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Turn ODSC into a kickstart bootcamp with our 3 day pass

Avail free access to on-demand pre-bootcamp training to win prizes.

Join Pre-Bootcamp Training

Start learning in the winter with the very fundamentals of data science on-demand training to build confidence and prepare for the bootcamp.

Learn Applied Skills at the Kickstart Bootcamp

Join the 3 day kickstart bootcamp and learn the languages, algorithms, and tools from some of the leading data scientists in the world

Career Support

 

Network with attendees from 1,600+ companies and meet over 40 companies hiring directly at ODSC, career days, webinars, resume reviews and more

Get Certified 

 

Get a certification of completion upon finishing 3 days of instruction

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 Kick-start your career in data science, from beginner to advanced in just 3 days. Get the skills you need, taught by world-renowned experts

ODSC Virtual Kickstart Bootcamp is the best way to gain in-demand data science skills in the shortest time with minimum investment. Starting in the winter, the on-demand pre-Bootcamp Training will provide the right preparation and confidence to put your practice to work in your learning path.

It’s truly an immersive experience. Over almost a week, you will access an expanse of industry-focused data science topics on a scale not offered elsewhere. Uniquely, you will also get invaluable insights by networking and connecting with hundreds of data science attendees, world renowned instructors, industry experts, and dozens of top companies seeking the next wave of talent.

Current Specialization Tracks

Attend one of our six specialization tracks or customize it to your own goals

  • Machine Learning with Python and R

    • Introduction to Scikit-learn: Machine learning in Python
    • Probabilistic Programming and Bayesian Inference with Python
    • Recommendation Systems in Python
    • Modern Machine Learning in R
    • Intermediate Machine Learning with Scikit-learn: Cross-validation, Parameter Tuning, Pandas Interoperability, and Missing Values
    • Solving the Data Scientist’s Cold-Start Problem with Machine Learning Examples
    • Scikit-learn: Machine learning in Python
    • Good, Fast, Cheap: How to do Data Science with Missing Data
    • Programming with Data: Python and Pandas
    • SQL for Data Science

  • Data Analytics

    • Linear Algebra, Calculus, and Probability: The Math ML Experts Master
    • Programming with Data: Python and Pandas
    • Good, Fast, Cheap: How to do Data Science with Missing Data
    • Data Mastering at Scale
    • Probabilistic Programming and Bayesian Inference with Python
    • Network Analysis Made Simple
    • Data Visualization and Lock-down Analysis Using Global Covid-19 Data
       

  • Deep Learning

    • Brand Voice: Deep Learning for Speech Synthesis
    • Applied Deep Learning: Building a Chess Object Detection Model with TensorFlow
    • Linear Algebra, Calculus, and Probability: The Math ML Experts Master
    • Art of BERT: Unlock the Full Potential of BERT for Domain-Specific Tasks(TensorFlow)
    • Do You See What I See: Using AR and AI
    • From Research to Production, Minus the Boilerplate
    • Programming with Data: Python and Pandas
    • Machine Learning & Modelling in Theory & Practice

  • Data Engineering / MLOps

    • Scaling Machine Learning with Dask
    • SQL for Data Science
    • Data Science and Machine Learning in the Cloud for Cloud Novices
    • Modern Machine learning in R
    • Intermediate Machine Learning with Scikit-learn: Evaluation, Calibration, and Inspection
    • Advanced Machine Learning with Scikit-learn: Text Data, Imbalanced Data, and Poisson Regression
    • Applied Deep Learning: Building a Chess Object Detection Model with TensorFlow

  • NLP

    • NLP Fundamentals
    • Art of BERT: Unlock the Full Potential of BERT for Domain-Specific Tasks(TensorFlow)
    • Exploratory Text Analysis in Python Using spaCy and textacy
    • Brand Voice: Deep Learning for Speech Synthesis
    • What are Transformers and How do They Work?
    • Programming with Data: Python and Pandas

  • Data Visualization

    • Painting with Data: Introduction to d3.js
    • Network Analysis Made Simple
    • Exploring the Interconnected World: Network/Graph Analysis in Python
    • Getting Started with D3.js for Data Visualization

  • Cybersecurity

    • Enhanced ML for Cybersecurity 
    • Building a Holistic Risk Profile: Near Real-Time Approach to Insider Threat Detection
    • Introduction to Scikit-learn: Machine learning in Python

Our Kickstart Bootcamp is fully customizable, you can attend the sessions you want live and on-demand after our conference. Check all available training here – Training Page.

