Hands-on Training Sessions
– Taught by World-Class Data Scientists –
Learn the latest data science concepts, tools, and techniques from the best. Forge a connection with these rock stars from industry and academia, who are passionate about molding the next generation of data scientists.
WHY ODSC?
Form a working relationship with some of the world’s top data scientists for follow up questions and advice.
Access to 50+ talks and workshops.
Recordings of workshop and talks sessions for later review.
Equivalent training at other conferences costs much more.
Professionally prepared learning materials, custom tailored to each course.
Opportunities to connect with other ambitious like-minded data scientists.
Meet some of our World Class Instructors
50+ More Training & Workshop Sessions Coming Soon. Check full speaker lineup here.
Training | Data Visualization | Machine Learning | Beginner-Intermediate
Have you ever wondered about how those data scientists at Facebook and LinkedIn make friend recommendations? Or how epidemiologists track down patient zero in an outbreak? If so, then this tutorial is for you. In this 3.5 hr tutorial, we will use a variety of datasets to help you understand the fundamentals of network thinking, with a particular focus on constructing, summarizing, and visualizing complex networks.
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).
Full-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.
Full-Day Training | Kick-starter | Open-source | Intermediate
Whether in R, MATLAB, Stata, or Python, modern data analysis, for many researchers, requires some kind of programming. The preponderance of tools and specialized languages for data analysis suggests that general purpose programming languages like C and Java do not readily address the needs of data scientists; something more is needed.
In this training, you will learn how to accelerate your data analyses using the Python language and Pandas, a library specifically designed for interactive data analysis. Pandas is a massive library, so we will focus on its core functionality, specifically, loading, filtering, grouping, and transforming data. Having completed this workshop, you will understand the fundamentals 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.
Training | Open-source | Intermediate
This tutorial will introduce you to the wonderful world of Bayesian data science through the lens of probabilistic programming in Python. In the first half of the tutorial, we will introduce the key concepts of probability distributions via hacker statistics, hands-on simulation, and telling stories of the data-generation processes. We will also cover the basics of joint and conditional probability, Bayes’ rule, and Bayesian inference, all through hands-on coding and real-world examples. In the second half of the tutorial, we will use a series of models to build your familiarity with PyMC3, showcasing how to perform the foundational inference tasks of parameter estimation, group comparison (for example, A/B tests and hypothesis testing), and arbitrary curve regression. By the end of this tutorial, you will be equipped with a solid grounding in Bayesian inference, able to write arbitrary models, and have experienced basic model checking workflow…more details
Hugo Bowne-Anderson is a Data Scientist at DataCamp and has had extensive experience teaching basic to advanced data science topics at institutions such as Yale University and Cold Spring Harbor Laboratory, conferences such as SciPy, PyCon, and with organizations such as Data Carpentry. He has developed over 25 courses on the DataCamp platform, impacting over 300,000 learners worldwide through his own courses. He previously also hosted DataFramed, the DataCamp podcast, loves teaching Bayesian data analysis and aspires to reduce as much “computational anxiety’ in the world as he can through pedagogy.
Training | Machine Learning | R-programming | All Levels
Temporal data requires special care to model as it violates several principles of standard machine learning models. R has long had top-of-the-line forecasting tools, though recently new ones have been developed which greatly ease working with time series data. We use the tsibble package for manipulating time series data, feasts for visualization , and fable for building forecasting models such as ETS and ARIMA…more details
Daniel is a PhD candidate in Genetics, Bioinformatics, and Computational Biology at Virginia Tech, currently working in the Social and Decision Analytics Laboratory in the Biocomplexity Institute at Virginia Tech. His current interests are in understanding how attitudes change and spread within social networks as well as performing analytics for precision medicine.
Daniel received his MPH in the Department of Epidemiology at Columbia University and hold a BA in Psychology – Behavioral Neuroscience with minors in Biology and Computer Science from the Macaulay Honors College at CUNY Hunter College.
Daniel enjoys teaching and volunteers some of his time to Software Carpentry by teaching, serving on the Mentoring subcommittee, and chair the Assessment subcommittee.
Training | MLOps & Data Engineering | ML for Programmers | Beginner-Intermediate
By attending this workshop, you will learn how you can become a contributor to Apache Airflow project. You will learn how to setup development environment, how to pick your first issue, how to communicate effectively within the community and how to make your first PR – experienced committers of Apache Airflow project will give you step-by-step instructions and will guide you in the process. When you finish the workshop you will be equipped with everything that is needed to make further contributions to the Apache Airflow project.
You will have a chance to pick a simple issue and get it through the whole process – starting from implementation, testing, going through review process and hopefully merging the changes. We will pick some simple changes for you so that you have a chance to implement it during the workshops...more details
Tomek is a Software engineer at Polidea, Apache Airflow committer and book lover. He fancies functional programming because he is graduated mathematician. Every day he tries to make the world a better place.
As the CTO Jared built software house 10-fold: from 6 to 60 people. After a few years of being the CTO, he decided to go back to a full-time engineering role and he works as a Principal Software Engineer in his company (and is super happy about it). Jarek has been working as an engineer in many industries – Telecoms, Mobile app development, Google, Robotics and Artificial Intelligence, Cloud and Open Source data processing. Jarek is currently PMC in the Apache Airflow project.
Training | Machine Learning | R-programming | All Levels
Linear regression is the foundation of supervised learning, though it has its limits. During this workshop we extend regression using penalization for automated variable selection and increased flexibility. We then introduce trees, and in particular boosted trees, via xgboost to get incredibly powerful predictions. We will go over some of the theory and also practical considerations such as hyperparameters…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.
Training | Data Visualization | Kick-starter | All Levels
In this complete hands-on training session, you will learn to turn your data into interactive dashboards, how to create stories with data and share these dashboards with your audience. We will begin with a quick refresher of basics about design and information literacy and discussions about practices for creating charts and storytelling utilizing best visual practices. Whether your goal is to explain an insight or let your audience explore data insights, using Tableau’s simple drag-and-drop user interface makes the task easy and enjoyable.
In this session, we’ll cover intermediate and advanced tableau functionality:
1. Database connectivity
2. Blending and joins using multiple data sources, web connectors and plugins,
3. Perform data analyses and create graphs from a real-world dataset, using Tableau Public (free to use)
4. Deeper Analysis – Trends, Clustering, Distributions, and Forecasting
5. Table Calculations, Sets, Filters, Level of Detail expressions, Parameters
6. Spatial analytics – Using Maps and Geocoding
7. Right and Wrong way to build Dashboards and Best Practices…more details
Nirav Shah is the Founder of OnPoint Insights, data analytics, software services, and staff augmentation consultancy based in Boston. He has 15 years of industry experience – mainly in consulting on data analytics, big data modeling, process analytics, and real-time data solutions, and training customers in data analytics, dashboards, and data visualization.
He consults and teaches in applying data analytics for manufacturing, operations, supply chain, process control strategies with clients to improve the manufacturing process and operational efficiency. He has implemented real-time process monitoring data analytics and fault detection systems for leading biopharma customers and clients from other industries such as chemical, pulp, and paper, food and beverages. He helps customers in providing better process insights using data-driven solutions.
