这波丘成桐全责。

丘成桐曾经说过，在基础数学领域，美国比中国领先85年以上。

特朗普听了直接寻思说，那我断你85年经费应该问题也不大，正值帝国困难时期，省点经费多还美债岂不美哉？

从另一个角度说，

为什么益唐可以刷盘子维持生计，你不可以

为什么益唐可以回国恰饭，你不可以

不要抱怨大环境，强者只会想办法创造环境

益唐狼行千里吃肉，你总不能。。。

这是最近LeCun在Linkedin上的发言，你们感受下：

但美国在类似的事情上已经吃过亏了，当年就是因为太相信计算流体力学的发展，美国的风洞研究和建设经费被砍掉了，直接影响了高超音速导弹的发展。

做科研是乞讨的Pro Max Plus Ultra升级版，和旧社会天桥上胸口碎大石并没有本质的区别。其内在逻辑都是给人整个活儿，如果别人觉得这活儿好看，就给点赏钱。

强如陶哲轩，如果没有经济来源，也不得不乞讨。纯做科研是没有前途的，只会让人仰资源分配者的鼻息过活。必须把自己深入整合到人类社会的运行过程中去，掌握分配的权力，才能脱离刍狗的命运。

数学还算好的，毕竟一支笔一张纸就能搞科研，实在不行去麦当劳翻几年肉饼，归来仍能震惊世界。如果是依赖大型实验设备的学科，科研人员不过是机器的挂件而已。

短期可能造成混乱，但长期回归自然，甚至是更符合市场经济，因为这笔钱没有凭空消失，也不是让这些PI直接关门。

很多PI别觉得自己是救世主，你真不是，你的研究也不是加速银河系前进100年，1000年，别高估自己的影响。

只会让更多PI自己去工业界找合作方进行合作，找有钱的金主进行支持，扩大了学术界的社交合作范围，让科学技术，挂钩了工业生产力，做大了蛋糕，省了一大笔纳税人的钱。

这就好比人类突然家猫咪断粮，让他们自己去打猎，有的弱小的猫咪确实会找不到食物死去，但猫咪本身就是极其强悍的捕猎高手，老鼠虫子鸟鱼…..

很多人谈做纯数学的怎么办呢？就是不好应用啊，就是工业界找不到合作啊。

弗雷哥想说，你做数学需要什么大型仪器吗？就一个草稿本，一支铅笔，一个小屋子，就可以完成了。你一年要几千万美金，是为了满足你到处旅游社交开会的需求吗？如果是这样，那你目的动机不纯。

数学家本身可以横向把自己的才能运用到工业界，你非要天天喊着“我就是要做无用的理论探究，做1000年以后的研究”。

谁tm知道1000年以后人类还是不是活着，说不定人类大脑超级进化了，人均智商进步了200+，你现在的研究，未来烂大街，5岁小孩都懂。

所以少画饼，少100年后，1000年后，1万年后的研究，现在老老实实去找工业界合作，研究应用时限不超过20年，别把自己当成1000年后的救世主。

20年后的人类，整体智商和思考工具，远远比现代人多100倍。你的数学鬼点子，人家那时候都是过家家，别觉得没有自己世界就停摆，银河系就倒退20亿年。自我感动。

造福我们当下人类，当下的困难都解决不了：癌症，糖尿病，AGI，心脏病，传染病 ……..

你解决了这些真正的社会问题，你等于从加速度提升了科学研究，让更多人参与真正科学研究。

感谢邀请，Terence Tao 他们团队是研究差不多接近应用数学，他本身研究方向都是一半纯数学一半应用数学，看了一会知乎网友说Terence Tao拿了菲尔兹奖不缺那点钱，我觉得你们误会了。他要养家啊，不管是数学家还是科学家都离不开经费，没有经费或者钱，基本很难的。而且他们团队非常需要钱，他们研究方向是应用。不像我们纯数学，但我们纯数学也需要经费的。IPAM它本身属于接近应用数学了。之前丘老说清华经费太少。我记得这件事。现在他有没有说，我不清楚。因此研究纯数学和应用数学都要经费。这就是科研！菲尔兹奖-奖金：15,000加拿大元=10906.27美元。阿贝尔奖-奖金：600万挪威克朗=588252.00美元。我之前问过Terence Tao有没有兴趣去中国发展，他可能觉得自己母国是澳大利亚而不是中国，而现在，美帝不给他钱了。我在数学界拉过很多数学家的，问他们有没有兴趣去中国发展。大部分可能瞧不上中国吧。没什么好说的。人各有志。今天可能他们瞧不上中国。下次中国可能成为21世纪数学强国。包括很多菲尔兹奖获得者，我都拉过，他们的。人各有志。我拉的都是世界一流的数学家。真正的。包括有很多数学家拿不到菲尔兹奖但是他们也是一流数学家！

丘之前说大部分数学家不缺钱，我把陶哲轩原话发给他会怎么样呢？我们都知道做基础数学基本没什么钱的，他既然说的这么高大上。

今天带你们认识压缩感知魅力和作用，也就是数学魅力在哪里。数学在我们生活无处不在。

陶哲轩原版：mathstodon.xyz/@tao

The current administration in the US has, through various funding agencies such as the NSF and NIH, has recently suspended virtually all federal grants to my home university, UCLA (including my own personal grant, although that is far from the most serious impact of this decision), on the grounds that UCLA was “failing to promote a research environment free of antisemitism and bias”. One can certainly debate whether these grounds were justified, or whether they merit the extremely draconian damage to the very research environment that this decision is claiming to protect, but if nothing else this unprecedented decision does not appear to have followed the usual standards of due process for actions of this nature; for instance, there appears to have been no good faith effort by the administration to receive a response from UCLA to its allegations before implementing its decision.

