Machine Learning for Services
The Machine Learning for Services (MLS) group at XRCE provides Xerox Corporation with state-of-the-art expertise in machine learning and computational statistics.
The second part of the 20th century saw the advent of information technology as a result of the surge of digital communication and computers. Now, our modern societies are about to experience a data processing revolution. The wealth of structured and unstructured data that are currently being generated by humans (and machines) will soon reach levels beyond our comprehension. This creates both new opportunities and challenges.
One emerging grand challenge is to enable machines to reason under uncertainty. By machine reasoning is understood the ability of computers to draw conclusions or propose solutions to an unseen problem, based on past noisy observations. A less ambitious goal, and pre-requisite to machine reasoning, is the ability of computers to convert information extracted from the deluge of data into knowledge readily comprehensible by non-experts. In order to succeed, it will be required to device specialized data models and to develop scalable learning and inference algorithms that can automatically organize, filter, aggregate, and summarize petabytes of distributed, possibly heterogeneous data. Once this new knowledge will be integrated into commercial products and services, it will drastically change the way individuals, group of individuals and organizations communicate, work, make decisions, or even innovate.
Our mission is to facilitate the adoption of data-intensive algorithms for automated decision making, the emergence of complex adaptive systems, and enable the widespread use of predictive analytics capabilities in organizations. Our statistical models capture individual preferences, enable self-learning services and/or borrow from behavioural economics in order to be incentive compatible. The resulting evidence-driven solutions are expected to play a key role in Xerox’ next generation document and business process outsourcing services (www.youtube.com/watch?v=kEnRQAz2Fqg).
Our current research activities are articulated along three main research lines:
- Predictive analytics
- Mechanism design (or reverse game theory)
- Knowledge generation
Our research aims to advance machine learning methodology, including generative and discriminative data modelling, structured prediction, collaborative filtering, choice modelling, and relational learning. We pay special attention to principled ways to deal with uncertainty (noise and partial information) and large-scale data intensive processes. Our work requires strong expertise in Bayesian inference, as well as convex and nonlinear optimization. Our data-driven solutions are applied in a wide range of domains, including customer care (white paper), healthcare (ITFoM: video), transportation (LA Express: video), and participatory governance (FUPOL: www.fupol.eu).
The team participates to the European network of Excellence PASCAL and is an associate partner of the European flagship IT Future of Medicine . Present research collaborations include co-supervision of one PhD student with Francis Bach at Ecole Normale Supérieure and two PhD students with Mark Girolami at University College London. The team also supports research projects funded by the Xerox Foundation with Frank Wood at Columbia University, with Bin Yu at University of California Berkeley and Y. Narahari at the Indian Institute of Science.
If you are interested in joining the group for an internship, a PhD or a Post-doc, please contact Cedric Archambeau (cedric.archambeau@xrce.xerox.com).