ODSC Webinar Calendar

ODSC’s free webinar series serves to educate our community on the languages, tools, and topics of AI and Data Science

ODSC Webinar: Rapid AI-powered Apps Prototyping, and Deep Learning Model to Detect Fraudulent Attacks

January 21, 2020
1 pm – 2:30 pm EST
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8/1/2020 10:00 AM
America/Los_Angeles
ODSC East 2020 Warm Up

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ODSC Webinar

Baha Abu Nojaim
Cofounder at Baseet.ai

Zero to Production: Rapid AI-powered apps prototyping and development

Building AI-powered applications is a complex and challenging process wrought with unforeseen costs and setbacks. But what if any developer could build and prototype a sophisticated and functional AI-powered application as quickly and efficiently as they could launch a new webpage on Wix or Squarespace? This would be a game-changer in the world of AI, and it’s exactly what Baha Abu Nojaim, Cofounder @ Baseet.ai, is going to show. 

Baha Abu Nojaim

A serial entrepreneur, technologist, MIT Innovator Under 35, and co-founder at Baseet.ai solving complex problems at the intersection between the digital world and the physical world with a passion to tackle world-class challenges in transformative tech.

Nicola Corradi
Deep Learning Research Engineer at DataVisor

Deep Learning Model to Detect Fraudulent Attacks

Fraudulent attacks such as application fraud, fake reviews, and promotion abuse have to automate the generation of user content to scale; this creates latent patterns shared among the coordinated malicious accounts. Nicola Corradi digs into a deep learning model to detect such patterns for the identification of coordinated content abuse attacks on social, ecommerce, financial platforms, and more.

Nicola Corradi, PhD

Nicola Corradi is a Research Scientist at DataVisor, where he uses his vast experience with neural networks to design and train deep learning models to recognize malicious patterns in user behaviour. He earned a PhD in cognitive science (University of Padua) and did a post-doc at Cornell in computational neuroscience and computer vision, focusing on the integration of computational model of the neurons with neural networks.


ODSC East 2020 Webinar Warm Up: Machine Learning & Computer Vision

February 19, 2020
1 pm – 2 pm EST
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19/2/2020 10:00 AM
America/Los_Angeles
ODSC East 2020 Warm Up

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ODSC East Webinar

Joy Payton
Supervisor, Data Education, Children’s Hospital of Philadelphia

Data Science and Machine Learning in the Cloud for Cloud Novices

In this hands-on training at the conference, we will use free-tier resources in the Google Cloud Platform (GCP) to introduce learners to the practical use of cloud computing resources in data science and machine learning. This training will be useful for those considering cloud adoption, interested in data engineering, or interested in working with public data as citizen scientists.
Topics covered will include: Cloud computing concepts and vocabulary; Cloud providers; Free tier and cost considerations; Public datasets and citizen science; Redundancy, security, and privacy; Continuum of management levels; Cloud data storage and analytics; Machine learning in the cloud.

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.

Patrick Buehler, PhD
Principal Data Scientist, Microsoft

How to Solve Real-World Computer Vision Problems Using Open-source

At the conference, the workshop will begin with an overview of common real-world tasks in the CV domain, including examples of problems our customers have faced in recent years. We will then give a brief introduction to deep learning models for CV. The main part of this session will demonstrate how to train and evaluate CV models by executing notebooks based on PyTorch’s Fast.ai and Torchvision libraries. We will start with image classification, how to fine-tune a pre-trained ImageNet model on a custom dataset, and show how to deploy the model to the cloud. Next, we will train an object detection model and extend the model to segmentation masks and keypoints. Finally, we will build an image similarity system and demo a fast image retrieval solution that can handle large amounts of images.

Patrick Buehler, PhD

Patrick Buehler is a principal data scientist at Microsoft’s Cloud AI Group. He obtained his PhD from the Oxford VGG group in Computer Vision with Prof. Andrew Zisserman. He has over fifteen years of working experience in academic settings and with various external customers spanning a wide range of Computer Vision problems.


ODSC East 2020 Webinar Warm-Up


Dr. Jon Krohn
Chief Data Scientist at untapt and author of Deep Learning Illustrated

Deep Learning (with TensorFlow 2)

Relatively obscure a few short years ago, Deep Learning is ubiquitous today across data-driven applications as diverse as machine vision, natural language processing, and super-human game-playing. This Deep Learning primer brings the revolutionary machine-learning approach behind contemporary artificial intelligence to life with interactive demos featuring TensorFlow 2.0, the major, cutting-edge revision of the world’s most popular Deep Learning library.