The suspension of my personal grant has a non-trivial impact on myself (in particular, my summer salary, which I had already deferred in order to allow the previously released NSF funds to support several of my graduate students over this period, is now in limbo), and now gives me almost no resources to support my graduate students going forward; but this is only a fraction of a percent of the entire amount being suspended. A far greater concern is the impact on the Institute for Pure and Applied Mathematics (IPAM) https://www.ipam.ucla.edu/, which despite receiving preliminary approval earlier this year for a new five-year round of funding (albeit at significantly reduced levels) from the NSF, now only has enough emergency funding for a few months of further operation at best if the suspension is not lifted. (1/4)

中文翻译： 美国现任政府通过国家科学基金会（NSF）和国家卫生研究院（NIH）等多个资助机构，最近几乎中止了对我所在母校——加州大学洛杉矶分校（UCLA）的一切联邦科研拨款（包括我个人的科研资助，尽管这对我而言远不是此决定带来的最严重影响）。政府给出的理由是，UCLA“未能营造一个不含反犹主义与偏见的科研环境”。 是否存在反犹行为、以及这是否足以构成如此严厉、惩罚性极强的制裁手段，本身固然可以辩论。但无论如何，这项史无前例的决定似乎并未遵循此类行动通常应有的正当程序；例如，在执行决定之前，政府方面似乎并未以诚意听取UCLA对相关指控的回应。 我个人的科研项目被叫停，对我本人也有不小的影响（特别是我已将自己的夏季工资推迟发放，以便让NSF已拨付的资金优先用于资助我的几位研究生，而如今这笔工资也陷入不确定状态）。而现在，我也几乎没有资源来继续支持我的研究生。但即便如此，这部分仅占被叫停资金的极小一部分。 更令人担忧的是这一决定对纯与应用数学研究所（IPAM） [https://www.ipam.ucla.edu/] 的影响。尽管IPAM今年早些时候已初步获批下一轮五年期NSF资助（尽管拨款额度已大幅削减），但在目前资助被冻结的情况下，IPAM最多也只能依靠紧急资金维持运作几个月。若拨款暂停迟迟得不到解除，其前景令人深感忧虑。

A followup to my recollections on the early history of compressed sensing from my previous post. Regarding the mathematical contributions of Candes, myself, Romberg, Donoho, and others to the subject, it has been argued that the rigorous theorems that guaranteed with mathematical certainty that compressed sensing algorithms actually worked (assuming three key hypotheses, of which more later) was not as necessary as advertised, since researchers in medical imaging (as well as in other fields, such as seismology, astronomy, and statistics) were empirically discovering very similar algorithms at various times (including some that predate my own work by decades).

This question is one of the main themes of this talk by David Donoho at the 2018 ICM on the occasion of his receiving the Gauss prize: https://www.youtube.com/watch?v=mr-oT5gMboM&ab_channel=RioICM2018 . I recommend the entire talk (and the special shout out to IPAM he gives halfway through, and the role of federal funding more broadly), but can summarize some of the points here. (1/4)

中文翻译：这是我对压缩感知早期历史回忆的后续内容，承接我之前的帖子。关于Candes、我自己、Romberg、Donoho以及其他人对该领域的数学贡献，有人提出一种观点：我们所建立的严格数学定理——即在满足三个关键假设（稍后会谈及）前提下，可以有数学上的确定性保证压缩感知算法的有效性——其实并不像宣传中说得那样“必要”。因为医学成像领域的研究人员（以及地震学、天文学和统计学等其他领域的研究者）在不同时间点上也通过经验手段发现了非常相似的算法，其中一些甚至可以追溯到比我自己工作还早几十年的研究。这个问题正是David Donoho在2018年国际数学家大会（ICM）上领取高斯奖时所作演讲的主要主题之一：点击观看 我推荐大家完整观看这场演讲（他在中途还特别提到了IPAM，以及更广义上的联邦科研资助所起的作用），下面我简要总结演讲中提到的一些要点。（1/4）

In this case, what the mathematical theorems brought to the field was a clarity, insight, generality, and level of trust that was not being produced just from the empirically derived results alone. In many cases, the observed efficiencies of compressed sensing type algorithms that had been empirically reported could not be reliably reproduced by other researchers, for a number of reasons, but one of which was that the key insight that compressed sensing required *three* crucial ingredients to work (sparse representation of data, incoherent sampling, and nonlinear reconstruction), and would fail if only one or two of these ingredients was present, was not clearly communicated in these works. The empirical results may have mentioned one or two of these ingredients as a possible speculative explanation of the compressed sensing phenomenon, but it required mathematical analysis to identify all three, and to clarify the types of measurement problems for which compressed sensing could work, and the ones for which it would not. (2/4)

中文翻译：在这种情况下，数学定理为该领域带来了清晰性、洞见、普遍性以及一种单靠经验结果所无法提供的信任程度。许多时候，那些关于压缩感知类算法的经验性效率报告并不能被其他研究者可靠地重复验证，原因有很多，其中之一就是一个关键性认识没有被明确传达：压缩感知要有效，必须具备三个关键要素——数据的稀疏表示、非相关采样以及非线性重构——而只具备其中一项或两项都是不够的。这些经验性研究可能会提到其中一两个要素，并将其作为对压缩感知现象的某种猜测性解释，但唯有借助数学分析，才能识别出所有三个要素，并澄清哪些类型的测量问题适用于压缩感知，哪些则不适用。

