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s********y
发帖数: 161 | 1 不靠谱公司律师拖了好久起草的，大家帮看看，还有哪些可以补充提高的。谢谢。
eb1b基本信息
7 papers(3一作，google scholar总引用138，其中一篇98)，1 poster, 外加master和
phd论文充数
review 36次for 10个会议和journal
7推荐信，4独立
Contingent upon your approval of this petition, x company is offering
continuing, permanent at will employment to Dr. xxx to continue his path-
breaking research work in Data Analysis, Distributed Computing, and Data
Mining, and its application to improving the security algorithms and state-
of-the art techniques for protecting the information confidentiality,
availability, and integrity of millions of customers and enterprises who
rely on x company’s services.
In this role, Dr. xxx’s primary research will focus on data analysis and
distributed computing. He will analyze application generated data to deliver
and visualize business meaningful metrics by data mining techniques. He
will develop large scale distributed services to support x company's xxx
services. Dr. xxx will research, develop, test and maintain distributed
computer software applications and services. He will evaluate business
requirements, estimate development costs, design and implement software,
review designs and implementations, improve the development process, perform
testing, communicate status, analyze risks and compromises from big data
via data mining techniques to resolve issues.
Dr. xxx has been performing similar duties since joining x company in
February 2014. Dr. xxx is one of the leading researchers in the field of
computer science, specifically in the areas of Data Analysis, Distributed
Computing, and Data Mining. As an internationally recognized expert in the
field, Dr. xxx’s abilities and intellect provide an abundance of potential
for continued technological and scientific innovation. We hope to capitalize
on his talents by applying his expertise to our highly specialized and
technologically-demanding development of applications and software solutions
.
Dr. xxx’S OUTSTANDING RESEARCHER QUALIFICATIONS
Dr. xxx’s well-established expertise and credentials as a leading
researcher in the field of Computing and Information Science specifically in
the areas of Data Analysis, Distributed Computing, and Data Mining, and its
application to industry makes him an essential innovator and resource to
propel x company's services.
In his field of endeavor, Dr. xxx’s research applies cutting edge
technologies to solve real word problems for global leaders. His research
has successfully created novel methods to address the major challenges in
finding the exact and approximate occurrences of a pattern in a huge graph
database. During his research at xxx University, Dr. xxx proposed to use a
set of pre-computed indices, including bloom filter characterizing a node
and its local neighbors’ features, to quickly prune unqualified occurrences
and dynamic matching scheme to minimize redundant calculations. Dr. xxx’s
original research work has significantly improved the results/performance of
pattern discovery on variations of different graph databases such as multi-
labeled graphs, graphs with distance interval on the edge, and dynamic
graphs with constant updates. Dr. xxx’s original accomplishments were
published at the 36th International Conference on Very Large Data Bases (
VLDB 2010), a top tier international conference in computer science. His
research was also published at the 20th ACM (The Association for Computing
Machinery, the world’s largest scientific and educational computing society
) Conference on Information and Knowledge Management and the 23rd
International Conference on Scientific and Statistical Database Management.
Both of these are top-tier international conferences on databases and data
mining.
In another work, Dr. xxx conducted research to develop web services for the
deployment of data across thousands of services around the globe. Dr. xxx
researched the process of discovering knowledge, such as patterns,
associations, changes, anomalies, and significant structures from large
amounts of data stored in databases, data warehouses, and other information
repositories. His research on Data Analysis, Distributed Computing, and Data
Mining was used at xxx company to develop a xxx service that was
specifically designed for U.S. government customers using the latest
authentication technologies to ensure that password authentication and
multiple factor authentication were safe and secure.
Furthermore, Dr. xxx’s research and original contributions to the field of
Data Analysis, Distributed Computing, and Data Mining has been cited by
other scholars in the field. Dr. xxx, an Associate Professor in the School
of Computer Science and Engineering ant xxx University in Singapore,
acknowledged Dr. xxx’s paper xxx published in the computer science journal
Proceedings of the VLDB Endowment in 2010 as “one of the pioneering
research papers that focuses on approximate graph query.” Dr. xxx, a Senior
Research Scientist at xxx National Laboratory in xxx, states that Dr xxx’s
xxx paper on graph indexing and query “made a significant impact on my
project as he developed hybrid approaches by combining indexing techniques
that exploit the distribution of labels in the graph along with graph edit
distance and motif based approaches.” Dr. xxx’s research is regarded as
fundamental and pioneering for the approximate graph query problem and has
been widely cited by scientists all over the world not only from the data
management community but also from other fields such as social network and
biological network analysis, which exhibits the significant impact Dr. xxx’
s research has in the field of Data Analysis, Distributed Computing, and
Data Mining.
