Her degrees are from the University at Buffalo and Princeton University. We compared the resulting portfolios from the different models with baseline methods. She received her master’s degree of statistics from University of California, Berkeley. 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. What is the state of the art in ML and how can our audience benefit from it? Dr. Mehta's areas of expertise are theory of machine/deep learning, and applications of machine learning in finance.. Talk: Beyond Standard Deep Learning Models for Time Series and Sequences, AI & Innovation Strategy Lead, National Bank of Canada. customer engagement, number of transactions, total profits. As machine learning systems advance in capability and increase in use, we must … 1 talking about this. The participants established 11 working groups, each of which developed and performed experiments relevant to existing industry needs. The following are the topics that will be covered by this workshop, 1. The event is casual and tickets are priced to remove all barriers to entry. 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. He received his PhD from Georgia Institute of Technology in 2019. When not in front of the computer, she enjoys the solitude of her garden, trying to recreate a bit of the magic from “the old world” where she spent summers walking across vineyards with a whistle to send away starlings, and falls picking hectares of grapes to make magic. Data discovery and data generation became the most challenging piece before putting ML solutions in production. Race is a concept, a tool, and a structure that defines a set of relationships between people. See Attendee Demographics and a list of the Attendee Titles from our past event here. Sought-after public speaker and facilitator, she’s counting over 30 conference presentations and speaking invitations, and 4 undergraduate courses on topics centered around human cognition and research approaches. Ontario Institute for Cancer Research (OICR). I will discuss how to make sophisticated machine learning models such as Neural networks (Deep Learning) as self-explanatory models. 2. She was a winner of the ACM RecSyS challenge on Context-Aware Movie Recommendations CAMRa2011, her 2012 UAI paper "Guess Who Rated This Movie: Identifying Users Through Subspace Clustering" was featured in an MIT TechReview article as “The Ultimate Challenge For Recommendation Engines”, and her work on inclusive AI was featured many press outlets, including FastCompany and Vogue Business. Talk: From Data Warehouse To Feature Warehouse: How DBT & AirFlow DAGs Orchestrate Building A Reliable & Reusable Feature Repository To Accelerate Model Development. We recently conducted an industry survey of firms that have natural language systems in production. We organize online Datathons, monthly challenges, digital meetups, webinars, … For a lot of the content, there is a large amount of textual data in the form of user reviews, synopsis, title plots and even Wikipedia. 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. Explanations for black box models are not reliable, and can be misleading. Finally, I will offer best practices to guide future industry collaborative projects. In this work, we apply a transfer learning approach to improve predictive power in noisy data systems with large variable confidence datasets. His work is on facilitating the communication between humans and machine learning models, which includes interpretability, trust, debugging, feedback, robustness, testing, etc. Ilnaz is VP, Data Science at BMO Capital Markets. Talk: Harnessing Geospatial Data for Machine Learning, Cognitive Data Specialist, Macadamian Technologies. Ours is also the first ever study of the weighted bipartite network representation of the funds-assets network. Data engineers, data analysts, and data scientists have to conduct time-consuming and repetitive tasks to understand the business logic within and across data components to get the desired features and datasets. She holds a Ph.D. degree from the Data Science Lab at Ryerson University, Canada, and earned her master’s degrees in both Chemical Engineering and Business of Administration. 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. 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. Data Science possibilities opened by using RAPIDS, Python. A pandemic, a world economy put on stand-by, radically changed work environments : how AI application in banking stepped up or had to be adapted to suit a world that radically changed onver night. This talk will provide answers, hopefully reasoned, to these questions. 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. Prior to co-founding Iguazio in 2014, Yaron was the Vice President of Datacenter Solutions at Mellanox (now NVIDIA), where he led technology innovation, software development and solution integrations. Earlier, she was a Staff Software Engineer in Machine Learning and the tech lead for the job recommendations team at LinkedIn, a principal research scientist at Technicolor Research lab, Palo Alto, and a postdoctoral researcher at the Massachusetts Institute of Technology, Research Laboratory of Electronics. The demands of explainable machine learning come not only from the quest for advancement in technology, but also from many non-technical considerations including laws and regulations such as GDPR (General Data Protection Regulation), which took effect in 2018. Professor Klabjan has led projects with large companies such as Intel, Baxter, Allstate, AbbVie and many others, and he is also assisting numerous start-ups with their analytics needs. His research has been cited over 34,000 times and has an h-index of 90. Rhys is also a part-time AI instructor at WeCloudData. Practical considerations in building real life recommendation systems. Arthur is an RBC Distinguished Technologist. Practical applications will be discussed, including personalized medicine, humanoid robotics and grammar learning. Methodology: We propose to use model based recursive partitioning (MOB) which use product characteristics and customer attributes as input and customer willingness to pay as output to segment customers. 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. She has also authored numerous papers and been awarded the prestigious scholarships including Mitacs Postdoctoral Award. Yes, attendees will have full access to both night's post-event networking socials. She is skilled in various machine learning and data mining techniques, and using them to tackle business problems. Graphical models such as PCA-KMeans, autoencoders, dynamic clustering, and structural learning can capture the time varying patterns in the covariance matrix and allow the creation of an optimal and robust portfolio. Machine learning (ML) as an academic research field is over 60 years old. Practical advise and mistakes from having launched two top tier ML tools companies. Pointed lessons learned and unique insights from leading data science organizations will be shared covering how to effectively manage your people, your process, and your technology. How to deal with data points with different levels of confidence in deep learning setting. Before coming to Treasury she studied backer behavior and what makes projects successful at Kickstarter. It examines how