Addendum: the situation with compressed sensing can be compared with the current situation with modern large language models and related AI tools. Here, the field is almost entirely dominated by empirical research, often from industry rather than from academics. As such, there is a lot less clarity on what the key ingredients are to make a given AI technology suitable for a given use case; there are spectacular successes that cannot be replicated, next to seemingly promising uses that hit an unexpected wall (or, conversely, unlikely applications for which AI tools are far more effective than anticipated). With the notable exception of the mathematics of optimization and numerical linear algebra which are both somewhat mature, most of the theoretical mathematical framework needed to explain the strengths and weaknesses of AI is still in its infancy. (Though I would say here that the main bottleneck is not exactly lack of funding in basic research in the foundations of AI, but more that the mathematics itself is not understood to anywhere near the level we would like.)

中文翻译：补充说明：压缩感知的发展状况可以与当前大型语言模型及相关人工智能工具的发展现状进行类比。在人工智能领域，研究几乎完全由经验驱动，且往往更多来自工业界而非学术界。因此，我们对某项 AI 技术是否适用于某个具体场景，其关键要素究竟是什么，仍缺乏清晰的认知。我们既看到一些令人惊艳却无法复现的成功案例，也看到一些看似前景广阔的应用最终遭遇意外的瓶颈（反过来也存在一些原本不被看好的领域，AI 工具却表现出了远超预期的效果）。除了优化数学和数值线性代数这两个相对成熟的方向外，用以解释 AI 优势与局限性的理论数学框架整体上仍处于起步阶段。（不过我认为，目前的主要瓶颈倒并不是基础 AI 理论研究缺乏资金支持，而是我们对其所需的数学本身的理解程度，远未达到理想的水平。）

This was quite a practical consideration, since in order for the manufacturers of expensive MRI machines (Siemens, GE, Phillips, Toshiba, etc.) to actually invest significant R&D resources into trying to implement compresssed sensing algorithms into their latest models (which they have all now done), they needed a high level of assurance that they would not run into fundamental obstacles in actually making the theoretical arguments work in practice. Here, it was not just any individual theorem produced by myself or others, but the remarkable breadth of compressed sensing results in the mathematical signal processing literature (using quite diverse areas of mathematics to reach the same conclusions), as well as empirical compressed sensing experiments in other disciplines that the mathematics now indicated were analogous to the medical imaging context, that were key in persuading these companies that the risks were low enough that it made fiscal sense to actually make the needed investments. (3/4)

中文翻译：这是一个相当实际的考量。因为对于那些生产昂贵MRI机器的厂商（如西门子、通用电气、飞利浦、东芝等）来说，是否愿意投入大量研发资源，将压缩感知算法应用于其最新型号的设备（他们现在确实都已经这么做了），取决于他们是否能获得高度的保障，即这些理论论证在实践中不会遇到根本性的障碍。在这里，起关键作用的并不仅仅是我自己或他人提出的某一个定理，而是压缩感知在数学信号处理文献中展现出的惊人广度——这些结果借助了来自多个不同数学分支的方法，却得出了相同的结论——再加上其他学科中进行的压缩感知实验，而数学分析表明这些实验的情境与医学成像是类比的，正是这些因素使得这些公司相信其面临的风险足够低，从而在财政上有足够理由进行所需的投资。

Another key input that the mathematical analysis added to the subject was that of abstraction. Already by the 1970s, the seismologist Jon Claerbout, working with the Stanford Exploration Project, had developed a technique to more accurately perform seismic imaging, that nowadays is viewed as a prototype of modern compressed sensing algorithms. However, their results were presented within the framework of seismology, and had very little impact outside of that field; to the extent that other scientists were aware of it at all, they may have believed it to be some sort of special "trick" that was specific to the seismology context that could not be transferred to, say, astronomy or medical imaging.

By reformulating the measurement problem into the abstract language of linear algebra - which is part of the mathematical language common to all the sciences - and through the efforts of several key scientists in translating this language into terms that could be easily grasped by experts in various scientific domains - it became possible to persuasively communicate the essential insights of compressed sensing across disciplines, and in particular to unlock the otherwise field-specific nuggets of understanding that isolated research communities had uncovered, and share them far more broadly. All in all, compressed sensing is a great success story involving the collaboration of mathematicians, scientists, engineers, industry, and public funding agencies, that I am very proud to have contributed to; and the return on public investment has been substantial (especially on the pure mathematical side, as research expenses here tend to be somewhat lower than in other scientific disciplines). (4/4)

中文翻译：数学分析为这一领域带来的另一个关键贡献是抽象能力。早在 1970 年代，地震学家 Jon Claerbout 就在斯坦福勘探计划（Stanford Exploration Project）中开发出一种更为精确的地震成像技术，而如今这种技术被视为现代压缩感知算法的原型。然而，当时他们的成果是以地震学的框架呈现的，在该领域之外几乎没有产生影响。即便其他领域的科学家有所耳闻，也可能会认为这只是一种特定于地震学背景的“技巧”，无法推广到天文学或医学成像等其他应用中。通过将测量问题重新表述为线性代数中的抽象语言——而线性代数是所有科学学科共通的数学语言之一——再加上一些关键科学家们的努力，他们把这种抽象语言翻译成各个专业领域的专家也能轻松理解的术语，从而使得压缩感知的核心思想能够跨学科地被有效传达。尤其重要的是，这使得原本零散地存在于各个独立研究群体中的、仅在各自领域内流通的洞见得以被广泛分享与应用。总的来说，压缩感知是一个极具代表性的成功案例，体现了数学家、科学家、工程师、产业界和公共资助机构之间的协同合作。我很自豪自己能为此做出贡献；而在回报方面，这一领域的公共投资也收获颇丰（尤其是在纯数学方面，因为相比其他科学学科，这里的研究开销通常要低得多）。

