ODSC West Schedule 2020
Day by Day Session Schedule
Schedule Guide for Pass Holders
West Talks/Workshops schedule includes October 28th through October 30th. It is available to General, Virtual Bootcamp 3-Day, Virtual Bootcamp 4-Day, Training Half-Day, Training Full-Day, and All Access Passes.
West Bootcamp and Trainings schedule includes October 27th through October 30th. It is available to Virtual Bootcamp 3-Day (Tue-Thurs), Virtual Bootcamp 4-Day (Tue-Fri), Training Half-Day (per 1/2 course Wed-Fri), Training Full-Day (per full day course Wed-Fri) and All Access Passes (Wed-Fri).
*Speaker and speaker schedule times are subject to change. More sessions added weekly.
ODSC West 2020 | October 27th - 30th
Register nowConfirmed Sessions
Please see full details of confirmed sessions and presenters below. Confirmed session times will be updated in the coming weeks.
AiX Business Talks’ schedule can be found here.
Full-Day Training | Machine Learning | R-programming | Intermediate
With hundreds of machine learning models available in R having one unified interface makes life so much easier. The author of the popular {caret} package has reinvented it for the modern age in the form of {tidymodels}. We’ll use the suite of packages to specify models, perform feature engineering, tune over hyperparameters and make predictions...more details
Jared Lander is the Chief Data Scientist of Lander Analytics a data science consultancy based in New York City, the Organizer of the New York Open Statistical Programming Meetup and the New York R Conference and an Adjunct Professor of Statistics at Columbia University. With a masters from Columbia University in statistics and bachelors from Muhlenberg College in mathematics, he has experience in both academic research and industry. His work for both large and small organizations ranges from music and fundraising to finance and humanitarian relief efforts.
He specializes in data management, multilevel models, machine learning, generalized linear models, data management and statistical computing. He is the author of R for Everyone: Advanced Analytics and Graphics, a book about R Programming geared toward Data Scientists and Non-Statisticians alike and is creating a course on glmnet with DataCamp.
Half-Day Training | Data Science Kick-Starter | Beginner
This tutorial offers a comprehensive introduction to the powerful pandas library for data analysis built on top of the Python programming language. Pandas represents a great step forward for graphical spreadsheet users looking to grow their data manipulation skills. I like to call it “Excel on steroids”. By completing this workshop, you’ll have a strong foundation for using Pandas in your day-to-day data analysis needs. We’ll start out with the basics — importing datasets, selecting rows and columns, filtering rows by criteria — and progress to advanced concepts like grouping values, joining multiple datasets together, and cleaning text…more details
Boris Paskhaver is a full-stack web developer based in New York City with experience building apps in React / Redux and Ruby on Rails. His favorite part of programming is the never-ending sense that there’s always something new to master — a secret language feature, a popular design pattern, an emerging library or — most importantly — a different way of looking at a problem.
Half-Day Training | R-Programming | Data Visualization | Beginner-Intermediate
Turning raw data into meaningful information and telling data driven stories is one of the great challenges of data science. When your data does not have you to speak for it in a live situation, your application needs to communicate your message clearly and provide simple interfaces and meaningful interactions to drive your message home to consumers.
In this session you will learn to use Shiny to build a dashboard from blank page to interactive application using the programming language R, the free R development environment rStudio and Redis. We will use free public data and open source libraries as we sculpt our dashboard together...more details
Bethany Poulin is a data scientist and educator with expertise in statistical analysis, data visualization and complex algorithmic problem solving. She has worked as a professional data scientist and educator for the last 4 years and loves sharing what she has learned with her students. Her unusual background in Fine Arts and experience teaching high school give her a unique perspective on both problem-solving and the learning-teaching process. Prior to teaching this part time course, she was lead instructor in our Data Science Immersive program on the Boston campus. She teaches simply because she loves students and enjoys being a part of their success. She holds a BFA in Professional Photography from Rochester Institute of Technology, did post bachelor’s studies and the University of Montana in Environmental Biology, where she was recognized nationally as a Morris K Udall Scholar, is one semester away from an MS in Data Science from City University of New York. She is has presented at PyOhio 2018 and 2019 and gave a presented at ODSC East in 2019. In here spare time, Bethany is an avid fly fisherman, potter and maker.
Half-Day Training | Machine Learning | Beginner
Scikit-learn is a machine learning library in Python that is used by many data science practitioners. Machine learning is a valuable tool used across many domains such as medicine, physics, and finance. We will start this training by learning about scikit-learn’s API for supervised machine learning. scikit-learn’s API mainly consists of three methods: fit, to build models, predict, to make predictions from models, and transform, to change the representation of the input data...more details
Thomas J. Fan is a Staff Associate at the Data Science Institute at Columbia University. He is one of the core developers of scikit-learn, an open source machine learning library written in Python. Thomas holds a Masters in Mathematics from NYU and Masters in Physics from Stony Brook University. He also maintains skorch, a scikit-learn compatible neural network library that wraps PyTorch. He believes that developing open source software is one of the best ways to maximize one’s impact.
Half-Day Training | Deep Learning | Intermediate
This workshop hopes to convince participants that Keras is a worthwhile addition to their Machine Learning toolbelt. It teaches them how to build their own Keras models, initially using components already available in Keras, then extend them by customizing some of these components, and finally exploit the underlying Tensorflow platform for maximum flexibility and performance. They will also be able to work with the many cool (sometimes SOTA) models shared by the Keras community…more details
Sujit Pal builds intelligent systems around research content that help researchers and medical professionals achieve better outcomes. His areas of interest are Information Retrieval, Natural Language Processing and Machine Learning (including Deep Learning). As an individual contributor in the Elsevier Labs team, he works with diverse product teams to help them solve tough problems in these areas, as well as build proofs of concept at the cutting edge of applied research.
Half-Day Training | Deep Learning | Advanced
Although supervised learning has dominated industry machine learning implementations, unsupervised and semi-supervised methods have started to be practically applied to real world problems (outside of playing video games). Generative Adversarial Networks (GANs) are being utilized to augment data and generate dialogue, and Reinforcement Learning (RL) is helping people plan marketing campaigns and control robots. In this training, you will develop a theoretical understanding of these and other related state-of-the-art AI methods along with the hands-on skills needed to train and utilize them. You will implement a variety of models in TensorFlow for tasks including object recognition, image generation and robotics…more details
Daniel Whitenack (aka Data Dan) is a PhD trained data scientist who has been developing artificial intelligence applications in the real world for over 10 years. He knows how to see beyond the hype of AI and machine learning to build systems that create business value, and he has taught these skills to 1000’s of developers, data scientists, and engineers all around the world. Now with the AI Classroom event, Data Dan is bringing this knowledge to an live, online learning environment so that you can level up your career from anywhere!
Full-Day Training | Deep Learning | Machine Learning | Beginner – Intermediate
Relatively obscure a few short years ago, Deep Learning is ubiquitous today across data-driven applications as diverse as machine vision, natural language processing, and super-human game-playing.
This Deep Learning primer brings the revolutionary machine-learning approach behind contemporary artificial intelligence to life with interactive demos featuring TensorFlow 2, the major, cutting-edge revision of the world’s most popular Deep Learning library. To facilitate an intuitive understanding of Deep Learning’s artificial-neural-network foundations, the essential theory will be introduced visually and pragmatically. Paired with tips for overcoming common pitfalls and hands-on Python code run-throughs provided in straightforward Jupyter notebooks, this foundational knowledge empowers you to build powerful state-of-the-art Deep Learning models…more details
Jon Krohn is Chief Data Scientist at the machine learning company untapt. He authored the book Deep Learning Illustrated, which was released by Addison-Wesley in 2019 and became an instant #1 bestseller that was translated into six languages. Jon is renowned for his compelling lectures, which he offers in-person at Columbia University, New York University, and the NYC Data Science Academy, as well as online via O’Reilly, YouTube, and his A4N podcast on A.I. news. Jon holds a doctorate in neuroscience from Oxford and has been publishing on machine learning in leading academic journals since 2010.
Full-Day Training | Machine Learning | Intermediate-Advanced
Ray RLlib implements a wide variety of reinforcement learning algorithms and it provides the tools for adding your own. It integrates with popular frameworks like OpenAI Gym, TensorFlow, and PyTorch. It provides concise abstractions for defining the algorithm and tools you want to use, and specifying the cluster resources available. It is extensible for new algorithms, agents, and environments. Ray does the work to leverage the resources, providing state-of-the-art performance…more details
Dean Wampler is an expert in data engineering for scalable streaming data systems and applications of machine learning and artificial intelligence (ML/AI). He is a Principal Software Engineer at Domino Data Lab. Previously he worked at Anyscale and Lightbend, where he worked on scalable ML with Ray and distributed streaming data systems with Apache Spark, Apache Kafka, Kubernetes, and other tools. Dean is the author of “Programming Scala”, “What Is Ray?”, “Fast Data Architectures for Streaming Applications”, “Functional Programming for Java Developers”, and the coauthor of “Programming Hive”, all from O’Reilly. He is a contributor to several open source projects, a frequent conference speaker. He also co-organizes several conferences around the world and several user groups in Chicago. Dean has a Ph.D. in Physics from the University of Washington. Find Dean on Twitter: @deanwampler.