ML applications differ from traditional software engineering applications, the scaling challenge, and the rise of MLOps. Applied AI solutions. - Some exposure to Git/GitHub is helpful, but not necessary. The goal of TMLS is to empower practitioners and business leaders with direct contact to the people that matter most. A global non-profit focused on the engagement, education, and empowerment of girls and women passionate about technology. These parameters were forecasted for a year using machine learning software package called Waikato Environment for Knowledge Analysis (WEKA)[8] and the predicted parameters were used for the computation in the optimization process. Valerii joined Cineplex last year to drive the best practice in development and serving of ML models. She enjoys empowering everyone who is curious about start-of-the-art deep learning algorithms with easy to understand instructions and innovative new teaching tools. Before joining BMO Capital Markets, Ilnaz worked as a Senior Quant at CIBC Capital Market Risk Management. She is an instructor at Amazon Machine Learning University and frequently presents at external events such as AWS Re:invent, Nvidia GTC, etc. For each customer segmentation, we found the demand curve function and formulate the nonlinear optimization problem that maximize the sale or revenue using PYOMO and IPOPT. Now, more than ever, society is looking to the global research community to respond to emerging challenges. Machine learning for comparative analysis of mutual funds using machine learning. When he’s not slapping away at the keyboard, you can find him practicing Spanish and hang out with his wife and seven children. Ari Kalfayan is a Senior Business Development Manager at AWS in charge of AI/ML startups. Alongside her work on PureFacts she is a member of the advisory board on Queens University’s InQUbate Program, first student-run AI startup incubator. Jenny Ni Zhan is a fourth year PhD student at Carnegie Mellon University. TMLS is a community of over 5,000 practitioners, researchers, … 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. ACL, NeurIPS, and NAACL, and appeared in the press. Product Manager Machine Learning, Amazon Web Services. What is the biggest blocker that’s blocking ML research and the adoption of AI/ML in the business world? Space, however, is limited. 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. 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. She has served on committees for INFORMS, the National Academies, the American Statistical Association, DARPA, the NIJ, and AAAI. I’ll present several healthcare case studies where these high-accuracy GAMs discover surprising patterns in the data that would have made deploying a black-box model risky, and also allow us to learn important new insights from our healthcare data. Machine Learning/deep learning PhDs and researchers, Enterprise innovation labs seeking to grow their teams, Community and university machine learning groups. We anticipate that FTL will enable the machine learning community to benefit from large datasets with uncertain labels in fields such as biology and medicine. Talk: The Algorithm is not Enough: UX Meets Data Science. Allow Facebook friends to see your upcoming events? From 2010-2016 Matt lived and worked in China, including as the first China correspondent for The World Post. Dr. Mamdani is Vice President of Data Science and Advanced Analytics at Unity Health Toronto. His work focuses on leveraging the power of machine learning to enhance the digital customer experience – solving problems for customers and driving tangible results. He brings with him cross-sector expertise and experience working with clients in industries such as aviation, telecom, and finance. However, much of the Deep Learning revolution has been limited to the Cloud and highly specialized hardware. As such, it is becoming more important for Financial firms to be able to incorporate dynamic ESG metrics into their investment processes. His research maps and quantifies the key inputs to AI ecosystems globally. Talk: Banorte’s AI Transformation Journey: How the Analytics Team of this Bank Yielded 3 Billion USD Revenue During the Past Five Years. We take a deeper dive into how to output only confident predictions in a dynamic fashion. Professor, Department of IEOR, Columbia University. Prior to joining the Li Ka Shing Knowledge Institute and Unity Health Toronto, Dr. Mamdani was a Director of Outcomes Research at Pfizer Global Pharmaceuticals in New York. We will unpack the idea of race as relationships and race as data in its historical and current contexts. In this talk, we show how to use A.I. 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. Glass-Box vs. Black-Box ML and explanation methods. Talk: Movie Attendance Forecasting: Machine Learning in Post-COVID Market, Vice President, Data Science and Advanced Analytics. He is also working towards his PhD in the same discipline, focused on scaling and accelerating algorithms for exploratory data analysis. Talk: Productionizing Deep Learning Models at Scale. Prediction of financial instruments is such an example. They develop and improve systems that serve everyday needs of society spanning from high-voltage engineering and sustainable energy, to breakthroughs in wireless technology. Explore the latest trends in machine learning. She has an engineering PhD from data science lab, Ryerson University, Canada. 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. Eventbrite, and certain approved third parties, use functional, analytical and tracking cookies (or similar technologies) to understand your event preferences and provide you with a customized experience. Q: Will you give out the attendee list? 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. 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. MD in the Time of Corona Virus in China See Abstract. How to build a system that utilizes both human and machine learning moderation to efficiently scale to millions of reader comments. She is working on variational quantum algorithms and computational tools for quantum simulation. He also lead the development of Zeus Technology, a publisher focused suite of advertising technologies that utilize NLP technology to drive contextual ad targeting. These three words have loomed large in the sociopolitical psyche of the United States since the founding of the movement in 2013. The audience will gain insights about which applications of machine learning in finance are likely to be successful. His latest AI project at hopupon.com. The focus of this talk is on ML product strategy and we can build meaningful and impactful ML product roadmaps. We hope this paper will serve as but the first step in the right direction. Recently the AI community has witnessed an increasing trend for training larger and larger neural models (e.g., GPT-3, T5, BERT) that achieve state-of-the-art results but require enormous computation, memory and energy resources on the Cloud. As such, more creative thinking is needed to convince stakeholders that your ML solutions can be trusted and bring value. In machine learning often a tradeoff must be made between accuracy and intelligibility: the most accurate models usually are not very intelligible, and the most intelligible models usually are less accurate.