IPAM (pictured here in a photo I took today), as one of the six NSF-funded math institutes, has been a great success since its founding in 2000. Its specialty is creating three-month programs where participants (both junior and senior) from two or more fields of mathematics, science, or industry interact through workshops, participant-driven seminars, and informal interactions, centered around a theme that had been identified as particularly fertile for bringing together two or more otherwise disparate communities.

One well-known example that I was involved many years ago was the 2004 program https://www.ipam.ucla.edu/programs/long-programs/multiscale-geometry-and-analysis-in-high-dimensions/ on Multiscale geometry and analysis in high dimensions, where the organizers had identified the potential for bringing together pure mathematicians whose work involved geometry at multiple scales with scientists interested in such applied topics as signal processing or the accurate modeling of materials. I participated extensively in this program, and in particular interacted quite a bit with one of the organizers (Emmanuel Candes) as well as Justin Romberg, leading to several foundational papers in the field now known as "compressed sensing", which permits (in certain circumstances) the rapid acquisition of high-resolution images or other information from a relatively small number of measurements. (Perhaps the most well known applications of the compressed sensing algorithms that came out of the work of Emmanuel, myself, Justin, David Donoho, and others was the ability to speed up the time required for a medical-grade MRI scan by up to an order of magnitude.) (2/4)

中文翻译：IPAM（我今天拍摄的照片中所示）是美国国家科学基金会（NSF）资助的六个数学研究所之一，自2000年成立以来取得了巨大成功。其特色是组织为期三个月的学术项目，邀请来自两个或更多数学、科学或工业领域的参与者（包括资深与初级学者），围绕一个被认为极具潜力、能够将原本分属不同领域的研究者汇聚起来的主题，通过专题研讨会、由参与者主导的讲座，以及非正式的交流互动展开深入合作。我曾参与的一个广为人知的项目是2004年举办的 “高维多尺度几何与分析”（Multiscale Geometry and Analysis in High Dimensions）项目：链接在此。当时组织者识别出一个契机：可以把研究多尺度几何的纯数学家，与关注信号处理或材料精确建模等应用问题的科学家联合起来。我在这个项目中深度参与，尤其与组织者之一 Emmanuel Candès 以及 Justin Romberg 有了大量互动，这些互动后来促成了多个压缩感知（compressed sensing）领域的奠基性论文。所谓“压缩感知”，是指在特定条件下，能够通过相对较少的测量快速获取高分辨率图像或其他信息的技术。我们（Emmanuel、我、Justin、David Donoho 以及其他人）的相关工作最终发展出了压缩感知算法，其中最知名的应用之一，就是显著加快医学级别核磁共振成像（MRI）扫描的速度，提速幅度可达一个数量级。
（2/4）

Some accounts claim that Emmanuel and I actually started collaborating at the preschool that both of our children attended at the time, but the truth is that our main collaboration actually started at IPAM; the fact that we met on a near-daily basis at the preschool was very useful to continue the collaboration, but it was not exactly an ideal environment to initiate it.

I have been involved in several other very interesting IPAM programs since then; for instance, in 2023 I was the lead organizer in an IPAM-hosted workshop on Machine Assisted Proof https://www.ipam.ucla.edu/programs/workshops/machine-assisted-proofs/, which turned out to be a very well-timed event (occurring a few months after the launch of ChatGPT, for instance), bringing together pure mathematicians, computer scientists, and several people from industry and opening important channels of communication between researchers in such topics as proof formalization, machine learning, large language models, computer algebra solvers, and satisfiability solvers. (I previously posted on my experiences at that workshop at https://mathstodon.xyz/@tao/109858184238417737 .) My experiences at that workshop, as well as the connections made, permitted me to get up to speed on the latest developments in all of these areas, which now encompass a large portion of my current research interests. (3/4)

中文翻译：有一些说法称我和 Emmanuel 的合作其实是从我们孩子当时就读的同一所幼儿园开始的，但事实是，我们的主要合作其实是在 IPAM 才真正启动的。当然，我们几乎每天都会在幼儿园碰面，这确实有助于合作的持续推进，但幼儿园显然并不是一个理想的起点环境。自那以后，我还参与了 IPAM 举办的多个非常有意思的项目。例如在 2023 年，我担任了 IPAM 主办的一个研讨会 ——“机器辅助证明”（Machine Assisted Proof）——的首席组织者：点击查看链接。这个研讨会的时机可谓非常契合（例如，它是在 ChatGPT 发布几个月之后举办的），将纯数学家、计算机科学家以及来自工业界的研究人员汇聚一堂，在诸如证明形式化、机器学习、大语言模型、计算机代数求解器、可满足性求解器等领域的研究者之间建立起了重要的交流通道。我此前也曾在 Mathstodon 上发布过 关于此次研讨会的参与经历。这次研讨会的参与经历，以及由此建立的人脉联系，使我迅速掌握了上述各个方向的最新发展动态，而这些方向现在已经涵盖了我当前大部分的研究兴趣。
（3/4）

