Sep 9–12, 2019

Ira Cohen
Cofounder, Chief Data Scientist, Anodot

Ira Cohen is a cofounder and chief data scientist at Anodot, where he’s responsible for developing and inventing the company’s real-time multivariate anomaly detection algorithms that work with millions of time series signals. He holds a PhD in machine learning from the University of Illinois at Urbana-Champaign and has over 12 years of industry experience.

Sessions

9:00am–12:30pm Tuesday, September 10, 2019

Location: LL21 C/D

Herding cats: Product management in the machine learning era

AI Business Summit, Executive Briefing/Best Practices

Secondary topics: Machine Learning

Ira Cohen (Anodot)

Average rating:

(3.50, 4 ratings)

While the role of the manager doesn't require deep knowledge of ML algorithms, it does require understanding how ML-based products should be developed. Ira Cohen explores what it takes to manage ML-based products, the cycle of developing ML-based capabilities (or entire products), and the role of the (product) manager in each step of the cycle. Read more.

1:45pm–2:25pm Wednesday, September 11, 2019

Location: LL21 C/D

Sequence to sequence modeling for time series forecasting

Models and Methods

Secondary topics: Deep Learning, Machine Learning, Temporal data and time-series

Arun Kejariwal (Independent), Ira Cohen (Anodot)

Average rating:

(4.40, 5 ratings)

Sequence to sequence (S2S) modeling using neural networks is increasingly becoming mainstream. In particular, it's been leveraged for applications such as speech recognition, language translation, and question answering. Arun Kejariwal and Ira Cohen walk you through how S2S modeling can be leveraged for the aforementioned use cases, visualization, real-time anomaly detection, and forecasting. Read more.