ODSC East 2021
Full Schedule
Fundamentals
Linear Algebra, Calculus, and Probability: Machine Learning Fundamentals
Intro to SQL for Data Analytics
Introduction to Data Analysis using Python
Introduction to Data Visualization using D3.js
Introduction to Machine Learning with Python
Introduction to Deep Learning for Object Detection
Fundamentals of Machine Learning on the Cloud
Non-technical
Ai Cross-industry: How Google Uses AI and Machine Learning in the Enterprise
Ai Healthcare: AI-Powered Best Healthcare for Everyone
Ai Social Good: Alternative Data Where It’s Needed The Most. Use Case: Satellite Imagery
Ai Management: Managing Data Science as Products
Ai Energy: Open Catalyst Project: Using AI to Model And Discover New Catalysts
Ai Finance: Using Survival Analysis to Model Credit Risk
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 Manager at EY and occasional adjunct at the University of Chicago’s Booth School of Business, teaching Python and R. Previously she’s taught a data science bootcamp and built risk models for large financial institutions at McKinsey & Co.
Full-Day Training | Machine Learning | ML for Programmers | 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.
Full-Day Training | Machine Learning | ML for Programmers | 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.
Half-Day Training | Machine Learning | MLOps | 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.
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 | Machine Learning | Intermediate-Advanced
How could a violation of the triangle inequality theorem in mathematics lead to a cure for cancer? How can a mathematical concept from the 18th century be used to estimate the mass density of galaxies across the Universe? How could a marketing segmentation algorithm protect astronauts traveling to Mars from certain death? How does a F1 race from the 1950’s inspire one of the greatest machine learning use cases for the Internet of Things? This workshop will answer these questions, and more, by presenting several examples of typical algorithms that were adopted for specific use cases or application domains, then showing how each one can be adapted to an atypical (often mind-bending) use case, producing significantly surprising results in some other domain…more details
Dr. Kirk Borne is the Principal Data Scientist and an Executive Advisor at global technology and consulting firm Booz Allen Hamilton. In those roles, he focuses on applications of data science, data management, machine learning, A.I., and modeling across a wide variety of disciplines. He also provides training and mentoring to executives and data scientists within numerous external organizations, industries, agencies, and partners in the use of large data repositories and machine learning for discovery, decision support, and innovation. Previously, he was Professor of Astrophysics and Computational Science at George Mason University for 12 years where he did research, taught, and advised students in data science. Prior to that, Kirk spent nearly 20 years supporting data systems activities on NASA space science programs, which included a period as NASA’s Data Archive Project Scientist for the Hubble Space Telescope. Dr. Borne has a B.S. degree in Physics from LSU, and a Ph.D. in Astronomy from Caltech. In 2016 he was elected Fellow of the International Astrostatistics Association for his lifelong contributions to big data research in astronomy. As a global speaker, he has given hundreds of invited talks worldwide, including conference keynote presentations at many dozens of data science, A.I. and big data analytics events globally. He is an active contributor on social media, where he has been named consistently among the top worldwide influencers in big data and data science since 2013. He was recently identified as the #1 digital influencer worldwide for 2018-2019. You can follow him on Twitter at @KirkDBorne.
Half-Day Training | Data Visualization | Machine Learning | Beginner-Intermediate
Upon completing this tutorial, you will be:
– familiar with how to use the NetworkX and nxviz Python packages for modelling and rationally visualizing networks,
– able to load node and edge data from a Pandas dataframe,
– familiar with object-oriented and matrix-oriented representations of graphs,
able to find paths between nodes, interesting structures in graphs, and projections of bipartite graphs.
– (if time permits) able to use matrix operations to simulate diffusion of information on networks..more details
Eric is an Investigator at the Novartis Institutes for Biomedical Research, where he solves biological problems using machine learning. He obtained his Doctor of Science (ScD) from the Department of Biological Engineering, MIT, and was an Insight Health Data Fellow in the summer of 2017. He has taught Network Analysis at a variety of data science venues, including PyCon USA, SciPy, PyData, and ODSC, and has also co-developed the Python Network Analysis curriculum on DataCamp. As an open-source contributor, he has made contributions to PyMC3, matplotlib, and bokeh. He has also led the development of the graph visualization package nxviz, and a data cleaning package pyjanitor (a Python port of the R package).
Half-Day Training | Data Analytics | Intermediate
In this training, you will learn how to accelerate your data analyses using the Python language and Pandas, a library specifically designed for tabular data analysis. We start by learning the core Pandas data structures, the Series and DataFrame. From these foundations, we will learn to use the split-apply-combine paradigm for grouped computations, manipulate time series, and perform advanced joins between datasets. Specifically, loading, filtering, grouping, and transforming data. Having completed this workshop, you will understand the fundamentals and advanced features of Pandas, be aware of common pitfalls, and be ready to perform your own analyses…more details
Daniel Gerlanc has worked as a data scientist for more than decade and been writing sofware for nearly 20 years. He frequently teaches live trainings on oreilly.com and is the author of the video course Programming with Data: Python and Pandas. He has coauthored several open source R packages, published in peer-reviewed journals, and is a graduate of Williams College.
Half-Day Training | Data Analytics | Machine Learning | Beginner
If you’ve never heard of the “good, fast, cheap” dilemma, it goes something like this: You can have something good and fast, but it won’t be cheap. You can have something good and cheap, but it won’t be fast. You can have something fast and cheap, but it won’t be good. In short, you can pick two of the three but you can’t have all three. If you’ve tackled a data science problem before, I can all but guarantee that you’ve run into missing data. How do we handle it? Well, we can avoid, ignore, or try to account for missing data…more details
Matt is currently Growth Manager at Roboflow, a YCombinator-backed startup focusing on responsibly democratizing computer vision. Matt is also Managing Partner + Principal Data Scientist at BetaVector. His full-time professional data work spans finance, education, consumer-packaged goods, and politics and he earned General Assembly’s 2019 “Distinguished Faculty Member of the Year” award. Matt earned his Master’s degree in statistics from Ohio State. Matt is passionate about mentoring folx in data and tech careers. Matt also volunteers with Statistics Without Borders and currently serves on their Executive Committee as the Marketing & Communications Director.