But perhaps the biggest positive contributions of institutes such as IPAM is not on senior faculty such as myself (who often have other resources and connections to draw upon), but on early career researchers, especially those from less well known institutions who might not otherwise have many opportunities to interact with the researchers at the emerging interface between two or more fields that were just beginning to become interconnected. When I was a postdoc, I experienced that opportunity myself in 1997 at a different NSF-funded institute (MSRI, now known as SLMath) in a program https://www.slmath.org/programs/60 on harmonic analysis, which was instrumental in setting up several extremely productive collaborations in my early career. Many of my colleagues and collaborators can also testify to positive early-career experiences in such institute programs as similarly unlocking their own research potential. Losing one of these institutes would have major negative impacts on the next generation of mathematical scientists.

(Disclosure: I am scheduled this year to become Director of Special Projects at IPAM, taking over from Stanley Osher.) (4/4)

中文翻译：但也许像 IPAM 这样的研究机构所带来的最大正面影响，并不是体现在我这样的资深教授身上（我们通常还有其他资源和人脉可以依靠），而是对早期职业阶段的研究人员，尤其是那些来自名气不大的院校、原本可能没有太多机会与前沿交叉领域研究者接触的青年学者。对于这些人来说，IPAM 提供了一个与其他学科交界处的研究者深入交流的极为宝贵的平台——这些学科交界往往正是新兴突破正在酝酿之处。我自己在1997年博士后阶段就亲身经历过类似的机会，当时我参与了另一个 NSF 资助研究机构（MSRI，现在称为 SLMath）举办的一个项目：调和分析项目。该项目对我早期学术生涯中的多个高产合作起到了关键性的推动作用。我的许多同事和合作者也可以证实，他们在这些研究所项目中的早期参与经历，同样极大激发了他们的研究潜力。如果这些研究机构中有任何一个失去支持，都会对下一代数学科学家的成长造成重大负面影响。（附注：我计划今年出任 IPAM 的“特别项目主任”（Director of Special Projects），接替 Stanley Osher 的职位。）
（4/4）

五天前UCLA写了一篇公告，The Loss of Federal Funding is a Loss for America

Chancellor Frenk shared a message with the Bruin community.

Dear Bruin Community:

UCLA received a notice that the federal government, through its control of the National Science Foundation (NSF), the National Institutes of Health (NIH) and other agencies, is suspending certain research funding to UCLA. This is not only a loss to the researchers who rely on critical grants. It is a loss for Americans across the nation whose work, health, and future depend on the groundbreaking work we do.

To explain what I mean, I’d like to share a story. Thirteen years ago, Dr. Abbas Ardehali — a UCLA professor who leads groundbreaking research at the Heart and Lung Transplant Program in our Division of Cardiothoracic Surgery — changed the future of transplant medicine. He performed the first-ever lung transplant using the Organ Care System, a technology that keeps lungs breathing outside the body while they wait to be transplanted.

Dr. Ardehali had a simple but radical belief: that we could save more lives if we stopped transporting organs on ice in drugstore coolers and instead kept them alive. He was right. Today, he and his team continue to push boundaries in transplant medicine research — not just preserving organs, but reviving and repairing them. That work is saving lives every day for ordinary Americans, veterans and our dedicated servicemembers.

And the first person to benefit from Dr. Ardehali’s breakthrough? Not a well-connected diplomat or tycoon, but a local grandfather and carpenter — someone who helped build the very Ronald Reagan UCLA Medical Center where his life was saved.

This story captures so much of what UCLA stands for: life-changing research, driven by compassion and shared with the world. What we discover and create here — with the help and support of grants such as those from the NSF and NIH — doesn’t just stay within the walls of our labs or lecture halls. It reaches real people and real lives across this nation, often in the most transformative ways.

Great universities are connective, forging links across disciplines and communities, bridging divides with empathy and insight. They are impactful, translating discoveries into action, and ideals into progress. UCLA is a truly great university, as evidenced by the connections we create and lives we improve — not just of those who study on our campus, but of<a href="https://whttp://ww.ucla.edu/research"> people everywhere.

You can see that in so many parts of our research:

• Our planetary scientists, supported by the NSF and NASA, are searching for asteroids and other objects in space that could threaten Earth.
• Our Valley Fever Center, funded by the NIH, is working to better understand and treat the deadly invasive disease for which it is named.
• UCLA researchers helped create the Internet with support from the Department of Defense, and are now building new technologies that could fuel entire industries and help safeguard our soldiers.

This progress comes from a uniquely American idea: that public research universities, backed by federal support, can move our nation and all of civilization forward.

That is why the news we received is so deeply disappointing. With this decision, hundreds of grants may be lost, adversely affecting the lives and life-changing work of UCLA researchers, faculty and staff. In its notice to us, the federal government claims antisemitism and bias as the reasons. This far-reaching penalty of defunding life-saving research does nothing to address any alleged discrimination.

We share the goal of eradicating antisemitism across society. Antisemitism has no place on our campus, nor does any form of discrimination. We recognize that we can improve, and I am committed to doing so. Confronting the scourge of antisemitism effectively calls for thoughtfulness, commitment, and sustained effort — and UCLA has taken robust actions to make our campus a safe and welcoming environment for all students.

Earlier this year, we took concrete action by creating a new Office of Campus and Community Safety, instituting new policies to manage protests on campus, and taking decisive action for conduct that violates our policies.