He is also an Adjunct Professor at the University of Massachusetts in Boston where he teaches Engineering Process Analytics, a graduate-level class in the Engineering Department, teaches Business Analytics and Dashboard Visualization at a technical college and conducts BootCamps and Workshops at General Assembly Boston. He has taught courses and conducted workshops to industry clients on Multivariate Data Analysis for ten years. He has spoken at various conferences ( ODSC East Boston, ODSC India, Global AI).
He completed his dual Masters in Chemical and Computer Engineering from the University of Massachusetts and an MBA in Entrepreneurship from Babson College.
Full-Day Training | MLOps & Data Engineering | Machine Learning | Intermediate-Advanced
In this tutorial we will consider the symbiosis of graphs and Machine Learning including the following topics:
*Graph-based Feature Engineering and Graph Algorithms*
We will start by looking at popular graph algorithm and their value for feature engineering for Machine Learning models.
*Graph Embeddings and Graph Neural Networks*
Utilizing graphs as input to Neural Networks is a very powerful combination. In this part, we will consider different Embedding strategies and the field of Graph Neural Networks.
*Graph-based Machine Learning Metadata*
We know about the value of high quality and quantity for building high-quality machine learning models, but for operating a production-grade machine learning pipeline Metadata is equally important. In this part, we will look at leveraging graphs to capture metadata and provenance information of our machine learning ecosystem...more details
Jörg Schad is Head of Development and Machine Learning at ArangoDB. In a previous life, he has worked on or built machine learning pipelines in healthcare, distributed systems at Mesosphere, and in-memory databases. He received his Ph.D. for research around distributed databases and data analytics. He is a frequent speaker at meetups, international conferences, and lecture halls.
Training | Open-source | Kick-starter | Beginner
Recommendation systems are fundamental to essentially every business on the planet. From providing the shows and movies you should watch, the books and articles you should read, the products you should purchase, or the people you should date making good recommendations can make or break your business. This workshop will give a general introduction to recommendation systems, as well as introduce practical tools for you to apply recommendation systems to your own use cases. Though the primary focus of this workshop will be to understand the different methods you might use to make recommendations, we will also touch briefly on common difficulties with building effective recommendation systems, how to deploy your recommendations, and how to evaluate whether your recommendations are effective…more details
Josh has been sharing his passion for data science with others more than a decade. He found his passion for teaching while pursuing a PhD in Statistics at Iowa State University. He then went on to teach graduate and undergraduate courses as a part of the Business Analytics program at the University of Colorado. Before jumping full time into industry, he worked as the Curriculum Lead for the School of Data Science at Udacity, where he built data science courses that train data scientists around the world.
In industry, Josh has used his skills to implement data science and machine learning solutions across industries from tax and auditing to healthcare. He currently works as a Data Scientist at NerdWallet, where he partners with product and marketing to help consumers make all the right money moves.
Bootcamp | Kick-starter | All Levels
The emergence of data science as a discipline has impacted businesses in a range of different ways. One primary impact has been to elevate the use of data in decision-making by using statistical methods to assess the ever-growing datasets companies are collecting. This workshop will review and introduce statistical techniques and touch on more advanced methods for dealing with noisy data and applying real-world constraints to analyses. This workshop assumes a working knowledge of standard statistical methods and will aim to connect theory to practice using real-world examples.
Lesson 1: Descriptive statistics and exploring data statistically
– (Re)familiarize yourself with basic descriptive statistics
– Use simple data exploration techniques to identify problems and limitations of a new dataset
Lesson 2: Statistical analyses...more details
Andrew is a Ph.D. Astrophysicist who made the switch from academia to data science (via the Insight Data Science program) in 2014. He was the first data scientist hired at Greenhouse Software where he has worked on many internal data science projects and a few customer-facing data-powered product features. Andrew lives in New Jersey with his wife and son.
Training | MLOps & Data Engineering | Beginner-Intermediate
In this workshop, we show how to maintain data science productivity as well as collaborate effectively and deliver value continuously and seamlessly. We demonstrate and guide the participants through CI/CD practices for machine learning and a new pattern of working that avoids most of the pitfalls of the typical approach.
Participants will learn how to utilize new patterns of repeatable continuous model development to collaborate effectively and deliver value continuously and seamlessly in industrial data science projects using Continuous Integration (CI) and Continuous Delivery (CD) practices...more details
Christoph Windheuser is the Global Head of Artificial Intelligence at ThoughtWorks Inc. Before joining ThoughtWorks, he gained more than 20 years of experience in the industry in several positions at SAP and Capgemini. Prior to that, he completed his Ph.D. in Neural Networks with a focus on Speech Recognition at the University of Bonn, Germany, Carnegie Mellon University in Pittsburgh, USA, Waseda University in Tokyo, Japan, and France Telekom (E.N.S.T.) in Paris, France.
David Johnston is a Principal Data Scientist and founding data scientist of the ThoughtWorks Data Science & Engineering practice. David has over 25 years of experience working with data, data processing pipelines, algorithms, optimization and statistical and machine learning models. David has a Ph.D. in physics and worked previously as a researcher at top universities, NASA and US government labs in the field of cosmology. Since leaving academia he has specialized in helping clients apply these techniques in their business environments with a focus on end-to-end delivery of valuable data-driven products and creating working, maintainable production systems. David is a frequent writer and speaker on data science, artificial intelligence and the importance of applying quality software development best practices toward data science-driven applications.
Eric Nagler serves ThoughtWorks, Inc. as a Lead Data Engineer with eight years of developing innovative batch and real-time data solutions for multiple different clients in multiple different domains. Eric holds a Masters Degree in Computer Science with a focus in Parallel and Distributed Algorithms. Eric’s data interests include Big Data Application Architecture and Design, Parallel Computation, Big Data ETL, NoSQL Data Storage, Natural Language Processing, Geospatial Analytics, Web Services, and Data Visualization.
Traning | Data Vizualization | ML for Programmers | Beginner-Intermediate
This workshop will take one through the steps associated with an end-to-end machine learning campaign: data retrieval; data curation; model construction, evaluation, selection and interpretation; and reporting. Particular attention will be paid to reporting, i.e., building a narrative. Examples will be presented demonstrating how one might generate multiple output formats (e.g., HTML pages, presentation slides, PDF documents) starting with the same code base.
The workshop will have three main foci:
1. infrastructure: instantiating the computational environment; loading packages; loading data
2. computation: data curation, transformation, and analysis; model construction, evaluation, and interpretation
3. communication: creating tables, charts, and graphs; weaving all components into data narrative…more details
Paul Kowalczyk is a Senior Data Scientist at Solvay. There, Paul uses a variety of toolchains and machine learning workflows to visualize, analyze, mine, and report data; to generate actionable insights from data. Paul is particularly interested in democratizing data science, working to put data products into the hands of his colleagues. His experience includes using computational chemistry, cheminformatics, and data science in the biopharmaceutical and agrochemical industries. Paul received his Ph.D. from Rensselaer Polytechnic Institute and was a Postdoctoral Research Fellow with IBM’s Data Systems Division.
