Day by Day Guide to ODSC West 2019

Conferences West 2019posted by ODSC Team October 21, 2019 ODSC Team

Next week is ODSC West 2019 in San Francisco and it’s time to start planning your schedule in earnest. With 300+...

Next week is ODSC West 2019 in San Francisco and it’s time to start planning your schedule in earnest. With 300+ hours of content and 100+ trainings and workshops led by our expert speakers ranging in level from beginner to advanced, we know it can be difficult to choose which sessions to attend. To facilitate this process, and provide the best possible conference experience, we’ve created recommended schedules for several different pathways based on your experience, interests, and goals.

[Read all of the articles from ODSC West 2019 speakers here!]

Newbie: (citizen data scientist)

You:

Want in on “the sexiest job of the 21st century”
Already know math and numbers but want to use it in a new way
Are a programmer/analyst ready for your next adventure
Are a student or recent graduate wondering if data science is right for you

Oct 29

9:00-1:00 | Half-Day Training: Introduction to Machine Learning with Andreas Mueller, PhD

—

2:00-6:00 | Half-Day Training: Intermediate Machine Learning with Scikit-learn with Andreas Mueller, PhD

Oct 30

9:00-1:00 | Half-Day Training: Advanced Machine Learning with Scikit-learn, Part I with Andreas Mueller, PhD

9:00-1:00 | Half-Day Training: Adapting Machine Learning Algorithms to Novel Use Cases with Dr. Kirk Borne

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2:00-6:00 | Half-Day Training: Advanced Machine Learning with Scikit-learn, Part II with Andreas Mueller, PhD

2:00-6:00 | Half-Day Training: From Numbers to Narrative: Turning Raw Data into Compelling Stories with Impact with Bill Shander

Oct 31

Keynotes until 10:30

—

11:00-11:45 | Talk: Model-based Learning and Optimization with Emo Todorov, PhD

and

12:00-12:45 | Talk: Machine Learning (ML) on Devices: Beyond the Hype with Divya Jain

11:00-12:30 | Workshop: Machine Learning Interpretability Toolkit with Mehrnoosh Sameki, PhD

—

2:00-2:45 | Talk: Learning from Small Datasets (Few Shot Learning) with Veysel Kocaman, PhD

and

3:00-3:45 | Talk: Simplified Data Preparation for Machine Learning in Hybrid and Multi Clouds with Bin Fan, PhD

2:00-3:30 | Workshop: MLOps: ML Engineering Best Practices from the Trenches with Sourav Dey, PhD

—

4:15-5:00 | Talk: AI Research at Bloomberg with Dr. Anju Kambadur

and

5:15-6:00 | Talk: Help! My Classes are Imbalanced! with Samuel Taylor

4:00-5:30 | Workshop: Declarative Data Visualization with Vega-Lite & Altair Kanit Wongsuphasawat, PhD

Nov 1

11:00-11:45 | Talk: Making the Best Possible Decisions with Evolutionary AI by Babak Hodjat, PhD

and

12:00-12:45 | Talk: When Your Big Data Seems Too Small: Accurate Inferences Beyond the Empirical Distribution with Gregory Valiant, PhD

11:00-12:30 | Workshop: Building an Industry Classifier with the Latest Scraping, NLP and Deployment Tools with Ido Shlomo

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2:00-2:45 | Talk: Scientific Annotation: Using Graphs to Facilitate Interdisciplinary Science with Simon Goring, PhD

and

3:00-3:45 | Talk: Causal Inference in E-Commerce Search: Tackling Small Data Challenge with Natural Experiments by Liang Wu, PhD

and

4:00-4:45 | Talk: Community-Specific AI: Building Solutions for Any Audience with Jonathan Purnell, PhD

2:00-3:30 | Workshop: Causal Inference for Data Science with Vinod Bakthavachalam

and

3:45-5:15 | Workshop: Optuna: A Define-by-Run Hyperparameter Optimization Framework with Crissman Loomis

Deep Learning Enthusiast

You:

