Bio
Tal is a Data Scientist at PayPal Risk, a global team of Data Scientists and Decision Scientists, responsible for balancing between loss and customer experience by providing innovative and cohesive models and solutions throughout the seller lifecycle. Prior to PayPal Tal graduated the ITC fellows’ program where she gained hands-on experience in machine learning, deep learning and statistical analysis.
Tal is a Biomedical engineer with a proven track record in the field of cyber-security and medical devices with over 7 years of experience designing embedded system and managing products throughout their full life cycle.
Bio
Tal is a Data Scientist at PayPal Risk, a global team of Data Scientists and Decision Scientists, responsible for balancing between loss and customer experience by providing innovative and cohesive models and solutions throughout the seller lifecycle. Prior to PayPal Tal graduated the ITC fellows’ program where she gained hands-on experience in machine learning, deep learning and statistical analysis.
Tal is a Biomedical engineer with a proven track record in the field of cyber-security and medical devices with over 7 years of experience designing embedded system and managing products throughout their full life cycle.
Abstract
Sequence models are vastly used in our daily routine, enabling numerous exciting applications in speech recognition, music synthesis, chatbots, machine translation, natural language understanding, and many more. But can we utilize sequence information to represent behavior?
In this poster, I will describe how we represented models’ predictions as sequential information and leveraged sequence modeling to detect and automatically mitigate merchants with risky modus operandi. I’ll present the various models we have considered, starting with TFIDF, Doc2Vec, followed by LSTM and 1D CNN and we’ll discuss the pros and cons in each approach.
Abstract
Sequence models are vastly used in our daily routine, enabling numerous exciting applications in speech recognition, music synthesis, chatbots, machine translation, natural language understanding, and many more. But can we utilize sequence information to represent behavior?
In this poster, I will describe how we represented models’ predictions as sequential information and leveraged sequence modeling to detect and automatically mitigate merchants with risky modus operandi. I’ll present the various models we have considered, starting with TFIDF, Doc2Vec, followed by LSTM and 1D CNN and we’ll discuss the pros and cons in each approach.
Planned Agenda
| 8:45 | Reception |
|---|---|
| 9:30 | Opening words by Shir Meir Lador, Data Science leader at Intuit |
| 9:45 | Yael Karov - AI For Assisting in Task Completion |
| 10:15 | Ofra Amir - Agent Strategy Summarization: Describing Agent Behavior to People |
| 10:45 | Break |
| 11:00 | Lightning talks |
| 12:30 | Lunch & Poster session |
|---|---|
| 13:30 | Roundtable session & Poster session |
| 14:30 | Roundtable closure |
| 14:45 | Gal Yona - How Fair Can We Be |
| 15:15 | Daphna Weissglas - Turning Data Science Into Precision Medicine Empowering Millions |
| 15:45 | Closing remarks |
Planned Agenda
| 8:45 | Reception |
|---|---|
| 9:30 | Opening words by Shir Meir Lador, Data Science leader at Intuit |
| 9:45 | Yael Karov - AI For Assisting in Task Completion |
| 10:15 | Ofra Amir - Agent Strategy Summarization: Describing Agent Behavior to People |
| 10:45 | Break |
| 11:00 | Lightning talks |
| 12:30 | Lunch & Poster session |
| 13:30 | Roundtable session & Poster session |
| 14:30 | Roundtable closure |
| 14:45 | Gal Yona - How Fair Can We Be |
| 15:15 | Daphna Weissglas - Turning Data Science Into Precision Medicine Empowering Millions |
| 15:45 | Closing remarks |