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 | 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...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 | Machine Learning | Intermediate-Advanced
Unsupervised learning models (including analysis of correlations, clusters, and associations in data) converge more readily to a useful solution if we start with good model parameterizations. Feature engineering is key, but selection of features often becomes guesswork. Similarly, in supervised machine learning, the choice of features in labeled data to use in training may still seem arbitrary. So, how does model-building start and move towards an optimal solution? This challenge is known as the cold-start problem! The solution to the problem is easy (sort of): We start with a guess, a totally random guess! That sounds so random and so wrong! But there is an orderly and productive way forward from such a start, which we will describe in this workshop…more details
Dr. Kirk Borne is the Principal Data Scientist and an Executive Advisor at global technology and consulting firm Booz Allen Hamilton. In those roles, he focuses on applications of data science, data management, machine learning, A.I., and modeling across a wide variety of disciplines. He also provides training and mentoring to executives and data scientists within numerous external organizations, industries, agencies, and partners in the use of large data repositories and machine learning for discovery, decision support, and innovation. Previously, he was Professor of Astrophysics and Computational Science at George Mason University for 12 years where he did research, taught, and advised students in data science. Prior to that, Kirk spent nearly 20 years supporting data systems activities on NASA space science programs, which included a period as NASA’s Data Archive Project Scientist for the Hubble Space Telescope. Dr. Borne has a B.S. degree in Physics from LSU, and a Ph.D. in Astronomy from Caltech. In 2016 he was elected Fellow of the International Astrostatistics Association for his lifelong contributions to big data research in astronomy. As a global speaker, he has given hundreds of invited talks worldwide, including conference keynote presentations at many dozens of data science, A.I. and big data analytics events globally. He is an active contributor on social media, where he has been named consistently among the top worldwide influencers in big data and data science since 2013. He was recently identified as the #1 digital influencer worldwide for 2018-2019. You can follow him on Twitter at @KirkDBorne.
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.
Workshop | MLOps | Beginner-Intermediate
Rajiv and Tim provide an easy explanation of critical issues around MLOps and get you started deploying a model.
After providing a short overview of typical model deployment patterns, the focus of the workshop is a hands-on walkthrough of deploying a model using reusable pipelines. This involves thinking about preprocessing, building a model, and getting a REST API endpoint. This workshop relies on DRUM, an open-source deployment framework, that provides automated testing and flexibility around deployment pipelines. DRUM provides built-in support for a variety of modeling frameworks including Keras, scikit learn, R, H2O, DataRobot, and more. So join us to see how we can making MLOps friendlier.
Rajiv Shah is a data scientist at DataRobot, where his primary focus is helping customers improve their ability to make and implement predictions. Previously, Rajiv has been part of data science teams at Caterpillar and State Farm. He has worked on a variety of projects from a wide ranging set of areas including supply chain, sensor data, acturial ratings, and security projects. He has a PhD from the University of Illinois at Urbana-Champaign.
Workshop | MLOps & Data Engineering
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.
Tutorial | Machine Learning | Data Visualization
During this tutorial, I will show you how to use Snakemake to create a scalable and reproducible data analysis pipeline. Snakemake is a workflow management system that uses sets of rules to define steps in the analysis process and it integrates smoothly with server, cluster, or cloud environments to allow easy scaling. Each rule defines the input files, output files, and the steps to get from input to output and wildcards allow the rules to be scaled. Snakemake is a very general framework for creating pipelines and I have used it for bioinformatics applications…more details
Laura Seaman is a Senior Machine Intelligence Scientist at Draper where she applies machine learning and bioinformatics algorithms to a variety of applications. Dr. Seaman’s graduate work focused on using genetic data to identify alterations to the genomic structure in cancer. She is currently using data science for many applications including analysis of financial networks and identification of genetically engineered organisms. Dr. Seaman has a Bachelor of Science in Biological Engineering from the Massachusetts Institute of Technology, a Masters of Arts in Statistics from the University of Michigan, and a Doctor of Philosophy in Bioinformatics from the University of Michigan.
Tutorial | Machine Learning | Intermediate
In this tutorial, we will review the main approaches and implementation (in R and python) to tackle this issue. We will start by the inferential framework, where the aim is to estimate at best the parameters and their variance in the presence of missing data. We will discuss the dangers of single imputation methods and detail multiple imputation. Then we will cover recent results in a supervised-learning setting. A striking one is that the widely-used method of imputing with the mean prior to learning can be consistent. That such a simple approach can be relevant may have important consequences in practice. We will also explain how to perform random forest with missing values and give first results on neural nets with incomplete data…more details
Julie Josse is a senior researcher at Inria and a scientific collaborator and teacher at at Ecole Polytechnique (Institut Polytechnique de Paris) in France. Julie Josse’s is an expert in handling missing values (inference, multiple imputation, matrix completion, missing non at random data, supervised learning with missing values) and has created a platform to collect works and give resources to users https://rmisstastic.netlify.app/, has organized workshops on the topic. Her vocation is to push methodological innovation to bring useful application of her research to the user in particular in bio-sciences and health. Her curent research focuses on causal inferences techniques for personalized medicine. She leads an important project with the Traumabase group dedicated to the management of polytraumatized patients to help emergency doctors making decisions. Julie Josse is dedicated to reproducible research with the R statistical software: she has developed packages including missMDA and FactoMineR to transfer her work, she is a member of the R foundation and of Rforwards to increase the participation of minorities in the community.
Workshop | Machine Learning | All Levels
The term XAI refers to a set of tools for explaining AI systems of any kind, beyond Machine Learning. Even though these tools aim at addressing explanation in the broader sense, they are not designed for all users, tasks, contexts, and applications. This presentation will describe progress to date on XAI by reviewing its approaches, motivation, best practices, industrial applications, and limitations….more details
Dr. Freddy Lecue is the Chief Artificial Intelligence (AI) Scientist at CortAIx (Centre of Research & Technology in Artificial Intelligence eXpertise) at Thales in Montreal – Canada. He is also a research associate at INRIA, in WIMMICS, Sophia Antipolis – France. Before joining the new R&T lab of Thales dedicated to AI, he was AI R&D Lead at Accenture Labs in Ireland from 2016 to 2018. Prior to joining Accenture he was a research scientist, the lead investigator in large-scale reasoning systems at IBM Research from 2011 to 2016, a research fellow at The University of Manchester from 2008 to 2011, and a research engineer at Orange Labs from 2005 to 2008.
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 | Machine Learning
A practical blitz on causal modeling and causal inference in the context of machine learning. We introduce case studies from the industry and provide Pytorch based Jupyter notebook tutorials…more details
Robert didn’t start in machine learning. He started his career by becoming fluent in Mandarin Chinese and moving to Tibet to do developmental economics fieldwork. He later obtained a graduate degree from Johns Hopkins School of Advanced International Studies.
After switching to the tech industry, Robert’s interests shifted to modeling data. He attained his Ph.D. in mathematical statistics from Purdue University, and then he worked as a research engineer in various AI startups (he is currently a ML research scientist at Gamalon). He has published in journals and venues across these spaces, including RECOMB and NeurIPS, on topics including causal inference, probabilistic modeling, sequential decision processes, and dynamic models of complex systems. In addition to startup work, he is a machine learning professor at Northeastern University.
