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Data By the Bay

Data By the Bay is the first Data Grid conference matrix with 6 vertical application areas spanned by multiple horizontal data pipelines, platforms, and algorithms. We are unifying data science and data engineering, showing what really works to run businesses at scale.

Nicole H. Romano

RadiumOne
Data Scientist
San Francisco

nicolehromano.com

I come to data science by way of a PhD in Materials Science and Engineering, in which I used machine learning to design an implant for spinal cord regeneration.

At RadiumOne, I develop machine learning algorithms that integrate data from the physical world (geolocation), the digital world, and third-party APIs to discover valuable user segments for targeted advertising. I also prototype lightweight automation of these algorithms to rapidly identify high-value users and optimal bid pricing in the Real-Time Bidding marketplace. I'm interested in technologies like python, distributed machine learning engines (e.g. Spark), graph-based tools (e.g. graphLab), and Hive on Hadoop.

9:50am PDT

Building a Realtime Receiver with Spark Streaming MarkovSal Uryasev

11:40am PDT

Characterizing and measuring the performance of Big Data processing platforms MarkovManish Singh

1:10pm PDT

Building domain specific databases with a distributed commit log [Kafka/Kubernetes] MarkovYunjing Xu

2:10pm PDT

Twitter Heron in Practice AdaKarthik Ramasamy

3:00pm PDT

Speed up app development with prefabricated, extensible, open-source backends GardnerSlava Akhmechet

4:00pm PDT

Quantitative Trading with Machine Learning AdaMartin Froehler

10:40am PDT

lda2vec AdaChristopher Erick Moody

1:40pm PDT

Of Rules and Probabilities: Computational Linguistics Methods for Improving Machine Learning Models AdaVita Markman

2:30pm PDT

Building AI that Searches and Sells AdaAnand Kulkarni

10:40am PDT

Predicting Hacker News with Beam and TensorFlow AdaEric Anderson The Promise of Heterogeneous Computing MarkovSubbu Rama

11:40am PDT

Recommendations for Building Machine Learning Software AdaJustin Basilico

1:10pm PDT

Fast deep recurrent net training GardnerSanjeev Satheesh Building realtime efficient queries and batch jobs in an IOT platform AdaDinesh Narayanan IoT security using data analysis MarkovDr. May Wang

2:10pm PDT

Scalable Training of RNNs for Speech Recognition GardnerErich Elsen Deep dive and best practices of Spark streaming AdaPrakash Chockalingam

3:00pm PDT

Hidden GEMMs: How Optimized Math Libraries Work AdaMarek Kolodziej

4:00pm PDT

Optimizing Machine Learning Models MarkovScott Clark

9:50am PDT

On the path to data Nirvana: Supporting OLTP and OLAP on Hadoop AdaMonte Zweben

1:40pm PDT

How to Use Big Data to Inspire Consumer Confidence AdaDaniel Doerr

3:30pm PDT

How Data helps UX evolves at realtor.com AdaIan Lin Urban Heartbeat: Data Experiments with Place GardnerSteve Pepple

4:00pm PDT

Extracting Medical Attributes from Clinical Trials GardnerSanghamitra Deb