Jon Krohn

Jon Krohn is Chief Data Scientist at the machine learning company untapt. He presents an acclaimed series of tutorials published by Addison-Wesley, including Deep Learning with TensorFlow and Deep Learning for Natural Language Processing. Jon teaches his deep learning curriculum in-classroom at the New York City Data Science Academy and guest lectures at Columbia University. He holds a doctorate in neuroscience from the University of Oxford and, since 2010, has been publishing on machine learning in leading peer-reviewed journals. His book, Deep Learning Illustrated, was published by Pearson in 2019.

Ali Vanderveld, PhD
Director of Data Science at ShopRunner

Using Deep Learning to Build a Unified E-commerce Marketplace

ShopRunner is an e-commerce company that receives feeds of product data from many different retailer partners, including large department stores and retailers that specialize in electronics, appliances, nutritional products, and more. In order to provide a great user experience on our website and in our mobile app, we need to have one easy-to-navigate product taxonomy. We also would like to have sets of attribute tags that make it easy to filter down to exactly what any shopper is looking for. In this talk I will describe how we are using computer vision and natural language processing to place all of the products from our retailer partners into one easy-to-navigate shopping experience.

Ali Vanderveld

Ali Vanderveld is Head of Data Science at ShopRunner, where her team leverages data from a network of over 140 retailers to build products for their 6 million members. Prior to ShopRunner, she was a staff data scientist at Civis Analytics, a consulting and software startup that helps companies, nonprofits, and political organizations better utilize their data. She has also worked at Groupon and as a technical mentor for the Data Science for Social Good Fellowship. Ali has a PhD in theoretical astrophysics from Cornell University and got her start working as an academic researcher at Caltech, the NASA Jet Propulsion Laboratory, and the University of Chicago, working on the development teams for several space telescope missions, including ESA’s Euclid.

Veysel Kocaman, PhD
Senior Data Scientist at John Snow Labs

Spark NLP for Healthcare: Lessons Learned Building Real-World Healthcare AI Systems

At the conference, the speaker will review case studies from real-world projects that built AI systems using Natural Language Processing (NLP) in healthcare. These case studies cover projects that deployed automated patient risk prediction, automated diagnosis, clinical guidelines, and revenue cycle optimization. He will also cover why and how NLP was used, what deep learning models and libraries were used, and what was achieved. Key takeaways for the conference attendees will include important considerations for NLP projects including how to build domain-specific healthcare models and using NLP as part of larger and scalable machine learning and deep learning pipelines in distributed environment.

Veysel Kocaman

Veysel Kocaman is a Senior Data Scientist and ML Engineer at John Snow Labs and have 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.


Adding Optimization to Your Data Science Analytics Toolkit


Dr. Gwyneth Butera
Sr. Support Engineer at Gurobi

Dr. Russell Halper
Principal at End-to-End Analytics

Adding Optimization to Your Data Science Analytics Toolkit

Mathematical optimization, specifically Mixed Integer Programming (MIP), is a technology that is used to solve a large variety of problems within multiple industries, including supply chain planning, electrical power generation and distribution, computational finance, sports scheduling, and many more. This powerful technology is complementary to Machine Learning and should be a part of every data scientist’s analytics toolbox. In this webinar, you will learn:
– The basics of optimization and MIP
– How to identify optimization problems within your organization
– When to use MIP vs Artificial Intelligence (AI) when developing a prescriptive analytics solution for your business problem
– How MIP can be used as a complementary technique to Machine Learning
We will present real-world examples of Machine Learning and optimization in action, illustrating the value it can bring to your organization. We will also provide you with next steps on how to get started with optimization as well as available resources.

Dr. Gwyneth Butera

Dr. Butera has over 20 years of optimization software experience. Prior to joining Gurobi Optimization, she worked as a software engineer for IBM ILOG CPLEX Optimization Studio. More recently, she completed an immersive course in Data Science. She has her PhD in Computational and Applied Mathematics from Rice University.

Dr. Russell Halper

Dr. Halper holds a PhD in Applied Mathematics from the University of Maryland. Russell is passionate about developing actionable, pragmatic analytics that align closely with business processes to generate world-class results. He has devoted his career to developing cutting-edge analytics solutions to problems in supply chain, manufacturing, and marketing.


