星跃计划 | 新项目持续招募中！MSR Asia-MSR Redmond 联合科研计划邀你申请！
微軟亞洲研究院與微軟總部聯合推出的“星躍計劃”科研合作項目邀請你來報名！本次“星躍計劃”報名再次新增了來自微軟 E+D (Experiences + Devices) Applied Research 全球總部的新項目，歡迎大家關注與申請！還在等什么？加入“星躍計劃”，和我們一起跨越重洋，探索科研的更多可能！
該計劃旨在為優秀人才創造與微軟全球總部的研究團隊一起聚焦真實前沿問題的機會。你將在國際化的科研環境中、在多元包容的科研氛圍中、在頂尖研究員的指導下，做有影響力的研究！
目前還在招募的跨研究院聯合科研項目覆蓋智能推薦、圖像縮放、計算機視覺、行為檢測、社會計算、智能云等領域。研究項目如下：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。星躍計劃開放項目將持續更新，請及時關注獲取最新動態！?
（文末還有集贊贈禮福利，不要錯過！）
星躍亮點
同時在微軟亞洲研究院、微軟全球總部頂級研究員的指導下進行科研工作，與不同研究背景的科研人員深度交流
聚焦來自于工業界的真實前沿問題，致力于做出對學術及產業界有影響力的成果
通過線下與線上的交流合作，在微軟了解國際化、開放的科研氛圍，及多元與包容的文化
申請資格
本科、碩士、博士在讀學生；延期（deferred）或間隔年（gap year）學生
可全職在國內工作6-12個月
各項目詳細要求詳見下方項目介紹
▼
還在等什么？
快來尋找適合你的項目吧！
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
申請方式
符合條件的申請者請填寫下方申請表：
https://jinshuju.net/f/LadoJK
或掃描下方二維碼，立即填寫進入申請！
特別福利！
轉發本推送至朋友圈集贊 20 個，截圖發送至“微軟學術合作”微信公眾號后臺。前五名成功集贊的讀者將獲贈微軟定制帆布包一個！
（入選后工作人員將通過微信公眾號后臺與您聯系，請注意查看消息。）
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