Entry level data scientists come to you for help
You’ve mastered machine learning and are ready to see if the hype behind reinforcement learning is real
Data science is a lifestyle, not a job

Oct 29

9:00-1:00 | Half-Day Training: Understanding the PyTorch Framework with Applications to Deep Learning with Robert Alvarez, PhD

—

2:00-6:00 | Full-Day Training: Hands-On Introduction to LSTMs in Keras/TensorFlow with Lukas Biewald, Chris Van Pelt, and Stacey Svetlichnaya

Oct 30

9:00-1:00 | Half-Day Training: Deep Learning (with TensorFlow 2.0) with Dr. Jon Krohn

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2:00-6:00 | Half-Day Training: Reinforcement Learning with TF-Agents & TensorFlow 2.0: Hands On by Oliver Zeigermann and Christian Hidber, PhD

Oct 31

Keynotes until 10:30

—

11:00-11:45 | Talk: Model-based Learning and Optimization by Emo Todorov, PhD

and

12:00-12:45 | Talk: Troubleshooting Deep Neural Networks with Josh Tobin, PhD

11:00 -12:30 | Workshop: Spark NLP: State of the Art Natural Language Processing at Scale by David Talby, PhD

—

2:00-2:45 | Talk: Incorporating Intent Propensities in Personalized Next Best Action Recommendation with Kexin Xie

and

3:00-3:45 | Talk: Visual Search on Hayneedle by Bugra Akyildiz

2:00-3:30 | Workshop: How to Build a Recommendation Engine That Isn’t Movielens with Max Humber

—

4:15-5:00 | Talk: How Advanced Hyperparameter Optimization Drives Performance without Compromising Privacy with Scott Clark, PhD

and

5:15-6:00 | Talk: Mining “Concept Embeddings” from Open-Source Data to Classify Previously Unseen Log Messages with David Nellinger Adamson, PhD

4:00-5:30 | Workshop: Pomegranate: Fast and Flexible Probabilistic Modeling in Python with Jacob Schreiber

Nov 1

Keynotes until 10:30

—

11:00-11:45 | Talk: EMI: Embed, Measure and Iterate with Mayank Kejriwal, PhD

and

12:00-12:45 | Talk: Combining Word Embeddings with Knowledge Engineering with Sanjana Ramprasad

11:00-12:30 | Workshop: Data Harmonization for Generalizable Deep Learning Models: from Theory to Hands-on Tutorial with Gerald Quon, PhD and Nelson Johansen

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2:00-2:45 | Talk: Planetary Scale Location-based Insights with Gopal Erinjippurath

and

3:00-3:45 | Talk: Lessons Learned Deploying a Deep Learning Visual Search Service at Scale with Scott Cronin, PhD

and

4:00-4:45 | Talk: Creating an Extensible Big Data Platform to Serve Data Scientists and Analysts – 100s of PetaBytes with Real-Time Access by Reza Shiftehfar, PhD

2:00-3:30 | Workshop: Tutorial on Deep Reinforcement Learning with Pieter Abbeel, PhD

and

3:45-5:15 | Workshop: Tutorial on Deep Reinforcement Learning with Pieter Abbeel, PhD

The Decision Maker (aka XAI)

You:

Your unofficial title is data science translator
Your company is falling behind the AI race and it’s time to catch up
You think “AI is neat” and now have to prove it to your boss
Dirty text: You think hiring a data scientist can help increase revenue

Oct 29

9:30-10:05 | Keynote: Title Coming Soon! with Cassie Kozyrkov, PhD

—

10:05-10:40 | Keynote: Accelerate AI: Moving AI Off Your Roadmap and Into Your Products with Ashok Srivastava, PhD

—

11:10-11:40 | Talk: Establishing a Data and Analytics Organization with Shanthi Iyer

11:10-11:40 | Talk: Enterprise Adoption of Reinforcement Learning with Dr. Ganapathi Pulipaka

—

11:50-12:20 | Talk: Robots Learning Dexterity: Robots That Learn with Peter Welinder, PhD

11:50-12:20 | Talk: Designing A User-Centric AI Product with Katie Malone, PhD and Annie Darmofal