Tutorial
In this 90-minute tutorial, we will evaluate a set of best practices and methods for each of the three approaches to statistical model evaluation. Leveraging common Python libraries such as scikit-learn and statsmodels, we will examine available methods and model output that generates the desired value for stakeholders depending on their specific questions. We will also discuss common stakeholder questions that fall into each of the 3 above categories, how to identify a question as belonging to a category, and common follow-up questions to anticipate in presenting your work…more details
Mona is a Senior Data Scientist at Greenhouse Software in New York City, where they contribute to data-informed decision making across the company and machine learning solutions to improve the hiring process for Greenhouse customers. They’ve previously worked in government, creating analytics and machine learning solutions to improve the lives of New Yorkers, and continue to be involved in civic projects through a number of volunteer and non-profit organizations. They’ve also been a statistics and data science educator with DataCamp, Emeritus, and in university settings. They hold a graduate degree in Developmental Psychology, and are passionate about contributing to the ethical use of data science methodology in the public and private sector.
Tutorial | Machine Learning | Deep Learning | Beginner-Intermediate
To be an outstanding data scientist or ML engineer, it doesn’t suffice to only know how to use ML algorithms via the abstract interfaces that the most popular libraries (e.g., scikit-learn, Keras) provide. To train innovative models or deploy them to run performantly in production, an in-depth appreciation of the math subjects underlying ML is required: linear algebra, calculus, and probability…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.
Workshop | Machine Learning
This workshop will harness the pipeline concept towards manageable high throughput experimentation in ML/DL research. We will distinguish between top-down pipelines used in production and a bottom-up design that we propose for researchers. We will see how to take a “conventional” flower detection example and employ the bottom-up design principle. While integrating typical research steps, we will mitigate some of the problematic aspects of moving from research to development…more details
Researcher first, developer second. Over the last 5 years, Ariel has worked on various projects; from the realms of quantum chemistry, massively-parallel supercomputing to deep-learning computer-vision. With AllegroAi, he helped build an open-source R&D platform (Allegro Trains), and later went on to lead a data-first transition for a revolutionary nanochemistry startup (StoreDot). Answering his calling to spread the word, he recently took up the mantle of Evangelist at ClearML. Ariel received his PhD in Chemistry in 2014 from the Weizmann Institute of Science. Ariel recently made the transition to the bustling startup scene of Tel-Aviv, and to cutting-edge Deep Learning research.
Tutorial | Deep Learning | Research Frontiers | Intermediate-Advanced
Deep learning has been tremendously successful, especially for solving problems in natural language understanding and computer vision. This is a great achievement, but the field still has a long way to go toward achieving the versatility of humans. After training on large amounts of data, conventional deep neural networks are frozen in time. Unlike humans, they cannot easily continue to learn more information. If this is attempted, conventional models will suffer from catastrophic forgetting, resulting in the loss of previously acquired skills…more details
Christopher Kanan is an Assistant Professor at the Rochester Institute of Technology (RIT), a Visiting Assistant Professor at Cornell Tech, and a Senior AI Scientist at Paige. At RIT, his lab works on lifelong machine learning and language driven computer vision, which has been supported by awards from NSF, AFOSR, ONR, DARPA, Adobe Research, and other industrial partners. He is also Associate Director of RIT’s Center for Human-aware AI and he is a member of RIT’s McNair Scholars advisory board. At Paige, a startup that has raised $95M to improve the diagnosis of cancer, he led the AI R&D team during its first 1.5 years and continues to advise its AI teams. He received a PhD in computer science from the University of California at San Diego, where he worked on brain-inspired algorithms for object recognition, neural networks, active vision, and cognitive modeling. He received an MS in computer science from the University of Southern California. Before joining RIT, he was a postdoctoral scholar at the California Institute of Technology, and later worked as a Research Technologist at NASA’s Jet Propulsion Laboratory, where he used deep learning to develop vision systems for autonomous ships. He is the recipient of the 2016 Rising Star Award and the 2019 Distinguished Scholarship Award in RIT’s College of Science. He is an IEEE Senior Member and has published over 50 refereed papers, many of which are in top venues across AI such as CVPR, ICCV, NeurIPS, AAAI, ICLR, ACL, etc.
Tyler Hayes is a PhD candidate in the Chester F. Carlson Center for Imaging Science at the Rochester Institute of Technology (RIT) in Rochester, NY. She works in Professor Christopher Kanan’s laboratory to advance online continual learning. Her current research interests include lifelong machine learning, computer vision, and computational mathematics. Previously, she earned a BS in Applied Mathematics from RIT in 2014 and an MS in Applied and Computational Mathematics from RIT in 2017.
Workshop
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…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.
Workshop | NLP | Deep Learning | Intermediate-Advanced
During the course of this project we, together with other teams (e.g. Mozilla), have tackled many of these issues and successfully trained the current state-of-the-art architectures such as Tacotron and Transformer-based models as well as developed their feed-forward counterparts and made all of it available open-source. Using these models, we created the first brand voice for Axel Springer, which now allows for audio content on the news website…more details
Francesco received his MS degree in Computational Mathematics in 2017 from the Department of Mathematics at the Technical University of Berlin. After a research experience in Bayesian machine learning, he pivoted into deep learning with generative models and computer vision. In his current position as a machine learning research engineer, he works on NLP and speech synthesis and has presented and authored a few open-source projects related to these topics.
Christian Schäfer is driving AI research within the Axel Springer group and helps to integrate machine learning systems in production. His goal is to create products that are both smart and pretty so that everyone will like them. Having an academic background in theoretical physics, Chris is interested in understanding complex phenomena, and his professional interests moved from network theory over neuroscience to deep learning, which all share some interesting similarities.
Tutorial | MLOps & Data Engineering
Moving data, transforming data types, taking small samples so they’ll fit in your sandbox – these are all things every data scientist puts up with as routine. And when you’re finished, a data engineer has to build full production pipelines to reproduce all that work at scale. But what if you could leave all the data where it is and analyze it in place?
What if you could jump straight to the meat of the work, and when you’re finished, a single line of code would push it all into production? In this tutorial, you’ll use familiar Pandas and SciKit code to build a churn reduction model without ever moving data…more details
In two decades in the data management industry, Paige Roberts has worked as an engineer, a trainer, a support technician, a technical writer, a marketer, a product manager, and a consultant.
She has built data engineering pipelines and architectures, documented and tested large scale open source analytics implementations, spun up Hadoop clusters from bare metal, picked the brains of some of the stars in the data analytics and engineering industry, championed data quality when that was supposedly passé, worked with a lot of companies in a lot of different industries, and questioned a lot of people’s assumptions.
Now, she promotes understanding of Vertica, MPP data processing, open source, high scale data engineering, and how the analytics revolution is changing the world.
Pranjal Singh has been a Data Scientist at Vertica since May 2020. Prior to joining Vertica, Pranjal received his Bachelor’s degree in Data Science from Northeastern University in Boston, MA. He has experience with Software Engineering, Data Analytics, and Machine Learning. Pranjal has a passion for ML and Predictive Analytics, and helping organizations make better decisions with data. He’s an avid sports fan, with a special interest in sports analytics and data.
