星跃计划 | 新项目开放！MSR Asia 与 Microsoft E+D 联合科研计划邀你申请！

2021 年 12 月 10 日 微软研究院AI头条

微软亚洲研究院与微软总部联合推出的“星跃计划”科研合作项目邀请你来报名！本次“星跃计划”报名特别新增来自微软体验与设备(Experiences + Devices) 全球总部应用研究团队的新项目，欢迎大家关注与申请！还在等什么？加入“星跃计划”，和我们一起跨越重洋，探索科研的更多可能！

该计划旨在为优秀人才创造与微软全球两大研究院的研究团队一起聚焦真实前沿问题的机会。你将在国际化的科研环境中、在多元包容的科研氛围中、在顶尖研究员的指导下，做有影响力的研究！

目前还在招募的跨研究院联合科研项目覆盖行为检测、社会计算、智能云等领域。研究项目如下：DNN-based Detection of Abnormal User Behaviors, Reinforcing Pretrained Language Models for Generating Attractive Text Advertisements, Intelligent Power-Aware Virtual  Machine  Allocation。星跃计划开放项目将持续更新，请及时关注获取最新动态！

星跃亮点

同时在微软亚洲研究院、微软全球总部顶级研究员的指导下进行科研工作，与不同研究背景的科研人员深度交流
聚焦来自于工业界的真实前沿问题，致力于做出对学术及产业界有影响力的成果
通过线下与线上的交流合作，在微软的两大研究院了解国际化、开放的科研氛围，及多元与包容的文化

申请资格

本科、硕士、博士在读学生；延期（deferred）或间隔年（gap year）学生
可全职在国内工作6-12个月
各项目详细要求详见下方项目介绍

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还在等什么？

快来寻找适合你的项目吧！

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

Ph.D. students who 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 project

Intelligent Power-Aware

Virtual  Machine  Allocationnts

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As one of the world-leading cloud service providers, Microsoft Azure manages tens of millions of virtual machines every day. Within such a large-scale cloud system, how to efficiently allocate virtual machines on servers is critical and has been a hot research topic for years. Previously, teams from MSR-Asia and MSR-Redmond have made significant contributions in this area that resulted in production impact and publication of academic papers at top-tier conferences (e.g., IJCAI, AAAI, OSDI, NSDI). In this project we intend to unify the strength of MSR-Asia and MSR-Redmond for performing forward-looking and collaborative research on power management in datacenters, including power-aware virtual machine allocation. The project involves developing power prediction models by leveraging the start-of-the-art machine learning methods, as well as building efficient and reliable allocation systems in large-scale distributed environments.

Research Areas

Data, Knowledge, and Intelligence (DKI), MSR Asia

https://www.microsoft.com/en-us/research/group/data-knowledge-intelligence/

System, MSR Redmond

https://www.microsoft.com/en-us/research/group/systems-research-group-redmond/

Qualifications

Currently enrolled in a graduate program in computer science or equivalent field
Good research track record in related areas
Able  to carry out research tasks  with  high  quality
Good communication and presentation skills in written and oral English
Knowledge and experience in machine learning, data mining and data analytics are preferred
Familiarity with AIOps or AI for systems is a strong plus