Training
Matt currently leads instruction for GA’s Data Science Immersive in Washington, D.C. and most enjoys bridging the gap between theoretical statistics and real-world insights. Matt is a recovering politico, having worked as a data scientist for a political consulting firm through the 2016 election. Prior to his work in politics, he earned his Master’s degree in statistics from The Ohio State University. Matt is passionate about making data science more accessible and putting the revolutionary power of machine learning into the hands of as many people as possible. When he isn’t teaching, he’s thinking about how to be a better teacher, falling asleep to Netflix, and/or cuddling with his pug.
Training
Andreas Mueller is an Associate Research Scientist at the Data Science Institute at Columbia University and author of the O’Reilly book “Introduction to machine learning with Python”. He is one of the core developers of the scikit-learn machine learning library and has co-maintained it for several years.
His mission is to create open tools to lower the barrier of entry for machine learning applications, promote reproducible science and democratize access to high-quality machine learning algorithms.
Training
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.
Training | Machine Learning | Intermediate
We will present at least 10 examples and suggested solutions of cold-start problems (i.e., that move from a bad initial random guess to a good, perhaps optimal, solution), covering a variety of different algorithms and applications, focused primarily on unsupervised learning, but with some supervised learning examples also. We will also introduce related concepts and their importance, including the objective function, genetic algorithms, backpropagation, gradient descent, and meta-learning. Those concepts represent the true keys that unlock performance in a cold-start challenge. Those are the magic ingredients in most of the examples that we will present. At the end of the workshop, you should be empowered, enabled, and emboldened to tackle similar machine learning challenges problems in other domains. After all, data are data, math is math, and good experience is transferable!..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.
Training | Kickstarter | Machine Learning | Beginner
Machine learning has become an indispensable tool across many areas of research and commercial applications. From text-to-speech for your phone to detect the Higgs boson, machine learning excels at extracting knowledge from large amounts of data. This talk will give a general introduction to machine learning, as well as introduce practical tools for you to apply machine learning in your research. We will focus on one particularly important subfield of machine learning, supervised learning. The goal of supervised learning is to “learn” a function that maps inputs x to an output y, by using a collection of training data consisting of input-output pairs. We will walk through formulating a problem as a supervised machine learning problem, creating the necessary training data and applying and evaluating a machine learning algorithm. This workshop should give you all the necessary background to start using machine learning yourself…more details
Andreas Mueller is an Associate Research Scientist at the Data Science Institute at Columbia University and author of the O’Reilly book “Introduction to machine learning with Python”. He is one of the core developers of the scikit-learn machine learning library and has co-maintained it for several years.
His mission is to create open tools to lower the barrier of entry for machine learning applications, promote reproducible science and democratize access to high-quality machine learning algorithms.
Training | Open-source | Kick-starter | Beginner
The goal of this tutorial is to guide new learners into R. We’ll introduce how to program in R by cleaning and processing data as the use case. This will help transition new learners who work with data in spreadsheets but want to utilize the power of R in their work.
The only pre-requsites needed for the tutorial is having R installed with the `tidyverse`, `data.table` packages installed. The basics of the R language will be taught from the perspective of data processing. The course will be mainly focused around the “tidyverse”, however, where appropriate, we will draw similarities to base R and data.table for completeness...more details
Daniel is a PhD candidate in Genetics, Bioinformatics, and Computational Biology at Virginia Tech, currently working in the Social and Decision Analytics Laboratory in the Biocomplexity Institute at Virginia Tech. His current interests are in understanding how attitudes change and spread within social networks as well as performing analytics for precision medicine.
Daniel received his MPH in the Department of Epidemiology at Columbia University and hold a BA in Psychology – Behavioral Neuroscience with minors in Biology and Computer Science from the Macaulay Honors College at CUNY Hunter College.
Daniel enjoys teaching and volunteers some of his time to Software Carpentry by teaching, serving on the Mentoring subcommittee, and chair the Assessment subcommittee.
Workshop
Workshop
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
Josh Benamram is Co-Founder of Databand, an APM and observability solution for data engineering teams. Josh comes from a background in Product Management and VC in the data infrastructure space. Prior to Databand he was a Product Manager at Sisense, a business analytics platform provider, where he led Sisense’s database, ETL, and predictive analytics product components. Before Sisense, Josh was a VC investor at Bessemer Venture Partners, focusing on machine learning and data infrastructure investments.
Evgeny is Cofounder of Databand, an APM and observability solution for data engineering teams. Evgeny is a data architect and engineer by background. Prior to Databand, Evgeny was first employee, data architect, and team lead at Crosswise, a big data startup acquired by Oracle Data Cloud. Before Crosswise and ODC, Evgeny was a senior developer, software engineering team lead, and researcher at various startups.
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.
The tutorial will use python, jupyter notebooks and the EchoTorch python module (https://github.com/nschaetti/EchoTorch).
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.
Tutorial | Machine Learning | Intermediate
Most implementations of supervised machine learning algorithms are designed to work with complete datasets but datasets are rarely complete. 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. I will spend most of the time describing three advanced methods for handling missing data: multiple imputation, the reduced-feature (aka pattern submodel) approach, and XGBoost, which is one of the few machine learning algorithms that works with incomplete datasets. We will discuss the advantages and limitations of each of these methods. By the end of the workshop, you will have a deeper understanding of the intricate nature of missing data and you will have multiple state of the art techniques in your arsenal to deal with them. I will use python to implement and visualize the techniques, relying on packages like pandas, scikit-learn, xgboost, and matplotlib or plotly for visualizations…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 | MLOps & Data Engineering | Intermediate
Kedro is a development workflow tool open sourced by QuantumBlack, a McKinsey company. Many data science teams have started using the library for their pipelines but are unsure how to integrate with other model tracking tools, such as MLflow. In this tutorial, we will give an overview of Kedro and MLflow and demo how to leverage the best of both. The goal of this session is to demonstrate how Kedro and MLflow fit together in a scalable AI architecture. To start, we will give an overview of Kedro and an overview of MLflow: – What are they used for? – What functionality do they provide? – How do they compare as tools? Next, we will walk through a demo of a Kedro project that has MLflow integrated into it. Finally, we will go over deployment options…more details
Tom is a Junior Principal Data Engineer at QuantumBlack, a McKinsey Company. Prior to consulting, Tom was CTO and co-founder of Commandiv, a wealth management startup.
Workshop | MLOps & Data Engineering | All Levels
Many enterprises found it challenging to scale AI solutions beyond pilots. The root cause of the challenges stem from the paradigm shift in how AI systems are built and work from the traditional systems. There are big gaps between AI algorithm frameworks on the market and enterprise scale AI systems; and between skill requirements and practice in developing AI systems and enterprise solutions. Talents that can bridge the gap are scarce. Wenju is a project at Futurewei that tackles such challenges. It is a platform that helps enterprises of any size to quickly and cost effectively build end-to-end AI solutions in hybrid and multi-cloud environments. The platform adds new architectural layers on top of existing technology stacks to address key requirements and concerns of enterprise AI systems, and provides components and new building blocks that empowers the development and operations team. It helps to reduce skills required to build AI solutions, while improving productivity by enabling end-to-end automation...more details
Changfeng Charles Wang is a technology consultant at Futurewei. He is also a serial entrepreneur and expert in AI & big data with over 20+ experience. His work spans across theoretical foundations, technology platforms, and real-world vertical applications surrounding machine learning and data. He is the founder of Boston Data Science, LLC, an AI and big data technology and strategy firm. Previously, he was the founder of Adelphic, an industry-leading mobile and cross-screen programmatic platform that uses AI to completely automate real-time advertising transactions, serving top advertising agencies and big brands across the globe. He began his career at Thinking Machines (Acquired by Oracle), where he built the world’s first commercial parallel Neural Networks and core Machine Learning platform. Other products that he built are now part of Apple (iAd), IBM (InfoSphere), Facebook (Atlas), and Nokia.
