detection systems for security analytics at the edge for IoT. This allows us to train quantum computers in largely the same way as we do neural networks, even using familiar software tools like TensorFlow and PyTorch. Ari focuses on helping early-stage co-founders, who are building machine learning startups, accelerate growth and achieve product market fit. Data also usually has sparse features with respect to time with tailored models accounting for them. Previously Joe was Lead Executive for Data at MaRS Discovery District in Toronto, North America’s largest urban innovation hub dedicated to growing Canada’s tech sector. Completing the Licensing Process Application Administrative Suspension and Reinstatement for Lawyers Robin McNa Agus holds several U.S. patents in both finance and engineering. In this talk, we provide concrete examples of intractable ML tasks that could be enhanced with near-term devices. Developments in the field are happening fast – for practitioners, it’s important to stay on top of … In order to achieve our objective, we proposed to convert our data which includes all the history about each client over time into images, and then apply deep neural network to predict client churn. Dr. Sujith Ravi is a Director at Amazon Alexa AI where he is leading efforts to build the future of multimodal conversational AI experiences at scale. Alán is a fellow of the American Association of the Advancement of Science and the American Physical Society. And we are awarded as the top 10 of the 100 best practices of AI medical solutions, by China Academy of Information and Communications Technology (CAICT) in April 2020. Miguel González-Fierro is a Sr. Data Scientist at Microsoft UK, where his job consists of helping customers leverage their processes using Big Data and Machine Learning. Chip Huyen works to bring the best practices to machine learning production. Tubi is an advertiser based video on demand service that allows its users to watch content online. Learn about challenges such as unintended user and societal harm, unfair bias, surveillance, adversarial attacks. Today, AI researchers & practitioners increasingly use deep neural networks for many applications across different modalities and areas such as NLP, Vision, Speech, Conversational and Multimodal AI. Canada Events Everyone is welcome. Rich received an NSF CAREER Award in 2004 (for Meta Clustering), best paper awards in 2005 (with Alex Niculescu-Mizil), 2007 (with Daria Sorokina), and 2014 (with Todd Kulesza, Saleema Amershi, Danyel Fisher, and Denis Charles), and co-chaired KDD in 2007 with Xindong Wu. Talk: Forget ROC scores, What Metrics Do your Stakeholders Care About? Dr. Goertzel also serves as Chairman of the Artificial General Intelligence Society, the OpenCog Foundation, the Decentralized AI Alliance and the futurist nonprofit Humanity+. The 2020 industry landscape for NLP use cases in production; the relative "market share" for the popular open-source libraries/frameworks; and analysis of cloud service usage and failure cases; plus industry drivers for accuracy vs. cost in new NLP advances. Talk: Machine Learning Deployment and Product Integration, Machine Learning Researcher, RiskLab Toronto. The failure modes of machine learning systems are also different from those of traditional software applications. Talk: Applications of NLP in Investment Research. Nathan Killoran is the Head of Software & Algorithms at Xanadu, and one of the founding developers of PennyLane, the world’s leading quantum machine learning software library. Emeli is a lecturer at the Graduate School of Management of St. Petersburg State University and Harbour.Space University, where she teaches courses on machine learning and data analysis tools. Among other academic and technology media writing, Patrick is the primary author of popular e-books on explainable and responsible machine learning. In this talk, we will discuss the evolution of autonomous robots for space exploration and planetary science. Rachel Hu is an applied scientist on the AWS AI working on deep learning. (1) some measurements showing the extent to which financial markets and traders dependent on machine learning algorithms were affected in the aftermath of COVID-19, (2) examples of how machine learning tools can be used to address macro shifts in the financial system, and (3) considerations for building financial algorithms that are more robust to changing regulatory policies, Talk: A Former Government Economist Discusses Opportunities at The Intersection of Financial Policy and Machine Learning, Senior Manager, Machine Learning and Asset Allocation, Vanguard. As machine learning systems advance in capability and increase in use, we must … AI for Discovery and Self-Driving Labs. Shahid has spent over a decade creating solutions that utilize the potential of technology and data to create real, measurable business outcomes. We focus on a critical vulnerability in the group fairness approach enforced in banking today. Talk: Scaling Global Models with Regional Data Strategies and Model Governance. Educated in several acronyms across the globe (UNISR, SFI, MIT), Jacopo was founder and CTO of Tooso, an A.I. Prior to joining AWS, he worked at Publicis Sapient as a data scientist focused on ML in the retail vertical. Avi is a PhD student in the Applied Math and Scientific Computation program at the University of Maryland. 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. She has published many top tier conference and journal papers. She led the development and establishment of the Anti-Racism Data Standards and provided strategic advice and support to public sector organizations regulated to collect race-based data under the Anti-Racism Act. Her PhD research is about atomic simulations of liquid alloys, and machine learning methods to aid the simulations and their analysis. Stefan Natu is a Sr. Machine Learning (ML) Specialist at Amazon Web Services with a focus on financial services. in EECS in 2005 both from Ecole Nationale Superieure des Telecommunications de Paris and EURECOM, France. His research includes reinforcement learning in continuous time and spaces, quantitative behavioral finance models that incorporate human emotions and psychology into financial decision makings, and intelligent wealth management solutions using stochastic control and machine learning techniques. Talk: Winning Your First 50 Enterprise Customers: Practical Strategies to Successfully Launch a Machine Learning Startup. No, we do our best to ensure attendees are not inundated with messages, We allow attendees to stay in contact through our slack channel and follow-up monthly socials. Ah, and how we saved 80% of our cost along the way. Choose from hundreds of free courses or pay to earn a Course or Specialization Certificate. Ilnaz is VP, Data Science at BMO Capital Markets. As such, it is becoming more important for Financial firms to be able to incorporate dynamic ESG metrics into their investment processes. I will discuss how to make sophisticated machine learning models such as Neural networks (Deep Learning) as self-explanatory models. Part of Computer Science at the University of Toronto. Electronic Structure Methods. In what ways are humans still fundamentally superior? These hurdles limit the accessibility many organizations have to NLP capabilities, putting the significant benefits advanced NLP can provide out of reach. Talk: The algorithm is not enough: UX meets Data Science. Yes, light breakfast, coffee and lunch are served both days, catered by Oliver & Bonacini Restaurants. Her current research focuses on conceptual abstraction, analogy-making, and visual recognition in artificial intelligence systems. Valerii joined Cineplex last year to drive the best practice in development and serving of ML models. Ontario Events & Ms. degrees in Computer Science. 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. Brad holds a Ph.D. in Computer In previous lives, he managed to get a Ph.D., do scienc-y things for a pro basketball team and simulate a pre-Columbian civilization. Google Chrome is recommended to run the Virtual Conference platform. Talk: Machine Learning for Space Exploration. Master best practices for experiment design and hypothesis testing. 2021 SoFiE Machine Learning Virtual Conference Sponsored by:The Kenan Institute of Private Enterprise at the University of North Carolina at Chapel Hill will host a virtual conference on machine learning in finance on March 5, 2021.The conference is co-sponsored by the Journal of Financial Econometrics (JFEC) and the International Center for Finance (ICF) at Yale University. Talk: Lessons Learned Transitioning to the Cloud. Oftentimes, these entities – such as individuals, organizations, or addresses – may not have a unique identifier that can be used as a key to detect duplicates or to merge datasets on.

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