toronto machine learning society
I'm a Bachelor of Mathematics graduate from the University of Waterloo. The Toronto Declaration: Protecting the rights to equality and non-discrimination in machine learning systems was launched on May 16, 2018 at RightsCon Toronto.. 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. In 2010, Dr. Mamdani was named among Canada’s Top 40 under 40. Our AI doctor assistant can answer patients’ queries, recommend what-to-do-next to the community doctors, supervise the quality and cost for the hospitals, and improve the doctor's productivity hundreds of times especially for online remote diagnosis. Talk: How Finnish Public Broadcaster Yle is the Only Streaming Service Beating Out Netflix, Snorkel AI - Machine Learning Engineer & Open Source Lead. In his spare time, he builds his own robots using Raspberry Pi and trains deep neural nets to teach the bots to recognize objects and understand human language. AI technology is redefining almost every industry by enabling transformation of established business models and products. This talk will focus on how Natural Language models can be leveraged to automatically adjust and influence portfolio construction methods away from sensitive ESG topics. Josh has a Masters in Applied Statistics from Cornell University. Jill is a data scientist at Shopify, where she tackles a wide range of fascinating data problems on the international team. The project involved 60 participants: 23 Vector researchers and staff with expertise in machine learning and NLP along with 37 industry technical professionals from 16 Vector sponsor companies. Patrick is the Director of Data Science at the Washington Post. Talk: Banorte’s AI Transformation Journey: How the Analytics Team of this Bank Yielded 3 Billion USD Revenue During the Past Five Years. Q: Are there ID or minimum age requirements to enter the event?There is not. Finally, we investigate the feasibility of realistic adversarial attacks in which an adversarial trader fools automated trading systems into making inaccurate predictions. Despite all the progress that has been made, machine learning explainers are still fraught with weakness and complexity. Workshop: Managing Data Science in the Enterprise. Emeli Dral is a Co-founder and Chief Technology Officer at Evidently AI, a startup developing tools to analyze and monitor the performance of machine learning models. 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. Easy of migrating to RAPIDS. Research Associate, National Research Council of Canada. TMLS consists of a community comprised of over 6,000 ML researchers, professionals and entrepreneurs. Her experience ranges from many years in research, in the development of smart assistant applications, to defining strategy of AI-based offers. 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. Talk: Fine-Grained Emotion Detection for Products & Research. Sentiment analysis, text classification, Named Entity recognition and QA using Bert and Spacy Models. 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. Race is a concept, a tool, and a structure that defines a set of relationships between people. At Loblaw Digital, we have abundant data resources. Background: Pricing is a famous business issue in many companies and organizations. 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. Many product companies have an established team of data science experts; many have an established team of UX experts. 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. She is a three time winner of the INFORMS Innovative Applications in Analytics Award, was named as one of the "Top 40 Under 40" by Poets and Quants in 2015, and was named by Businessinsider.com as one of the 12 most impressive professors at MIT in 2015. Learn about challenges such as unintended user and societal harm, unfair bias, surveillance, adversarial attacks. We argue that to reach this target, the focus should be on areas where ML researchers are struggling, such as generative models in unsupervised and semi-supervised learning, instead of the popular and more tractable supervised learning tasks. to assess A.I. Shahid Amlani is the Director of Machine Learning and Automation at Rogers Communications. During his fellowship, Dr. Mamdani obtained a Master of Arts degree in Economics from Wayne State University in Detroit, Michigan with a concentration in econometric theory. We believe these events should be as accessible as possible and set our ticket passes accordingly, *Please Scroll down for full Program/Abstracts*. Come expand your network with machine learning experts and further your own personal & professional development in this exciting and rewarding field. Why today's group fairness algorithms can result in blatantly unfair outcomes (and what we can do about it), Talk: Black Loans Matter: Algorithms for Racial Justice. Talk: Inclusive Search and Recommendations. The capacity to implment and demonstrate high ROI AI projects changes this dynamic. 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, … Dr Dhagash Mehta is a Senior Investment Strategies Manager (Machine Learning and Asset Allocation) at Investment Strategies Group at Vanguard, and prior to that was a Principal Research Data Scientist at Vanguard. 