Tutorial | Open-source | All Levels
Responsible AI is an umbrella term for many themes associated with the intersection of ethics and AI. One reasonable enumeration is Microsoft’s 6 Principles for AI development: Four core principles of fairness, reliability/safety, privacy/security, and inclusiveness, underpinned by two foundational principles of transparency and accountability. For this presentation, we focus on Transparency (Interpretability), Fairness and Inclusiveness, and Privacy as major principles of responsible AI and cover best practices and state-of-the-art open source toolkits and offerings that target researchers, data scientists, machine learning developers, and business stakeholders to be able to build trustable, more transparent AI systems.
Attendees will leave the session with the basic understanding of responsible AI principles, best practices and open source tools around responsible development and deployment of AI systems. They will be able to incorporate the introduced tools and products in their machine learning life cycle, running them on their previously-trained models to understand the factors that went into their model predictions, and verify their model fairness across protected attributes and mitigate the existing bias...more details
Mehrnoosh Sameki is a senior technical program manager at Microsoft, responsible for leading the product efforts on machine learning interpretability and fairness within the Azure Machine Learning platform. Previously, she was a data scientist at Rue Gilt Groupe, incorporating data science and machine learning in the retail space to drive revenue and enhance customers’ personalized shopping experiences. She earned her PhD degree in computer science at Boston University.
Minsoo is a Program Manager in the Responsible AI team at Microsoft focusing on building out offerings for the OSS Interpretability Toolkit and its integration into Azure Machine Learning Platform. She recently graduated from Microsoft’s pilot AI rotation program as one of three first PMs in the first cohort working on a variety of ML/AI application projects within Microsoft to accelerate its adoption of the AI-first initiative. She has Bachelor’s degrees in Applied Math and Painting from Brown University and Rhode Island School of Design (RISD). Coming from an interdisciplinary background with experience in building models and applications, analyzing data, and designing UX, she is looking to work in the intersection of AI/ML, design, and social sciences to empower data practitioners to work ethically and responsibly end-to-end.
Ehi Nosakhare is a Data and Applied Scientist in the AI development and acceleration program at Microsoft. She designs, develops and leads the implementation of machine learning (ML) solutions in application projects across Microsoft’s products and services. She is currently focused on developing a toolkit that enables text interpretability and machine learning transparency more broadly. Prior to Microsoft, she completed a Ph.D. in Electrical Engineering and Computer Science (EECS) from the Massachusetts Institute of Technology (MIT). She is very passionate about using ML to solve real-world problems and studying the ethical implications of ML/AI. In her spare time, she enjoys reading and re-learning to play the cello.
Workshop | Deep Learning | Beginner-Intermediate
PyTorch has become the dominant tool used in machine learning research. As for a more concrete plan, the session will be centered around extensibility, performance and model packaging, and will be presented as a case study of potential modifications that could be applied to the OpenNMT-py repository. Firstly, because we will care about investigating performance, we’ll kick off the session with a short description of the library’s execution model and best practices for benchmarking PyTorch code. Then, we’ll move on to custom extensions — a feature that allows loading plugins at run-time to, for example, expose third-party libraries or hand-written C or CUDA code. Finally, we will talk a fair amount about the JIT compiler which aims to both optimize the run-time performance of your models and enable easy export and later deployment on mobile devices….more details
Adam is an author and maintainer of PyTorch. He has worked with large organizations like Facebook AI Research, NVIDIA and Google, despite the fact that he has graduated from the Master’s program in Computer Science at the University of Warsaw only last year. Currently, he is also finishing his second major in Mathematics. His general interests include graph theory, programming languages, numerical computing and machine learning.
Tutorial | Deep Learning | Computer Vision | Beginner-Intermediate
There has been huge advancements in the field of Artificial Intelligence in the last decade. While this has been fueled by better compute, more data and cheaper storage, the real success which has revolutionized AI applications is a combination of deep learning and transfer learning. This session will give a gentle introduction to deep learning, deep transfer learning, cover its scope, advantages, applications and limitations. We will then do a deep-dive into three industry focused use-cases pertaining to Semiconductors and Manufacturing where we have leveraged a combination of deep transfer learning and traditional computer vision to solve these challenging, yet interesting problems at nanoscale...more details
Dipanjan (DJ) Sarkar is a Data Science Lead at Applied Materials, leading advanced analytics efforts around computer vision, natural language processing and deep learning. He is also a Google Developer Expert in Machine Learning. He has consulted and worked with several startups as well as Fortune 500 companies like Intel and Open Source organizations like Red Hat. He primarily works on leveraging data science, machine learning and deep learning to build large- scale intelligent systems. He holds a master of technology degree with specializations in Data Science and Software Engineering.
Dipanjan has been an analytics practitioner for several years now, specializing in machine learning, natural language processing, computer vision and deep learning. Having a passion for data science and education, he also acts as an AI Consultant and Mentor at various organizations like Springboard, where he helps people build their skills on areas like Data Science and Machine Learning. Dipanjan is also a published author, having authored several books on R, Python, Machine Learning, Social Media Analytics, Natural Language Processing, and Deep Learning. In his spare time he loves reading, gaming, watching popular sitcoms and football and writing interesting articles on https://medium.com/@dipanzan.sarkar and https://www.linkedin.com/in/dipanzan. He is also a strong supporter of open-source and publishes his code and analyses from his books and articles on GitHub at https://github.com/dipanjanS.
Sachin is a Deputy Director at Applied Materials leading efforts in Data Science and Advanced Analytics with a team of data scientists, working to provide innovative solutions with a focus on AI enablement in various products and systems for the semiconductor equipment manufacturing industry. He has a BS in Information Technology and over 15 years of diverse experience in the Industry around Software, Data Science and Artificial Intelligence.
Workshop | NLP | MLOps & Data Engineering | Intermediate – Advanced
In this hands-on workshop we will use AdaptNLP to:
– Provide an overview of recent advances in NLP
– Demonstrate our approach to initially setup and build these advanced NLP capabilities
– Train a simple Question Answering (QA) model
– Combine QA and NER to develop a custom application to automatically build visual timelines on documents by asking predefined questions on dates, places and people found in the document
– Demonstrate how to deploy the application as a scalable service in the cloud...more details
Brian Sacash is a Machine Learning Engineer in Novetta’s Machine Learning Center of Excellence. He helps various organizations discover the best ways to extract value from data. His interests are in the areas of Natural Language Processing, Machine Learning, Big Data, and Statistical Methods. Brian holds a Master of Science in Quantitative Analysis from the University of Cincinnati and a Bachelor of Science in Physics from Ohio Northern University.