Half-Day Training | Machine Learning | Intermediate
Scikit-learn is a machine learning library in Python that is used by many data science practitioners. In this training, we will learn about model evaluation, model calibration, and model inspection. We will start by learning about evaluating a machine learning model after it is trained. We will compare various metrics such as ROC AUC and mean average precision and see how they behave on datasets with different characteristics. We will use scikit-learn’s plotting API to easily visualize the performance of a model and to compare multiple models. Next, we will learn about how to calibrate a machine learning model with scikit-learn. A well-calibrated model will predict probabilities that reflect the true likelihood of an event...more details
Thomas J. Fan is a Staff Associate at the Data Science Institute at Columbia University. He is one of the core developers of scikit-learn, an open source machine learning library written in Python. Thomas holds a Masters in Mathematics from NYU and Masters in Physics from Stony Brook University. He also maintains skorch, a scikit-learn compatible neural network library that wraps PyTorch. He believes that developing open source software is one of the best ways to maximize one’s impact.
Full-Day Training | Machine Learning | R-programming | Intermediate
This second day of modern machine learning has us fitting forecasting models on time series data using the new {fable} package. Then we’ll take our fitted models and turn them into an API using {plumber} and expose them in Docker containers so they are ready for production.…more details
Jared Lander is the Chief Data Scientist of Lander Analytics a data science consultancy based in New York City, the Organizer of the New York Open Statistical Programming Meetup and the New York R Conference and an Adjunct Professor of Statistics at Columbia University. With a masters from Columbia University in statistics and bachelors from Muhlenberg College in mathematics, he has experience in both academic research and industry. His work for both large and small organizations ranges from music and fundraising to finance and humanitarian relief efforts.
He specializes in data management, multilevel models, machine learning, generalized linear models, data management and statistical computing. He is the author of R for Everyone: Advanced Analytics and Graphics, a book about R Programming geared toward Data Scientists and Non-Statisticians alike and is creating a course on glmnet with DataCamp.
Full-Day Training | NLP | Advanced
Natural Language Processing (NLP) has recently experienced its own “ImageNet” moment. Rapidly evolving language models have enabled practitioners to decipher long lost languages, translate speech in one language to speech in another language directly without converting to text, generate long form text that adapts to the style and content of human prompts, and translate between language pairs never seen explicitly by computer systems (among many other impressive results).
In this training, you will develop a theoretical understanding of modern NLP along with the hands-on skills needed to develop state-of-the-art models. You will implement a variety of recurrent layer and transformer based architectures in both TensorFlow and PyTorch for tasks including text classification, machine translation, and predictive text…more details
Daniel Whitenack (aka Data Dan) is a PhD trained data scientist who has been developing artificial intelligence applications in the real world for over 10 years. He knows how to see beyond the hype of AI and machine learning to build systems that create business value, and he has taught these skills to 1000’s of developers, data scientists, and engineers all around the world. Now with the AI Classroom event, Data Dan is bringing this knowledge to an live, online learning environment so that you can level up your career from anywhere!
Half-Day Training | Data Visualization | Intermediate
Data visualization is fundamental to the data science process. Using plots and graphs to convey a complex idea makes your data more accessible to everyone. In this session, you will learn the fundamentals of plotting with Pandas in Jupyter by building an interactive visualization prototype that can also run as a standalone web application/dashboard. This session is for anyone who wants to be more familiar with data visualization, hands-on, with Python, Pandas, Matplotlib, interactive widgets, and Flask...more details
At the age of 8, David began learning the BASIC programming language while living in the outskirts of Alaska. He studied music performance but found the beginning of his career building a small software and consulting company in the late 90’s. David’s career spans almost 20 years including many startups as a lead engineer, building scalable data services from prototype to production. During his time at Sony/Gracenote, leading the development of a handful of prototypes were featured multiple years in the Consumer Electronics Show, spanning problems with recommender systems, content classification, and profiling type problems. More recently, David was a data scientist at a YC backed dating app company researching and building scalable recommendation pipelines.
Currently, David works at General Assembly as a Global Data Science Instructor, where he helped architect the first version of the data science immersive curriculum and pilot many new programs developed internally. Regularly delivering lectures in a hybrid format classroom on topics ranging from engineering, statistics, and ML.
Half-Day Training
Ever wondered how quantum computers work, and how they do machine learning? With quantum computing technologies nearing the ear of commercialization and quantum advantage, machine learning has been proposed as one of the most promising applications. One of the areas in which quantum computing is showing great potential is in generative models in unsupervised and semi-supervised learning. In this training you will develop a basic understanding of quantum computing and how it can be used in machine learning models, with special emphasis on generative models. We will focus on a particular architecture, the quantum circuit Born machine (QCBM), and use it to generate a simple dataset of bars and stripes…more details
Luis Serrano is a Quantum AI Research Scientist at Zapata Computing. He is the author of the book Grokking Machine Learning and maintains a popular YouTube channel where he explains machine learning in pedestrian terms.
Luis has previously worked in machine learning at Apple and Google, and at Udacity as the head of content for AI and data science. He has a PhD in mathematics from the University of Michigan, a master’s and bachelors from the University of Waterloo, and worked as a postdoctoral researcher in mathematics at the University of Quebec at Montreal.
Kaitlin Gili is a Quantum Applications Intern at Zapata Computing. She has previously worked at Los Alamos National Laboratory and the IBMQ hub within Keio University as a quantum algorithm intern, and at the University of Oxford as a visiting quantum hardware research student. Kaitlin is passionate about quantum computing outreach for young scientists and has previously delivered quantum computing workshops to Girls Who Code middle/high school programs. She received her Bachelors’s in Physics from Stevens Institute of Technology and will be starting her PhD in Physics at the University of Oxford in January 2021.
Half-Day Training | Deep Learning | Machine Learning | Beginner-Intermediate
Reinforcement Learning recently progressed greatly in the industry as one of the best techniques for sequential decision making and control policies.DeepMind used RL to greatly reduce energy consumption in Google’s data center. It has been used to do text summarization, autonomous driving, dialog systems, media advertisements and in finance by JPMorgan Chase. We are at the very beginning of the adoption of these algorithms as systems are required to operate more and more autonomously.
In this workshop we will explore Reinforcement Learning, starting from its fundamentals and ending creating our own algorithms. We will use OpenAI gym to try our RL algorithms. OpenAI is a non profit organization that want committed to open source all their research on Artificial Intelligence…more details
Leonardo De Marchi holds a Master in Artificial intelligence and has worked as a Data Scientist in the sports world, with clients such as the New York Knicks and Manchester United, and with large social networks, like Justgiving.
He now works as Head of Data Scientist and Analytics in Badoo, the largest dating site with over 420 million users. He is also the lead instructor at ideai.io, a company specialized in Reinforcement Learning, Deep Learning, and Machine Learning training.
Half-Day Training | Data Visualization | Intermediate-Advanced
In this workshop we will build an interactive data visualization from scratch using d3.js in the browser. The posibilities shown in d3 examples are exciting but the API surface of d3 and the various browser standards like HTML, CSS, SVG and JavaScript, can be overwhelming. Think of this workshop as a guided tour that will point out the important things to pay attention to as we go step-by-step from CSV file to interactive visualization...more details
Ian Johnson is a User Experience Engineer at Google. He also organizes of Bay Area d3, starts with SVG and then dives deep into d3 including DOM manipulation, categorical and quantitative scales, axis, brushes, color schemes, events and histograms. Ian likes to make sense of data by exploring it visually with D3.js!
Half-Day Training | Machine Learning | Intermediate
Scikit-learn is a machine learning library in Python that is used by many data science practitioners. In this training, we will learn about cross validation, tuning machine learning algorithms and pandas interoperability. We will start by learning about cross validation for machine learning. Cross validation enables us to evaluate our machine learning models by splitting our data into training and testing datasets. We will cover cross validation schemes such as K-Fold cross-validation and the importance of stratifying your data. Next, we will learn about tuning algorithms in scikit-learn with grid search and random search. These hyper-parameter searching techniques help find hyper-parameter combinations that are suited for your dataset...more details
Thomas J. Fan is a Staff Associate at the Data Science Institute at Columbia University. He is one of the core developers of scikit-learn, an open source machine learning library written in Python. Thomas holds a Masters in Mathematics from NYU and Masters in Physics from Stony Brook University. He also maintains skorch, a scikit-learn compatible neural network library that wraps PyTorch. He believes that developing open source software is one of the best ways to maximize one’s impact.