Workshop | NLP | Deep Learning | Intermediate-Advanced
In this workshop, I would be talking about transformers and their inner workings. I would in fact review two google papers:
– Presentation: Attention is what you need.
– NB: Create Translator from Scratch.
– Presentation: BERT Transformers.
– NB: How to Use BERT for a task. like QA or Classification…more details
Rahul Agarwal is currently working as a Machine Learning Engineer at Facebook. Before this he has worked at WalmartLabs and Citi India.
In his free time, Rahul likes to write about Data Science and Machine Learning and you could find him at Medium, his own Website, or linkedin profile which is the place where he is the most active
Workshop
Most implementations of supervised machine learning algorithms are designed to work with complete datasets, but datasets are rarely complete. This dichotomy is usually addressed by either deleting points with missing elements and losing potentially valuable information or imputing (trying to guess the values of the missing elements), which can lead to increased bias and false conclusions. I will quickly review the three types of missing data (missing completely at random, missing at random, missing not at random) and a couple of simple but often misleading ways to impute…more details
Andras Zsom is a Lead Data Scientist in the Center for Computation and Visualization group at Brown University, Providence, RI. He works with high-level academic administrators to tackle predictive modeling problems, he collaborates with faculty members on data-intensive research projects, and he was the instructor of a data science course offered to the data science master students at Brown.
Workshop | Machine Learning | Intermediate
In our workshop, we will focus on the state of the art in automatic machine learning pipeline construction and neural architecture search (NAS), and provide hands-on guidance on using different methods that are used to optimize pipelines, such as evolutionary optimization and Bayesian optimization. Much like human-machine learning experts learn from experience, we also want to leverage prior knowledge to find better models, faster. This is also known as meta-learning, which we apply in an AutoML context by learning which hyperparameters are more important, or which pipeline structures or neural architectures should be explored…more details
Joaquin Vanschoren is Assistant Professor in Machine Learning at the Eindhoven University of Technology. His research focuses on machine learning, meta-learning, and understanding and automating learning. He founded and leads OpenML.org, an open science platform for machine learning. He received several demo and open data awards, has been tutorial speaker at NeurIPS and ECMLPKDD, and invited speaker at ECDA, StatComp, AutoML@ICML, CiML@NIPS, DEEM@SIGMOD, AutoML@PRICAI, MLOSS@NIPS, and many other occasions. He was general chair at LION 2016, program chair of Discovery Science 2018, demo chair at ECMLPKDD 2013, and he co-organizes the AutoML and meta-learning workshop series at NIPS and ICML. He is also co-editor of the book ’Automatic Machine Learning: Methods, Systems, Challenges’.
Pieter Gijsbers is a Ph.D. student at the Eindhoven University of Technology. His areas of interest are automated machine learning and meta-learning. He is the main author of GAMA, a research-focused open-source AutoML tool. While GAMA is easy to use for end-users, it also allows researchers to try different search techniques and visualize the optimization process. He is a co-author of the Open Source AutoML Benchmark. He is also part of the team working on the openml-python package, providing an easy to use Python interface to OpenML.
Tutorial | Machine Learning | Intermediate
In this tutorial, we will look at some of the recent advances in multi-task RL. We will start by discussing the nuances of the relationship between single-task and multi-task setup. For example, a multi-task RL problem can be modeled as a single-task RL problem. In that setup, all the multi-task environments are considered different parts of a large single task environment. We will see how this observation can be used to develop some general components and strategies that can convert any single task RL algorithm into a multi-task agent…more details
Shagun Sodhani is a Research Engineer in the Facebook AI Research Group. He is primarily interested in lifelong reinforcement learning – training AI systems that can interact with and learn from the physical world and consistently improve as they do so without forgetting the previous knowledge. He did his MS from Mila, University of Montreal., where he was supervised by Dr. Yoshua Bengio and Dr. Jian Tang.
Workshop | NLP | Data Analytics | Intermediate
In this workshop, we will start with an open-source data set of text and convert it to a knowledge graph. We will use standard natural language processing (NLP) packages and approaches in Python to clean that text and create a knowledge graph data model within a graph database that can be queried and turned into data insights. This data model will include the nodes, edges, and attributes identified through the NLP process that can be used to create the necessary ontologies for the graph. We will experience the problems associated with generating such knowledge graphs, such as entity disambiguation and the lack of sufficient training data (zero-shot learning). Attendees of this workshop will create and put to use a complete pipeline for knowledge graph generation and analysis…more details
Dr. Clair Sullivan is currently a graph data science advocate at Neo4j, working to expand the community of data scientists and machine learning engineers using graphs to solve challenging problems. She received her doctorate degree in nuclear engineering from the University of Michigan in 2002. After that, she began her career in nuclear emergency response at Los Alamos National Laboratory where her research involved signal processing of spectroscopic data. She spent 4 years working in the federal government on related subjects and returned to academic research in 2012 as an assistant professor in the Department of Nuclear, Plasma, and Radiological Engineering at the University of Illinois at Urbana-Champaign. While there, her research focused on using machine learning to analyze the data from large sensor networks. Deciding to focus more on machine learning, she accepted a job at GitHub as a machine learning engineer while maintaining adjunct assistant professor status at the University of Illinois. Additionally, she founded a company, La Neige Analytics, whose purpose is to provide data science expertise to the ski industry. She has authored 4 book chapters, over 20 peer-reviewed papers, and more than 30 conference papers. Dr. Sullivan was the recipient of the DARPA Young Faculty Award in 2014 and the American Nuclear Society’s Mary J. Oestmann Professional Women’s Achievement Award in 2015.
Tutorial | Machine Learning | Intermediate
Regardless of your industry or functional areas, in order to build modern products and services, product managers will benefit the most by taking a data-driven decision-making process. The A/B testing or random controlled trial (RCT), is the core of the product development process. Instead of limiting to traditional A/B testing alone, using techniques such as Reinforced Learning (RL) techniques, multi-arm bandits, Markov Decision Process, exploration, and exploitation, enterprises are able to build superior products with low opportunity costs. In this session, the audience will be able to learn emerging techniques to building superior products…more details
A proven leader in Data Sciences & Applied AI. Experience in managing large scale projects and teams in data engineering, ML operations, and ML modeling. Helped in developing and implementing enterprise AI strategy; I have lead Cloud Migration and Integration efforts. Good understanding of State of the Art (SOTA) ML tools, framework, and algorithms. Experience in building distributed machine learning workflows using Apache Spark, DASK, Rapids and Kubernetes.
Vamsi enjoys reading the latest ML papers and staying up-to-date with relevant trends in the field.