Andrew Chang is an Applied Machine Learning Researcher in Novetta’s Machine Learning (ML) Center of Excellence. Andrew is a graduate from Carnegie Mellon University who has a focus on researching state of the art machine learning models and rapid prototyping ML technologies and solutions across the scope of customer problems. He has an interest in open source projects and research in natural language processing, geometric deep learning, reinforcement learning, and computer vision. Andrew is the author and creator of NovettaNLP.
Workshop | NLP | Beginner-Intermediate
In this session, I’ll start by introducing the recent breakthroughs in NLP that resulted from the combination of Transfer Learning and Transformer architectures. Then, we’ll learn to use the open-source tools released by HuggingFace like the Transformers and Tokenizers libraries and the distilled models.
Learning outcomes: understanding Transfer Learning in NLP, how the Transformers and Tokenizers libraries are organized and how to use them for downstream tasks like text classification, NER and text generation...more details
Thomas leads the Science Team at Huggingface Inc., a Brooklyn-based startup working on Natural Language Generation and Natural Language Understanding.
After graduating from Ecole Polytechnique (Paris, France), he worked on laser-plasma interactions at the BELLA Center of the Lawrence Berkeley National Laboratory (Berkeley, CA). Got accepted for a PhD at MIT (Cambridge, MA) but ended up doing his PhD in Statistical/Quantum physics at Sorbonne University and ESPCI (Paris, France), working on superconducting materials for the French DARPA (DGA) and Thales.
Thomas is interested in Natural Language Processing, Deep Learning, and Computational Linguistics. Much of his research is about Natural Language Generation (mostly) and Natural Language Understanding (as a tool for better generation).
Workshop | 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 PhD 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.
Workshop
Veysel Kocaman is a Senior Data Scientist and ML Engineer at John Snow Labs and has a decade long industry experience. He is also pursuing his PhD in CS as well as giving lectures at Leiden University (NL) and holds an MS degree in Operations Research from Penn State University. He is affiliated with Google as a Developer Expert in Machine Learning.
Workshop
Sanjay Kottaram is a detail-oriented technical leader well-versed in design, development, and operations of distributed, highly concurrent, data ingestion and processing systems. Extensive experience in first-of-a-kind multi-year projects from inception to realization. Focused on cognitive applications and devices, machine learning (ML), artificial intelligence (AI), Blockchain, edge computing. Real-world business common sense, proficient in collaborating with solution engineering and reference architecture teams to ensure alignment with enterprise architecture standards and governance processes. Partner with product, architecture, and operations teams to design and implement engineering solutions which support roadmap. Skilled in adapting technologies to specific industries, domains, and applications in scalable manner.
Workshop
Vishal Hawa is a Principal Scientist at The Vanguard Group, where he works closely with marketing managers to design attribution, propensity, and attrition modeling. Vish has over 15 years of experience in the retail and financial services industries. He has training in executive management from the Wharton School and holds postgraduate degrees in information sciences, statistics, and computer engineering from the Indian Statistical Institute.
Tutorial | Machine Learning | Research Frontiers | Intermediate
This tutorial will cover both introductory and advanced topics. I will first introduce the uplift concept, contrast with the traditional response modeling method, and review various predictive analytics approaches to Uplift Modeling. Our discussion extends from experimental data to observational data, by integrating Uplift Modeling with Causal Inference. I will also discuss the multiple treatment situation where the optimal treatment for each person needs to be determined. Prescriptive analytics from the optimization field will be employed to handle the uncertainty of lift estimates. I will illustrate the application and methodologies with examples from multiple industries…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.
Tutorial | Machine Learning | Beginner-Intermediate
We introduce algorithms that use predictions from machine learning applied to the input to circumvent worst-case analysis. We aim for algorithms that have near optimal performance when these predictions are good, but recover the prediction-less worst case behavior when the predictions have large errors. We look at examples of recent results in job scheduling, caching, and network measurement to show how predictions can be used effectively while still allowing for theoretical guarantees....more details
Michael Mitzenmacher is a Professor of Computer Science in the School of Engineering and Applied Sciences at Harvard University. Michael
has authored or co-authored over 200 conference and journal publications on a variety of topics, including algorithms for the Internet, efficient hash-based data structures, erasure and error-correcting codes, power laws, and compression. He is interested in both algorithms for AI applications, and how predictors from AI systems can yield better algorithms with rigorous performance bounds. He is an ACM Fellow and an IEEE Fellow. He has a widely used textbook on randomized algorithms and probabilistic techniques in computer science published by Cambridge University Press.
Michael Mitzenmacher graduated summa cum laude with a B.A. in mathematics and computer science from Harvard in 1991. After studying
mathematics for a year in Cambridge, England, on the Churchill Scholarship, he obtained his Ph. D. in computer science at U.C. Berkeley in 1996. He then worked at Digital Systems Research Center until joining the Harvard faculty in 1999. He served as the chair for computer science from 2010 to 2013 and co-chair in the 2018-2019 academic year.
Tutorial | Research Frontiers | Intermediate-Advanced
Thanks to advances in imitation and reinforcement learning techniques, we can now train intelligent agents to accomplish a diverse range of goals. But if we want to create household robots or personal assistants that can take advantage of this diversity, we need to give users some way to tell them what to do! This tutorial will focus on humans’ favorite tools for communicating goals and plans: natural language. We’ll assume basic familiarity with supervised learning and RL, and begin with a review of core machine learning techniques useful for natural language instruction following problems. The body of the talk will focus on modeling techniques for instruction following problems in different kinds of environments and data conditions. We’ll conclude with a survey of other applications for the tools we’ve built, including instruction generation, interpretability, and machine teaching…more details
Jacob Andreas is an assistant professor at MIT and a senior researcher at Microsoft Semantic Machines. His research focuses on language learning as a window into reasoning, planning and perception, and on more general machine learning problems involving compositionality and modularity. Jacob earned his Ph.D. from UC Berkeley, his M.Phil. from Cambridge (where he studied as a Churchill scholar) and his B.S. from Columbia. He has been the recipient of an NSF graduate fellowship, a Facebook fellowship, and paper awards at NAACL and ICML.
Workshop | NLP | Machine Learning | Intermediate-Advanced
This workshop teaches you the use of transformer neural networks and their incarnations (BERT, RoBERTa, GPT-2) for solving real-world natural language use cases. NLP has advanced tremendously over the last few years and BERT is at the forefront of this success having achieved state-of-the-art results on 11 different NLP tasks. For businesses, BERT has unlocked new NLP use cases that have been previously unattainable.
This workshop will teach you what transformers and systems like BERT and GPT-2 are and how to use and modify them for your needs. Organizations have a wealth of unstructured text sources in every line of business, such as employee feedback in human resources, purchase orders and legal documents in contracting and procurement, communication records throughout the org, and many more. Making sense of this information and organizing it into knowledge and actionable insights to improve business outcomes is a key function every data scientist should be aware of…more details
Niels Kasch, PhD is a Founding Partner at Miner & Kasch, a leading AI and Data Science firm. He combines extensive experience in machine learning, analytics, and business knowledge to solve his customers’ key business challenges. An expert in natural language processing, he has developed large-scale, state-of-the-art semantic analysis technologies for combating financial fraud in the finance industry, and text analytics solutions for mitigating risk in the construction industry. He has delivered high quality analytics and data products to Fortune 500 clients and startups alike, covering industries from automotive to utilities. His leadership in framing strategic analytics visions for enterprises has led to the building of several innovative data science teams for his customers.
