Accelerate AI East 2019

Preliminary Session Schedule

Accelerate AI East Session Schedule

Schedule Guide for Pass Holders

Accelerate AI has passes for 2 days and 4 days available

The Accelerate AI session schedule below is for Tuesday April 30th and Wednesday May 1st September 19th. It is accessible to Accelerate AI 2 Day & 4 Day and all VIP pass holders

The ODSC Talks/Workshop schedule includes Thursday May 2nd andFriday May 3rd. It is available is to Accelerate AI 4 Day Pass holders plus ODSC Silver, Gold, Platinum, Platinum Business, and all VIP Pass holders

Speaker and speaker schedule times are subject to change. More sessions added weekly

East 2019 Preliminary Schedule

We are delighted to announce our East 2019 Preliminary Schedule which lists ~45% of our sessions.  Session times will be added in the coming week in addition to more talks, workshop, and training sessions. 
-Accelerate AI East
East Talks & Workshops
Thursday, May 2nd
Friday, May 3rd
Thursday, May 2nd
Friday, May 3rd
Continuous Improvement of Chat, Social, and Survey Interactions Using AI “Idea analysis”

Business Talk | Cross Industry | Beginner-Intermediate-Advanced


How does customer experience/digital marketing know what customers are saying to our human chat, bot chat, survey, or social? Why are they not satisfied or not moving to the next action? The first step is to deeply analyze customer conversations. A new generation of AI technology makes this possible, extracting the ideas contained in text to summarize, organize, and display for analysis…more details

Ben Vigoda, PhD
CEO | Gamalon
Big Data and Mobility Analytics: What can we learn from the way things (and humans!) move?

Business Talk | Cross industry | Intermediate


Things (IoT) will mean that the amount of devices that connect to the Internet will rise massively. This is already giving rise to the creation of massive amounts of data. Spatial and temporal mobility patterns of things and societies as a whole can be characterized based on the interactions that we are able to capture from the IoT sensors.
In this presentation, we will review what we can learn from human mobility patterns, how they can be used to optimize traffic, city planning and tourist attractions. We will review the challenges associated with privacy and security regulation when analyzing mobility patterns. As an application we will present an study on AIS data that describes the locations of vessels traveling in Norwegian seas. We will close the presentation with an overview of the kind of AI techniques we can apply to analyze mobility patterns…more details

Arturo Amador, PhD
Senior Consultant | Capgemini Norway
Generating Programs for Living Cells

Talks | Research Bridge | Deep Learning | Intermediate


Living cells naturally perform information processing using genetic circuits — networks of DNA-encoded genes that dynamically sense and respond to their environment. The ability to engineer new genetic circuits has broad applications in biotechnology, personalized medicine and human health. Biologically informed, predictive, and robust genetic circuit design tools are necessary to realize the next generation of intelligent treatments.

Despite progress over the past two decades, genetic circuit design remains imprecise. Computer-aided design tools have been coupled with genetic part libraries to design complex genetic circuits. However, many circuits fail to function as predicted due to hidden genetic interactions, contextual effects at the DNA-level, and other subtle biophysical phenomena.

Here, we discuss a radically new approach for designing and inferring performance of genetic circuits. We use machine learning to fit complex biophysical models that map to semantic structures to enable more accurate forward inference of circuit performance. Methods adapted from Bayesian inference, sequence-to-sequence models and deep generative models are applied to realize and introspect biophysical systems…more details

Joe Isaacson
Vice President of Engineering | Asimov
AIOps: Anomaly detection with Prometheus

Talk | Open Source Data Science | Beginner-Intermediate


As IT operations become more agile and complex, at the same time the need to enhance operational efficiency and intelligence grows. Monitoring applications and kubernetes clusters with Prometheus has become quite common. Yet identifying relevant metrics and thresholds for your setup is getting harder.

In this talk, Marcel will show the tooling used to collect and store metrics gathered by Prometheus for the long term. Then analyze those on a large scale using Spark. This includes extracting trends and seasonality but also forecasting of expected values for a given metric. Finally, he will integrate the predicted metrics back into the Prometheus monitoring and alerting stack to enable dynamic thresholding and anomaly detection…more details

Marcel Hild
Principal Software Engineer | Red Hat
Coming soon!

