Software Engineering for Machine Learning Applications (SEMLA)

The Software Engineering for Machine Learning Applications (SEMLA) international symposium, to be held on May 23 and 24 2019, aims at bringing together leading researchers and practitioners in software engineering and machine learning to reflect on and discuss the challenges and implications of engineering complex data-intensive software systems.

A growing concern is getting ahold of today’s software engineers in a world where data science and deep learning are becoming increasingly pervasive. While everyone agrees that the development of new machine learning algorithms will enhance the potential of data analytics, only their implementation in software systems will allow them to realize their full potential. Dave Parnas expressed this concern in his recent ACM Communications paper.

From an engineering perspective, once an algorithm is implemented, it requires a solid architecture, model/data validation, proper monitoring for changes, dedicated release engineering strategies, judicious adoption of design patterns and security checks, and thorough user experience evaluation and adjustment. All these activities require a combined knowledge in software engineering, data science, and machine learning.

Hence, SEMLA invites all practitioners and researchers at the cross-bridge of software engineering and machine learning to get to know the results and opinions of world leaders in both machine learning and software engineering and to discuss their views on the above problems.

The event will include KEYNOTES by:

• Thomas G. Dietterich
• Lionel Briand
• Stephen J. Eglash
• François Laviolette

INDUSTRY TALKS by:

• Tina Yang (SAP)
• Danny Tarlow (Google Brain)
• Mathieu Nayrolles (Ubisoft)

ACADEMIC TALKS by:

• Ahmed Hassan
• Tim Menzies
• David Lo
• Fuyuki Ishikawa
• Lei Ma
• Jie Zhang
• Amal Bennaceur
• Zhen Ming (Jack) Jiang
• Giovanni Beltrame

PLUS: panels and hands-on sessions, reception with a poster session, and ample time for networking and discussion!

Please visit https://semla.polymtl.ca for information and registration.

Registration for Polytechnique professors and full-time graduate students are free but required.