Workshop | Machine Learning | Beginner-Intermediate
Want to learn more about trends like AI, IoT and wearable tech? In this workshop, we will cut through the hype by building a “smart” fitness tracker using your own mobile device. We’ll do hands-on exercises: you’ll acquire data from sensors, design a step counter and train a human activity classifier. You will leave motivated and ready to use machine learning and sensors in your own projects!
The goal for this session is twofold: show our participants how easy it is to get started with Machine Learning and wearables and set them up for success to take on challenging projects in this domain. A clear instruction sheet will guide the participants through exercises ranging from beginner to intermediate level. This workshop is highly interactive with hands-on exercises in which the participants will create a “smart” wearable device and test it out. We aim to demonstrate how powerful applications can be built when we combine machine learning, sensor analytics, and IoT. Participants will use the free MATLAB Mobile App to access built-in sensors and analyze data on their mobile devices...more details
Louvere Walker-Hannon is a MathWorks 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. Louvere has worked in three different engineering roles throughout her 20 year career while at MathWorks. A prominent theme in her career is communication of technical concepts to various audiences and being involved with STEM education. 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. Louvere is a member of the Society of Women Engineers (SWE) and the National Society of Black Engineers (NSBE). She has a long history of serving as a STEM mentor. Louvere volunteers with Black Girls Code and the Society of Women Engineers. 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 presented and continues to present at several STEM related conferences on various topics. She recently presented her work on a Natural Language Processing application at the 100th American Meteorological Society in January 2020.
Maria Gavilan-Alfonso is a Senior Online Content Developer at MathWorks, where she helps integrating MATLAB and Simulink in online learning experiences worldwide. She is one of the developers and instructors of the Coursera specialization “Practical Data Science with MATLAB.” Maria holds a MSc in Aerospace Engineering from Purdue University, a BSc in Physics from Universidad Nacional de Colombia, and she’s currently an MBA candidate from University of Illinois at Urbana-Champaign. In her professional career Maria has supported and trained engineers using simulation software for aerodynamics, aeroacoustics, thermal management and flight dynamics. Her current interests are focused on autonomous aerial vehicles and machine learning techniques applied to aerodynamics. Maria also volunteers in multiple events looking to increase the representation of women in science and engineering.
Vaidehi is a principal engineer at MathWorks with over 10 years of software engineering experience. She leads the efforts to generate embedded efficient deep learning network representations using quantization and model compression techniques. In the past, she has developed software tools for automatic deployment of embedded applications on microprocessors, ECUs and FPGAs. Vaidehi has a bachelor’s degree in Information Systems and master’s degree in Computer Science with a specialization in Machine Learning. Prior to MathWorks, Vaidehi was a software engineer at Bloomberg working on solutions that provide e-communication surveillance. Vaidehi has presented technical talks at the MathWorks Research Summit and the MathWorks Advisory Board meetings.
Workshop | Machine Learning | Beginner-Intermediate
This workshop shows how interpretability tools can give you not only more confidence in a model, but also help to improve model performance. Through this interactive workshop, you will learn how to better understand the models you build, along with the latest techniques and many tricks of the trade around interpretability. The workshop will largely focus on interpretability techniques, such as feature importance, partial dependence, and explanation approaches, such as LIME and Shap.
The workshop will demonstrate interpretability techniques with notebooks, some in R and some in Python. Along the way, workshop will consider issues like spurious correlation, random effects, multicollinearity, reproducibility, and other issues that may affect model interpretation and performance. To illustrate the points, the workshop will use easy to understand examples and references to open source tools to illustrate the techniques...more details
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 | Data Visualization | Deep Learning | Intermediate
For compute-intensive simulation models with high-dimensional input parameters and output spaces, repeated execution of the expensive simulations can become computationally prohibitive and the analysis of the large output is non-trivial. In this talk, I will discuss our recent work on assisting simulation paramter exploration using deep neural networks that won a best paper and an honorable mention awards in the IEEE Visualization conference. To capture the characteristics of
simulation and provide a rapid visualization of the simulation output without huge computation and storage overhead, we develop DNN models to serve as surrogates for prediction of simulation data and visualization images with novel simulation parameters. To achieve these, at simulation time we will collect reduced simulation data and visualization images. Using them as the training data, in one of our recent works we train a generative adversarial networks (GANs) that can take novel simulation and visualization parameters as input to generate new visualization images, which will serve as a quick preview of the simulation output. In another work, we collaborated with computational biologists to design an interactive visual analysis framework, backed by a neural network-based surrogate model that can assist computational biologists in analyzing and visualizing a complex yeast cell polarization simulation model. Our model also allows uncertainty quantification and sensity analysis of the input parameters and simulation output...more details
Han-Wei Shen is a Full Professor at The Ohio State University. He is currently the Associate Editor in Chief of IEEE Transactions on Visualization and Computer Graphics. He is also the chair of the steering committee for IEEE SciVis conference. His primary research interests are scientific visualization and computer graphics. Professor Shen is a winner of National Science Foundation’s CAREER award and US Department of Energy’s Early Career Principal Investigator Award. He has served as an Associate Editor for IEEE Transactions on Visualization and Computer Graphics, a paper chair for IEEE Visualization, IEEE Pacific Visualization, and IEEE Parallel Visualization and Graphics. He is currently on the IEEE Visualization conference executive committee, and IEEE SciVis steering committee. He has published more than 50 papers in IEEE Transactions on Visualization and Computer Graphics and IEEE Visualization conference, the very top journal and conference. He received his BS degree from Department of Computer Science and Information Engineering at National Taiwan University in 1988, the MS degree in computer science from the State University of New York at Stony Brook in 1992, and the PhD degree in computer science from the University of Utah in 1998. From 1996 to 1999, he was a research scientist at NASA Ames Research Center in Mountain View California.
Tutorial | Machine Learning | Data Visualization | Beginner-Intermediate
I will cover what snakemake is, how to think about your problem in this type of pipeline, walk through examples of pipelines that can perform RNA-sequencing analysis, and discuss a FELIX, a program on which I have used Snakemake. FELIX is a program whose goal is to identify genetically engineered organisms in complex samples. For Felix, I use Snakemake to create pipelines that have been deployed onto clusters to process 100 samples (100s of Gb of data) and perform over a thousand hours of compute time in about two days. The pipeline includes alignment of DNA sequences, assembling sequences that contain signs of engineering into larger constructs, identifying the makeup of those constructs, and creating visualizations so that subject matter experts can quickly identify if a sample has been genetically engineered or not…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.
Workshop | Deep Learning | Data Visualization | Intermediate
Variational Auto-Encoders (VAEs) are cutting edge deep learning technology. The purpose of a VAE is to decomplexify data. Another name for this task is dimension reduction. The idea is that you have data that has lots of variables in it, but with so many variables it’s hard to understand what’s going on. You want to think about your customers, not as this huge spreadsheet with hundreds or thousands of pieces of information about each customer, but as something simple. You want to think of just a couple of customer types, with the idea that you know people are not very different from those ideal types.