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. She is currently the head of AI strategy within the Wealth Management division of the National Bank of Canada. Selika was formerly the Appointed Deputy Administrator for the State of New Jersey Motor Vehicle Commission in charge of all Operations for state services, the Director of Field Operations for the FMCSA (Federal Motor Carrier Safety Administration) and has over 18 years’ experience as an attorney litigator and was most recently the Senior Advisor to the Administrator of the FMCSA. We demonstrate that using FTL to learn stepwise, across the label confidence distribution, results in higher performance compared to deep neural network models trained on a single confidence range. In what ways are humans still fundamentally superior? All models are wrong and when they are wrong they create financial or non-financial harm. Program Information. We focus on a critical vulnerability in the group fairness approach enforced in banking today. Practical applications will be discussed, including personalized medicine, humanoid robotics and grammar learning. While the tech unicorns and their proxies have conducted almost an "arms race" since early 2018, sometimes publishing papers twice monthly to outdo their competitors' most recently published benchmarks -- how are these advances diffusing into practical use cases, and becoming adopted by mainstream businesses for their needs? This talk will highlight some roles machine learning is playing in finance and financial markets today -- from the aftermath of COVID-19 to lending applications, and how policy makers and practitioners might use machine learning to promote financial stability and build more sustainable business models. Jesika holds a bachelor's degree in manufacturing engineering with a specialization in Total Quality Management (TQM) and a Master of Engineering Entrepreneurship and Innovation (MEEI) from McMaster University. 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. However, in aggregated data environments, confidence in the individual data points vary in a quantifiable manner by primary data source or measurement type. He works in the integration of robotics, machine learning and high-throughput quantum chemistry for the development of materials acceleration platforms. Professor, Department of IEOR, Columbia University. 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 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. How emotions can be detected from textual content for business use cases & research purposes 2. Talk: The algorithm is not enough: UX meets Data Science. Find local Machine Learning groups in Toronto, Ontario and meet people who share your interests. He obtained his PHD in mathematics from Temple University in 1989. 1 talking about this. Shirin is a senior data scientist at Artificial intelligent and machine learning team at Scotiabank. Moreover, some classical approaches of the market simulation are contrasted with simulation using generative modelling and the advantages and drawbacks of the new approaches are highlighted. Working with organizations such as Women in Communications and Technology, Canadian Women in Business and Girls Who Code, Jules actively promotes the importance of encouraging women to pursue STEM careers and leadership roles. degree in Electronics and Communications Engineering from Manipal University, India. Corey has a passion for using data to make better sense of the world. TMLS is not a sales pitch - It's a connection to a deep community that is committed to advancing ML/AI and to create and deliver value and exciting careers for Businesses and Individuals. While most existing reinforcement learning (RL) research is in the framework of Markov Decision Processes (MDPs), it is important and indeed necessary, both theoretically and practically, to consider RL in continuous time with continuous feature and action spaces, for which stochastic control theory offers a natural underpinning. Working with orgs who are new to AI and how to manage their expectations. Corey has a passion for using data to make better sense of the world. The related research is still in its infancy, and this talk reports some of the latest developments and suggests several directions for investigation. The joint collaboration from AI2, Microsoft, the NLM at the NIH, and other prestigious research institutes aims at empowering the world’s AI researchers with a text and data mining tools to help accelerate COVID-19 related research. We live in an age of data; so much data that it’s overwhelming. Ontario Institute for Cancer Research (OICR). Mai was senior research/policy advisor at the Anti-Racism Directorate. Artificial Intelligence (AI) and Machine learning (ML) have exploded in importance in recent years and garnered attention in a wide variety of application areas, including computer vision (e.g., image … Join a group and attend online or in person events. This talk is designed to help you land your first 50 enterprise machine learning customers. Christina leads at the intersection of operations, finance and culture building to lay a strong foundation of people, investors and industry advisors to propel Knowtion’s growth. Abdul is also leading discussions with academia in cybersecurity analytics on behalf of Telus to establish strategic partnerships for pushing state-of-the-art in security analytics and extending the Telus cybersecurity analytics data lake capabilities beyond IT security. The focus of this talk is on ML product strategy and we can build meaningful and impactful ML product roadmaps. It helped Scotiabank to capture international banking customer behaviour and their price sensitivity more promptly . Previously, Prof. Rudin held positions at MIT, Columbia, and NYU. Yes, the Virtual Conference is accessible via a smartphone or tablet. The AES serves its members, the industry and the public by stimulating and facilitating advances in the constantly changing field of audio. For more information please review our cookie policy. She contributed to the development of the Systemic Racial Barriers Identification and Removal Program to support advancement of workplace racial equity and inclusion within the Ontario Public Service. Ashish is a Director of Recommendations at Twitch where he works on building scalable recommendation systems across a variety of product surfaces, connecting content to people. Prior to that, as a Human Rights Advisor in the Ministry of Community Safety and Correctional Services, Mai supported initiatives to address systemic discrimination and remove barriers in employment and service delivery in correctional services. This presentation will address certain important questions including, Talk: Craft, Communicate and Deliver an Ompactful ML Product Strategy- Observations and Learnings. This talk will provide answers, hopefully reasoned, to these questions. We translate those 800 million EHRs into structured clinical routes, one by one, so that we have 800 million structure clinical routes. He is also an adjunct professor of computer science at the University of California, San Diego, where he was previously a tenured full professor. Originally from Pittsburgh, PA but currently residing in Austin, TX, Peter built his career developing full-stack applications for over 25 years. 