toronto machine learning society
Rebecca Knowles is a Research Associate at the National Research Council of Canada. A creative exploration of developments in AI and machine learning, from research to industry to career exploration. These huge complex models trained on billions of words of text have been made available to researchers and industry to solve real-world problems. His research has been cited over 34,000 times and has an h-index of 90. He works in the integration of robotics, machine learning and high-throughput quantum chemistry for the development of materials acceleration platforms. Should we use deep learning? Jules’ team facilitates money movement for clients, suppliers and employees and ensures branches and ATMs have cash. Practitioners are leveraging and expanding their expertise to become high-impact global leaders. In this tutorial, participants will learn to: 1. perform image segmentation using the state-of-art deep learning approaches and customized datasets; 2. build an end-to-end image segmentation pipeline: dataset customization and transformation, model training, validating, and testing, techniques for post-processing; 3. convert trained models to ios and tensorflow-lite for mobile deployment for building downstream applications. Jesika is responsible for the business and partnership development and program management activities of the Autonomous Vehicle Innovation Network's (AVIN) Toronto Regional Technology Development Site (RTDS). Arthur Berrill is the Head of Data Science at the Royal Bank of Canada. LinkedIn: https://www.linkedin.com/in/sujithravi, Talk: Efficient AI: Building Efficient Neural Computing Machines on the Edge & Cloud. Together with his team, he combines product thinking with technical innovation on a variety of topics, presenting original findings at major conferences (KDD, HCOMP, ACL, ECAI, RecSys, etc.). Talk: Applied Machine Learning In Healthcare - Practical And Legal Considerations. In this talk, I will provide an overview of the NLP project and share how industry participants gained practical knowledge through pre-training large scale language models, learned theoretical concepts from leading NLP practitioners, and broadened their professional network through collaborations with participating sponsors. She has published many top tier conference and journal papers. 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. Yes, light breakfast, coffee and lunch are served both days, catered by Oliver & Bonacini Restaurants. Elle is a data scientist at Iterative, a startup building open source software tools for machine learning. I’ll also talk about how the culture changes within organisations as they start to benefit more from progressive data solutions – what are the future skills that every organisation should have and how to get started with the change. How to set out an enterprise approach to responsible use of data and AI, how to translate that into global data strategy elements and frameworks and then how to use regional or country specific data and model building strategies. Selika is an innovative strategist, transportation executive, and motivational speaker. She graduated from Stanford, where she taught TensorFlow for Deep Learning Research. In this talk we will demonstrate our novel method of detecting client churn based on image processing. Ling Jiang is a data scientist at the Washington Post. She has published research on machine learning for finance topics including graphical models for portfolio selection and modeling bank deposits using bank financial data and macroeconomic variables. At the academia he taught economics at Rice University, ITAM and El Colegio de Mexico. services are deployed to produce improvements to important business metrics, e.g. We focus on a critical vulnerability in the group fairness approach enforced in banking today. You may also find my experience helpful, which is that we have never needed a black box model for a high stakes decision, because we have always been able to construct an interpretable model that is at the same level of predictive performance as the best black box we could find. Talk: Political Economy of Future Transportation and Equity, The concept of the innovation and transportation and real world issues of public engagement, political policy and equity, Policy considerations when designing for future mobility. This talk is designed to help you land your first 50 enterprise machine learning customers. Find local Machine Learning groups in Toronto, Ontario and meet people who share your interests. This work suggests that graphical models can effectively learn the temporal dependencies in time series data and are proved useful in asset management. Previously, he was CEO and founder of Samsamia Technologies, a company that created a visual search engine for fashion items allowing users to find products using images instead of words, and founder of the Robotics Society of Universidad Carlos III, which developed different projects related to UAVs, mobile robots, small humanoids competitions, and 3D printers. 