Star leap plan | new projects are continuously being recruited! MSR Asia MSR Redmond joint research program invites you to apply!

Microsoft research Asia And Microsoft headquarters Jointly launched “ Star leap program ” You are invited to sign up for the scientific research cooperation project ！ This time “ Star leap program ” To sign up Add again From Microsoft E+D (Experiences + Devices) Applied Research New project of global headquarters , Welcome to pay attention and apply ！ What are you waiting for ？ Join in “ Star leap program ”, Cross the ocean with us , Explore more possibilities for scientific research ！

The program aims to create opportunities for outstanding talents to focus on real cutting-edge issues with the research team in Microsoft's global headquarters . You will be in an international scientific research environment 、 In a pluralistic and inclusive scientific research atmosphere 、 Under the guidance of top researchers , Do influential research ！

At present, the cross Institute joint scientific research projects still being recruited cover Intelligent recommendation 、 Image zoom 、 Computer vision 、 Behavior detection 、 Social Computing 、 Intelligent Cloud Other fields . The research projects are as follows ：Online Aesthetic-Aware Smart Image Resizing, UserBERT: Pretrain User Models for Recommendation, Visual representation learning by vision-language tasks and its applications, DNN-based Detection of Abnormal User Behaviors, Reinforcing Pretrained Language Models for Generating Attractive Text Advertisements. The open project of star leap plan will Continuous updating , Please keep up with the latest developments ！ 

（ At the end of the article, there is also a set of praise gifts and benefits , Don't miss ！）

Star leap highlights

• At Microsoft Research Asia 、 Microsoft global headquarters Guidance of top researchers Carry out scientific research work under , In depth communication with researchers from different research backgrounds

• focusing Real frontier issues from industry , We are committed to making achievements that have influence on academic and industrial circles

• Through offline and online communication and cooperation , Learn about at Microsoft internationalization 、 Open research atmosphere , And Diverse and inclusive culture

Apply for qualification

• Undergraduate 、 master 、 The doctor is reading students ; delay （deferred） Or every other year （gap year） Student

• Can work full-time in China 6-12 Months

• See the project introduction below for detailed requirements of each project

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What are you waiting for ？

Come and find the right project for you ！

Online Aesthetic-Aware Smart Image Resizing

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For the new Designer app and Designer in Edge, we need to resize templates to different sizes, since different social media platforms require different target dimensions of the media, e.g., Facebook Timeline Post for personal accounts and business pages (1200 x 628), LinkedIn timeline post (1200 x 1200), Twitter timeline post (1600 x 900), etc. Image is the center of a template design. We need an ML-powered technique to automatically resize (including aspect ratio change, crop, zoom in/out) an image and put it into a resized template (more specifically speaking, resized image placeholder) for the target platform, so that the image placement looks good (i.e., maintaining the aesthetic values).

Research Areas 

Computer Vision and Machine Learning

Qualifications

• Ph.D. students majoring in computer science, applied mathematics, electrical engineering or related technical discipline

• Relevant experience in the development and application of computer vision and/or machine learning algorithms to solve challenging image understanding problems

• Strong scientific programming skills, including C/C++, MATLAB, Python

• Independent analytical problem-solving skills

• Experience collaborating within research teams to develop advanced research concepts, prototypes, and systems

• Strong communication skills

UserBERT: Pretrain User Models for Recommendation

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Pretrained language models such as BERT and UniLM have achieved huge success in many natural language processing scenarios. In many recommendation scenarios such as news recommendation, video recommendation, and ads CTR/CVR prediction, user models are very important to infer user interest and intent from user behaviors. Previously, user models are trained in a supervised task-specific way, which cannot achieve a global and universal understanding of users and may limit they capacities in serving personalized applications.

In this project, inspired by the success of pretrained language models, we plan to pretrain universal user models from large-scale unlabeled user behaviors using self-supervision tasks. The pretrained user models aim to better understand the characteristics, interest and intent of users, and can empower different downstream recommendation tasks by finetuning on their labeled data. Our recent work can be found at https://scholar.google.co.jp/citations?hl=zh-CN&user=0SZVO0sAAAAJ&view_op=list_works&sortby=pubdate.

Research Areas 

Recommender Systems and Natural Language Processing

Qualifications

• Ph.D. students majoring in computer science, electronic engineering, or related areas
  

• Self-motivated and passionate in research

• Solid coding skills

• Experienced in Recommender Systems and Natural Language Processing

Visual representation learning by vision-language tasks and its applications

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Learning visual representation by vision-language pair data has shown highly competitive compared to previous supervised and self-supervised approaches, pioneered by CLIP and DALL-E. Such vision-language learning approaches have also demonstrated strong performance on some pure vision and vision-language applications. The aim of this project is to continually push forward the boundary of this research direction.