Leading research in SOTA in areas such as Natural Language Inference, Text Entailment, Q&A, Text Classification, and Semantic Similarity using BERT, RoBERTa, Fairseq, Huggingface, LSTM, Transformers and others.
In the past established highly talented engineering teams at various enterprises. Vamsi has a successful track record of tactical execution in fast-paced software and technology development environments.
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
In this hands-on session, attendees will learn how to create a GCP project that uses Google’s public BigQuery data. In addition to sharpening their SQL skills in BigQuery, attendees will also learn how to use Google Colab notebooks to do additional analysis of data. Finally, attendees will learn how to create a map that displays data creatively…more details
Joy Payton is a cloud engineer, data scientist, and adjunct professor who specializes in helping biomedical professionals conduct reproducible computational research. In addition to moving medicine forward through principles of open science and reproducibility, Joy also enjoys teaching citizen scientists how to use public data repositories to understand their own communities better and advocate for change from a data-centric perspective. Her various roles allow Joy to lead efforts to teach people how to write their first line of code and help anyone who’s interested climb the data science learning curve. Currently employed by the Children’s Hospital of Philadelphia and Yeshiva University, Joy is always open to hearing about open-source, data-centric volunteer opportunities for herself and her students.
Workshop | Research Frontiers | Data Analytics | Beginner
We propose a method to assess the sensitivity of data analyses to the removal of a small fraction of the data set. Our metric, which we call the Approximate Maximum Influence Perturbation, approximately computes the fraction of observations with the greatest influence on a given result when dropped. The exact computation is prohibitive; for example, even with a mere 100 data points and 1-second data analysis, dropping every subset of 5 data points and re-running each analysis would take years...more details
Tamara Broderick is an Associate Professor in the Department of Electrical Engineering and Computer Science at MIT. She is a member of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), the MIT Statistics and Data Science Center, and the Institute for Data, Systems, and Society (IDSS). She completed her Ph.D. in Statistics at the University of California, Berkeley in 2014. Previously, she received an AB in Mathematics from Princeton University (2007), a Master of Advanced Study for completion of Part III of the Mathematical Tripos from the University of Cambridge (2008), an MPhil by research in Physics from the University of Cambridge (2009), and an MS in Computer Science from the University of California, Berkeley (2013). Her recent research has focused on developing and analyzing models for scalable Bayesian machine learning. She has been awarded an Early Career Grant (ECG) from the Office of Naval Research (2020), an AISTATS Notable Paper Award (2019), an NSF CAREER Award (2018), a Sloan Research Fellowship (2018), an Army Research Office Young Investigator Program (YIP) award (2017), Google Faculty Research Awards, an Amazon Research Award, the ISBA Lifetime Members Junior Researcher Award, the Savage Award (for an outstanding doctoral dissertation in Bayesian theory and methods), the Evelyn Fix Memorial Medal and Citation (for the Ph.D. student on the Berkeley campus showing the greatest promise in statistical research), the Berkeley Fellowship, an NSF Graduate Research Fellowship, a Marshall Scholarship, and the Phi Beta Kappa Prize (for the graduating Princeton senior with the highest academic average).
Workshop | Deep Learning | Research Frontiers | Beginner-Intermediate
In this talk, we’ll discuss how PyTorch Lightning is trying to mitigate this problem and remove roadblocks on the journey from Github to deployment, and help researchers and data scientists focus on the research, not engineering…more details
William Falcon is the creator of the popular open-source project PyTorch Lightning, and the recently announced Grid AI. William created Lightning while doing his PhD at NYU and as a PhD researcher at Facebook AI; Lightning allows users to scale models without the boilerplate and Grid enables large-scale training on the cloud. Previously he co-founded the now acquired NextGenVest and spent time at Goldman Sachs. His PhD (currently on leave to focus on Lightning), is funded by Google Deepmind and NSF Foundation. His research interest is in unsupervised learning and the intersection of AI and neuroscience. William is a native of Venezuela and holds a BA from Columbia University in Computer Science and Statistics, with a minor in Math.
Workshop | Deep Learning | DS for Good| Beginner-Intermediate
Welcome to the world of augmented reality and artificial intelligence! In this workshop, we demonstrate AR and AI via hands-on exercises where you will interact with your augmented world. We describe some of the novel applications of AR+AI, their limitations, and social impacts amplified by the COVID-19 pandemic. We leave attendees armed with code, inspiration and ethical considerations for future projects…more details
Louvere Walker-Hannon is a MathWorks Senior Application Engineer, who provides direction and recommendations on technical workflows for various applications. Specifically, she assists with the following topics image processing, computer vision, machine learning, deep learning, geospatial analysis, and data analytics when discussing technical workflows. She has a bachelor’s degree in Biomedical Engineering and a master’s degree in Geographic Information Technology with a specialization in Remote Sensing. Louvere has worked in three different engineering roles throughout her 20-year career while at MathWorks. She was also a Program Presenter; in this role she was the lead educator of STEM topics for Cahners ComputerPlace within the Boston Museum of Science. A prominent theme in her career is communication of technical concepts to various audiences and being involved with STEM education. Louvere is a member of the Society of Women Engineers (SWE), the National Society of Black Engineers (NSBE), the Society of Hispanic Professional Engineers (SHPE), and other organizations. She has a long history of serving as a STEM mentor. Louvere volunteers with Black Girls Code and the Society of Women Engineers. She is a Curriculum Lead for the Boston Chapter of Black Girls CODE, a Co-Lead of the SWE Latinos Affinity Group, and a Society Engagement Lead for the SWE African American Affinity Group. Louvere has presented and continues to present at several STEM related conferences on various topics. She presented her work on a Natural Language Processing application at the 100th American Meteorological Society in January 2020 and at the American Association of Geographers (AAG) virtual conference in April 2020. She presented content on Augmented Reality, Artificial Intelligence, and their challenges at the virtual Grace Hopper Celebration in October 2020. She served as one of the Virtual Chairs for the thirty-seventh International Conference on Machine Learning (ICML) in July 2020 and thirty-fourth conference on Neural Information Processing Systems (NeurIPS) which occurs in December 2020. Louvere is scheduled to serve as one of the MeetUp Chairs for the upcoming thirty-fifth conference on Neural Information Processing Systems (NeurIPS) in 2021. She is helping her company begin an initiative regarding STEM Outreach with a DEI focus.
Sarah Mohamed is a Software Engineer at MathWorks working on deep learning interoperability. She develops tools that allow AI models to be exchanged and deployed among frameworks such as MATLAB, Pytorch, TensorFlow, and ONNX. In the past, she has also worked as an Application Support Engineer, where she interacted directly with customers developing deep learning applications. She obtained her M.Sc. in Computational Science and Engineering from Harvard University, and holds dual bachelor’s degrees in Computer Science and Biology from the University of Virginia. She has experience organizing and delivering hands-on workshops at the Grace Hopper Celebration, the world’s largest conference for women in computing, as well as TechTogether Boston, the largest student-led hackathon for women and nonbinary individuals in Boston.