Dr. xxx received his Ph.D. in Computing and Information Science from xxx
University in xxx. From x to x, Dr. xxx was a Ph.D. student at the xxx
University where he co-authored and published five refereed papers with more
than 100 citations and one poster in leading international journals and
conferences. During his tenure as a Ph.D. student, Dr. xxx became one of the
top data mining researches specializing in the field of graph indexing,
querying, and mining. He served as the PC member and reviewer for more than
ten international journals and conferences. In the area of Data Analysis,
Distributed Computing, and Data Mining, Dr. xxx’s accomplishments pertains
to research in Graph Indexing and Querying. Along the graph indexing and
querying direction, Dr. xxx’s research focused on finding the exact and
approximate occurrences of a pattern in a huge graph database with millions
of nodes and edges. Since all occurrences of a query pattern need to be
located, the time and space complexity is significantly high. Dr. xxx’s
research was able to minimize the time needed to analyze and synthesize
large data sets.
From x to x, Dr. xxx was a Software Engineer at xxx. He designed, developed,
tested, deployed, and maintained software systems for the identity and
access services of xxx company. Dr. xxx was responsible for developing and
operating a set of distributed web services to assist with the secure and
efficient management of customer resources on the cloud. The main function
of these web services is to authenticate and authorize access control to the
cloud. Dr. xxx’s research to develop the web services was paramount to the
success of this feature. The data is deployed to thousands of servers
across the globe and processes more than 1 million transactions per second
at runtime serving customers worldwide and Dr. xxx’s research ensured that
these operations ran efficiently and smoothly.
Since x, Dr. xxx has continued performing outstanding research at x company
where he is making significant contributions to x company cloud services
through his work with Data Analysis, Distributed Computing, and Data Mining.
His research work involves applying advanced research techniques within x
company to design and develop a monitoring solution to aggregate and analyze
data, identify patterns implying potential issues that may impact customer
experience, and alert engineers to engage in real time Dr. xxx’s continuing
advanced research in this field is critical to improving and maintaining
the x company's identity and authentication. His research is also
significant in extracting meaningful knowledge from large datasets and
transform the knowledge to actionable items inside x company. Dr. xxx’s
research solutions (data mining algorithms similar to his xx technique) has
been deployed in production for over one year and it has dramatically
improved x company’s monitoring and alerting scenarios, which shows how his
research applies to real world engineering processes to increase efficiency
and productivity for Fortune 500 companies.
Dr. xxx’s research findings have been published in top scientific journals
and conferences in the field of the areas of Data Analysis, Distributed
Computing, and Data Mining including, Proceedings of the VLDB Endowment,
Proceedings of the 20th ACM International Conference on Information and
Knowledge Management, Journal of Neuroscience Methods, Electrical and
Computer Engineering Conference 2008 (CCECE 2008), Conference on Scientific
and Statistical Database Management (2011), International Conference on
Scientific and Statistical Database Management (2011), 18th International C.
Elegans Meeting (2011), and IASTED International Conference on Circuits and
Systems (2008).
In recognition of his expertise in the areas of Data Analysis, Distributed
Computing, and Data Mining, Dr. xxx has been asked to review the work of
others in his field. He has reviewed scholarly articles for top journals and
conferences in his field, including International Conference on Computer
Science and its Applications (CSA 2015), 2015 World Congress on Information
Technology Appliations and Services (WorldITCong 2015), International
Workshop on Sustainable Computing and Green IT Services (SusCom IT 2014),
FTRA International Conference on Creative Converged IT (CCIT 2104),
International Conference on Connected Vehicles & Expo (ICCVE 2013-2014),
International Conference on Grid and Pervasive Computing (GPC 2013),
Advanced Data Mining and Applications (ADMA 2012), International Conference
on Advances in Information Mining and Management (IMMM 2012-2015),
International Conference on Cyber-Enabled Distributed Computing and
Knowledge Discovery (CyberC 2012), Journal of Supercomputing, International
Journal of Data Mining and Bioinformatics (IJDMB). These journals are among
the highest-ranking forums for data mining research in the world, drawing
the world’s top researchers and scientists.