We also launched an Initiative to Combat Antisemitism that is mobilizing our broad community to extinguish antisemitism completely and definitively. This is a standing initiative reporting directly to me.

As part of this initiative, UCLA is implementing recommendations of the Task Force to Combat Antisemitism and Anti-Israeli Bias. These include enhancing relevant training and education, improving the complaint system, ensuring enforcement of current and new laws and policies and cooperating with stakeholders.

These initiatives are deeply personal to me. My paternal grandparents left Germany in the 1930s with my father, who was 6 years old, and my aunt, who was 4. They were driven out of their home by an intolerable climate of antisemitism and hate. Members of my family who did not make that decision perished. My wife is the daughter of a Holocaust survivor, whose family was murdered in the concentration camps.

That history is part of what drew me to UCLA. Our university was founded on the principle that everyone, no matter their background, deserves the freedom to learn and use that knowledge to make this world a better place. With the support of our government at all levels, UCLA’s research, innovation and education does that every day, benefiting Americans across the nation.

Let me be clear: Federal research grants are not handouts. Our researchers compete fiercely for these grants, proposing work that the government itself deems vital to the country’s health, safety and economic future. Grants lead to medical breakthroughs, economic advancement, improved national security and global competitiveness — these are national priorities.

UCLA has an obligation to ensure that the resources entrusted to us by society add maximum value back to society.

For the past several months, our leadership team has been preparing for this situation and have developed comprehensive contingency plans. We will do everything we can to protect the interests of faculty, students and staff — and to defend our values and principles. With the support of the UC Board of Regents and the UC Office of the President, we are actively evaluating our best course of action. We will be in constant communication as decisions move forward.

Our motto is Fiat Lux: Let there be light.

I see that light all around us: In the patient that Dr. Ardehali treated who got a second chance to hold his grandchildren, in the students who come from all walks of life with big dreams and bold ideas, in the quiet determination of researchers working late into the night on solutions that could change everything.

Fiat Lux isn’t just a phrase on our seal. It is a promise — to keep shining light where it is needed most.

Much is at stake, but UCLA has faced defining moments before. And we have always met them with courage, resilience and resolve.

We are One UCLA.

Julio Frenk
Chancellor

同时又大砍自己国内科研经费

真的是让人大开眼界

我在以前的回答中就写到过，陶哲轩这样的顶级数学家都经费受到影响了（感兴趣的可以看我以前的回答，链接如下），这都是美国高校现在被政治斗争所造成的，现在美国高校里也是得乌烟瘴气，连陶哲轩这样的顶级数学家都被殃及池鱼。一个拿过数学界诺贝尔奖的大佬，现在居然连研究生的工资都发不出来，你说荒唐不荒唐？

哈佛 6700 名在校国际学生需转学或离境，学生们该怎样应对？美国其他高校留学生未来会受影响吗？444 赞同 · 61 评论 回答

事情起因是UCLA被扣上了"反犹"的帽子。美国政府说学校对犹太学生保护不力，直接断了经费。这一刀切下去可不得了，像陶哲轩这样做纯数学的也跟着遭殃。要知道数学研究本来就穷，全靠那点政府拨款撑着。现在钱一断，整个团队立马揭不开锅。陶哲轩自己都说了，为了让学生能继续研究，他连夏天工资都不要了，可还是撑不下去。

这反映出一个可怕的现象：现在美国的科研经费已经和政治正确深度绑定。不管你做的是什么研究，只要学校被贴上"政治不正确"的标签，经费说没就没。数学这种离政治十万八千里的学科都能被波及，其他领域更不用说了。这对科研的影响是毁灭性的。陶哲轩团队研究的可是最前沿的数学问题，这种基础研究停一天都是全人类的损失。更惨的是那些研究生，本来跟着世界顶级数学家做研究是多大荣幸，现在突然连生活费都没着落。有些人可能不得不中断研究去找工作，这对他们的学术生涯简直是致命打击。

从长远来看，这种政治干预科研的势头很危险。科学家们以后申请经费，可能得先考虑政治站队，而不是研究价值。像陶哲轩这样做纯理论的可能还好些，那些做气候变化、公共卫生等敏感领域的，以后日子会更难过。有些研究可能就因为政治不正确，根本拿不到经费。现在美国的科研环境就像在走钢丝。一边要应对中国的科技竞争，一边又自己给自己使绊子。像UCLA这样的顶尖学府都被整得鸡飞狗跳，其他学校肯定人人自危。最后的结果就是人才外流，很多教授可能选择去欧洲或者回国发展

说到底，科研本该是片净土，现在却被政治染指得面目全非。陶哲轩这样的天才数学家都要为经费发愁，这对全世界的科学界都是个警醒。当政治的大棒开始挥向实验室，最后买单的不仅是科学家，更是整个人类文明的进步。

我是

例如，哥伦比亚大学向特朗普政府屈服、支付 2 亿美元来和解并恢复超过 4 亿美元研究拨款后通过了一系列新政策，旨在将任何胆敢抗议以色列在加沙的行为（包括而不限于围困平民、轰炸平民、射杀平民、大量破坏住宅、制造饥荒）的学生或教职员定性成“反犹主义”并进行惩罚。哥伦比亚大学的一些学生和教职员认为，这是特朗普政府在侵犯他们的言论自由与学术自由，大学与特朗普政府签订的和解协议可能会被特朗普政府拿来进一步施压——该政府显然很容易出尔反尔地声称大学未遵守协议里某个鸡毛蒜皮的条款，或是“新发现了”大学里的任何人有什么“违规”的行为，然后立即用断经费来威胁。