Half-Day Training | Machine Learning | ML for Programmers | Intermediate
If you can write a model in sklearn, you can make the leap to Bayesian inference with PyMC3, a user-friendly intro to probabilistic programming (PP) in Python. PP just means building models where the building blocks are probability distributions! And we can use PP to do Bayesian inference easily. Bayesian inference allows us to solve problems that aren’t otherwise tractable with classical methods...more details
Lara is a data science instructor at Metis, a 12-week accredited data science bootcamp. She teaches a wide-ranging curriculum, starting from linear regression and ending at deep learning. She comes to Metis from McKinsey, where she worked with financial institutions on risk modeling. Prior to settling into the practical world of consulting, Lara received a master’s from the University of Chicago, where she had the privilege of thinking about abstract problems while drinking too much coffee. If forced to snap out of her mathematical reveries, she’s probably doing yoga or riding a bike.
Half-Day Training | Kick-starter | Machine Learning | All Levels
The field of machine learning and data science has gained sudden resurgence in the last few years. The contributions of machine learning in solving data-driven problems and creating intelligent applications cannot be overemphasized. This field which intersects statistics and probability, mathematics, computer science and algorithms can be used to learn iteratively from complex data and find hidden insights. Understanding the mathematics behind machine learning allows us to choose the right algorithms for our problem, make good choices on parameter settings and validation strategies, recognize under- and over-fitting, troubleshoot ambiguous results and put appropriate confidence bounds on results...more details
Adewale (Wale) Akinfaderin is a Data Scientist, Team Lead at Duke Energy Innovation Center where he works on the intersection of machine learning and renewable energy. His expertise is in machine learning, deep learning, statistical experimentation and general information theory. He has broad experience implementing and extending ML techniques to solve practical and business problems. In his spare time, he conduct research on Machine Learning for the Developing World and he was on the Program Committee for NeurIPS 2019 Workshop on Machine Learning for the Developing World. He is affiliated with Google as a Google Developer Expert in Machine Learning.
Half-Day Training | NLP | Machine Learning | Beginner-Intermediate
Learn why the truly open source HPCC Systems platform is Better at Big Data and learn how ECL can empower you to build powerful data queries with ease. HPCC Systems is a comprehensive, dedicated data lake platform makes combining different types of data easier and faster than competing platforms — even data stored in massive, mixed schema data lakes — and it scales very quickly as your data needs grow...more details
Bob Foreman has worked with LexisNexis and the open-source big data HPCC Systems technology platform and the ECL programming language for more than 9 years, and has been a technical trainer for over 25 years. He is the developer and designer of the HPCC Systems Online Training Courses and is the Senior Instructor for all classroom and remote training. This includes: Introduction to ECL (Part 1 and 2) – Concepts and Queries, the Extract, Transform and Load (ETL) Process, Advanced ECL (Part 1 and 2) – Working with Relational Data, Super files, Working with XML, and Free-form Text Parsing, ROXIE ECL – Indexes and Queries, Complex Query Development, and Applied ECL – ECL Code Generation Tools.
Hugo supports the development and delivery of training programs for the HPCC Systems platform in the Brazil region since 2019. Hugo has worked for over 15 years on various technical roles in the IT industry with a focus on High-Performance Computing. He is also a part-time researcher on Information Systems and a member of the UK Academy for Information Systems.
Alejandro is a Ph.D. Candidate at Stanford University and Professor Ron Howard’s last doctoral student. His research focus is on approximation of multi-attribute utility functions with the use of thresholds and the implications that this approach has on the creation of better decision support automation. He is passionate about operationalizing decision making within organizations in a way that creates an “alive process”. Before coming to Stanford, he was an Operations consultant and an Operations Manager for several manufacturing businesses in his native Venezuela. He has led large teams in delivering results in operations rich fields such as food and heavy metal manufacturing.
Half-Day Training | Machine Learning | Intermediate
Scikit-learn is a machine learning library in Python that is used by many data science practitioners. In this training, we will learn about processing text data, working with imbalanced data and Poisson regression. We will start by learning about processing text data with scikit-learn’s CountVectorizer and TfidfVectorizer. The CountVectorizer converts a collection of text documents into a matrix of token counts. We will explore the hyper-parameters that the CountVectorizer provides for creating these token counts. The TfidfVectorizer weights the count features into floating point values using the term frequency and inverse document-frequency...more details
Thomas J. Fan is a Staff Associate at the Data Science Institute at Columbia University. He is one of the core developers of scikit-learn, an open source machine learning library written in Python. Thomas holds a Masters in Mathematics from NYU and Masters in Physics from Stony Brook University. He also maintains skorch, a scikit-learn compatible neural network library that wraps PyTorch. He believes that developing open source software is one of the best ways to maximize one’s impact.
Half-Day Training | Computer Vision | Deep Learning | Intermediate
In this tutorial, we will introduce how to build an object detection model. Specifically, we will build an object detection model that identifies chess pieces (a custom dataset provided by the presenter). In doing so, participants will gain insight into the fundamentals of computer vision: structuring a good problem for object detection, dataset collection and annotation, data preparation through preprocessing, data augmentation to support a well-fit model, training a model, debugging a model’s fit, and using the model for inference.
We will introduce Keras and TensorFlow as our specific libraries for writing computer vision models and train a Yolov3 (You Only Look Once) model for real-time detection. Participants will leverage Colab for GPU compute.
Session Outline
– Introduction to Computer Computer Vision Problems
– Data Preparation
– Data Preprocessing and Augmentation
– Training Our Yolov3 Model
– Inference and Q&A...more details
Joseph Nelson is co-founder and CEO of Roboflow, a computer vision developer tool. Roboflow enables anyone to build better computer vision models quickly. Joseph previously co-founded Represently (acq. 2018). He has been named Distinguished Faculty at General Assembly and worked at companies big (Facebook) and small (failed startups). Joseph is a managing partner at BetaVector, a data science consultancy he co-founded. He’s easily reached on Twitter: @josephofiowa
Half-Day Training | DataOps and Management | ML for Programmers | Beginner-Intermediate
Data analysts and data scientists often struggle with getting data into a usable form. Indeed research shows that it can consume up to 90% of a data scientist’s time preparing data. In this interactive workshop, you will learn how to use Apache Drill to rapidly explore a wide variety of data, from a variety of sources without having to write code…more details
Charles Givre recently joined JP Morgan Chase works as a data scientist and technical product manager in the cybersecurity and technology controls group. Prior to joining JP Morgan, Mr. Givre worked as a lead data scientist for Deutsche Bank. Mr. Givre worked as a Senior Lead Data Scientist for Booz Allen Hamilton for seven years where he worked in the intersection of cyber security and data science. At Booz Allen, Mr. Givre worked on one of Booz Allen’s largest analytic programs where he led data science efforts and worked to expand the role of data science in the program. Mr. Givre is passionate about teaching others data science and analytic skills and has taught data science classes all over the world at conferences, universities and for clients. Mr. Givre taught data science classes at BlackHat, the O’Reilly Security Conference, the Center for Research in Applied Cryptography and Cyber Security at Bar Ilan University. He is a sought-after speaker and has delivered presentations at major industry conferences such as Strata-Hadoop World, Open Data Science Conference and others. One of Mr. Givre’s research interests is increasing the productivity of data science and analytic teams, and towards that end, he has been working extensively to promote the use of Apache Drill in security applications and is a committer and PMC Member for the Drill project. Mr. Givre teaches online classes for O’Reilly about Drill and Security Data Science and is a coauthor for the O’Reilly book Learning Apache Drill. Prior to joining Booz Allen, Mr. Givre, worked as a counterterrorism analyst at the Central Intelligence Agency for five years. Mr. Givre holds a Masters Degree in Middle Eastern Studies from Brandeis University, as well as a Bachelors of Science in Computer Science and a Bachelor’s of Music both from the University of Arizona. Mr. Givre blogs at thedataist.com and tweets @cgivre.
Half-Day Training | Deep Learning | Beginner – Intermediate
Objective: Natural Language Processing (NLP) is the fastest-growing field of deep learning with interest and funding from top AI companies to solve problems of language, text, and unstructured information. This has resulted in a tremendous focus on model building that combines language, mathematics, and computer science.This workshop will focus on problems of text summarization, question answering, and sentiment classification using modern approaches to model-building (GNMT, BERT, and GPT2). We will apply this to real-world problems to create an NLP pipeline on top of the PyTorch framework and spaCy…more details
Yashesh Shroff is a Lead Strategy Planner at Intel where he focuses on enabling the AI ecosystem on heterogenous compute. Recently, as product manager, he was responsible for the AI and media/game graphics software ecosystem showcasing Intel’s latest gen graphics architecture (10nm). He has over 15 years of technical and enabling experience, spanning optical modeling, statistical analysis, and capital equipment supply chain at Intel. He has over 20 published papers and 4 patents. He has a Ph.D. in EECS from UC Berkeley and a joint MBA from UC Berkeley Haas & Columbia Graduate School of Business.