Shruti Karulkar is the Quality Engineering Manager MATLAB Hardware at MathWorks. Shruti’s team of highly creative engineers work to design and implement cutting-edge tools to qualify MATLAB and Simulink support for hardware ranging from simple – Arduino/Raspberry Pi/iOS and Android phones – to high-end RF Signal Generators. Shruti has a long history of serving as a STEM mentor and is a STEM Ambassador for MathWorks. Shruti was a speaker at GHC18, GHC19 and GHC20 delivering successful and popular hands-on AI workshops.
Workshop | NLP | Beginner-Intermediate
The Python ecosystem has many libraries for natural language processing (NLP), which can make it confusing to get started analyzing text as data. This workshop will introduce spaCy as a powerful, opinionated library for NLP that facilitates analysis of text data, along with textacy, a library that adds information retrieval and corpus analysis features...more details
Scott Bailey is the Digital Research and Scholarship Librarian at the NC State University Libraries, where he collaborates with faculty and other scholars in applying digital and computational tools and methods to open new possibilities in research and learning. He regularly teaches workshops using programming languages like Python and R to introduce data analysis and visualization, machine learning, and computational approaches to text data. He was previously the Head of Social Science Data and Software in the Center for Interdisciplinary Digital Research (CIDR) at Stanford Libraries, where he oversaw a group of Ph.D. students in delivering expert consultation on statistical computing, organized and taught in the CIDR workshop series, and collaborated with colleagues across Stanford University to provide better access to data and support for data-driven research.
Tutorial | Machine Learning | Intermediate
AI model inference on the phone is important to deliver a real-time experience that requires execution locally on the device. But there are many constraints to build a scalable solution addressing all the different hardware specs and platform requirements across Android and iOS. Devices have limited storage and memory, and the platform app stores impose restrictions on the size of the app package.
ONNX Runtime Mobile is a new feature to address the needs for developers to build solutions for Mobile devices. You can build a reduced size binary package to integrate with the phone application and inference your ONNX models locally on devices.
ONNX Quantization techniques can be used to reduce the model size by converting FP32 weights to INT8. Improved performance is achieved with INT8 execution on ARM processors on the mobile phone…more details
Emma Ning is a Senior Product Manager in the AI Framework team under Microsoft Cloud + AI Group, focusing on AI model operationalization and acceleration with ONNX/ONNX Runtime for open and interoperable AI. She has more than five years of product experience in search engines taking advantage of machine learning techniques and spent more than three years exploring AI adoption among various businesses. She is passionate about bringing AI solutions to solve business problems as well as enhancing product experience.
Guoyu Wang is a senior software engineer at Microsoft, where he works on Onnx Runtime to bring accelerated inferencing to mobile platforms. In the past, he has also worked on Microsoft Office and shipped multiple versions of Microsoft PowerPoint. Guoyu earned his master’s degree in Computer Science from University of Toronto.
Workshop | Data Analytics | Beginner-Intermediate
A/B testing (experimentation) moves beyond correlation to identify if a causal relationship exists between two (or possibly more) variables. Because this is the only approach we can currently take to establish causality, A/B testing is critical to understand for any data scientist. In this workshop, you will have familiarity with the types of questions A/B testing can answer, how to design your own experiment, and what to watch out for on a conceptual level…more details
Dr. Boardman has over a decade of experience as a scientist, professor, and consultant in various sectors and disciplines, including digital marketing, health tech, intelligence, entrepreneurship, economic development, and health/medical policy. Her knowledge and experience includes A/B testing, machine learning, agent-based modeling, statistics, and intelligence analysis. In her work, she places heavy emphasis on clearly and empathetically communicating insights derived from small, qualitative data up to the big data she currently works with in digital marketing. Invigorated by messy, unconventional data and problems, she has also designed and modified several techniques and approaches to work with this. She believes that science is an ongoing process and we should adapt our approach to the problems and data, not find problems and data that work well with what we know. Currently, Dr. Boardman is a Senior Data Scientist at TI Health and holds a B.B.A. in International Business from the University of Oklahoma, an M.P.P. in Public Policy from Pepperdine University, a PhD in Public Policy from George Mason University, and a Masters of Information and Data Science from Berkeley.
Workshop | NLP | Deep Learning| Intermediate-Advanced
You probably have heard buzz-words like BERT and even how they revolutionized the way we solve NLP problems. BERT is a deep learning model pretrained on large amounts of text, so they can readily provide powerful numerical representations of text for downstream outcome-driven NLP models. Models like BERT are so commoditized you might be using them in-house…more details
Thushan is a Senior Data Scientist at QBE Insurance, Australia, where he works on marrying artificial intelligence with business acumen to solve business problems. He has extensive knowledge of deep learning techniques, that has been poured into two books and a video course on deep learning (TensorFlow). In his free time, Thushan answers questions on StackOverflow creates YouTube content or presents at local meetups. He obtained his Ph.D. from the University of Sydney, specializing in deep learning and reinforcement learning. When not indulging in the world of data science, Thushan enjoys meditation, swimming, and hiking.
Tutorial | MLOps & Data Engineering | Intermediate
In this talk, attendees will get an introduction to Dask, a distributed computing framework in the PyData ecosystem.
The first half of the talk will describe the current state of the project and its ecosystem including distributed data collections, cloud deployment options, distributed machine learning projects, and workflow orchestration.
The second half of the talk will be a live demo showing the programming model for machine learning on Dask. Dask’s potential for speeding up machine learning workflows will be demonstrated with an intermediate-level tutorial on training XGBoost and LightGBM models with Dask…more details
James Lamb is a software engineer at Saturn Cloud, where he works on a managed data science platform built on Dask and Kubernetes. Before Saturn Cloud, James worked on the industrial internet of things (IIoT) problems as a data scientist at AWS and Chicago-based Uptake. He is a core maintainer on LightGBM, and has contributed on other open-source data science and data engineering projects such as XGBoost and prefect. James holds Masters degrees in Applied Economics (Marquette University) and Data Science (University of California, Berkeley).