Customize your learning

Discover Your Bootcamp Learning Path

East Virtual Kickstart Bootcamp Overview

Begin, this winter, with a pre-bootcamp on-demand training to build confidence on the core fundamentals of data science, and steadily progress through the bootcamp week with our hands-on training and expert-led sessions and projects. Refer to the timeline below.

Pre-Bootcamp On-demand Training

Get started in your learning track with free access to introductory concepts with short, to-the-point and industry driven on-demand courses.

Schedule: Self-paced

Pre-bootcamp Live Training (Free & Paid)

Build a foundation of essential hands-on concepts as part of your Bootcamp pass and get complimentary access to one Live training of your choice on the Ai+ training platform.

Upcoming live training include:

Free Literacy Pre-Bootcamp Sessions

These 60-minute sessions will help you understand core concepts in Data, Ai, and Programming, to get you to reach for our Bootcamp Program.  Register for FREE or as part of your Bootcamp Pass. 

March 11, 2021 – Warm-up Tutorial: Data Literacy for Careers in AI

March 17, 2021 – Warm-up Tutorial: AI Literacy for Careers in AI

March 24, 2021 – Tutorial: Coding Literacy for Careers in AI

Kickstart Bootcamp Day 1 - Fundamental Sessions (March 30th)

Recommend fundamental sessions:

  • Machine Learning & Modelling in Theory & Practice
  • Introduction to Scikit-learn: Machine learning in Python
  • Introduction to NLP Fundamentals
  • Introduction to SQL for Data Science

Additional Recommended Sessions

  • Recommendation Systems in Python
  • Data Science and Machine Learning in the Cloud for Cloud Novices
  • Good, Fast, Cheap: How to do Data Science with Missing Data

Kickstart Bootcamp Day 2 - Introductory to Advanced sessions (March 31st)

Fundamentals sessions include:

  • Programming with Data: Python and Pandas
  • Modern Machine Learning in R
  • Intermediate Machine Learning with Scikit-learn: Evaluation, Calibration, and Inspection
  • An Overview of Methods to Handle Missing Values

Additional Recommended Sessions:

  • A typical Applications of Typical Machine Learning Algorithms
  • Advanced Machine Learning with Scikit-learn: Text Data, Imbalanced Data, and Poisson Regression
  • Painting with Data: Introduction to d3.js
  • Scaling Machine Learning with Dask
  • Art of BERT: Unlock the Full Potential of BERT for Domain-Specific Tasks(TensorFlow)

Kickstart Bootcamp Day 3 - Introductory to Advanced Sessions (April 1st)

Fundamental sessions include:

  • Probabilistic Programming and Bayesian Inference with Python
  • Modern Data Acquisition using Python

Additional Recommended Sessions:

  • Brand Voice: Deep Learning for Speech Synthesis
  • What are Transformers and How do They Work?
  • Multi-Task Reinforcement Learning
  • Applied Deep Learning: Building a Chess Object Detection Model with TensorFlow

Certificate of Completion & More

  • Obtain an ODSC Certification of Completion
  • Get access to all ODSC East 2021 recorded sessions
  • Get access to over 100+ additional recorded sessions from prior ODSC events, including ODSC West and ODSC Europe

What You’ll Learn

 Learn Fundamentals

Prep for your immersive training by learning core fundamentals, including:

  • Fundamentals of Python Programming

  • Data Analysis in Python

  • Introduction to Statistics for Data Science

  • Fundamental Analytics for Data Engineering

Learn Frameworks & Tools

Get hands-on experience with in-demand data science tools and frameworks, including:

  • Python, Jupyter Notebooks

  • R programming, Julia, Scala, Stan

  • Apache Spark, MLlib, Streaming

  • Tensorflow, MXNet, Caffe, CNTK

  • Scikit-learn, Theano, Shogun, Pylearn2

Learn Core Topics

Get hands-on experience in the most important data science topics with over 40 beginner to intermediate sessions to choose from, including