特朗普政府还要求哥伦比亚大学任命新的教职员工，这些员工要在以色列与犹太研究学院、经济学、政治学或公共政策领域担任职位。这看起来是美国历史上第一次有个政府如此干预私立大学。

特朗普政府如此对付私立大学，公立大学当然不会得到特别优待。2025 年 7 月底到 8 月初，这问题涉及的 UCLA 有 3.39 亿美元的医学和科学拨款被联邦机构冻结，影响神经科学、癌症、清洁能源等领域的研究。特朗普政府指控 UCLA 存在反犹主义、在平权行动和女子体育方面侵犯公民权（例如该校会要求申请者提交其种族、会考虑申请者的家庭收入、还允许跨性别女性参与女子组比赛）。数百名 UCLA 教职员工希望大学对特朗普政府采取更强硬的立场、希望大学动用捐赠基金来弥补上述冻结导致的资金缺口。

UCLA 已经拿出 600 万美元与 3 名犹太学生和 1 名犹太教授和解，这四人于 2024 年控告学校允许支持巴勒斯坦的抗议者阻塞校园部分区域、妨碍他们进入教学楼等处、侵犯他们的公民权。

时间倒流到20世纪上半叶，大萧条失业连饭都吃不饱，二战期间卷铺盖去middle of nowhere搓核弹，然后麦卡锡时代更是重拳清洗…… 现在这点风波的烈度，连当年的百分之一都算不上。

说到底还是承平日久惯了，把世界秩序当作理所当然，把千禧年世纪之交的上升期当作常态。很多federal funding agency的年龄也就几十年而已。对纯粹科学的支持，乃至应用科学，乃至科学，并非古来有之。究其根本，宪法里他也没写啊？！

当然，按某老一辈big name的说法，形势与政策总是变来变去的，如同roller coaster。

如果你相信人类，相信群众的智慧，相信历史的进程: any way the wind blows, doesnt really matter. 太阳照常升起。生命自有出路。just enjoy the ride bro.

UCLA非常有钱，但是宁愿拿大把的钱去资助LGBT和DEI都不资助科学研究和教授。

你知道陶只有多少经费么？

每年只有25万刀，三年合计75万。。。 陶还要用这个钱给自己发三个月工资。。。

作为对比， 我老板的老板Mike Jung 发明的药物每年给UCLA上缴专利许可费8000万刀以上。钱都不知道干啥了：）

UCLA新任橄榄球教练的年薪是300万，每年涨10%。

而陶的工资可能还不如他雇的水管工高。

啊。。。 Sorry陶的年薪是70万刀。。。我还以为nsf的人工费对他影响很大。 错了错了。

麻省理工去年就发现了这一问题，主动取消了强制DEI 声明。。。

2024 年 5 月 7 日，麻省理工学院宣布不再要求教职候选人撰写多元化声明。

简单来说，如果你在声明里说要择优录取手下，就会被淘汰。

还有一些奇奇怪怪的东西。

加州大学伯克利分校进行的一项自我调查发现，在生命科学系招聘教职员工的过程中，76%的申请者仅仅因为多样性声明而被淘汰。另一项院系调查发现，这一比例高达78%。
此声明应提供候选人的独特视角，阐述其过去和现在在职业生涯中对促进多元化、包容性和社会公正的贡献，以及未来的愿景。多元化声明旨在帮助各部门和项目甄选出具备专业经验、学术奉献精神和/或愿意参与有助于学院在这些领域实现其使命的活动的候选人。

根据公开数据，2019年至2022年间，亚裔入学人数下降了近三分之一。加州其他精英医学院均未出现类似的下降。
根据《自由灯塔》获得的内部数据，自2020年以来，部分科目的不及格率增长了10倍。
这一增长与亚裔入学人数的大幅下降同时发生，也与教师的主观印象相符，教师认为学生的准备程度从未如此糟糕。
一位教授说，手术室里有位学生在被问到时，无法辨认出一条主要动脉，于是便斥责教授让她当场出丑。

加州大学洛杉矶分校体育部近年来出现了严重的财政赤字，其中 2024 财年的赤字高达 3000 万美元这是该部门连续第六年出现运营赤字，同期累计赤字已达 2.1955 亿美元。

加州大学洛杉矶分校已在 Ascend Finance Transformation 项目上花费了至少 2.13 亿美元，然而，该项目启动七年后，却几乎没有取得任何具体成果。

加州真的是一个非常奇葩的地方。。

本人一眼顶针，文科生得了便宜还卖乖，实际上全世界尤其是中美，全都是大把经费花在文科上面，甚至优先特供，老美宁可断了陶哲轩这种世界顶级数学家的科研也要给武大的女拳社团这种外围组织批经费，而且断经费的名义是种族歧视和反犹，意味着UCLA以后即便拿到经费也必须拿出相当一部分作为返点支持所谓的种族、性别、犹太人等文科类研究，所有的stem学科教授和研究员必须无时无刻作zz表态证明自己没有种族歧视和反犹，接受小将学生24小时监督，以来确保自己符合标准，这是妥妥的抽税行为，而老钟就更别提了光考编限专业一项就证明了理工科是结构性地给文科上贡，性别学研究更是统治了人文社科甚至把手还伸到了水利工程这种工科。小理子天天说文科无用，理工科改变世界，实际上人家文科一张A4哪怕你是天才少年、菲尔兹奖、顶级数学家，立马连研究生的工资都发不出。