Ravi Ilango is a Principal Data Scientist at StatesTitle. He is passionate in developing deployable deep learning solutions. Previously he was at Foghorn Systems as a Sr. Data Scientist and has over 10 years of experience at Apple as a data Scientist & at Applied Materials in Supply Chain Program Management. Ravi has a Graduate Certificate in Data Mining & Machine Learning from Stanford and completed a Masters Program in Aeronautics and Production Engineering from IIT Madras. He has a BS in Mechanical Engineering, Madras University.
Half-Day Training | Deep Learning | Machine Learning | Beginner-Intermediate
We will use OpenAI gym to try our RL algorithms. OpenAI is a non profit organization that want committed to open source all their research on Artificial Intelligence. To foster innovation OpenAI created a virtual environment, OpenAi gym, where it’s easy to test Reinforcement Learning algorithms. In particular, we will start with some popular techniques like Multi-Armed Bandit, going thought Markov Decision Processes and Dynamic Programming. We then will also explore other RL frameworks and more complex concepts like Policy gradients methods and Deep Reinforcement learning, which recently changed the field of Reinforcement Learning. In particular, we will see Actor-Critic models and Proximal Policy Optimizations that allowed OpenAI to beat some of the best Dota players. We will also provide the necessary Deep Learning concepts for the course…more details
Leonardo De Marchi holds a Master in Artificial intelligence and has worked as a Data Scientist in the sports world, with clients such as the New York Knicks and Manchester United, and with large social networks, like Justgiving.
He now works as Head of Data Scientist and Analytics in Badoo, the largest dating site with over 420 million users. He is also the lead instructor at ideai.io, a company specialized in Reinforcement Learning, Deep Learning, and Machine Learning training.
Jeffrey is currently the lead lecturer at UC Berkeley, School of Information. He was the Chief Data Scientist at AllianceBernstein, a global investment firm managing over $500 billions. He was responsible for building and leading the data science group, partnering with investment professionals to create investment signals using data science, and collaborating with sales and marketing teams to analyze clients. Graduated with a Ph.D. in economics from the University of Pennsylvania, he has also taught statistics, econometrics, and machine learning courses at UC Berkeley, Cornell, NYU, the University of Pennsylvania, and Virginia Tech. Previously, Jeffrey held advanced analytic positions at Silicon Valley Data Science, Charles Schwab Corporation, KPMG, and Moody’s Analytics.
Tutorial | Data Visualization | Beginner-Intermediate
Networks, also known as graphs are one of the most crucial data structures in our increasingly intertwined world. Social friendship networks, the world-wide-web, financial systems, infrastructure (power grid, streets), etc. are all network structures. Knowing how to analyze the underlying network topology of interconnected systems can provide an invaluable skill in anyone’s toolbox. This tutorial will provide a hands-on guide on how to approach a network analysis project from scratch and end-to-end: how to generate, manipulate, analyze and visualize graph structures that will help you gain insight about relationships between elements in your data…more details
Noemi Derzsy is a Senior Inventive Scientist at AT&T Chief Data Office within the Data Science and AI Research organization. Her research is centered on understanding and modeling customer behavior and experience through large-scale consumer and network data, using machine learning, network analysis/modeling, spatio-temporal mining, text mining and natural language processing techniques. She is an organizer of Data Umbrella meetup group and NYC Women in Machine Learning and Data Science meetup group, and she is a NASA Datanaut.
Prior to joining AT&T, Noemi was a Data Science Fellow at Insight Data Science NYC and a postdoctoral research associate at Social Cognitive Networks Academic Research Center at Rensselaer Polytechnic Institute. She holds a PhD in Physics, MS in Computational Physics, and has a research background in Network Science and Computer Science.
Workshop | R Programming | Intermediate
Apache Arrow is a cross-language development platform for in-memory analytics. In this tutorial, I’ll show how you can use Arrow in Python and R, both separately and together, to speed up data analysis on datasets that are bigger than memory. We’ll cover the fundamentals of Arrow in Python in R, then explore in depth Arrow’s Dataset feature, which provides for fast, efficient querying of large, multi-file datasets. Finally, we’ll discuss Flight, an Arrow-native client-server framework for transporting data, and show how to set up a server and query against it...more details
Experienced product manager and engineering leader with a focus on data science and data products, currently working on the Apache Arrow project with the Ursa Labs team. Previously, he led product development at Crunch.io, where he grew a geographically distributed, cross-functional team from 5 to 25, shaped the product vision and roadmap, and developed processes to deliver a high-quality product tailored to the needs of survey researchers.
He is also an open-source software developer, author of several packages for the R language, and contributor to many others in R and Python. Many of his projects sit at the intersection of data science and web services. In his projects and public talks, he is a strong advocate for intuitive user experience/API design and for comprehensive test coverage.
Prior to working in software development, he received a Ph.D. in Political Science from UC Berkeley and worked in data science at YouGov, where analyzed survey data and developed tools to streamline data pipelines and workflows. Among the statistical tools and methods he has used professionally and in peer-reviewed publications are experimental and quasi-experimental methods, text classification and sentiment analysis, survey weighting, and web scraping.
Workshop | Machine Learning | Open-source | Intermediate
Gradient boosting is a powerful machine-learning technique that achieves state-of-the-art results in a variety of practical tasks. For a number of years, it has remained the primary method for learning problems with heterogeneous features, noisy data, and complex dependencies: web search, recommendation systems, weather forecasting, and many others.
Some problems contain different types of data, including numerical, categorical and text data. In this case the best solution is either buiding new numerical features instead of text and categories and pass it to gradient boosting, or using out-of-the box solutions for that…more details
Workshop | Machine Learning | ML for Programmers | Beginner-Intermediate
The talk will emphasize both data preparation (ETL) and machine learning operations, with a hands-on demonstration of porting a typical workflow from CPU to GPU and measuring the speedup. We’ll go into more detail on real-world applications taking advantage of these speed improvements, including hyperparameter optimization for machine learning models, single cell genomics analysis, and applications in finance. For large-data users, we’ll discuss some of the options for scaling RAPIDS to multiple GPUs or multiple nodes, emphasizing the tight integration with the Dask ecosystem…more details
Workshop | Deep Learning | Beginner-Intermediate
Adoption of messaging communication and voice assistants has grown rapidly in the last years. This creates a huge demand for tools that speed up development of Conversational AI systems. An open-source DeepPavlov framework is created for development of multi-skill conversational agents. It prioritizes efficiency, modularity, and extensibility with the goal to make it easier to develop dialogue systems from scratch and with limited data available. It also supports modular as well as end-to-end approaches to the implementation of conversational agents...more details
Mikhail Burtsev is a head of Neural Networks and Deep Learning Laboratory at Moscow Institute of Physics and Technology. He is also founder and leader of open-source conversational AI framework DeepPavlov. Mikhail had proposed and co-organize a series of academic Conversational AI Challenges (including NIPS 2017, NeurIPS 2018, EMNLP 2020). His research interests are in fields of Natural Language Processing , Machine Learning, Artificial Intelligence and Complex Systems. Mikhail Burtsev has published more than 20 technical papers including – Nature, Artificial Life, Lecture Notes in Computer Science series and other peer reviewed venues.
Tutorial | NLP | Machine Learning | Intermediate
This is a hands-on tutorial on applying the latest advances in deep learning and transfer learning for common NLP tasks such as named entity recognition, document classification, spell checking, and sentiment analysis. Learn to building complete text analysis pipelines using the highly accurate, high performant, open-source Spark NLP library in Python.This is a hands-on tutorial on applying the latest advances in deep learning and transfer learning for common NLP tasks such as named entity recognition, document classification, spell checking, and sentiment analysis. Learn to building complete text analysis pipelines using the highly accurate, high performant, open-source Spark NLP library in Python…more details
David Talby is a chief technology officer at Pacific AI, helping fast-growing companies apply big data and data science techniques to solve real-world problems in healthcare, life science, and related fields. David has extensive experience in building and operating web-scale data science and business platforms, as well as building world-class, Agile, distributed teams. Previously, he was with Microsoft’s Bing Group, where he led business operations for Bing Shopping in the US and Europe and worked at Amazon both in Seattle and the UK, where he built and ran distributed teams that helped scale Amazon’s financial systems. David holds a Ph.D. in computer science and master’s degrees in both computer science and business administration.
