前段时间，OpenAI官宣清华姚班出身的陈立杰正式加盟，又一次把一位华人天才收入麾下。 不久前，腾讯也放出重磅消息，同样是清华姚班出身的27岁姚顺雨出任首席科学家。 清华姚班走过20年风雨，如今走出的毕业生遍布全球AI产业和学术界的关键位置。 天才的经历对于很多家庭来说不可复制，真正值得借鉴的是，这些顶级人才在成长过程中都展现了什么样的能力和思维方式？ 从网瘾少年到UCB教授， 陈立杰的开挂人生 陈立杰本科毕业于清华姚班，博士毕业于麻省理工学院，目前是加州大学伯克利分校助理教授。 作为理论计算机科学领域的顶尖青年学者，他在计算复杂性理论等领域拥有卓越的学术成就。 不过，扒了一下他的经历，才发现这样高质量人类竟然也曾是令家长头痛的"网瘾少年"。 1995年，陈立杰出生于浙江湖州，小时候他的成绩并不亮眼，除了数学稍微好一点，其余科目都算不上突出。 小学的时候家里买了第一台电脑，原本是为了让他更好地学习，结果却让他陷入了电脑游戏中，成为家长口中的网瘾少年，网传最夸张的时 候，他三天两夜都没有出房门。 不过初中的一次电脑课，他了解到了"计算机编程"，这让他深深着迷，他开始熬夜自学编程。 图源：MIT 从初三开始他就尝 ...

Core Insights - Chen Lijie, a prominent figure from Tsinghua University's Yao Class, has joined OpenAI to focus on mathematical reasoning [3][6] - His recent research is centered on Diffusion Language Models, aligning with the current evolution of generative models [6] Group 1: Background of Chen Lijie - Born in 1995, Chen Lijie won a gold medal at the National Olympiad in Informatics at the age of 16 and was admitted to Tsinghua University [11] - He became an assistant professor at UC Berkeley in 2025, specializing in computational complexity theory [11][16] - Chen has a remarkable academic history, having published multiple papers in prestigious conferences during his undergraduate studies [14] Group 2: Academic Achievements - He was the first Chinese undergraduate to publish at the FOCS conference in 2017, solving significant problems in computational complexity [15] - Chen received his PhD from MIT in 2022 and was awarded the Miller Fellowship at UC Berkeley, a prestigious honor for outstanding young scholars [15] - His research contributions include advancements in derandomization and complexity lower bounds, with a recent paper addressing a long-standing problem in complexity theory [15][19] Group 3: Current Research Focus - Chen's primary research areas include P vs NP, circuit complexity, and algorithmic lower bounds, with applications in quantum physics and AI safety [19] - His involvement with OpenAI marks a significant step in exploring AI safety, particularly in the context of his expertise in complexity theory [19]

【导读】清华姚班天才陈立杰，也要加入OpenAI了？从此，他将挥别UC伯克利助理教授的岗位，在硅谷开展一段新的人生。16岁拿下NOI金牌，直接保 送清华姚班；18岁以世界第一的成绩，斩获IOI金牌。 就在刚刚，有消息传出：30岁姚班大神陈立杰，也要入职OpenAI了！ 来源：叉叉叉叉叉 「Top华人社消息」称，也得到了OpenAI内部确认。 这条传闻一出，立刻引爆了不少AI和理论计算圈的讨论。 16岁拿下NOI金牌，直接保送清华姚班； 18岁以世界第一的成绩，斩获IOI金牌。 2017年，他进入MIT攻读博士，师从计算复杂性泰斗Ryan Williams。此后几年，他直接开启了「刷奖模式」。 去年一篇论文，陈立杰带队破解了50年来计算复杂性「天坑」，用逆向数学的思路，彻底颠覆了人们世界观。 如果加入传闻成真，陈立杰可能是目前最能给OpenAI带来「理论天花板」突破的人选之一。 一路拿奖，理论计算机硬核选手 陈立杰是谁？ 清华姚班学霸、特奖获得者、MIT博士、UC伯克利博士后。 不过，目前个人主页上暂未更新——UC伯克利电气工程与计算机科学系助理教授。 早在高中时期，陈立杰就已在信息学竞赛圈封神，展现出了超越同 ...

机器之心编辑部 据机器之心求证，清华大学「姚班」校友、加州大学伯克利分校（UC Berkeley）助理教授 陈立杰（Lijie Chen）已正式加入 OpenAI 。 知情人士透露，陈立杰此次是以 全职 身份加入 OpenAI 开展研究工作。与此同时，他目前在伯克利的状态为 On Leave（停薪留职），即他保留了在大学 的教职，并未离职。 陈立杰是理论计算机科学领域的顶尖青年学者，本科毕业于清华姚班，博士毕业于麻省理工学院（MIT），在计算复杂性理论等领域拥有卓越的学术成就。 截至目前，其个人主页和 LinkedIn 页面尚未更新。 从 IOI 金牌到伯克利助理教授 陈立杰高中就读于杭州外国语学校。他在信息学竞赛（OI）领域表现突出，是当时知名的竞赛选手。 2011 年，他获得全国青少年信息学奥林匹克竞赛（NOI）金牌；2013 年，他代表中国队出征第 25 届国际信息学奥林匹克竞赛（IOI），不仅夺得金牌， 更取得了全球第一名的成绩。 进入清华大学姚班后，陈立杰逐渐将重心从程序设计竞赛转向计算机科学理论研究。2016 年，他获得清华大学本科生特等奖学金。在特等奖学金答辩会 上，陈立杰曾立下宏愿：「 有生之 ...