4. Natalia Bailey is a Policy Advisor with the Digital Finance Department at the IIF, she focuses on the digital transformation of the financial system, particularly the application of new technologies such Machine Learning to the domain of risk management, compliance and financial sector supervision. Textbook deep learning models of course work but their performance can be improved with tailored approaches for data and problem in question. As the field continues to advance, responsibility is becoming increasingly important to meet expectations of all stakeholders. Professor Zhou is known for his work in indefinite stochastic LQ control theory and application to dynamic mean-variance portfolio selection, in asset allocation and pricing under cumulative prospect theory, and in general time-inconsistent problems. AI technology is redefining almost every industry by enabling transformation of established business models and products. AI Research Scientist, NASA Jet Propulsion Laboratory. She has spoken nationally and internationally providing unique strategies for companies and governments in a variety of policy areas. His group has been recognized as a success story within the data and analytics industry. In her spare time, Jesika works as a strategic partnership consultant and advisor in the auto-tech sector and volunteers as an Advisory Council Member and Chair for Girls in Tech - Toronto Chapter. Workshop: MLOps & Automation Workshop: Bringing ML to Production in a Few Easy Steps, How to deploy ML/AI faster to production using various MLOps technologies, Familiarity with Jupyter Notebooks, Pandas, and common ML tools, Sr. Workshop: Machine Learning Simplified: From Ideation to Deployment in Minutes with Automated Machine Learning. Prior to Banorte, Jose was a top ranking official at Mexico’s Central Bank and participated for more than a decade at the Monetary Policy Committee holding the staff’s view on inflation –the key variable for policy decision. The goal of TMLS is to empower practitioners and business leaders with direct contact to the people that matter most. The Toronto Declaration: Protecting the rights to equality and non-discrimination in machine learning systems was launched on May 16, 2018 at RightsCon Toronto.. Talk: Harnessing the Power of NLP: A Vector Institute Industry Collaborative Project. As well, to help data practitioners, researchers and students fast-track their learning process and develop rewarding careers in the field of ML and AI. His charter is to research, guide and deliver data science capabilities including location intelligence, new data content, artificial intelligence, ontology and climate change studies across all departments of the bank. He spends his time working with enterprise financial services customers from investment banking, asset management and investment research on building secure environments, best practices on model development, model governance and operationalizing ML workflows. The ecosystem for deploying SaaS applications includes countless tools for delivering an app to production, monitoring its performance, and deploying in real-time. Deadline to submit a talk is Sept 15th, however, we will continue to review submissions. Lastly, we will share how organizations could use this dataset to train custom models for their use cases. 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. Deploy machine learning algorithms to mine your data. Find out more about how your privacy is protected. This virtual summit will examine how academia, government and industry can align to support all facets of society. Director of Machine Learning, Rogers Communications. Q: Why should I attend the TMLS?Developments are happening fast - it's important to stay on top. Shreyansh received his M.S. Workshop: Intrusion Detection Systems - An Overview. Mai was senior research/policy advisor at the Anti-Racism Directorate. 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. She completed her PhD at the University of Washington where she conducted research on speech and hearing using mathematical models. His research interests spans computer vision, machine learning and autonomous robotics, with a focus on real-time computation, safety and adaptability. The audience will learn about how practical applications of NLP are incorporated into the investment research process in order to generate alpha on a discretionary and systematic basis. The leading professional association worldwide for professionals and students involved in the audio industry. Before joining the Washington Post, he lead engineering teams at Amazon Web Services building cloud networking solutions used by some of the largest companies in the world. Patrick is the Director of Data Science at the Washington Post. Abstract: A critical component of data management and enrichment pipelines is connecting large datasets from various sources to form a holistic view; to make connections between entities across data sources. Professor Zhou received his Ph.D. in Operations Research and Control Theory from Fudan University in China in 1989. Alán also develops quantum computer algorithms for quantum machine learning and has pioneered quantum algorithms for the simulation of matter. We are part