Keynote
The tragedy of the commons, first proposed by William Lloyd in 1833, is an economic problem in which every individual has an incentive to consume a resource at the expense of every other individual with no way to exclude anyone from consuming. It results in overconsumption, underinvestment, and ultimately depletion of the resource. While the direct application of these principles to data seems like a bit of a reach, it becomes clear that data sharing risks much of the same problem – people wishing to protect their own data while having access to other people’s…more details
James Hendler is the Director of the Institute for Data Exploration and Applications and the Tetherless World Professor of Computer, Web, and Cognitive Sciences at RPI. He also is acting director of the RPI-IBM Artificial Intelligence Research Collaboration and serves on the Board of the UK’s charitable Web Science Trust. Hendler has authored over 400 books, technical papers, and articles in the areas of Semantic Web, artificial intelligence, agent-based computing, and high-performance processing. Hendler was the recipient of a 1995 Fulbright Foundation Fellowship, is a former member of the US Air Force Science Advisory Board, and is a Fellow of the AAAI, BCS, the IEEE, the AAAS, and the ACM. He is also the former Chief Scientist of the Information Systems Office at the US Defense Advanced Research Projects Agency (DARPA) and was awarded a US Air Force Exceptional Civilian Service Medal in 2002. He is also the first computer scientist to serve on the Board of Reviewing Editors for Science. In 2010, Hendler was named one of the 20 most innovative professors in America by Playboy magazine and was selected as an “Internet Web Expert” by the US government. In 2013, he was appointed as the Open Data Advisor to New York State and in 2015 appointed a member of the US Homeland Security Science and Technology Advisory Committee. In 2016, became a member of the National Academies Board on Research Data and Information and in 2018 became chair of the ACM’s US technology policy committee and was elected a Fellow of the National Academy of Public Administration.
Keynote
You may have heard, the world is going hybrid with IBM. Fueled by the power of data, smarter organizations are infusing the intelligence of AI and the agility of the hybrid cloud to better predict and shape future outcomes and automate decisions and processes. Join us today to hear the latest and greatest from IBM to empower people to do higher-value work, and hyper-personalized digital experiences…more details
Ritika Gunnar is the Vice President of Data and AI Expert Labs. In this role, she and her team of ‘ninjas’ work with clients on their transformation journey through a data & AI first methodology and help implement Data & AI solutions using deep knowledge-based skills to accelerate the adoption of data & AI enterprise capabilities. Prior to this role, Ritika was the Vice President of Offerings for IBM Watson. In this role, she has responsibility for all of Watson’s Data and AI portfolio where she is responsible for defining the portfolio strategy, execution of product offerings, and driving business results. Previous to this, as Vice President of IBM’s World-Wide Cloud and Cognitive GTM organization she drove IBM’s mission to apply deep cloud, data, and AI expertise to our clients’ most pressing needs Ritika has also been the Vice President of IBM’s Data and Analytics Business where she was responsible for setting the strategy and execution of IBM’s data platform, data science, and analytics practices. Prior to this, she led and managed IBM’s Master Data Management & Information Integration and Governance business and IBM’s Data Warehousing & Analytics business. Ritika joined IBM as a software engineer in 1999 and has held numerous development and support roles in her career. Ritika holds a Bachelor of Science in Computer Science and an Executive Masters in Business from the University of Texas at Austin. She currently resides in New York.
Keynote | Machine Learning
Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge, a Fellow at The Alan Turing Institute in London, and a Chancellor’s Professor at UCLA.
Mihaela was elected IEEE Fellow in 2009. She has received numerous awards, including the Oon Prize on Preventative Medicine from the University of Cambridge (2018), a National Science Foundation CAREER Award (2004), 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award.
Mihaela’s work has also led to 35 USA patents (many widely cited and adopted in standards) and 45+ contributions to international standards for which she received 3 International ISO (International Organization for Standardization) Awards.
In 2019, she was identified by National Endowment for Science, Technology, and the Arts as the most-cited female AI researcher in the UK. She was also elected as a 2019 “Star in Computer Networking and Communications” by N²Women. Her research expertise spans signal and image processing, communication networks, network science, multimedia, game theory, distributed systems, machine learning, and AI.
Mihaela’s research focus is on machine learning, AI, and operations research for healthcare and medicine.
In addition to leading the van der Schaar Lab, Mihaela is founder and director of the Cambridge Centre for AI in Medicine (CCAIM).
Keynote | MLOps & Data Engineering
To be competitive, organizations have to put more models into production more rapidly than ever before. At the same time, data scientists have to ensure trust, repeatability and transparency in those models. ModelOps, a superset of MLOps, gives data scientists the capabilities they need to adapt quickly while maintaining model integrity…more details
As Executive Vice President and Chief Technology Officer, Bryan Harris is responsible for setting the technology direction for SAS and working with the executive leadership team to translate the organization’s strategic objectives and priorities into products and solutions. Harris has more than 20 years of experience researching and developing analytic techniques, enterprise search technologies, distributed computing and cloud architectures, and user experiences for both the federal and commercial industries. For nearly 10 years, he has been a critical senior leader of SAS R&D. Most recently, as Senior Vice President and Head of Engineering, Harris was instrumental in releasing the latest advancement of SAS® software, SAS® Viya®
Chris Wright is Senior Vice President and Chief Technology Officer (CTO) at Red Hat. He leads the Office of the CTO, which is responsible for incubating emerging technologies and developing forward-looking perspectives on innovations like artificial intelligence, cloud and edge computing, distributed storage, software-defined networking and network functions virtualization, containers, automation and continuous delivery, and distributed ledgers. During his 25 years as a software engineer, Wright has worked in the telecommunications industry and beyond on high-availability and distributed systems. He also has experience in the Linux® industry on security, virtualization, and networking. Wright has been a Linux developer for more than 15 years, with most of this time spent working deep in the Linux kernel. He is passionate about open source software serving as the foundation for next-generation IT systems.
Ai x Keynote
Business leaders desire data-driven insights to help improve customer experience. Data engineers, data scientists, and software developers desire a self-service, cloud-like experience to access tools/frameworks, data, and compute resources anywhere to collaborate, build, and scale. This keynote will highlight AI/ML use cases, execution challenges, and tools to help accelerate AI/ML projects from pilot to production, and accelerate delivery of intelligent applications. Finally, the session will share real-world success stories across a number of open source projects led by Red Hat, including Open Data Hub…more details
Mike Piech is Vice President and General Manager of the Cloud Storage and Data Services organization, managing a team responsible for the product management, strategy, and marketing of Red Hat’s portfolio of data services offerings. These include cloud-native capabilities for storing, moving, transforming, and combining data for such applications as machine learning and edge computing. Prior to Red Hat, Piech held executive product roles at Magnet Systems (mobile app server) and Engine Yard (PaaS), after Oracle and BEA with product responsibility for WebLogic, Java™, and other enterprise platform technologies. He holds several patents.
Track Keynote
In this talk, we discuss the different demand forecasts we need to generate to optimize the interventions we make in the three-sided marketplace of on-demand food delivery. We go through different data preparation and modeling techniques for different prediction horizons and granularity. Further, we discuss some of the downstream secondary models which consume these forecasts to make optimal business decisions…more details
As Head of Data Science and Machine Learning at DoorDash, Alok Gupta directs teams building models to optimize critical metrics necessary to balance a three-sided marketplace. With previous roles as Director of Data Science for both Lyft and Airbnb, Alok has abundant experience in using data science to make predictions at scale. Prior to this industry experience, Alok was a Research Fellow in Mathematics at Oxford University, earning his PhD in derivative pricing, work that he applied on Wall Street as a high frequency trader to predict movements in foreign exchange rates at high frequency.