Workshop | NLP | Deep Learning | Intermediate-Advanced
NLP is one of the fastest-growing fields within AI. A wide variety of tasks can be tackled with NLP such as text classification, question-answering (e.g. chatbots), translation, topic modelling, sentiment analysis, summarization, and so on. In this workshop, we focus on text summarization, as it is not commonly showcased in tutorials despite being a powerful and challenging application of NLP. We see a trend towards pre-training Deep Learning models on a large text corpus and fine-tuning them for a specific downstream task (also known as transfer learning). In this hands-on workshop, you’ll get the opportunity to apply a state-of-the-art summarization model to generate news headlines. We finetuned this model on Reuters news data, which is professionally produced by journalists and strictly follows rules of integrity, independence and freedom from bias…more details
Nadja Herger is a Data Scientist at Thomson Reuters Labs, based in Switzerland. She is primarily focusing on Deep Learning PoCs within the Labs, where she is working on applied NLP projects in the legal and news domains, applying her skills to text classification, metadata extraction, and summarization tasks. Before joining Thomson Reuters, she obtained her Ph.D. in Climate Science from the University of New South Wales, Australia. She has successfully made the transition from working with spatio-temporal data to working with text-based data on the job. Nadja is passionate about education, which is reflected in her ongoing mentorship of students within Thomson Reuters, as well as South African students from previously disadvantaged groups who are aspiring to get into Data Science.
Nina Hristozova is a Data Scientist at Thomson Reuters (TR) Labs. She has a BSc in Computer Science from the University of Glasgow, Scotland. As part of her role at TR she has worked on a wide range of projects applying ML and DL to a variety of NLP problems. Her current focus is on applied summarization of legal text. She is actively engaged with local technology Meetups to spread the love and knowledge for NLP through tech talks and workshops. In her free time she plays volleyball for the local team in Zug, enjoys going to the mountains and SUP in the lakes.
Viktoriia Samatova is a Head of Applied Innovation team of Data Scientists within Reuters Technology division focused on discovering and applying new technologies to enhance Reuters products and improving efficiency of news content production and discoverability. Prior to joining Reuters, Viktoriia spent over 5 years at State Street Bank’s Global Exchange division where she was managing product development of academic and financial industry content ingestion platform, which was the first company-wide product application utilizing emerging technologies of AI and machine learning.
Tutorial | Machine Learning | Beginner-Intermediate
In this session, participants will be introduced to Echo State Networks (a type of recurrent neural network) including theory, key parameters in implementation and practical considerations. Participants will have the opportunity to use a publicly available Echo State Network implementation on open data. Additional results will be shown based on a highly customized implementation.
Participants will come away with a basic understanding of Echo State Networks, how to use the EchoTorch python module, the impact key parameters have on algorithm performance and potential application areas…more details
Teal Guidici is a Senior Machine Intelligence Scientist at Draper where she uses statistical techniques and machine learning algorithms to develop creative solutions for interesting data-driven problems in areas including biomedicine, finance, and remote sensing. Prior to Draper, she did graduate work creating new methods to analyze patterns of co-variation in complex datasets and applied these methods applied to high throughput metabolomics data. She has additional experience in survey design and data analysis in consumer marketing research. Dr. Guidici has a B.S. in Theoretical Mathematics from MIT, a M.S. in Bioinformatics and a Ph.D. in Statistics from the University of Michigan.
Workshop | Machine Learning | Beginner-Intermediate
Most deployed Machine Learning models use Supervised Learning powered by human training data. Selecting the right data for human review is known as Active Learning. This talk will introduce a set of Active Learning methods that help you understand where your model is currently confused (Uncertainty Sampling) and to identify gaps in your model knowledge (Diversity Sampling). We’ll cover techniques that are only a few lines of code through to techniques that build on recent advances in transfer learning. We’ll use code examples from my open source PyTorch Active Learning library...more details
Robert Munro is an expert in combining Human and Machine Intelligence, working with Machine Learning approaches to Text, Speech, Image and Video Processing. Robert has founded several AI companies, building some of the top teams in Artificial Intelligence. He has worked in many diverse environments, from Sierra Leone, Haiti and the Amazon, to London, Sydney and Silicon Valley, in organizations ranging from startups to the United Nations. He has shipped Machine Learning Products at startups and at/with Amazon, Google, IBM & Microsoft.
Robert has published more than 50 papers on Artificial Intelligence and is a regular speaker about technology in an increasingly connected world. He has a PhD from Stanford University. Robert is the author of Human-in-the-Loop Machine Learning (Manning Publications, 2020)
Workshop | Deep Learning | Data Visualization | Intermediate
In this hands-on tutorial, we will introduce attendees to the siVAE (scalable, interpretable VAE) model that infers a set of factor loadings that explicitly map latent dimensions to the input features that define them, during training of the VAE model. Using standard datasets from computer vision (MNIST, Fashion-MNIST and CIFAR-10), we will walk attendees through the process of training the siVAE model, visualizing the sample embeddings inferred by classic VAEs, and extracting and visualizing the features that contribute to individual latent dimensions. We will also teach attendees how to estimate and visualize feature awareness, a new metric for measuring the overall importance of individual features for embedding a sample in the latent space. At the end of the tutorial, attendees will be able to train an siVAE model on their own datasets and interpret and visualize the latent dimensions inferred…more details
Gerald Quon is an Assistant Professor in the Department of Molecular and Cellular Biology at the University of California at Davis. He obtained his Ph.D. in Computer Science from the University of Toronto, M.Sc. in Biochemistry from the University of Toronto, and B. Math in Computer Science from the University of Waterloo. He also completed postdoctoral research training at MIT. His lab focuses on applications of machine learning to human genetics, genomics and health, and is funded by the National Science Foundation, National Institutes of Health, the Chan Zuckerberg Initiative, and the American Cancer Society.
Tutorial | Machine Learning | Data Visualization | Beginner-Intermediate
Data scientists often get asked questions of the form “Does X Drive Y”: (1) did recent PR coverage drive sign ups, (2) does customer support increase sales, or (3) did improving the recommendation model drive revenue? Supporting company stakeholders requires every data scientist to learn techniques that can answer questions like these, which are centered around issues of causality. Often times we cannot use AB testing to answer these questions and must turn to causal inference techniques instead…more details
Vinod Bakthavachalam is a Data Scientist working with the Content Strategy and Enterprise teams, focusing on using Coursera’s data to understand what are the most valuable skills across roles, industries, and geographies. Prior to Coursera, he worked in quantitative finance and studied Economics, Statistics, and Molecular & Cellular Biology at UC Berkeley.
Workshop | Machine Learning | ML for Programmers | Intermediate-Advanced
A brief introduction to Reinforcement Learning (RL), and a walkthrough of using the Dopamine library for running RL experiments…more details
Pablo Samuel Castro was born and raised in Quito, Ecuador, and moved to Montreal after high school to study at McGill. He stayed in Montreal for the next 10 years, finished his bachelors, worked at a flight simulator company, and then eventually obtained his masters and PhD at McGill, focusing on Reinforcement Learning. After his PhD Pablo did a 10-month postdoc in Paris before moving to Pittsburgh to join Google. He has worked at Google for over 8 years, and is currently a Staff Research Software Developer in Google Brain in Montreal, focusing on fundamental Reinforcement Learning research, as well as Machine Learning and Creativity. Aside from his interest in coding/AI/math, Pablo is actively trying to increase the presence of the latin american community in the AI research ecosystem. On the side, he’s an active musician (https://www.psctrio.com/), loves running (5 marathons so far, including Boston!), and enjoys discussing politics and activism.
Workshop | Machine Learning | Research Frontiers | Intermediate
Randomized Controlled Trial (RCT) has been the gold standard for determining the Average Treatment Effect of a program for marketing, medical, economic, political, and policy applications. Uplift modeling is an additional step to uncover individuals who are most positively influenced by treatment through predictive analytics and machine learning by identifying Heterogeneous Treatment Effects. This approach enables us to identify the likely “”persuadables”” in order to maximize treatment impact through optimal target selection. It has gained significant attention in recent years from multiple fields such as personalized medicine, political election, personalized marketing, and personalized healthcare with growing publications and presentations from industry and academic experts across the world...more details
Victor S.Y. Lo is a seasoned Big Data, Marketing, Risk, and Finance leader with over 25 years of extensive consulting and corporate experience employing data-driven solutions in a wide variety of business areas, including Customer Relationship Management, Market Research, Advertising Strategy, Risk Management, Financial Econometrics, Insurance Analytics, Product Development, Healthcare Analytics, Operations Management, Transportation, and Human Resources. He is actively engaged with causal inference and is a pioneer of Uplift/True-lift modeling, a key subfield of data science.
Victor has managed teams of quantitative analysts in multiple organizations. He currently leads the AI and Data Science Center of Excellence, Workplace Investing at Fidelity Investments. Previously he managed advanced analytics/data science teams in Personal Investing, Corporate Treasury, Managerial Finance, and Healthcare and Total Well-being at Fidelity Investments. Prior to Fidelity, he was VP and Manager of Modeling and Analysis at FleetBoston Financial (now Bank of America), and Senior Associate at Mercer Management Consulting (now Oliver Wyman).