  • Deep Learning

  • Reinforcement Learning and Deep Reinforcement Learning

  • Machine Learning

  • Transfer Learning

  • Natural Language Processing

  • Text Analytics

  • Data Visualization

  • Data Modeling and Data Wrangling

  Build your Network

A career in data science opens up the possibility of rewarding and exciting work. Build your network and help grow your career

  • Learn from and connect with world-leading data science experts

  • Stop by our Career Expo and meet top hiring companies like Google, DataRobot, and dozens of others

  • Join our various networking events, such as Meet the Speakers, Meet the Experts, Dinner with Data Scientists; plus other networking opportunities

  • Network with hundreds of data science attendees to learn what it’s like to be a data scientist

Bootcamp Experience

.

How It Works

  • Virtual conference experience includes networking lounge area, speaker auditorium, expo halls, and prizes

  • Access multiple livestream tracks on Tuesday, Wednesday, Thursday, or Friday

  • Switch between sessions or tracks as your interests dictate

  • Multiple focus areas including deep learning, machine learning, NLP, research frontiers, AI X for business, and more

  • Sessions you missed can be viewed on demand at your leisure

  • Engage virtually with fellow attendees, speakers, and Expo partners

  • Participate in Q&A sessions with your speaker over live chat

  • Directly download slides and other session materials

  • (Training only) Access training and workshops prerequisites, notebooks, and other materials prior to training session starting

  • (Training only) Access hands-on training and workshops with instructor-led code labs and notebooks.

Registration

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Choose your Pass

Pre-Bootcamp On-Demand Training

Tuesday - Thursday | Data Science Training  - 20 Sessions

Tuesday - Thursday | Workshops & Talks - 30 Sessions

On-demand Access to All Conference recordings 

Tuesday & Wednesday | Virtual AI Expo and Demo Talks

Thursday | Virtual Career Expo 

1 year of Premium Subscription on aiplus.odsc.com 

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Current ODSC East Instructors

Kirk borne
Principal Data ScientistBooz Allen Hamilton

Dr. Kirk Borne

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.

Solving the Data Scientist’s Cold-Start Problem with Machine Learning Examples(Half-Day Training)

Atypical Applications of Typical Machine Learning Algorithms(Half-Day Training)

Open Source Relations ManagerVertica

Paige Roberts

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.

In-Database Machine Learning in Jupyter(Tutorial)

Machine Intelligence ScientistDraper

Teal Guidici, PhD

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.

Echo State Networks for Time-Series Data (Tutorial)

Chief Data Scientist, Author of "R for Everyone"Lander Analytics

Jared Lander

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.

Machine Learning in R Part I(Full-Day Training)

Machine Learning in R Part II(Full-Day Training)

Head of Data Science and AnalyticsBadoo (now MagicLab, which owns several apps)

Leonardo De Marchi

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.

NLP Fundamentals(Full-Day Training)

Staff Associate - Machine LearningColumbia University in the City of New York

Thomas Fan

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.

Introduction to Scikit-learn: Machine Learning in Python(Half-Day Training)

Intermediate Machine Learning with Scikit-learn: Cross-validation, Parameter Tuning, Pandas Interoperability, and Missing Values(Half-Day Training)

Intermediate Machine Learning with Scikit-learn: Evaluation, Calibration, and Inspection(Half-Day Training)

Advanced Machine Learning with Scikit-learn: Text Data, Imbalanced Data, and Poisson Regression(Half-Day Training)

deep learning with TensorFlow
Chief Data ScientistUntapt

Dr. Jon Krohn

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.

Linear Algebra, Calculus, and Probability: The Math ML Experts Master(Tutorial)

Machine Intelligence ScientistDraper

Laura A. Seaman, PhD

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.