—

1:30-2:00 | Talk: Challenges of Digital Transformation and AI with Rashed Haq, PhD

1:30-2:00 | Talk: Identifying and Labeling Fraudulent Store Return Activities with Henry Chen, PhD, Vidhya Raman, and Jingru Zhou, PhD

—

2:10-2:40 | Talk: Scaling 200B+ Pins Using a Mix of Machine Learning and Human Curation with Chuck Rosenberg, PhD

2:10-2:40 | Talk: Race Your Facts: Making AI Work for Enterprises with Rama Akkiraju

—

2:50-3:20 | Talk: Accelerating AI-Driven Innovation in Your Enterprise with Pallav Agrawal

2:50-3:20 | Talk: Sources of Bias: Strategies for Tackling Inherent Bias in AI with Harry Glaser

—

3:50-4:20 | Talk: On AI ROI: The Questions You Need to Be Asking with Kerstin Frailey

3:50-4:20 | Talk: AI in Healthcare: the State of Adoption with Alex Ermolaev

—

4:30-5:00 | Talk: Building AI Products: Delivery Vs Discovery with Charles Martin, PhD

4:30-5:00 | Talk: Transaction Data Enrichment – an Opportunity for Business Growth and Risk Mitigation with Pramod Singh, PhD

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5:10-5:40 | Talk: Bringing AI Out of the Lab and Into Production with Irina Farooq

5:10-5:40 | Talk: Weaponizing Distraction: How to Use Analytics on Call Rotations for Improving Team Focus, Onboarding New Employees, and Making Space for Career Growth with Katie Bauer

Oct 30

9:30-10:05 | Keynote: Title Coming Soon! with Dr. Anand S Rao

—

10:05-10:40 | Keynote: Title Coming Soon! with Michael I. Jordan, PhD

—

11:10-11:40 | Talk: Scaling Computer Vision in the Cloud and AI Chips with Reza Zadeh, PhD

11:50-12:20 | Talk: From R&D to ROI: Realize Value by Operationalizing Machine Learning with Diego Oppenheimer

—

11:50-12:20 | Talk: Developing Machine Learning-Driven Customer-Facing Product Features with Marsal Gavalda, PhD

11:50-12:20 | Talk: Harnessing AI: Data Evangelism Must Be Data-Driven with Jennifer Redmon

—

1:30-2:00 | Talk: Data-Driven Approaches to Forecasting with Javed Ahmed, PhD

1:30-2:00 | Talk: The Last Frontier of Machine Learning – Data Wrangling with Alex Holub, PhD

—

2:10-2:40 | Talk: Why We Should Hire More Analysts for Data Science Teams with Benn Stancil

2:10-2:40 | Talk: An Introduction to AI’s Impact in the Life Sciences with Mark DePristo

—

2:50-3:20 | Talk: Natural Language Processing: Deciphering the Message within the Message – Stock Selection Insights using Corporate Earnings Calls with Frank Zhao

2:50-3:20 | Talk: Data Literacy and Democratization of Data in the 4th Industrial Revolution with Jacob Dockendorf

—

3:50-4:20 | Talk: AI in Medicine: Avoiding Hype and False Conclusions with Michael Zalis, MD

3:50-4:20 | Talk: The Great Voice Migration: How Voice Will Make the Leap from Consumer to Enterprise with Omar Tawakol

—

4:30-5:00 | Talk: Building a Center of Excellence for Data Science with Michael Xiao

4:30-5:00 | Talk: Beyond Conventional AI—Proven in Space, Now Available on Earth with Giovanni Gentile

—

5:10-5:40 | Talk: From Silos to Platform: Building Twitter’s Feature Marketplace with Wolfram Arnold, PhD

5:10-5:40 | Talk: Deployment of Strategic AI in the Enterprise with Dr. Fernando Nunez-Mendoza

Senior Data Scientist / Researcher

You print out articles from ArXiv.org and keep them in your briefcase
You fondly remember when you had to compete in kaggle competitions
Coding is still fun for you
You need an excuse to stay in-the-loop with the latest frameworks and languages