For academic services, Victor has been a visiting research fellow and corporate executive-in-residence at Bentley University. He has also been serving on the steering committee of the Boston Chapter of the Institute for Operations Research and the Management Sciences (INFORMS) and on the editorial board for two academic journals. He is also an elected board member of the National Institute of Statistical Sciences (NISS). Victor earned a master’s degree in Operational Research and a PhD in Statistics and was a Postdoctoral Fellow in Management Science. He has co-authored a graduate-level econometrics book and published numerous articles in Data Mining, Marketing, Statistics, and Management Science literature, and is completing a graduate-level book on causal inference in business.
Workshop | Machine Learning | Intermediate
Effective predictive modeling projects follow the analytics life cycle, from data and discovery to deployment and decisions. Data scientists use a variety of tools, both commercial and open-source, to collaborate and develop enterprise applications of analytics and artificial intelligence. SAS Viya provides a unified platform to perform all these from one graphical user interface or through programming APIs. In this workshop, you will load data into memory, prepare input variables for modeling and build complex analytics pipelines to demonstrate powerful machine learning models. Need to integrate open source models? No problem. We’ll show you how you to do that and deploy any model. Then you can save and package the best performing model for deployment while keeping the ability to retrain it on new data…more details
Jordan Bakerman holds a Ph.D. in statistics from North Carolina State University. His dissertation centered on using social media to forecast real world events, such as civil unrest and influenza rates. As an intern at SAS, Jordan wrote the SAS Programming for R Users course for students to efficiently transition from the R to SAS using a cookbook style approach. As an employee, Jordan has developed courses demonstrating how to integrate open source software within SAS products. He is passionate about statistics, programming, and helping others become better statisticians.
Ari Zitin holds bachelor’s degrees in both physics and mathematics from UNC-Chapel Hill. His research focused on collecting and analyzing low energy physics data to better understand the neutrino. Ari taught introductory and advanced physics and scientific programming courses at UC-Berkeley while working on a master’s in physics with a focus on nonlinear dynamics. While at SAS, Ari has worked to develop courses that teach how to use Python code to control SAS analytical procedures, including machine learning and optimization.
Workshop | Machine Learning | Research Frontiers | Intermediate-Advanced
The values of a categorical variable frequently have a structure that is not ordinal or linear in nature. For example, the months of the year have a circular structure, and the US States have a geographical structure. Standard approaches such as one-hot or numerical encoding are unable to effectively exploit the structural information of such variables. In this tutorial, we will introduce the StructureBoost gradient boosting package, wherein the structure of categorical variables can be represented by a graph, and exploited to improve predictive performance. Moreover, StructureBoost can make informed predictions on categorical values for which there is little or no data, by leveraging the knowledge of the structure. We will walk through examples of how to configure and train models using StructureBoost and demonstrate other features of the package…more details
Brian Lucena is a Principal at Lucena Consulting and a consulting Data Scientist at Agentero. An applied mathematician in every sense, he is passionate about applying modern machine learning techniques to understand the world and act upon it. In previous roles, he has served as SVP of Analytics at PCCI, Principal Data Scientist at Clover Health, and Chief Mathematician at Guardian Analytics. He has taught at numerous institutions including UC-Berkeley, Brown, USF, and the Metis Data Science Bootcamp.
Tutorial | Machine Learning | NLP | Intermediate-Advanced
xNLP driven AI is a fast-growing technology domain with diverse applications in customer engagement, employee collaboration, marketing, and social media. Does having an accurate model by itself mean, that we have a successful product, service, or solution? No! The most difficult phase begins after that. We still have the following challenges to solve. How do we Create a sellable service around this model? How to Scale this service to handle millions of inference requests, in a cost-effective manner? How to build automated deployment pipelines for software and models? How to add security, privacy, manageability, and observability for the service? How to track model drift and analyze model performance? In this session, we will discuss these questions and explore the answers. We will go through the unique challenges that NLP serving poses and the solutions and best practices to overcome them…more details
Kumaran Ponnambalam is a seasoned veteran in everything data, with a reputation for delivering high performance database and SaaS applications and currently specializing in leading Big Data Science and Engineering efforts
Workshop | Machine Learning
Bayesian statistical methods are becoming more common, but there are not many resources to help beginners get started. People who know Python can use their programming skills to get a head start. In this workshop, I introduce Bayesian methods using grid algorithms, which help develop understanding, and MCMC, which is a powerful algorithm for real-world problems.
As the primary example, we will estimate goal scoring rates in hockey and soccer. This example is meant to be fun, but it is also useful; the same methods apply to any system well-modeled by a Poisson process, including customers arriving at a business, requests coming in to a server, and many other applications…more details
Allen Downey is a Professor of Computer Science at Olin College of Engineering in Needham, MA. He is the author of several books related to computer science and data science, including Think Python, Think Stats, Think Bayes, and Think Complexity. Prof Downey has taught at Colby College and Wellesley College, and in 2009 he was a Visiting Scientist at Google. He received his Ph.D. in Computer Science from U.C. Berkeley, and M.S. and B.S. degrees from MIT.
Tutorial | AI for Good | Beginner
I will talk about the vision, motivation, as well as the design and development of Ubenwa, an AI-driven technology to provide global access to early diagnosis of birth asphyxia. Birth asphyxia is one of the top 3 causes of newborn mortality today. Ubenwa aims democratise access to clinical-grade screening through a mobile tool that is non-invasive, swift, easy to use and cost-effective...more details
Charles is a PhD candidate at McGill University and Mila – the Québec AI Institute. He is interested in the design and development of improved diagnostic and decision-making tools in healthcare. He leads the Ubenwa Health, a collaboration between researchers in Canada and Nigeria, developing low-cost, AI-powered mobile app for the diagnosis of perinatal asphyxia from the infant cry. At the Montreal Children’s Hospital, Charles is involved in the APEX project, where he has been developing machine learning algorithms for analyzing cardiorespiratory behaviour of preterm newborns in order to determine their readiness for extubation. He has worked with Health Experiences Research Canada contributing to the design and leading the development of the HERS mobile app – a personalized recommendation tool for breast cancer patients. Charles is a Jeanne Sauvé Fellow and a Vanier Doctoral Scholar.
Workshop
During the workshop, the audience will gain not only a holistic overview of the TensorFlow ecosystem but will also learn the necessary steps to bring ML projects from experiments to production. With the knowledge, the participants can translate their ML projects into TFX pipelines and simplify their ML model production processes…more details
Hannes Hapke is a Senior Machine Learning Engineer at SAP ConcurLabs. He’s been a machine learning enthusiast for many years and is a Google Developer Expert for machine learning.
Eric P. Xing is a Professor of Computer Science at Carnegie Mellon University, and the Founder, CEO, and Chief Scientist of Petuum Inc., a 2018 World Economic Forum Technology Pioneer company that builds standardized artificial intelligence development platform and operating system for broad and general industrial AI applications. He completed his undergraduate study at Tsinghua University, and holds a PhD in Molecular Biology and Biochemistry from the State University of New Jersey, and a PhD in Computer Science from the University of California, Berkeley. His main research interests are the development of machine learning and statistical methodology, and large-scale computational system and architectures, for solving problems involving automated learning, reasoning, and decision-making in high-dimensional, multimodal, and dynamic possible worlds in artificial, biological, and social systems. Prof. Xing currently serves or has served the following roles: associate editor of the Journal of the American Statistical Association (JASA), Annals of Applied Statistics (AOAS), IEEE Journal of Pattern Analysis and Machine Intelligence (PAMI) and the PLoS Journal of Computational Biology; action editor of the Machine Learning Journal (MLJ) and Journal of Machine Learning Research (JMLR); member of the United States Department of Defense Advanced Research Projects Agency (DARPA) Information Science and Technology (ISAT) advisory group. He is a recipient of the Carnegie Science Award, National Science Foundation (NSF) Career Award, the Alfred P. Sloan Research Fellowship in Computer Science, the United States Air Force Office of Scientific Research Young Investigator Award, the IBM Open Collaborative Research Faculty Award, as well as several best paper awards. Prof Xing is a board member of the International Machine Learning Society; he has served as the Program Chair (2014) and General Chair (2019) of the International Conference of Machine Learning (ICML); he is also the Associate Department Head of the Machine Learning Department, founding director of the Center for Machine Learning and Health at Carnegie Mellon University; and he is a Fellow of the Association of Advancement of Artificial Intelligence (AAAI), and an IEEE Fellow.
Workshop | R Programming | Machine Learning | Intermediate
With customer privacy laws, missing data is becoming more of a normal situation. Many approaches are either computationally expensive, or throw out the baby with the bath water. There are certain models which allow missing data directly, and variants of common models which can be adapted to do so. Surprisingly, we can create effective predictability even in scenarios where data is missing not at random and at rates of higher than 80%. Hands on workshop using R or Python...more details
Anne Lifton has ten years of experience in data science and 3 years in data science management. She has worked across a range of industries from medical devices to retail to engineering, and specializes in reducing the cycle time to delivery of models.