Evolutionary AI is the new Deep Learning


Babak Hodjat, PhD
Siri Co-Inventor, VP Evolutionary AI, Founder | Cognizant, Sentient Technologies

Evolutionary AI is the new Deep Learning

Today’s AI systems are heavily engineered, data-hungry, and costly to build, as they require immense amounts of processing capacity. Once deployed these ML-based systems, while robust, do not quite keep up with a changing environment, priorities, and needs. Furthermore, while the state-of-the-art in AI in discovering insights from raw data is promising, the decision-making based on these insights is often left to human judgement or rigid predetermined rules. In this talk, we will introduce a complementary technology called Evolutionary AI, which, by virtue of being creative, addresses many of the problems noted above. We will show how Evolutionary AI can automatically design Deep Learning systems, and help generate adaptive decision strategies by constructing prescriptive models.

Babak Hodjat, PhD

Babak Hodjat is VP of Evolutionary AI at Cognizant, and former co-founder and CEO of Sentient, responsible for the core technology behind the world’s largest distributed artificial intelligence system. Babak was also the founder of the world’s first AI-driven hedge-fund, Sentient Investment Management. Babak is a serial entrepreneur, having started a number of Silicon Valley companies as main inventor and technologist. Prior to co-founding Sentient, Babak was senior director of engineering at Sybase iAnywhere, where he led mobile solutions engineering. Prior to Sybase, Babak was co-founder, CTO and board member of Dejima Inc. Babak is the primary inventor of Dejima’s patented, agent-oriented technology applied to intelligent interfaces for mobile and enterprise computing – the technology behind Apple’s Siri. Babak is a published scholar in the fields of Artificial Life, Agent-Oriented Software Engineering, and Distributed Artificial Intelligence, and has 31 granted or pending patents to his name. He is an expert in numerous fields of AI, including natural language processing, machine learning, genetic algorithms, distributed AI, and has founded multiple companies in these areas. Babak holds a PhD in Machine Intelligence from Kyushu University, in Fukuoka, Japan.


Machine Learning Operationalization (MLOps): Harness the real power of AI


Nitin Aggarwal
Technical Program Manager, Machine Learning at Google Cloud

ML Operationalization (MLOps): Harness the real power of AI

Recent boom in AI pushed lot of organizations to harness it’s real power and make real impact on the business. Even after having some good ML talent, majority of the AI initiatives ended as a science project. It is very important to productionize and operationalize such solutions with existing systems. There are many ways to do it. Different cloud providers has their own ways to reduce overall burden of building and managing such systems on the businesses. Let’s discuss what are the important factors that we should keep in our mind while productioning any AI/ML system and how the landscape looks like.

Nitin Aggarwal

Nitin is a Technical Program Manager who works with Google Cloud to solve challenging business problems for Google’s strategic customers using cutting edge AI/ML technologies. Nitin believes that responsible and explainable AI can solve real world problems. He has worn many hats during his stint of professional career – Software Engineer, Management Consultant, Data Scientist and ML leader. Nitin holds a degree in engineering and business administration.


ODSC Europe Warm-Up

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Michal Mucha
Senior Data Scientist (independent consultant) at create.ml

Make Beautiful Web Apps from Jupyter Notebooks

Are you a data scientist looking for new and powerful ways to ship data products to users within your organization?
This session offers a crash course in new open source Python, Jupyter and PyData tools to rapidly prototype interactive apps that are as expressive and powerful as any notebook you can build!
Come and learn how to quickly turn Jupyter notebooks – a central element in a data science workflow – into beautiful web apps to share with team members and stakeholders in your organization!

Michal Mucha

Michal leads a successful data science consultancy, delivering strategy and execution on data science, data engineering and ML projects, with clients in retail, transportation, finance, film, and building automation. Beyond technical contributions, he has developed successful approaches to create lasting change in the alignment in organizational collaboration, data literacy, and unlock unrealized human potential that is being stopped by elusive inefficiencies in process and culture.
He holds a Masters degree in Econometrics and Information engineering form Poznan University of Economics. He participated in commercial research projects, including mobile phone data research with published results.


Opening The Black Box - Interpretability In Deep Learning

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Matteo Manica, PhD
Research Staff Member at Cognitive Health Care & Life Sciences, IBM Research Zürich

Opening The Black Box - Interpretability In Deep Learning

The recent application of deep neural networks to long-standing problems has brought a break-through in performance and prediction power. However, high accuracy often comes at the price of loss of interpretability, i.e. many of these models are black-boxes that fail to provide explanations on their predictions. This webinar will be an introduction to the ODSC Europe 2019’s training, which will focus on illustrating some of the recent advancements in the field of interpretable artificial intelligence. We will show some common techniques that can be used to explain predictions on pretrained models and that can be used to shed light on their inner mechanisms. The training is aimed to strike the right balance between theoretical input and practical exercises. The session has been designed to provide the participants not only with the theory behind deep learning interpretability, but also to offer a set of frameworks, tools and real-life examples that they can implement in their own projects.