Oct 29

9:00-1:00 | Full-Day Training: Hands-On Introduction to LSTMs in Keras/TensorFlow with Lukas Biewald, Chris Van Pelt, and Stacey Svetlichnaya

—

2:00-6:00 | Full-Day Training: Hands-On Introduction to LSTMs in Keras/TensorFlow with Lukas Biewald, Chris Van Pelt, and Stacey Svetlichnaya

Oct 30

9:00-1:00 | Tutorial: Deep Implicit Learning with Laurent El Ghaoui, PhD

—

| Workshop: Deciphering the Black Box: Latest Tools and Techniques for Interpretability with Rajiv Shah, PhD

Oct 31

Keynotes until 10:30

—

11:00-12:30 | Workshop: Advanced Methods for Explaining XGBoost Models with Brian Lucena, PhD

—

2:00-2:45 | Talk: Deploying AI for Near Real-Time Engineering Decisions with Heather Gorr, PhD

—

3:00-3:45 | Talk: Coming soon! with Sudeep Pillai, PhD

—

4:15-5:00 | Talk: AI Research at Bloomberg with Dr. Anju Kambadur

—

5:15-6:00 | Talk: AutoML at Scale: Integrating Data as Part of Hyperparameter Optimization with Moses Guttmann

Nov 1

Keynotes until 10:30

—

11:00-11:45 | Talk: Quantamental Factor Investing Using Alternative Data and Machine Learning with Arun Verma, PhD

—

12:00-12:45 | Talk: Advanced Techniques in Natural Language Understanding with FakeNews Use Cases by Michael Tamir, PhD and Jacob Baumbach

—

2:00-3:30 | Workshop: Causal Inference for Data Science with Vinod Bakthavachalam

—

3:45-5:15 | Tutorial: The Robustness Problem with Justin Gilmer, PhD

The AI Engineer

You:

Were a software engineer in a past life
Want to work on self-driving cars and robots
Are ready to be an innovator
You want to see how Google, Facebook, and Uber get ahead

Oct 29

9:00-1:00 | Half-Day Training: Programming with Data: Foundation of Python & Pandas with Daniel Gerlanc

—

2:00-6:00 | Half-Day Training: Scalable Machine Learning with Kubernetes and Kubeflow by John Tate

Oct 30

9:00-1:00 and 2:00-6:00 | Full-Day Training: Introduction to Deep Learning for Engineers with Lukas Biewald, Chris Van Pelt, and Stacey Svetlichnaya

Oct 31

Keynotes until 10:30

—

11:00-11:45 | Talk: Model-based Learning and Optimization with Emo Todorov, PhD

—

12:00-12:45 | Talk: Machine Learning (ML) on Devices: Beyond the Hype with Divya Jain

—

2:00-2:45 | Talk: Deploying AI for Near Real-Time Engineering Decisions with Heather Gorr, PhD

—

3:00-3:45 | Talk: Scaling Machine Learning from 0 to Millions of Users with Shashank Prasanna

—

4:00-5:30 | Workshop: Pomegranate: Fast and Flexible Probabilistic Modeling in Python with Jacob Schreiber

Nov 1

Keynotes until 10:30

—

11:00 – 11:45 | Talk: diff software_dev software_dev*ai with Jana Eggers

—

12:00-12:45 | Talk: Combining Word Embeddings with Knowledge Engineering by Sanjana Ramprasad

—

2:00-2:45 | Talk: Building Modern ML/AI Pipelines with the Latest Open Source Technologies with Chris Fregly

—

3:00-3:45 | Talk: Product Search in E-Commerce: What to Optimize? with Liang Wu, PhD

—

4:00-4:45 | Talk: Looking from Above: Object Detection and Other Computer Vision Tasks on Satellite Imagery with Xiaoyong Zhu

In just a few days thousands of data scientists will come together to learn, connect, and be inspired. We hope that the above pathways help you have the best possible conference learning experience, but don’t forget to also take some time to relax and have fun at our community-focused events. We can’t wait to see you soon.