Research Areas 

Computer vision

https://www.microsoft.com/en-us/research/group/visual-computing/

https://www.microsoft.com/en-us/research/people/hanhu/

Qualifications

• Currently enrolled oversea Ph. D. students with promised or deferred offer, and is now staying in China

• Major in computer vision, natural language processing, or machine learning

DNN-based Detection of 

Abnormal User Behaviors

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Are you excited to apply deep neural networks to solve practical problems? Would you like to help secure enterprise computer systems and users across the globe? Cyber-attacks on enterprises are proliferating and oftentimes causing damage to essential business operations. Adversaries may steal credentials of valid users and use their accounts to conduct malicious activities, which abruptly deviate from valid user behavior. We aim to prevent such attacks by detecting abrupt user behavior changes.

In this project, you will leverage deep neural networks to model behaviors of a large number of users, detect abrupt behavior changes of individual users, and determine if changed behaviors are malicious or not. You will be part of a joint initiative between Microsoft Research and the Microsoft Defender for Endpoint (MDE). During your internship, you will get to collaborate with some of the world’s best researchers in security and machine learning.  

You would be expected to: 

• Closely work with researchers in China and Israel towards the research goals of the project.

•  Develop and implement research ideas and conduct experiments to validate them.

• Report and present findings.

Microsoft is an equal opportunity employer.

Research Areas 

Software Analytics, MSR Asia

https://www.microsoft.com/en-us/research/group/software-analytics/

Microsoft Defender for Endpoint (MDE)

This is a Microsoft engineering and research group that develops the Microsoft Defender for Endpoint, an enterprise endpoint security platform designed to help enterprise networks prevent, detect, investigate, and respond to advanced threats

https://www.microsoft.com/en-us/security/business/threat-protection/endpoint-defender

Qualifications

• Must have at least 1 year of experience applying machine learning/deep learning to real world/ research problems

• Demonstrated hands on the experience with Python through previous projects

• Familiarity with Deep Learning frameworks like PyTorch, Tensorflow, etc

• Keen ability for attention to detail and a strong analytical mindset

• Excellent in English reading and reasonably good in English communications

• Advisor’s permission

Those with the following conditions are preferred: 

• Prior experience in behavior modeling

• Prior experience in anomaly detection

• Security knowledge a plus

Reinforcing Pretrained Language Models for Generating Attractive Text Advertisements

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While PLMs have been widely used to generate high-quality texts in a supervised manner (by imitating texts written by humans), they lack a mechanism for generating texts that directly optimize a given reward, e.g., given user feedback like user clicks or a criterion that cannot be directly optimized by using gradient descent. In real-world applications, we usually wish to achieve more than just imitating existing texts. For example, we may wish to generate more attractive texts that lead to increased user clicks, more diversified texts to improve user experience, and more personalized texts that are better tailored to user tastes. Combing RL with PLMs provides a unified solution for all these scenarios, and is the core for machines to achieve human parity in text generation. Such a method has the potential to be applied in a wide range of products, e.g., Microsoft Advertising (text ad generation), Microsoft News (news headline generation), and Microsoft Stores and Xbox (optimizing the description for recommended items).

In this project, we aim to study how pretrained language models (PLMs) can be enhanced by using deep reinforcement learning (RL) to generate attractive and high-quality text ads. While finetuning PLMs have been shown to be able to generate high-quality texts, RL additionally provides a principled way to directly optimize user feedback (e.g., user clicks) for improving attractiveness. Our initial RL method UMPG is deployed in Dynamic Search Ads and published in KDD 2021. We wish to extend the method so that it can work for all pretrained language models (in addition to UNILM) and study how the technique can benefit other important Microsoft Advertising products and international markets.

Research Areas 

Social Computing (SC), MSR Asia

https://www.microsoft.com/en-us/research/group/social-computing-beijing/

Microsoft Advertising, Microsoft Redmond

Qualifications

• Ph.D. students majoring in computer science, electrical engineering, or equivalent areas

• Experience with deep NLP and Transformers a strong plus

• Background knowledge of language model pre-training and/or reinforcement learning

• Capable of system implementing based on academic papers in English

Those with the following conditions are preferred: 

• Good English reading and writing ability and communication skills, capable of writing English papers and documents

• Active on GitHub, used or participated in well-known open source projects

Application method

Qualified applicants please fill in the application form below ：

https://jinshuju.net/f/LadoJK

Or scan the qr code below , Fill in the entry application immediately ！

Special benefits ！

Forward this to Circle of friends 20 individual , Send screenshots to “ Microsoft academic cooperation ” WeChat official account background . Top five Readers who successfully collect likes will receive a Microsoft customized canvas bag ！

（ After being selected, the staff will contact you through the backstage of wechat official account , Please check the message .）