Workshop | Machine Learning | Deep Learning | All Levels
Leveraging data to produce algorithmic rules is nothing new. However, we used to second guess these rules, and now we don’t. Therefore, a human used to be accountable, and now it’s the algorithm… But can it? How do we solve this accountability gap and ethical quandary?…more details
Serg Masís has been at the confluence of the internet, application development, and analytics for the last two decades. Currently, he’s a Data Scientist at Syngenta, a leading agribusiness company with a mission to improve global food security. Before that role, he co-founded a search engine startup, incubated by Harvard Innovation Labs, that combined the power of cloud computing and machine learning with principles in decision-making science to expose users to new places and events efficiently. Serg is passionate about providing the often-missing link between data and decision-making. His book titled “Interpretable Machine Learning with Python” is scheduled to be released in early 2021 by UK-based publisher Packt.
Tutorial | Deep Learning | Machine Learning | Intermediate
Deep generative models have made great progress to synthesize data realistically in various domains such as image generation and speech synthesis. Generative modeling brings a new paradigm shift in AI from content classification and regression to content analysis and creation. This tutorial aims at introducing the basics of deep generative models such as Variational AutoEncoder (VAEs) and Generative Adversarial Networks (GANs) as well as the user interaction with the models for content manipulation and creation. By completing this workshop, you will have an understanding of the deep generative models, their strengths and weaknesses, and their promising applications in content analytics and creation. The workshop will focus on the generative models used in image synthesis but the introduced methodology is able to be extended in different domains…more details
Bolei Zhou is an Assistant Professor with the Information Engineering Department at the Chinese University of Hong Kong. He received his PhD in computer science at the Massachusetts Institute of Technology. His research is on machine perception and decision, with a focus on visual scene understanding and interpretable AI systems. He received the MIT Tech Review’s Innovators under 35 in Asia-Pacific award, Facebook Fellowship, Microsoft Research Asia Fellowship, MIT Greater China Fellowship, and his research was featured in media outlets such as TechCrunch, Quartz, and MIT News. More about his research is at http://bzhou.ie.cuhk.edu.hk/.
We live in the era of Analytics Heterogeneity – where analytics is not limited to one single methodology, tool or algorithm, but is able to leverage the full potential of the fast-growing and rapid-changing ecosystem of analytical solutions and technologies available. However, one byproduct of this evolution has been increasing complexity of how to turn analytics into value. Which new capabilities are needed to be successful in this new era? What can we do to be champions and convert this complexity into a competitive advantage? In this session, I will walk you through the best practices to move from sandbox to production…more details
Marinela Profi is a Global Strategist for AI, with a focus on ModelOps solutions. She is interested around strategies for putting machine learning in production and turning models into trusted decisions efficiently. Marinela started her career as a Sales Engineer for Advanced Analytics, developing solutions for banking, retail, and manufacturing industries. Her main contributions have been around combination of forecasting and machine learning techniques. Her background is a mix of Business Administration, Statistics, and Marketing. She holds a Master of Science in Business Administration and a Master in Statistics from the University of Rome. Marinela is a Global Ambassador and member of Women Tech Network, an organization that enables women’s empowerment in tech through leadership development, and speaker at the International Symposium of Forecasting. She is also the author of the ebook “Mastering Model Lifecycle Orchestration: An interactive guide”.
Many problems in systems and chip design are in the form of combinatorial optimization on graph structured data. In this talk, I will motivate taking a learning based approach to combinatorial optimization problems with a focus on deep reinforcement learning (RL) agents that generalize. I will discuss our work on a new domain-transferable reinforcement learning methodology for optimizing chip placement, a long pole in hardware design…more details
Azalia Mirhoseini is a Senior Research Scientist at Google Brain. She is the co-founder/tech-lead of the Machine Learning for Systems Team in Google Brain where they focus on deep reinforcement learning based approaches to solve problems in computer systems and metalearning. She has a Ph.D. in Electrical and Computer Engineering from Rice University. She has received a number of awards, including the MIT Technology Review 35 under 35 award, the Best Ph.D. Thesis Award at Rice and a Gold Medal in the National Math Olympiad in Iran. Her work has been covered in various media outlets including MIT Technology Review, IEEE Spectrum, and Wired.
AI for Good | Machine Learning | All Levels
Every field has data. We use data to discover new knowledge, to interpret the world, to make decisions, and even to predict the future. The recent convergence of big data, cloud computing, and novel machine learning algorithms and statistical methods is causing an explosive interest in data science and its applicability to all fields. This convergence has already enabled the automation of some tasks that better human performance. The novel capabilities we derive from data science will drive our cars, treat disease, and keep us safe. At the same time, such capabilities risk leading to biased, inappropriate, or unintended action. The design of data science solutions requires both excellence in the fundamentals of the field and expertise to develop applications which meet human challenges without creating even greater risk...more details
Jeannette M. Wing is Avanessians Director of the Data Science Institute and Professor of Computer Science at Columbia University. From 2013 to 2017, she was a Corporate Vice President of Microsoft Research. She is Adjunct Professor of Computer Science at Carnegie Mellon where she twice served as the Head of the Computer Science Department and had been on the faculty since 1985. From 2007-2010 she was the Assistant Director of the Computer and Information Science and Engineering Directorate at the National Science Foundation. She received her S.B., S.M., and Ph.D. degrees in Computer Science, all from the Massachusetts Institute of Technology. Professor Wing’s general research interests are in the areas of trustworthy computing, specification and verification, concurrent and distributed systems, programming languages, and software engineering. Her current interests are in the foundations of security and privacy, with a new focus on trustworthy AI. She was or is on the editorial board of twelve journals, including the Journal of the ACM and Communications of the ACM. Professor Wing is known for her work on linearizability, behavioral subtyping, attack graphs, and privacy-compliance checkers. Her 2006 seminal essay, titled “Computational Thinking,” is credited with helping to establish the centrality of computer science to problem-solving in fields where previously it had not been embraced. She is currently a member of: the National Library of Medicine Blue Ribbon Panel; the Science, Engineering, and Technology Advisory Committee for the American Academy for Arts and Sciences; the Board of Trustees for the Institute of Pure and Applied Mathematics; the Advisory Board for the Association for Women in Mathematics; and the Alibaba DAMO Technical Advisory Board. She has been chair and/or a member of many other academic, government, and industry advisory boards. She received the CRA Distinguished Service Award in 2011 and the ACM Distinguished Service Award in 2014. She is a Fellow of the American Academy of Arts and Sciences, American Association for the Advancement of Science, the Association for Computing Machinery (ACM), and the Institute of Electrical and Electronic Engineers (IEEE).
An AI expert and health AI pioneer, Suchi Saria’s research has led to myriad new inventions to improve patient care. Her work first demonstrated the use of machine learning to make early detection possible in sepsis, a life-threatening condition (Science Trans. Med. 2015). In Parkinson’s, her work showed a first demonstration of using readily-available sensors to easily track and measure symptom severity at home, to optimize treatment management (JAMA Neurology 2018). On the technical front, her work at the intersection of machine learning and causal inference has led to new ideas for building and evaluating reliable ML (ACM FAT 2019). Suchi currently holds a John C. Malone endowed chair at Johns Hopkins University, with appointments across engineering, public health, and medicine. She is also the Founder of Bayesian Health, aiming to revolutionize the delivery of healthcare by empowering providers and health systems with real-time access to essential clinical inferences. She is the recipient of numerous prizes and honors, including being named a Sloan Research Fellow, a National Academy of Medicine Emerging Leader in Health and Medicine, MIT Technology Review’s 35 Innovators Under 35, and a World Economic Forum Young Global Leader.
Zoubin Ghahramani is Chief Scientist of Uber and a world leader in the field of machine learning, significantly advancing the state-of-the-art in algorithms that can learn from data. He is known in particular for fundamental contributions to probabilistic modeling and Bayesian approaches to machine learning systems and AI. Zoubin also maintains his roles as Professor of Information Engineering at the University of Cambridge and Deputy Director of the Leverhulme Centre for the Future of Intelligence. He was one of the founding directors of the Alan Turing Institute (the UK’s national institute for Data Science and AI), and is a Fellow of St John’s College Cambridge and of the Royal Society.
John leads Program Management for Microsoft Azure AI and is responsible for designing products and services that data scientists and ML experts around the world love and use. He leads a team of program managers, researchers, and designers responsible for products and services including Azure Machine Learning, Azure Cognitive Services, ML.NET, and ONNX Runtime. Prior to this role, John has led the Program Management team for Microsoft’s Developer Division, including Visual Studio, Visual Studio Code, and Azure Notebooks.