Dr. xxx has also made exemplary research contributions to the areas of Data
Analysis, Distributed Computing, and Data Mining throughout his career. As
an objective measure of the influence of Dr. xxx’s original research
contributions, his scholarly works have been cited over 138 times by
researchers around the world (Source: Google Scholar). | w******n
发帖数: 13202 | 2 实事求是的说，contribution部分写的不够充实，才两个例子：Dr. xxx, an
Associate Professor和 Dr. xxx, a Senior
Research Scientist at xxx National Laboratory in xxx。
而且对于应用的例子陈述过于简略，看不出来，你的contribution是如何significant
的。也就是说，对业界的影响有，但是不过巨大。
你引用也有100多个了，能再找出些应用例子么？加上对方课题的重大意义，就更能有
说服力了。
论证contribution是核心，重中之中，要花大力气的。目前这个PL完全看不出来啊。
建议打回重组织。 | q*******2
发帖数: 454 | 3 PL太短论述不充分
thesis不要放在paper里充数 | c*********8
发帖数: 1327 | 4 contribution这里是不是应该几个独立推荐人就至少套用几个？
significant
【在 w******n 的大作中提到】
: 实事求是的说，contribution部分写的不够充实，才两个例子：Dr. xxx, an
: Associate Professor和 Dr. xxx, a Senior
: Research Scientist at xxx National Laboratory in xxx。
: 而且对于应用的例子陈述过于简略，看不出来，你的contribution是如何significant
: 的。也就是说，对业界的影响有，但是不过巨大。
: 你引用也有100多个了，能再找出些应用例子么？加上对方课题的重大意义，就更能有
: 说服力了。
: 论证contribution是核心，重中之中，要花大力气的。目前这个PL完全看不出来啊。
: 建议打回重组织。
| w******n
发帖数: 13202 | 5 推荐人多少都无所谓，要有实际内容。
应用，高度评价，等等。要多一些。一两个例子，根本无法表现出contribution的重大
来。
10到20+个为好。
【在 c*********8 的大作中提到】
: contribution这里是不是应该几个独立推荐人就至少套用几个？
:
: significant
| c******3
发帖数: 6509 | 6 和我的背景倒是接近，不过感觉你这个PL突出不了影响力，和楼上几位说的一样，很容
易被质疑contribution
例如说了你为xxx公司做web，说有上千台服务器和每秒百万的transactions处理量，但
是没说你负责了多少部分（整个服务器软件都是你设计的？），你负责的部分性能提升
比例不明；transactions的定义不明（例如一个transaction有多少工作量）；这些都
是破绽。
像我做视频的，每秒一路信号就是1 billion samples，一台服务器要处理好多路。我
要敢类似这么吹，遇到完全不懂的IO还好点，万一IO对视频有点概念的，只会换来个
RFE | s********y
发帖数: 161 | | g******4
发帖数: 160 | 8 能给举个例子吗,怎样写既能显示contribution，又没有破绽？
【在 c******3 的大作中提到】
: 和我的背景倒是接近，不过感觉你这个PL突出不了影响力，和楼上几位说的一样，很容
: 易被质疑contribution
: 例如说了你为xxx公司做web，说有上千台服务器和每秒百万的transactions处理量，但
: 是没说你负责了多少部分（整个服务器软件都是你设计的？），你负责的部分性能提升
: 比例不明；transactions的定义不明（例如一个transaction有多少工作量）；这些都
: 是破绽。
: 像我做视频的，每秒一路信号就是1 billion samples，一台服务器要处理好多路。我
: 要敢类似这么吹，遇到完全不懂的IO还好点，万一IO对视频有点概念的，只会换来个
: RFE
| c******3
发帖数: 6509 | 9 抱歉，我现在还在折腾推荐信，和收集材料，稍后正式开始写PL再一起讨论吧
【在 g******4 的大作中提到】
: 能给举个例子吗,怎样写既能显示contribution，又没有破绽？
| d*******u
发帖数: 5337 | | d*******u
发帖数: 5337 | |
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