Once you have someone’s type, you know almost everything you really need to know from that huge dataset. The type gives you a good summary of the data that you’ve been collecting about the customer, but thanks to the VAE it’s a simple description. For sure you’re going to lose some detail, but not much. And that simple description produced by the VAE is going to be super useful...more details
Yaniv Ben-Ami loves seeing data with my own eyes. It’s frustrating to him that it’s impossible to observe the geometry of multidimensional data that’s more than 3d. Yaniv first became obsessed with this challenge as a Bioinformatics undergrad at Tel Aviv University. Little did Yaniv knows that my obsession would become an epic journey. As an Economics Master student, Yaniv met the ultimate beast of a data set. This beast is The American Time Use Survey (ATUS), which has 389 dimensions. To Yaniv, it appeared as a 389-head Hydra that nobody would dare to slay. Gazing upon that beast, Yaniv initially fell into despair. Fast forward 15 years, 3 kids and a Ph.D. in Economics from Yale, Yaniv reads Deep Learning in Python by François Chollet. Within this tome of wisdom, Yaniv found a gift from the Gods. A deep learning algorithm by the name of Variational Auto-Encoder. This mythical weapon can map multidimensional data to a one-dimensional range and maintain relative distances. Finally, Yaniv Ben-Ami can see the whole data set. Yaniv will show you how he used this weapon to slay the ATUS monster. Just in case there’s a monster you want to slay.
Workshop | Machine Learning | Beginner
The Julia language is known for combining performance, ease of use, and flexibility. In the past year, this foundation has continued to expand and Julia users have built on it to push the envelope in several areas of data science and scientific computing. This talk will explain some of these developments and how to take advantage of them. First, we introduced a new composable multi-threading system that makes it easy to take advantage of multiple processor cores in all Julia code and libraries. This has been used, for example, to multi-thread CSV parsing in CSV.jl (written in 100% Julia), bringing its performance on par with the best CSV readers. Julia is at the forefront of the emerging field of scientific machine learning, which combines data-driven learning approaches with traditional modeling techniques such as differential equations. Julia packages now exist for applying scientific ML to a range of real-world problems...more details
Jeff is one of the creators of Julia, co-founding the project at MIT in 2009 and eventually receiving a Ph.D. related to the language in 2015. He continues to work on the compiler and system internals, while also working to expand Julia’s commercial reach as a co-founder of Julia Computing, Inc.
Workshop | Machine Learning | Open Source | Intermediate
In this workshop, we will work hands-on using XGBoost with real-world data sets to demonstrate how to approach data sets with the twin goals of prediction and understanding in a manner such that improvements in one area yield improvements in the other. Using modern tooling such as Individual Conditional Expectation (ICE) plots and SHAP, as well as a sense of curiosity, we will extract powerful insights that could not be gained from simpler methods. In particular, attention will be placed on how to approach a data set with the goal of understanding as well as prediction…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.
Training | NLP | Deep Learning | Intermediate
Natural language processing (NLP) has become an important field with interest from many important sectors that leverage modern deep learning methods for approaching several NLP problems and tasks such as text summarization, question answering, and sentiment classification, to name a few. In this tutorial, we will introduce several of the fundamental NLP techniques and more modern approaches (BERT, GTP-2, etc.) and show how they can be applied via transfer learning to approach many real-world NLP problems. We will focus on how to build an NLP pipeline using several open-source tools such as spaCy and TensorFlow. Then we will learn how to use the NLP model to search over documents based on semantic relationships. We will use open-source technologies such as BERT and Elasticsearch for this segment to build a proof of concept. In essence, the learner will take away the important theoretical pieces needed to build practical NLP pipelines to address a wider variety of problems in the real world…more details
Elvis Saravia is an educator at Elastic and an independent research scientist. He earned his Ph.D. in Computer Science at National Tsing Hua University. His topics of interest include but are not limited to information retrieval, data analysis, transfer learning, sentiment analysis, intent discovery, natural language modeling, conversational AI, and computational health. Elvis is also lead and editor of dair.ai which aims to democratize AI research, education, and technologies.
Training | Machine Learning | Intermediate
How can an idea from an 18th-century Presbyterian minister be used to estimate the mass density function of galaxies across the Universe? How can a marketing segmentation algorithm protect astronauts traveling to Mars from certain death? How does a Formula 1 race from then 1950’s inspire one of the greatest data science use cases for the Internet of Things? How can a violation of the triangle inequality theorem in mathematics lead to a cure for cancer? This workshop will answer these questions, and more, by presenting several examples of one of the key aptitudes of successful data science practice, which is adaptability. In particular, I will present several well-known algorithms (including some that we would not even call “algorithms”) that may have been adopted for specific use cases or applied in specific business domains, and then I will show how each one can be adapted to a novel use case that may be less obvious, perhaps producing significantly surprising results in some other domain. Plus, I will suggest some new opportunities that may come from interesting combinations of data and algorithms. The point of these exercises is to demonstrate how data scientists can create even more value, beyond that which is expected, from our data assets and our algorithmic talents…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.
Training
Anish Asthana is an experienced Software Engineer with a demonstrated history of working in the computer software industry. Skilled in Python, C++, and cloud technologies such as Kubernetes, Prometheus, and the ELK stack. Strong information technology professional with a Master of Science in Electrical and Computer Engineering Specializing in Data Analytics from Boston University College of Engineering.
Training | Machine Learning | ML for Programmers | Intermediate
scikit-learn is a machine learning library in Python, that has become a valuable tool for many data science practitioners. This workshop will go beyond the basics and show how to effectively evaluate and tune algorithms. We will also discuss the most important machine learning algorithms that you’re likely to see in practice, how and when to use them, and some details about how they work internally. The session will focus on linear models for classification and regression and tree-based models, including random forests…more details
Andreas Mueller is an Associate Research Scientist at the Data Science Institute at Columbia University and author of the O’Reilly book “Introduction to machine learning with Python”. He is one of the core developers of the scikit-learn machine learning library and has co-maintained it for several years.
His mission is to create open tools to lower the barrier of entry for machine learning applications, promote reproducible science and democratize access to high-quality machine learning algorithms.
Training
Matt currently leads instruction for GA’s Data Science Immersive in Washington, D.C. and most enjoys bridging the gap between theoretical statistics and real-world insights. Matt is a recovering politico, having worked as a data scientist for a political consulting firm through the 2016 election. Prior to his work in politics, he earned his Master’s degree in statistics from The Ohio State University. Matt is passionate about making data science more accessible and putting the revolutionary power of machine learning into the hands of as many people as possible. When he isn’t teaching, he’s thinking about how to be a better teacher, falling asleep to Netflix, and/or cuddling with his pug.
Training
Andreas Mueller is an Associate Research Scientist at the Data Science Institute at Columbia University and author of the O’Reilly book “Introduction to machine learning with Python”. He is one of the core developers of the scikit-learn machine learning library and has co-maintained it for several years.
His mission is to create open tools to lower the barrier of entry for machine learning applications, promote reproducible science and democratize access to high-quality machine learning algorithms.
Training | Machine Learning | MLOps & Data Engineering | Intermediate-Advanced
In this workshop, you will learn the best techniques that can be applied manually or automatically to validate and monitor statistical models.