Talks | Data Science Management


Coming soon..more details

Peter Wang
CTO | Anaconda
Secrets to Building a Top Trending Alexa skill

Workshop | AI for Engineers | Big Data | Beginner-Intermediate


Throughout the history of computing, humans had to interact with machines in an abstract and complex ways starting with punch cards to machine code to command line to graphic user interfaces. Machines still force us to communicate with them on their own terms. However with commercialization of Voice Enabled Devices like Amazon Echo, finally time has come where we are able to communicate with machines in a more natural way using our voice.Voice as an interface for communication with devices is going to very prevalent in the coming years. This workshop focuses on jumpstarting developers who are curious about building skills on Amazon Alexa. Topics like Conversational UX, Interaction Schema, Entity Resolution, Dialog Management, Command Line Utilities for Skill Management and Alexa Skills Kit SDK. By the end of the workshop, attendees will walk out with a fully functional Alexa skill that they can test on the simulator of deploy to their Amazon Echo devices….more details

Shanthan Kesharaju
Director of Product Management | Fidelity Investments
Coming soon!

Workshops | Open Source Data Science


Coming soon..more details

Yunus Genes, PhD
Pan-cancer machine learning predictors of primary site of origin and molecular subtype

Talks | Data Visualization | Research Bridge | Intermediate


It is estimated by the American Cancer Society that approximately 5% of all metastatic tumors have no defined primary tissue of origin and are classified as cancers of unknown primary origin (CUPs). The current standard of care for CUP patients depends on immunohistochemistry (IHC) based approaches to identify the primary site. The addition of post-mortem evaluation to IHC based tests helps to reveal the identity of the primary site for only 25% of the CUPs, emphasizing the acute need for better methods of determination of the site of origin. CUP patients are therefore given generic chemotherapeutic agents resulting in poor prognosis. When the tissue of origin is known, patients can be given site specific therapy with significant improvement in clinical outcome. Similarly, identifying the primary origin of metastatic cancer is of great importance for designing treatment. Identification of the primary site of origin is an import first step but may not be sufficient information for optimal treatment of the patient. Recent studies, primarily from The Cancer Genome Atlas (TCGA) project, and others, have revealed molecular subtypes in several cancer types with distinct clinical outcome. The molecular subtype captures the fundamental mechanisms driving the cancer and provides information that is essential for the optimal treatment of a cancer. Thus, along with primary site of origin, molecular subtype of a tumor is emerging as a criterion for personalized medicine and patient entry into clinical trials. However, there is no comprehensive toolset available for precise identification of tissue of origin or molecular subtype for precision medicine and translational research. Methods and Findings: We posited that metastatic tumors will harbor the gene expression profiles of the primary tissue of origin of the cancer. Therefore, we decided to learn the characteristics of the primary tumors using the large number of cancer genome profiles available from the TCGA project. Our predictors were trained for 33 cancer types and for the 11 cancers where there are established molecular subtypes. We estimated the accuracy of several machine learning models using cross-validation methods and external validation sets. The extensive testing using independent test sets revealed that the predictors had a median sensitivity and specificity of 97.2% and 99.9% respectively without losing classification of any tumor. Subtype classifiers achieved median sensitivity of 87.7% and specificity of 94.5% via cross validation and presented median sensitivity of 79.6% and specificity of 94.6% in two external datasets of 1,999 total samples. Importantly, these external data shows that our classifiers can robustly predict the cancer primary origin from microarray data, metastatic cancer, and patient-derived xenograft (PDX) mouse models. Conclusion: We have demonstrated the utility of gene expression profiles to solve the important clinical challenge of identifying the primary site of origin and the molecular subtype of cancers based on machine learning algorithms. We show, for the first time to our knowledge, that our pan-cancer classifiers can predict multiple cancers’ primary tissue of origin from metastatic samples. The predictors will be made available as open source software, freely available for academic non-commercial use…more details