Matteo Manica, PhD

Matteo is a Research Staff Member in Cognitive Health Care and Life Sciences at IBM Research Zürich. He’s currently working on the development of multimodal deep learning models for drug discovery using chemical features and omic data. He also researches in multimodal learning techniques for the analysis of pediatric cancers in a H2020 EU project, iPC, with the aim of creating treatment models for patients. He received his degree in Mathematical Engineering from Politecnico di Milano in 2013. After getting his MSc he worked in a startup, Moxoff spa, as a software engineer and analyst for scientific computing. In 2019 he obtained his doctoral degree at the end of a joint PhD program between IBM Research and the Institute of Molecular Systems Biology, ETH Zürich, with a thesis on multimodal learning approaches for precision medicine.


Accelerating AI-driven Innovation in Your Enterprise

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Pallav Agrawal
Director, Data Science at Levi Strauss & Co.

Accelerating AI-driven Innovation in Your Enterprise

The presentation will focus on best practices to develop ML powered applications that can move the needle on business critical KPIs. We will walk through a rapid prototyping framework to develop effective personalization experiences, the mindsets and skills required to execute on an innovation roadmap, how to evaluate and work with vendors that provide ‘AI-powered’ solutions, and how to design experiments to quickly iterate towards a better experience for customers.

Pallav Agrawal

During daytime, Pallav works as a Data Scientist and tries to extract meaningful signals from the noisy world we live in. As the moon rises and evening sets in all bets are off and one might find Pallav on his bike riding through the Berkeley hills in bright colored lycra or performing never-before-scenes of Dramedy with his Improv troupe.

Pallav is a part-time Human Centered Design Thinking coach and has helped non-profits and early-age startups develop clarity on their mission and recognize growth areas. He moved to the Bay Area in 2010 and somehow managed to acquire a Masters in Structural Engineering after spending two years actually learning how to think.

He is an avid follower of Seth Godin, Ken Robinson, and Nicholas Taleb, and is currently looking at ways to explain algorithms through cute, anthropomorphized animals.


Introduction to Deep Learning Models for Computer Vision

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Haidar Altaie
Data Scientist at SAS UK&I

Spiros Potamitis
Data Scientist at SAS

Introduction to Deep Learning Models for Computer Vision

In this Webinar, we will discuss the application of DL models using DLPy focusing on Computer Vision. DLPy is a high-level Python API designed to provide an efficient way to apply Deep Learning functionalities using friendly Keras-like APIs to solve Computer Vision, Natural Language Processing, Forecasting, and Speech Processing problems. We explain how DLPy can be applied to data preparation, data processing, model building, assessment and deployment.

This will be a preview of our more in-depth presentation, specifically focused around Multi-Task Deep Learning For Image Tagging, during ODSC Europe in London this November.

Haidar Altaie

Haidar Altaie is a Data Scientist at SAS UK&I. He joined SAS in September 2018 after graduating with a Mathematics and Statistics degree, and is now passionate to integrate Advanced Analytics, Machine Learning, Forecasting and Computer Vision techniques across various industries to enable customer to solve complex real life problem.

Spiros Potamitis

Spiros Potamitis is a data scientist at SAS, a leading software and services provider in advanced analytics. Having acquired an MSc in Information Management from the University of Manchester, Spiros is specialising in the application and implementation of analytics to drive business outcomes. Prior of joining SAS, Spiros has acquired a wealth of predictive modelling experience while working in advanced analytics positions in Credit Risk, Customer Insights and CRM.

Free access to ODSC talks and content is available at our

AI Learning Accelerator

ODSC EAST | Boston

– April 30th – May 3rd, 2019 –

The World’s Largest Applied Data Science Conference

ODSC EUROPE | London

– Nov 19th – 22nd, 2019 –

Europe’s Fastest Growing Data Science Community

ODSC WEST | San Francisco

– Oct 29th – Nov 1st, 2019 –

The World’s Largest Applied Data Science Conference

Accelerate AI

Business Conference

The Accelerate AI conference series is where executives and business professionals meet the best and brightest innovators in AI and Data Science. The conference brings together top industry executives and CxOs that will help you understand how AI and data science can transform your business.

Accelerate AI East | Boston

– April 30th – May 1st, 2019 –

The ODSC summit on accelerating your business growth with AI

Accelerate AI Europe | London 

– Nov 19th – 20th, 2019 –

The ODSC summit on accelerating your business growth with AI

Accelerate AI West | San Francisco 

– Oct 29th – 30th, 2019 –

The ODSC summit on accelerating your business growth with AI