Techniques below will be discussed and demonstrated to perform a full model validation:
Techniques used for initial validation:
Bias and variance
Model selection
Hyperparameters selection
Model interpretation
Adversarial validation o
Techniques used for model monitoring
Bias and variance consistence
Model degradation
Model interpretation consistence…more details
Olivier is a data science expert whose leading field of expertise and cutting-edge knowledge of AI and machine learning led him to support many companies’ digital transformations, as well as implementing projects in different industries. He has led the data team and put in place a data culture in companies like Pratt & Whitney Canada, L’Oréal, GSoft and now Moov AI.
Training | Open-source | MLOps & Data Engineering | Beginner-Intermediate
The intention of this VERY hands on workshop is to get you introduced and playing with some of the great features you never knew about in PostgreSQL. You know, and probably already love, PostgreSQL as your relational database. We will show you how you can forget about using ElasticSearch, MongoDB, and Redis for a broad array of use cases. We will add some nice statistical work with R embedded in PostgreSQL. Finally, we will bring this all together using the gold standard in spatial databases, PostGIS. Unless you have a specialized use case, PostgreSQL is the answer. The session will be very hands on with plenty of interactive exercises.
This is a VERY hands on workshop introducing you to other great features in PostgreSQL to use with PostGIS. We will show you how you can forget about using ElasticSearch, MongoDB, and Redis for a broad array of use cases. We will also work through R embedded in PostgreSQL...more details
Steve is a Partner, and Director of Developer Relations for Crunchy Data (PostgreSQL people). He goes around and shows off all the great work the PostgreSQL community and Crunchy Committers do. He can teach you about Data Analysis with PostgreSQL, Java, Python, MongoDB, and some JavaScript. He has deep subject area expertise in GIS/Spatial, Statistics, and Ecology. He has spoken at over 75 conferences and done over 50 workshops including Monktoberfest, MongoNY, JavaOne, FOSS4G, CTIA, AjaxWorld, GeoWeb, Where2.0, and OSCON. Before Crunchy Data, Steve was a developer evangelist for DigitalGlobe, Red Hat, LinkedIn, deCarta, and ESRI. Steve has a Ph.D. in Ecology.
Training | MLOps & Data Engineering | Intermediate
MLOps (Machine Learning operations) is all about helping you bring your machine learning workflows to production. This workshop will show you how to get to production faster with optimized experiences for reproducible model training, packaging, validation, deployment and monitoring. Using an MLOps process you can store, version and track ML assets and collect continuous feedback on model behavior. As a result, you can maintain quality models to keep your business and customers ahead...more details
Shivani Patel is a Program Manager working on MLOps in Azure Machine Learning.
Jordan Edwards is a Principal Program Manager working on MLOps in Azure Machine Learning
Training | Machine Learning | R-programming | All Levels
After we build various machine learning models we need to make them accessible to others. We use modeldb so we can store models in a database and plumber to expose our model as a REST API that can be hosted in a Docker container…more details
Daniel is a PhD candidate in Genetics, Bioinformatics, and Computational Biology at Virginia Tech, currently working in the Social and Decision Analytics Laboratory in the Biocomplexity Institute at Virginia Tech. His current interests are in understanding how attitudes change and spread within social networks as well as performing analytics for precision medicine.
Daniel received his MPH in the Department of Epidemiology at Columbia University and hold a BA in Psychology – Behavioral Neuroscience with minors in Biology and Computer Science from the Macaulay Honors College at CUNY Hunter College.
Daniel enjoys teaching and volunteers some of his time to Software Carpentry by teaching, serving on the Mentoring subcommittee, and chair the Assessment subcommittee.
Training | Kick-starter | Machine Learning | Beginner
This workshop will provide an overview of how to use machine learning to forecast complex operational problems. This workshop is a hands-on, Python-based, introduction to how machine learning can be used to tackle time-series problems. Topics to be covered include: why time-aware ML problems are different from non-time-aware ML problems; why time-series and forecasting problems in particular are challenging; and how to use to leverage both deep-learning and non-deep-learning approaches to successfully tackle real world problems. This course is a code-based workshop using a variety of Python based tools so familiarity with Python and data science fundamentals will be helpful, but time series experience is not assumed...more details
As Data Science Engineering Architect at DataRobot, Mark designs and builds key components of automated machine learning infrastructure. He contributes both by leading large cross-functional project teams and tackling challenging data science problems. Before working at DataRobot and data science he was a physicist where he did data analysis and detector work for the Olympus experiment at MIT and DESY.
Training | MLOps & Data Engineering | Intermediate
Ray is an open-source distributed framework from U.C. Berkeley’s RISELab that easily scales Python applications from a laptop to a cluster, with an emphasis on ML/AI systems, such as reinforcement learning. It is now used in many production deployments. In this tutorial, we’ll use several hands-on examples to explore the problems that Ray solves and the useful features it provides, such as rapid distribution, scheduling, and execution of “tasks” and management of distributed stateful “serverless” computing. We’ll see how it’s used in several ML libraries (and play with examples using those libraries). You’ll learn when to use Ray and how to use it in your projects…more details
Dean Wampler (@deanwampler) is an expert in streaming data systems, focusing on applications of ML/AI. He is Head of Evangelism at Anyscale.io, which is focused on distributed Python for ML/AI. Previously, he was an engineering VP at Lightbend, where he led the development of Lightbend CloudFlow, an integrated system for building and running streaming data applications with Akka Streams, Apache Spark, Apache Flink, and Apache Kafka. Dean has written books for O’Reilly and contributed to several open source projects. He is a frequent conference speaker and a co-organizer of several conferences and user groups in Chicago. Dean has a Ph.D. in Physics from the University of Washington.
Training | Machine Learning | Open-source | Beginner-Intermediate
The rapid progress in machine learning (ML) and the massive increase in the availability and diversity of data has enabled novel approaches to quantitative investment. It has also increased the demand for the application of data science to develop both discretionary and algorithmic trading strategies.
In this workshop, we will cover popular use cases for ML in the investment industry, and how data science and ML fit into the workflow of developing a trading and investment strategy from the identification and combination of alpha factors to strategy backtesting and asset allocation.
The workshop uses Python and various standard data science and machine learning libraries like pandas, scikit-learn, gensim, spaCy as well as TensorFlow and Keras. The code examples will be presented using jupyter notebooks and are based on my book ‘Machine Learning for Algorithmic Trading’…more details
Stefan is the founder and Lead Data Scientist at Applied AI. He advises Fortune 500 companies, investment firms and startups across industries on data & AI strategy, building data science teams, and developing machine learning solutions. Before his current venture, he was a partner and managing director at an international investment firm where he built the predictive analytics and investment research practice. He also was a senior executive at a global fintech company with operations in 15 markets.
Earlier, he advised Central Banks in emerging markets, worked for the World Bank, raised $35m from the Gates Foundation to cofound the Alliance for Financial Inclusion, and has worked in six languages across Asia, Africa, and Latin America. Stefan holds Master degrees in Computer Science from Georgia Tech and in Economics from Harvard and Free University Berlin and is a CFA Charterholder. He is the author for ‘Machine Learning for Algorithmic Trading’ and has been teaching data science at Datacamp and General Assembly.
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…