Snakemake: a Python Pipeline Toolbox(Tutorial)

Data ScientistMediaMath

Jayeeta Putatunda

Jayeeta is a Data Scientist with 5+ years of industry experience. She recently led six-week NLP workshops in association with Women Who Code, Data Science track. Jayeeta has also been a speaker at International Conference on Machine Learning (ICML 2020), MLConf EU, WomenTech Global Conference, and Data Summit Connect. She works extensively on NLP projects where she gets to explore a lot of state-of-the-art models and build cool products and firmly believes that data is the best storyteller. Recently, Jayeeta joined MediaMath, a leader in the programmatic AdTech domain. Prior to this, she worked at Indellient, Omnicom, Deloitte, and Volvo Group. Jayeeta is also engaged with some amazing organizations to promote and inspire more women to take up STEM. Jayeeta received her Master of Science in Quantitative Methods and Modeling from City University of New York, NY, and Bachelor of Science in Economics and Statistics from West Bengal State University, India.
Website – https://jayeetap.wixsite.com/helloworld

Build a Question Answering System using DistilBERT in Python (Workshop)

Data Visualization DeveloperObservable

Ian Johnson

Ian Johnson is a User Experience Engineer at Google. He also organizes of Bay Area d3, starts with SVG and then dives deep into d3 including DOM manipulation, categorical and quantitative scales, axis, brushes, color schemes, events and histograms. Ian likes to make sense of data by exploring it visually with D3.js!

Painting with Data: Introduction to d3.js(Half-Day Training)

Author of nxviz Package

Eric Ma, PhD

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).

Network Analysis Made Simple(Half-Day Training)

PresidentEnplus Advisors, Inc.

Daniel Gerlanc

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.

Programming with Data: Python and Pandas(Half-Day Training)

Chief AI Scientist | Research AssociateThales | INRIA

Freddy Lecue, PhD

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.

XAI – Explanation in AI: From Machine Learning to Knowledge Representation & Reasoning and Beyond(Workshop)

Research EngineerFacebook AI Research Group.

Shagun Sodhani

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.

Multi-Task Reinforcement Learning(Tutorial)

Co-Founder, Principal Data Scientist & FacultyRoboflow.com, BetaVector, General Assembly

Joseph Nelson

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

Applied Deep Learning: Building a Chess Object Detection Model with Computer Vision(Half-Day Training)

Associate ProfessorMIT

Tamara Broderick, PhD

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).

An Automatic Finite-Sample Robustness Metric: Can Dropping a Little Data Change Conclusions?(Workshop)

Principal Program ManagerMicrosoft

Manash Goswami

Manash Goswami is a product management leader with a passion to solve problems and build products that meet end-user needs. Grown from development – coding, test & verification, systems design & architecture, to product management and business development. Delivered consumer electronics products from smartwatch to tablets. Deep understanding of the Android, Chrome & Windows eco-system. Demonstrated leadership by managing through influence across a matrixed org structure. Capable of driving new initiative/agenda internally or with customers, enable collaboration across functional teams and drive for financial results. Excel at dealing with ambiguity and managing complex, cross-group interactions. Can successfully tie long-term strategic thinking with near-term execution.

Ml Inference on Mobile Device With Onnx Runtime(Tutorial)

Professor, Lab Director, Sr. AI ScientistCornell Tech, Rochester Institute of Technology, Paige

Christopher Kanan, PhD

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.

Continual Learning in Deep Neural Networks: Methods and Applications(Tutorial)

Software EngineerSaturn Cloud

James Lamb

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 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).

Scaling Machine Learning with Dask(Tutorial)

Senior Data Scientist, AI&ML Instructor QBE Insurance, DataCamp

Thushan Ganegedara

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 PhD 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.

Art of BERT: Unlock the Full Potential of BERT for Domain-Specific Tasks(TensorFlow)(Workshop)

Quality Engineering ManagerMathWorks

Shruti Karulkar

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.

Do You See What I See: Using AR and AI (Workshop)

Software Engineer MathWorks

Sarah Mohamed

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.

Do You See What I See: Using AR and AI (Workshop)

Senior Application EngineerMathWorks

Louvere Walker-Hannon

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.

Do You See What I See: Using AR and AI(Workshop)

Machine Learning Research EngineerAxel Springer

Francesco Cardinale

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.

Brand Voice: Deep Learning for Speech Synthesis(Workshop)

Senior Machine Learning EngineerAxel Springer

Christian Schäfer, PhD

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.