of the Department of Computer Science at the University of Toronto. How to set up your model monitoring from scratch, and how to prioritise different metrics. She received her master’s degree of statistics from University of California, Berkeley. The challenges, the solutions, the effectiveness, and the remaining issues, including technology progress and institution reform. The discussion will cover a broad-spectrum of considerations on moving Analytics journey to cloud. As such, it is becoming more important for Financial firms to be able to incorporate dynamic ESG metrics into their investment processes. Recurrent neural networks and transformers are well suited for temporal data and sequences however their performance can be improved by using novel concepts. Learn about challenges such as unintended user and societal harm, unfair bias, surveillance, adversarial attacks. 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. She has also authored numerous papers and been awarded the prestigious scholarships including Mitacs Postdoctoral Award. She enjoys working on data mining and knowledge discovery from large volume of data. We hope this paper will serve as but the first step in the right direction. Moderator: Trishala Pillai, Applied AI Partner Myplanet, Advanced Research Track: Adversarial Examples and Understanding Neural Network Representation SpaceNick Frosst , rSWE, GoogleBrain, ML in Production - Implementation, Tooling & Engineering, Data/ML Ops: Building Private Machine Learning Models with TensorFlowChang Liu, Applied Research Scientist, Georgian Partners, Advanced Research Track: Temporal Concept Localization on YouTube 8M DatasetSatya Krishna Gorti Machine Learning Scientist, Layer6 AI, Business Talk - Transformation from Research Lab to Product Centers Daniel Weimer, Head of AI Volkswagen of America, Inc, ML Case Study Talk - Machine Learning for Space ExplorationShreyansh Daftry AI Research Scientist, NASA, Advanced Research Talk - Machine Learning for SystemsAzalia Mirhoseini, Senior Research Scientist, Google Brain, Business Panel – Autonomous Vehicles and the Future of Mobility Ted Graham Head of Open Innovation, GM, Shreyansh Daftry AI Research Scientist, NASA, Steven Lake Waslander , Associate Prof, University of Toronto Moderator: Arif Virani, COO, DarwinAI, Applied Machine Learning Case Study Talk – Data Science at the New York Times, Christopher Wiggins, Chief Data Scientist, New York Times, ML in Production: Implementation, Tooling & Engineering, Data/ML Ops Talk: DevOps for Machine Learning and other Half-Truths: Processes and Tools for the ML Life Cycle Kenny Daniel, Founder, Algorithmia, Advanced Research Talk - Explaining with Impact: A Machine-centric Strategy to Quantify the Performance of Explainability Algorithms Sheldon Fernandez, CEO at DarwinAI, Applied ML in Production Case Study Talk- Rearchitecting Legacy Machine Learning Systems Amit Jain, Machine Learning Team Lead, TradeRev, Advanced Technical Talk - A Flexible Framework for Entity Resolution, A Flexible Framework for Entity ResolutionHoyoung Jang, Data Scientist, ThinkData Works, Cheng Lin, McGill University. Application of Generative Models in Financial Time Series Modelling. The participants established 11 working groups, each of which developed and performed experiments relevant to existing industry needs. Melanie is the author or editor of six books and numerous scholarly papers in the fields of artificial intelligence, cognitive science, and complex systems. How to measure AI contribution to the bottom line 2. contributions to revenues in eCommerce: in particular, we will show how deep learning models can be used to assess how much revenues in a digital shop comes from interactions with search and recommendation APIs. Ali leads the machine learning team at Cyclica Inc focusing on improving the company's technology for predicting interaction between drugs and target proteins. 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. Her latest book is Artificial Intelligence: A Guide for Thinking Humans (Farrar, Straus, and Giroux). Firms are using AI to create unprecedented business advantages that are reshaping the global - but more specifically Canadian - economic landscape. Shahid Amlani is the Director of Machine Learning and Automation at Rogers Communications. Whether you are developing your first machine learning application, creating an enterprise ML infrastructure startup, or creating new Machine/Deep Learning tools, this hands-on session is designed to share practical strategies, growth hacks, and specific techniques to use that will win you your first customers and scale. Toronto, Canada, June 16 - 18 - Toronto Machine Learning Society (TMLS) hosts MLOps, Production & Engineering 2020 through an interactive conference to enable attendees the opportunity to virtually engage with speakers and establish a stronger network within the AI community. Scientist at Coveo, building A.I. Before joining Lloyds, he was an executive and head of Quantitative Risk at Bank of America. The talk ends with a survey of the MLOps landscape by analyzing over 200 available tools, where they fit into the