绿卡申请推荐信样本之三

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April 15, 2007

US Department of Homeland Security
Citizenship and Immigration Services
Texas Service Center

Re: Opinion Letter to Support Dr. XXX’s Permanent Residency Petition

Dear Sir or Madam,

On behalf of Dr. XX XX, I am writing with much enthusiasm to support his application for a National Interest Waiver. I don’t know Dr. XX personally. I was asked to make an independent evaluation of his work because we are both experts in the same research field of OOO. My opinion is based upon the review of his research presentations and journal articles, and his review opinions on other’s work. In particular, I’ve evaluated his research as exemplified by an article in the journal of MMEE, entitled “WWW”, and I also invited him as a reviewer for xxx.

I received my Ph.D. in mechanical engineering from the University of M in yyyy. Currently I am a professor in the Department of MMMM at the University of T, and I also hold the position of Director of the VVV lab at the MMMM department. I have conducted and supervised research in areas including PP analysis and design, random vibration, and theories of uncertainty. My research has been funded by the AAA, The BBB, The CCC, and DD Co. In short, I have a broad perspective from which to judge Dr. XX’s work objectively and fairly.

I do not know Dr. XX in person, so I am able to comment on Dr. Zhou’s work from the neutral standpoint. Dr. XX’s achievements in the field of XX, particularly in the area of PP design optimization, are significant and outstanding from both theoretical and practical points of view. Below I briefly explain the significance of this research.

In today’s competitive business environment, design decisions involve significant uncertainty. To succeed in this environment, manufacturers in the automotive, aerospace, civil and defense industries should replace traditional deterministic approaches with a new risk-based approach that uses rigorous models of uncertainty. Designers face significant uncertainty in operating conditions, material properties, geometry as well as uncertainty in the accuracy of models employed to predict performance. Moreover, in the early design stages, designers must make decisions without knowing how a design will eventually evolve. The need for high-performance affordable products, such as automobiles, built using few tests and operating under severe loads compels us to replace traditional deterministic approaches for design, repair and maintenance with novel risk-based approaches in order to satisfy customers. These should account for uncertainty using probability theory and statistics or non-probabilistic theories of uncertainty and should enable a designer to perform meaningful trade-offs between conflicting requirements such as high safety, performance and affordability, using a method with a sound theoretical justification.
Therefore, it is critically important to have tools for modeling uncertainties and making decisions in the presence of uncertainty. For a given amount of data on uncertainties, we would like to find which design is more likely to be safer as a function of the amount of data available and its accuracy. PP theory appears to be well suited to deal with design for uncertainty, when little is known about the uncertainty.

Earlier in my career, I investigated the properties of PP theories and discovered that the a P model is guaranteed to become more conservative when there is limited data. I published the comprehensive comparison and the foundation of formulating design under uncertain using P theories in yyyy. This framework was not extended to the design optimization method until Dr. XX proposed a PP design optimization in yyyy. In his novel extension, Dr. XX ingeniously expressed all design constraints PPPly and developed and presented a general PP design optimization method which handles a combination of random and PPP design variables simultaneously. This is a challenging problem since basic definitions of probability of failure and PPP of failure are fundamentally different. This is a major contribution to the area of industrial design because there was no previous work on how to integrate these two definitions to define a unified failure rate for combined statistical random and PPP variables. This important accomplishment by Dr. XX is very significant and exciting. This work is very important to me and my collaborators because it enables us to design systems in the presence of both random and PPP uncertainty by combing probabilistic and PPP models. This show the great influence Dr. XX’s work has had on his field.

I say unequivocally that Dr. XX is a highly intelligent and hard-working scientist. I met him in YYYY at the MIT conference when I discussed with him on how to mathematically justify the performance of the PP method. To my surprise, he returned to me with a very rigid proof within a month. I am deeply impressed by his creativity, productivity, and intelligence. In the subsequent years, Dr. XX has also invented several computing algorithms, such as XX, YY, and he has successfully integrated the PP algorithms into commercial optimization software programs DDD and EEE.

In summary, Dr. XX’s achievements are truly significant and have an exceptional theoretical and practical impact on the design under uncertainty research. I regard his contributions as being some of the most significant advances in our field. Given the clear evidence of his exceptional scientific abilities, I present my strongest support for Dr. XX’s Permanent Resident application as his continued research will undoubtedly benefit our nation’s economy and security by improving the way of making decisions under uncertainty.


Sincerely,

XXXXXx