Brand Voice: Deep Learning for Speech Synthesis(Workshop)

Brian Lucena
Consulting Data ScientistAgentero

Brian Lucena, PhD

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.

StructureBoost: Gradient Boosting with Categorical Structure(Workshop)

Co founder & CEOGrid

William Falcon, PhD

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.

From Research to Production, Minus the Boilerplate(Workshop)

 

Sr Analytical Training ConsultantSAS

Jordan Bakerman, PhD

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.

End to End Modeling and Machine Learning (Workshop)

Analytical Training ConsultantSAS

Ari Zitin

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.

End to End Modeling and Machine Learning (Workshop)

Chief Scientist and Head of Conversational AIGot It AI

Chandra Khatri

Chandra Khatri is the Chief Scientist and Head of AI at Got It AI, wherein, his team is transforming the AI space by leveraging state-of-the-art technologies in order to deliver Self-Discovering, Self-Training, and Self-Optimizing products. Under his leadership, Got It AI is democratizing Conversational AI and related ecosystems through automation. Prior to Got-It, Chandra was leading various kinds of applied research projects at Uber AI such as Conversational AI, Multi-modal AI, and Recommendation Systems.

Prior to Uber AI, he was the founding member of the Alexa Prize Competition at Amazon, wherein he was leading the R&D and got the opportunity to significantly advance the field of Conversational AI, particularly Open-domain Dialog Systems, which is considered as the holy-grail of Conversational AI and is one of the open-ended problems in AI. Prior to Alexa AI, he was driving NLP, Deep Learning, and Recommendation Systems related Applied Research at eBay. He graduated from Georgia Tech with a specialization in Deep Learning in 2015 and holds an undergraduate degree from BITS Pilani, India.

His current areas of research include Artificial and General Intelligence, Democratization of AI, Reinforcement Learning, Language Understanding, Conversational AI, Multi-modal and Human-agent Interactions, and Introducing Common Sense within Artificial Agents

Advances in Conversational AI and NLP through Large Scale Language Models such as GPT-3(Workshop)

Senior Data Scientist, InstructorTI Health, University of South Florida

Mary C. Boardman, Ph.D

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.

A/B Testing for Data Science Using Python(Workshop)

Lead Data Scientist and Adjunct Lecturer in Data ScienceBrown University, Center for Computation and Visualization

Andras Zsom, PhD

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.

Missing Data in Supervised Machine Learning(Workshop)

Sr. Data ScientistJohn Snow Labs

Veysel Kocaman, PhD

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.

Introduction to Spark NLP(Talk)

Supervisor, Data EducationChildren's Hospital of Philadelphia

Joy Payton

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.

Using Google BigQuery Public Data With Colab Notebooks: From Insight to Knowledge Using Cloud Computing, SQL, and Python to Map NYC Fall Tree Color!(Workshop)

Assistant Professor of Machine LearningEindhoven University of Technology

Joaquin Vanschoren, PhD

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’.

Tutorial on Automated Machine Learning(Workshop)

Senior Data ScientistGreenhouse Software

Mona Khalil

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.

Evaluation of Statistical Models based on Stakeholder Needs(Tutorial)

ML EngineerFacebook

Rahul Agarwal

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

What are Transformers and How do They Work?(Workshop)

Senior Program ManagerMicrosoft

Emma Ning

Emma Ning is a senior Product manager in 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 engine 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.

Fostering AI Innovations Through Open Source Projects(Keynote)

ML Reseacrh Scientist, Professor of PracticeGamalon, Inc., Northeastern University

Robert Osazuwa Ness, PhD

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.

Causal Machine Learning Blitz(Workshop)

Graph Data Science AdvocateNeo4j

Dr. Clair Sullivan

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.

Going from Text to Knowledge Graphs: Putting Natural Language Processing and Graph Databases to Work(Workshop)

Data Science and Machine Learning LeaderUnify Consulting

Vamsi Sistla

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.

A/B Testing, Statistical Experimentation and Reinforced Learning Are the Future of Product Development(Tutorial)

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Who Should Attend

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Intermediates

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  • Data Journalists 



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