ecosystem, and what’s missing in the ecosystem. Business Executives, PhD researchers, Engineers and Practitioners ranging from Beginner to Advanced. Deep learning provides a computationally efficient and implementation friendly way to approximate derivative valuation function, a critical component of XVA models. We acknowledge the Talk: Artificial Intelligence for Molecular Design and Self Driving Labs. Director of Data Science, The Washington Post, Race Data Collection Expert, Toronto Police Service, Sr. Director – Robotics and Machine Learning, Rogers Communications, Senior Data Scientist, Manager, Scotiabank, Data Scientist/Machine Learning Specialist & Senior ML Specialist, Amazon Web Services, Sr. Although it is a fundamental step for many data science tasks, an efficient and standard framework is absent. The quality of online comments is critical to the Washington Post. Despite all the progress that has been made, machine learning explainers are still fraught with weakness and complexity. Things to do in Toronto, Canada Aim: Our main objectives is to design a pricing product that can help to: 1) Identify groups of elastic and inelastic customers, 2) Determine the optimal rate for each group of customers, 3) Be agonistic pipeline and can be reusable for other pricing use cases. Electrical and computer engineers shape the future through innovation. Content provided by Toronto Machine Learning Society. This tutorial will outline a series of approaches to this task based on neural networks. You will learn about various machine learning methods that can be used to address this problem. He is also a researcher in the artificial intelligence and machine learning field. Race is a concept, a tool, and a structure that defines a set of relationships between people. He holds masters and doctorate degrees in engineering and management from Wayne State University and the Massachusetts Institute of Technology. He is both an IEEE Fellow and a SIAM Fellow. Glass-Box vs. Black-Box ML and explanation methods. Ah, and how we saved 80% of our cost along the way. We will start with the basics of containers and work our way up to a full example. An Electrical Engineer by technical training, Phoenix holds graduate degrees in Business Administration and Analytics. She holds a Master’s of Science in Management from Purdue University. Toronto Conferences Talk: Beyond Standard Deep Learning Models for Time Series and Sequences, AI & Innovation Strategy Lead, National Bank of Canada. Breakthroughs in the usage of deep learning, as well as the availability of more sophisticated hardware and cloud resources, led to sudden advances in natural language. Danit serves as the former chair and vice chair of the P7009 IEEE standard on the Fail-Safe Design of Autonomous and Semi-Autonomous Systems, and the executive committee of The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems. Avi is a PhD student in the Applied Math and Scientific Computation program at the University of Maryland. Talk: Deep XVA Her current research focuses on conceptual abstraction, analogy-making, and visual recognition in artificial intelligence systems. Educated in several acronyms across the globe (UNISR, SFI, MIT), Jacopo was founder and CTO of Tooso, an A.I. The Department of Computer Science at the University of Toronto has several faculty members working in the area of machine learning, neural networks, statistical pattern recognition, probabilistic planning, … Part of Computer Science at the University of Toronto. Details about data for training own models, Navid is an applied research scientist with a master's degree in computer science from the University of Toronto. Talk: Productionizing Deep Learning Models at Scale. Agus holds several U.S. patents in both finance and engineering. Working with orgs who are new to AI and how to manage their expectations. At Loblaw Digital, we have abundant data resources. She is currently working at Data Cognition Team in Global Market Engineering Group. 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. Jaakko Lempinen works as a Head of Customer Experience at Yle – Finnish public broadcaster. Winston is the founder of Arima, a synthetic database that captures individual consumer-level behavioural and demographic attributes across Canada. Senior Vice President, Enterprise Operations and Payments, RBC, Jules Andrew has held leadership financial and operational positions across the globe with IBM and RBC for over 20 years. Mining Massive Datasets. His work has been featured in press: Wired, Forbes, Forrester, New York Times, TechCrunch, VentureBeat, Engadget, New Scientist, among others, and also won the SIGDIAL Best Paper Award in 2019 and ACM SIGKDD Best Research Paper Award in 2014. The NLP Project addressed these challenges by familiarizing industry participants with advanced NLP techniques and the workflows for developing new methods that could achieve high performance while using relatively small data sets and widely accessible computing resources. Talk: How Finnish Public Broadcaster Yle is the Only Streaming Service Beating Out Netflix, Snorkel AI - Machine Learning Engineer & Open Source Lead.