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. 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. Identifying similar mutual funds (including exchange-traded funds) with respect to the underlying portfolios has found many applications in fund recommender systems, competitors analysis, marketing and sales of the products. Talk: Quantum - Assisted Machine Learning with Near-Term Quantum Devices. Dr. Ravi has authored over 100 scientific publications and patents in top-tier machine learning and natural language processing conferences. 河南发恩德矿业有限公司 | 在领英上有 27 位关注者。河南发恩德矿业有限公司 is a company based out of China. 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. Senior Manager, Connected and Autonomous Vehicles, MaRS Discovery District. services are deployed to produce improvements to important business metrics, e.g. Part of Computer Science at the University of Toronto. How to set up your model monitoring from scratch, and how to prioritise different metrics. Ala is a Data Scientist at LVMH - Moët Hennessy Louis Vuitton in Paris, Kaggle Expert, and speaker with interests in the fields of machine learning, programming, and big data, working on real-life problems and a passion holder for deploying predictive and deep learning models. Her past experience in the field includes the Globe and Mail, Scribd and Slyce. 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. ", Workshop: Reaching Lightspeed Data Science: ETL, ML, and Graph with NVIDIA RAPIDS, Performance gains possible using GPUs and RAPIDS. 3) The amazing things we can learn about healthcare by using this kind of model on medical data. How we operationalize model findings quickly in an. Jules is the current chair of the T&O Diversity Leadership Council where she supports T&O employees to be champions, advocates and examples of diversity, inclusion & belonging at RBC. Before joining Amazon, Rachel also worked on natural language processing projects to promote user engagements in multiple industries. The quality of online comments is critical to the Washington Post. She holds a Master’s in Machine Learning from University College London and a B. We further discuss how these attacks can be used as an analysis tool to study and evaluate the robustness properties of financial models. He was also the Co-Chair for ICML 2019, NAACL 2019, and NeurIPS 2018 ML workshops and regularly serves as Senior/Area Chair and PC of top-tier machine learning and natural language processing conferences like NeurIPS, ICML, ACL, NAACL, AAAI, EMNLP, COLING, KDD, and WSDM. Avi is a PhD student in the Applied Math and Scientific Computation program at the University of Maryland. Originally focused on predictive maintenance / asset optimization of industrial assets and commercial vehicles, we're now making our internal tech stack available to a broad audience. The audience will get an overview of techniques used by intrusion detection systems, both AI/Machine Learning and non machine learning, for identifying intrusions and malicious behaviours in systems. Melanie is the author or editor of six books and numerous scholarly papers in the fields of artificial intelligence, cognitive science, and complex systems. Q: Can I get a training certificate? Previously, Dr. Mamdani founded the Ontario Drug Policy Research Network (ODPRN), which is among the world’s most impactful collaborations between researchers and drug policy decision-makers. He is jointly appointed as a Professor of Chemistry and Computer Science at the University of Toronto. Applied machine learning has the potential to transform healthcare, particularly in the areas of automation, prediction, and optimization. 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. 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. By contrast, while we’ve seen explosive growth in the adoption of machine and deep learning (ML/DL) across industries, putting ML/DL models into production isn’t as well supported. 1) The risk of using machine learning in healthcare when you can't understand what the model is learned. Miguel also worked as a robotics scientist at Universidad Carlos III of Madrid (UC3M) and King’s College London, where his research focused on learning from demonstration, reinforcement learning, computer vision, and dynamic control of humanoid robots. Expect 1 day of workshops and 2-days of high-quality networking, food, drinks, workshops, breakouts, keynotes and exhibitors. I will present what are the state-of-the-art quantum algorithms, its advantages and limitations. * Who should join: Describe your ideal members already, or a learning … She completed her PhD at the University of Washington where she conducted research on speech and hearing using mathematical models. At MaRS Joe founded and led the data practice, building strategic partnerships to scale Canadian data and AI businesses in sectors including retail, finance, energy, and healthcare. For each stage, it breaks down the steps needed, the tradeoffs of different solutions at each step. The next part covers the four main stages in the iterative process of ML systems design. She earned her PhD degree in computer science at Boston University, where she currently serves as an adjunct assistant professor and lecturer, offering courses in responsible AI. Do you want to make predictions based on irregularly-sampled, sparse time series? Dr. Mamdani is also Professor in the Department of Medicine of the Faculty of Medicine, the Leslie Dan Faculty of Pharmacy, and the Institute of Health Policy, Management and Evaluation of the Dalla Lana Faculty of Public Health. 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.). He also spends a lot of time of his career on both the engineering side but mostly in the data science side, developing active learning models to help descision maker, using scalable machine learning application and most importantly doing the research behind them.