Machine Learning Internship at Adobe Research, San Jose

Machine Learning Group in Big Data Experience Lab (BEL) at Adobe Research (https://research.adobe.com/about-the-labs/bigdata-...) in San Jose is looking for interns to work on a range of problems in machine learning, deep learning, digital marketing, and analytics. Our interns will have opportunity to work on real-world terabyte-scale problems in Adobe Marketing Cloud (http://www.adobe.com/marketing-cloud.html). The interns will be supervised by researchers in the group who have excellent publication record with dozens of papers at top-tier machine learning and AI conferences and journals in recent years. Our research topics include:
- Large-scale reinforcement learning, online learning, bandits (contextual and combinatorial), A/B and hypothesis testing
- Graphical models and approximate inference
- Deep learning, deep reinforcement learning, and representation learning
- Time-series prediction and spatial-temporal analysis
- Causal inference
- Risk analysis and risk-sensitive optimization
- Anomaly and change detection in high-dimensional data
- Data cleansing: imbalanced data, categorical variables, missing values, dimensionality reduction, feature selection
- Large-scale recommender systems
- Activity recognition from web, mobile, and location data
- Clustering / similarity metrics / embeddings of user trajectories
- Big data visualization
The internship will be in San Jose, California, at the heart of the Silicon Valley. The duration of the internship is 12 weeks and it can start any time from April 1, 2017.
Beyond Adobe's traditional strength in media technologies, the BEL lab is focusing on areas related to digital marketing and analytics, in particular problems related to Adobe's Digital Marketing Cloud. Adobe is the leading provider of digital marketing and analytics solutions with customers including big banks, hotels, online retails, insurance and entertainment companies.
The successful candidate will be mentored and work closely with one or more of the following Adobe researchers:
- Yasin Abbasi Yadkori (http://webdocs.cs.ualberta.ca/~abbasiya)
- Hung Bui (https://sites.google.com/site/buihhung)
- Mohammad Ghavamzadeh (http://chercheurs.lille.inria.fr/~ghavamza)
- Branislav Kveton (http://www.bkveton.com)
- Georgios Theocharous (http://www.adobe.com/technology/people/san-jose/ge...)
- Nikos Vlassis (https://sites.google.com/site/nikosvlassis)
- Zheng Wen (http://www.zheng-wen.com)
* Requirements *
The applicants should be either at the final stage of a Master's or in a PhD program in Computer Science, Statistics, Operations Research, Applied Mathematics, or related fields; with a strong background in AI, machine learning, and good programming skills. We are particularly interested in candidates with prior exposure to deep learning, optimization, statistics, reinforcement learning, bandits, and scalable machine learning.
* Application Submission *
The deadline for the application is January 31, 2017, but we encourage the applicants to apply as soon as possible. The screening of the candidates will start on February 1, 2017, and will continue until the positions are filled. The application should include a brief description of the applicant's research interests and past experience, plus a CV that contains the degrees, GPAs, relevant publications, names and the contact information of references, and other relevant documents.
To apply, please send your application to machine-learning-internships-AT-adobe.com.