He has also held positions as director of marketing for Visual Studio, as well as a program manager for Microsoft’s participation in several standards organizations, including ISO, IETF, and ECMA.
Prior to joining Microsoft in 1998, John worked as a writer and editor for several computer and technology publications, including BYTE Magazine, PC/Computing, and Corporate Computing, as well as being the Chief Information Officer for Imagine Publishing.
Multiple organizations often wish to aggregate their sensitive data and learn from it, but they cannot do so because they cannot share their data. For example, banks wish to train models jointly over their aggregate transaction data to detect money launderers because criminals hide their traces across different banks. To address such problems, my students and I developed MC^2, a framework for secure collaborative computation. My talk will overview our MC^2 platform, our technical approach, results, and adoption…more details
Raluca Ada Popa is an assistant professor of computer science at UC Berkeley. She is interested in security, systems, and applied cryptography. Raluca developed practical systems that protect data confidentiality by computing over encrypted data, as well as designed new encryption schemes that underlie these systems. Some of her systems have been adopted into or inspired systems such as SEEED of SAP AG, Microsoft SQL Server’s Always Encrypted Service, and others. Raluca received her PhD in computer science as well as her two BS degrees, in computer science and in mathematics, from MIT. She is the recipient of an Intel Early Career Faculty Honor award, George M. Sprowls Award for best MIT CS doctoral thesis, a Google PhD Fellowship, a Johnson award for best CS Masters of Engineering thesis from MIT, and a CRA Outstanding undergraduate award from the ACM.
Ion Stoica is a Professor in the EECS Department at University of California at Berkeley. HHe does research on cloud computing and networked computer systems. Past work includes Apache Spark, Apache Mesos, Tachyon, Chord DHT, and Dynamic Packet State (DPS). He is an ACM Fellow and has received numerous awards, including the SIGOPS Hall of Fame Award (2015), the SIGCOMM Test of Time Award (2011), and the ACM doctoral dissertation award (2001). In 2013, he co-founded Databricks a startup to commercialize technologies for Big Data processing, and in 2006 he co-founded Conviva, a startup to commercialize technologies for large scale video distribution.
Track Keynote | Machine Learning | AI for Good | All Levels
Automated Machine Learning (Auto ML) refers broadly to technologies that automate the process of generating, evaluating, and selecting an ML pipeline optimized for a specific dataset. I will focus on recent advances that will have a dramatic impact on driving automation across the entire AI/ML lifecycle, from data discovery and curation; to advanced model building with business and fairness constraints; to automation to monitor models in deployment, recognizing deficiencies and recommending corrections. Automation for this end-to-end AI/ML lifecycle is sometimes referred to as AutoAI. I will also describe frontiers of Auto ML/AI, including research into automating knowledge augmentation for AI/ML models, specifically by mining and incorporating domain knowledge from open data sources, enterprise structured knowledge, Jupyter notebooks and text documents. Another example targets expanding the scope of Auto ML/AI to novel and more powerful tasks. Current AutoML focuses on supervised learning for classification, prediction, and forecasting tasks. I will describe research into automation for tackling novel tasks such as unsupervised learning and decision optimization. This session will be of interest to practicing data scientists and AI/ML engineers, as well as AI/ML researchers...more details
Dr. Lisa Amini is the Director of IBM Research Cambridge, which is also home to the MIT-IBM Watson AI Lab, and of IBM’s AI Horizons Network. Lisa was previously Director of Knowledge & Reasoning Research in the Cognitive Computing group at IBM’s TJ Watson Research Center in New York, and she is also an IBM Distinguished Engineer. Lisa was the founding Director of IBM Research Ireland, and the first woman Lab Director for an IBM Research Global (i.e., non-US) Lab (2010-2013). In this role she developed the strategy and led researchers in advancing science and technology for intelligent urban and environmental systems (Smarter Cities), with a focus on creating analytics, optimizations, and systems for sustainable energy, constrained resources (e.g., urban water management), transportation, and the linked open data systems that assimilate and share data and models for these domains. She earned her PhD degree in Computer Science from Columbia University.
Track Keynote | AI for Good | Research Frontiers | All Levels
This talk will describe the dramatic creation of the COVID-19 Open Research Dataset (CORD-19) and the broad range of efforts, both inside and outside of the Semantic Scholar project, to garner insights into COVID-19 and its treatment based on this data...more details
Dr. Oren Etzioni has served as the Chief Executive Officer of the Allen Institute for AI (AI2) since its inception in 2014. He has been a Professor at the University of Washington’s Computer Science department since 1991, and a Venture Partner at the Madrona Venture Group since 2000. He has garnered several awards including Seattle’s Geek of the Year (2013), the Robert Engelmore Memorial Award (2007), the IJCAI Distinguished Paper Award (2005), AAAI Fellow (2003), and a National Young Investigator Award (1993). He has been the founder or co-founder of several companies, including Farecast (sold to Microsoft in 2008) and Decide (sold to eBay in 2013). He has written commentary on AI for The New York Times, Nature, Wired, and the MIT Technology Review. He helped to pioneer meta-search (1994), online comparison shopping (1996), machine reading (2006), and Open Information Extraction (2007). He has authored over 100 technical papers that have garnered over 2,000 highly influential citations on Semantic Scholar. He received his Ph.D. from Carnegie Mellon in 1991 and his B.A. from Harvard in 1986.
Gaël Varoquaux is an Inria faculty researcher working on data science and brain imaging. He has a joint position at Inria (French Computer Science National research) and in the Neurospin brain research institute. His research focuses on using data and machine learning for scientific inference, applying it to brain-imaging data to understand cognition, as well as developing tools that make it easier for non-specialists to use machine learning. Years before the NSA, he was hoping to make bleeding-edge data processing available across new fields, and he has been working on a mastermind plan building easy-to-use open-source software in Python. He is a core developer of scikit-learn, joblib, Mayavi and nilearn, a nominated member of the PSF, and often teaches scientific computing with Python using the scipy lecture notes.
Talk | NLP
NLP models are big – they take a lot of time and money to train and can be hard to deploy. You’re going to want all the help you can get to reduce costs, improve agility, and enable production deployments. In this session you’ll see how massive scale products like Microsoft Office are using PyTorch, ONNX Runtime, and Azure Machine Learning to significantly accelerate their training and inferencing to increase agility and reduce costs. You’ll learn how you can do the same for your own NLP scenarios…more details
Prasanth Pulavarthi is Principal Program Manager for the AI Frameworks team at Microsoft. His team works on making ML practitioners and engineers more efficient through optimized libraries, tools, and communities. ONNX Runtime (https://onnxruntime.ai) is an open-source engine from his team that integrates with TensorFlow, PyTorch, and other frameworks to accelerate inferencing and training on a variety of cloud and edge hardware.
Prasanth is also the Co-Founder of ONNX (https://onnx.ai), the open standard for machine learning interoperability. ONNX is now a graduate projected in Linux Foundation Artificial Intelligence. He serves on the ONNX Steering Committee and is actively involved in the ONNX community.
Track Keynote | NLP | Intermediate
Measuring the impact of scientific articles is important for evaluating the research output of individual scientists, academic institutions and journals. While citations are raw data for constructing impact measures, there exist biases and potential issues if factors affecting citation patterns are not properly accounted for. In this work, we address the problem of field variation and introduce an article level metric useful for evaluating individual articles’ visibility. This measure derives from joint probabilistic modeling of the content in the articles and the citations among them using latent Dirichlet allocation (LDA) and the mixed membership stochastic blockmodel (MMSB). Our proposed model provides a visibility metric for individual articles adjusted for field variation in citation rates, a structural understanding of citation behavior in different fields, and article recommendations which take into account article visibility and citation patterns...more details
Tian Zheng is Professor and Department Chair of Statistics at Columbia University. She develops novel methods for exploring and understanding patterns in complex data from different application domains. Her current projects are in the fields of statistical machine learning, spatiotemporal modeling and social network analysis. Professor Zheng’s research has been recognized by the 2008 Outstanding Statistical Application Award from the American Statistical Association (ASA), the Mitchell Prize from ISBA and a Google research award. She became a Fellow of American Statistical Association in 2014. Professor Zheng is the recipient of the 2017 Columbia’s Presidential Award for Outstanding Teaching. From 2018-2020, she has been the chair-elect, chair and past-chair for ASA’s section on Statistical Learning and Data Science.
Track Keynote | Machine Learning | All levels
ML Models in production lose accuracy over time. A number of factors contribute to this change, like Demographic Mix, Consumer Behavior change etc. Using these models’ output resultz in incorrect decisions that could lead to catastrophic failures for the organization. This existing whitespace calls for a solution(s) to help Data Science teams predict the failure in advance...more details
Aravind heads the Data Science Org at Tredence, and his team works on the R&D & algorithm development for new Data Science solutions