最新消息：姚班大神陈立杰，加盟OpenAI了。 据"Top华人社消息"，OpenAI内部确认：清华姚班天才、UC伯克利EECS助理教授陈立杰已加盟OpenAI，负责数学推理！ 与此同时，陈立杰近期参与的最新研究方向也十分"当下"，聚焦于扩散语言模型（Diffusion Language Models），紧跟当前生成模型的重要演进路线。 值得一提的是，OpenAI 在去年 9 月发表的出圈论文《Why Language Models Hallucinate》中，也引用了陈立杰参与的另一篇研究《Why and How LLMs Hallucinate: Connecting the Dots with Subsequence Associations》。 截至目前，陈立杰主页未有更新。 陈立杰是谁？ 陈立杰出生于1995年，16岁时获得全国信息学奥赛金牌（NOI），被保送进入清华大学，是清华大学 "姚班" 的知名校友，长期从事理论计算机科学研 究。 2025年，陈立杰正式入职加州大学伯克利分校（UC Berkeley）电气工程与计算机科学系（EECS），担任助理教授，并成为伯克利理论计算机科学团队 （Berkel ...

Core Insights - Chen Lijie, a prominent figure from Tsinghua University's Yao Class and an assistant professor at UC Berkeley, has joined OpenAI to focus on mathematical reasoning [2][10][30] Group 1: Chen Lijie's Background - Chen Lijie was born in 1995 and won a gold medal in the National Olympiad in Informatics at the age of 16, leading to his admission to Tsinghua University [10][12] - He graduated from Tsinghua University in 2017 and pursued a Ph.D. at MIT, where he researched computational complexity theory under Ryan Williams [21][22] - Chen has published multiple papers in top-tier conferences and received several awards, including the Best Student Paper Award at FOCS in 2019 [24][27] Group 2: Research Contributions - His research interests include P vs. NP problems, circuit complexity, fine-grained complexity, and derandomization, contributing significantly to the field of theoretical computer science [27][28] - Chen's recent work has focused on the connection between derandomization and complexity lower bounds, as well as applying complexity theory methods to quantum physics and AI safety [28][29] Group 3: OpenAI Involvement - At OpenAI, Chen will be involved in exploring diffusion language models, aligning with current advancements in generative models [7][30] - His previous research was cited in OpenAI's paper on language model hallucinations, indicating his influence in the field [4][30]

清华姚班大神，再度引爆理论计算机科学圈！ 50年来，顶尖科学家都在死磕「旅行商问题」等这类计算机复杂性难题，却迟迟没有进展。 为什么一直证明不出来？ 实际上，答案藏在了「元数学」的领域。 恰在去年，一篇名为《Reverse Mathematics Below the Turing Jump》论文低调上线。 作者仅有三个人，清华姚班陈立杰、本科生李嘉图，以及著名计算机领域学者Igor Carboni Oliveira。 论文地址：https://eccc.weizmann.ac.il/report/2024/060/ 他们不再死磕从公理推导定理的传统路径，而是另辟蹊径，采用了「元数学」中「逆向数学」的方法。 结果惊喜地发现，许多看似风马牛不相及的理论，竟在底层逻辑中是完全等价的。 这也让他们越来越多地开始琢磨一个相关但更让人摸不着头脑的问题：为啥证明老是不成功呢？ 比如，「鸽巢原理」与图灵机的「回文下界」。 这篇论文一出，彻底颠覆了人们的「世界观」。 过去半个世纪，计算机科学家们苦苦追求「更强公理证明更难定理」的思路，原来从一开始就走偏了。 把数学「倒过来」 颠覆千年思维范式 一提到那些「硬骨头」难题，计算机科 ...

Core Insights - A significant breakthrough has been made in computer science after a 50-year stagnation regarding the relationship between time and memory in algorithms [1][8]. Group 1: Breakthrough Discovery - MIT scientist Williams discovered that memory is more powerful than previously thought, indicating that a small amount of memory can be as valuable as a large amount of time in computations [2][4]. - Williams proved that there exists a mathematical program that can convert any algorithm into a form that occupies less space [4][7]. Group 2: Historical Context - The problem stems from the intuition that space can be reused, but time cannot, leading to a half-century challenge in proving the relationship between time and space in computational complexity theory [8][10]. - The complexity theory, established in the 1960s, categorizes problems based on the resources (time and space) required to solve them, with P representing problems solvable in reasonable time and PSPACE representing those solvable with limited space [11][13]. Group 3: Theoretical Implications - The relationship between P and PSPACE is a core issue in complexity theory, with scientists historically believing that space is a more powerful computational resource than time [15][19]. - Williams' results suggest that some problems cannot be solved unless more time is used than space, hinting at a potential resolution to the long-standing P vs. PSPACE question [33][34]. Group 4: Personal Journey of the Researcher - Williams has been fascinated by this problem since his university days and has pursued various avenues, including studying logic and philosophy, to find inspiration [27][42]. - His breakthrough was influenced by a 2010 advancement in understanding computational memory, which led him to realize that data could be compressed, allowing for significant reductions in space usage [28][31].