Talk
Managing your machine learning experiments can be a painful and tedious process. But the good news is that it doesn’t have to be. Managing experiments can be straightforward and simple. This introductory course will help you understand the key concepts of experiment management and how to implement them in your organization…more details
Ayodele Odubela is a Data Science Advocate for Comet ML. She combines her background in marketing and passion for data and analytics to educate Data Scientists on model reproducibility and experiment tracking. She earned her Master’s degree in Data Science from Regis University after working in various digital marketing roles at agencies and startups. She’s passionate about data justice, kayaking, and hockey.
Business Talk
Natural language processing has achieved remarkable progress over the last decade, and many companies now rely on NLP-based insights to drive their business intelligence. One of the more challenging goals is to elevate the capabilities of AI models from identifying companies and events in text-based news to building clusters that define a topic, to discerning trends around those topics from which financial professionals can derive actionable insights. Up until recently the last step of that process relied on manual intervention, but recent developments involving deep learning models now make the seamless automation of topic modeling and trend detection within reach…more details
Marko is the Head of Quantitative Research for the Americas at RavenPack with over 10 years of experience in the finance industry. He focuses on exploring novel approaches and techniques for combining fundamental drivers with big data quantitative frameworks to identify alpha opportunities from a wide universe of securities across multiple asset classes. Previously, as the head trader/investment analyst at an event-driven hedge fund in New York, he was responsible for macro research, idea generation, and risk management. Marko has experience in utilizing quantitative methods in portfolio construction, developing hedging strategies, and trading structured derivative instruments.
Demo Talk
Fraud is a multi-billion dollar problem facing everyone from banks to credit card issuers to payment processors to merchants. Detecting and preventing fraud has traditionally been a challenge for these types of businesses, which rely on a clunky combination of heuristic-based rule engines and home-grown analytics solutions. In this talk, Danny D. Leybzon will be presenting a new methodology for anomaly detection and analysis that can be applied to everything from fraud detection to factory accident prevention. This system uses a combination of the Imply analytics platform (built on top of the open-source Apache Druid) and the anomaly detection system Sherlock (built on top of the open-source Yahoo EGADS). It leverages both human and machine expertise, allowing both actors to play to their strengths while offsetting each other’s weaknesses.
Danny lives and breathes data science. He is constantly finding connections and similarities between the world of data science and the world around us. Danny loves what he does. The feeling of solving interesting problems is exhilarating to Danny, and he loves seeing the impact of machine learning and advanced analytics to improve society and lives.
Talk | Machine Learning
AI/ML governance is the overall process for how an organization controls access, implements policy, and tracks activity for models and their results. Effective governance is the bedrock for both industrializing ML and minimizing risk to an organization’s bottom line and to its brand. But what does that really mean ML professionals should do about it?
Join Diego Oppenheimer, CEO, and founder of Algorithmia, as he demystifies what ML governance is and more importantly what is required to manage it effectively.
Attendees will walk away with a comprehensive understanding of the component parts of ML operational governance and the data, metrics, and KPIs required to support it.
Diego Oppenheimer, founder and CEO of Algorithmia, is an entrepreneur and product developer with an extensive background in all things data. Prior to founding Algorithmia he designed, managed and shipped some of Microsoft’s most used data analysis products including Excel, Power Pivot, SQL Server and Power BI.
Diego holds a Bachelors degree in Information Systems and a Masters degree in Business Intelligence and Data Analytics from Carnegie Mellon University.
Talk
The influence of environmental exposures on human health and disease is gaining focus from healthcare professionals, scientists, and policymakers. By combining datasets generated by the CDC with those from academic and industry research studies, IndiOmics has created multi-omics methods for modeling how chemical exposures can impact cellular behavior and health…more details
As a North Carolina native, Dr. Strong moved back to the Wilmington area to study water contamination and health effects concerns in 2017. Prior to that, she attended NC State University for Biomedical Engineering, East Carolina University for a Biology MS, and The Medical University of South Carolina for a Ph.D. in Marine Biomedicine and Environmental Sciences. After starting a non-profit in 2012, she founded IndiOmics in 2019 and combined the two as a social enterprise with the common goals of assessing and addressing environmental exposures of concern.
Track Keynote | DS for Good | NLP | All Levels
This talk will discuss projects focused on advancing AI for the common good, including 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.
Track Keynote | MLOps
In this keynote, KPMG and Red Hat will provide an overview of how KPMG went about developing the Ignite AI Platform using open source technologies such as MLFlow, Jupyter notebooks, Red Hat OpenShift Kubernetes Platform, PostgreSQL, to run in public or private cloud. You will hear lessons learned across people, process, and technology transformation initiatives to operationalize the solution, and how it has helped enhance work for data scientists and DevOps teams…more details
Abhinav Joshi is a Senior Manager, Product Marketing in the Cloud Platforms Business Unit at Red Hat. He and his team are focused on cloud-native workloads on OpenShift, including AI/ML, data analytics, databases, etc. Abhinav has over 20 years of industry experience around Hybrid Cloud, AI/ML, Data Analytics, Digital Workspace, and Software Defined Data Center focused products and solutions. Throughout his career, Abhinav has held strategic roles in Product Management, Product Marketing, Sales, and Consulting Services at Red Hat, VMware, Cisco, NetApp, etc. He holds MS in Systems Engineering from the University of Maryland College Park, Strategic Management Graduate Certificate from Harvard University, and a B.Tech in Chemical Engineering from Nagpur University, India. is a Senior Manager, Product Marketing in the Cloud Platforms Business Unit at Red Hat. He and his team are focused on cloud-native workloads on OpenShift, including AI/ML, data analytics, databases, etc. Abhinav has over 20 years of industry experience around Hybrid Cloud, AI/ML, Data Analytics, Digital Workspace, and Software Defined Data Center focused products and solutions. Throughout his career, Abhinav has held strategic roles in Product Management, Product Marketing, Sales, and Consulting Services at Red Hat, VMware, Cisco, NetApp, etc. He holds MS in Systems Engineering from the University of Maryland College Park, Strategic Management Graduate Certificate from Harvard University, and a B.Tech in Chemical Engineering from Nagpur University, India.
Kevin works with clients to design, engineer, deliver and operate advance analytic/AI software solutions on hyper scale platforms that align to enterprise requirements. Kevin and his team support clients to drive digital transformations leveraging advance techniques to take advantage of cloud and containers services. Kevin is a leader in AI/ML and engineering cloud at KPMG.
Track Keynote | MLOps & Data Engineering | Intermediate