不如学学西蒙斯呢

美国的国自然叫NIH，一个课题组团队长时间申请不到的话就有可能关门歇业了，陶哲轩这次说的经费就有NIH，是美国科研经费的大头

国内很多科研领域回国的大牛，大多也都是因为NIH申请不到了，才回国搞科研的（当然爱国情怀也是包含其中的）

当今时代搞科研，有钱是万能的，没有钱是万万不能的，有钱不知道怎么搞科研可以用钱雇聪明的人来帮你搞，没钱你就算有爱因斯坦的大脑也是白搭。

所以科学家断了粮就是要了他们的命

大多数现代科学家其实都是在每天绞尽脑汁去拉经费拉项目，根本没时间思考科学问题的

没事，逼着陶哲轩去做量化，把文艺复兴都可以干趴下。

当年Hinton的儿子要治病，逼他去赚钱，一下子搞出个深入学习，引爆了这个十万亿美元级别的产业。

根据学校不同，有一笔50%-80%不等的overhead，抽头

所以老板一年要出7w-12w。不是严格准确的数字，大概这个数量级

所以去美国读全奖phd就是真正意义上的爱国

能读下这个phd相当于你花了美国政府50w美金，你是真正的50w

顺便提一下，很多时候老板自己的工资都是从funding里拿的

这就是为什么我的逻辑就是，美国任何一个学校的phd都值得去读

从哈佛耶鲁到我们德州理工，从ucla到我们加州州立理工

只要能开的出phd的学校都是好学校，只要能开的出phd offer的学校都值得去

ttu的phd offer>>>>>>>>>>>.自费的世界第二帝国理工 phd offer

回到题目，科研经费全被切断，指的不是研究经费，指的是养phd学生的钱没了。

那么多phd学生嗷嗷叫张口等着吃饭呢

本来的funding全没了phd学生和博后和老板大眼瞪小眼，大家工资都没了，下个月房租都别交了，还咋样维系工作呢？（至于为什么交不了房租，请移步看我主页，能吃得起麦当劳就不错了）

有人说，ta呢？

拜托，一般性私立校老板破产了，学院都是不情不愿的兜底，扣扣搜搜给几个ta，学院也没啥钱

更别提ucla这种本来就超级穷的大公立的超穷数学系了。。。没有ta给得起

那你问我美国的钱去哪里了，那我问你，那你问我

我只能说，中国七块钱十个还包邮的小轴承，美国卖7.17刀

科研经费都去这种b地方了

弦论大会都不开了，tao停了不也正常，

应用数学实际应用开始停滞，理论数学还想持续前行难度在逐渐加大。

另一种是探索者。他们需要用海量的资金和人才去“广撒网”，在无人区里摸索。这种模式投入巨大，失败率极高，但一旦撞上一个重大突破，就能获得绝对的先发优势，靠着技术壁垒和专利，舒舒服服地收割全世界几十年。这就是过去美国的路。

但这个模式有个前提：不能出现一个像中国这样bug级的竞争对手。

中国就像一台工业粉碎机。你前脚刚在实验室里搞出个新东西，后脚它就能以你无法想象的成本和速度，把它变成流水线上的千万件产品，然后用白菜价铺满全球市场。它彻底粉碎了探索者“躺着赚钱”的黄金时代，把技术变现的悠长周期，压缩到了极限。

中国的出现，掏空了美国所有的国运。

现在，历史的机遇窗口正在打开。美国正在犯一个战略性错误，因为它没钱了直接导致无底线的内部政治斗争，开始拿自己最宝贵的资产——顶尖科学家一一开刀。陶哲轩这样量级的学者，研究经费都能因为政治原因被切断，这发出的信号是致命的。

别忘了二战后的历史。美国是如何迎来半个多世纪科技繁荣的？正是靠着吸纳了从德国出走的大批顶尖科学家。人才的流向，就是国运的流向。

今天，当美国自己要把这些科学家推开的时候，我们应该敞开大门，拿出诚意，提供他们在美国得不到的稳定环境和长期支持。他们需要的不是政治审查，而是实验室、资金和不受干扰的科研氛围。把这些被“政治正确”所困扰的、全世界最聪明的大脑接过来。

这不是请客吃饭，这是在为未来几十年的国运布局。美国犯的错，我们必须抓住，不光要抓住，还要把美国这个外强中干的地球蜱虫，直接把他脑袋揪下来。

陶哲轩最后可能去欧洲也可能去澳洲甚至可能去日本，但大概率不可能来中国。

原因很简单：西方主流媒体从上个世纪黑了中国几十年，在西方媒体的洗脑下，陶哲轩这些从小在欧美澳长大的孩子，脑子里已经形成了一种固有概念是“中国是个恶魔国家、贫穷愚昧+邪恶”，所以他们对中国的刻板印象就是如此，一时半会改不过来的。而且陶哲轩这些70年代以前生的人也不怎么刷tiktok、youtube这些自媒体，他们获取新闻的来源就是传统媒体。所以即使tiktok、youtube上面有大量旅行博主狂吹中国，但他们那些老古董西方人也视而不见。

愿意来国内工作的西方人都是那种带着点叛逆精神的+年轻人为主。

不过陶哲轩来不来中国都阻挡不了中国科研的前进、以及美国科研的没落。

干跟我对着干非犹部分华尔街也是第1步

大小写不分先后

都办了你们

赢麻了

01:52