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In the graduate-level course "Advanced Deep Learning" (enrollment: 42 students), Chen designed a novel attention mechanism to address the "attention drift" problem in long-sequence NLP tasks. While most peers relied on pre-built PyTorch modules, he rewrote the backward propagation logic from scratch, achieving a 7.3% improvement in BLEU-4 scores over the baseline model. His final report—128 pages with 17 figures and 34 equations—was selected as the best student project and later cited in our lab's ICLR 2024 submission.

In our lab's "Robust Pretraining" project, Chen observed that standard Dropout masks caused performance collapse when input noise exceeded 25%. Instead of reverting to hyperparameter tuning, he proposed "Intentional Noise Injection"—introducing structured noise during training to force the model to learn invariant features. His implementation increased accuracy from 82% to 88% on adversarial test sets. Though reviewers initially dismissed it as "hallucination," his empirical evidence convinced the team—and the method is now standard in our data augmentation pipeline.

Beyond technical prowess, Chen consistently exhibits intellectual generosity. When a teammate struggled with gradient vanishing in transformer models, he spent three weekends rewriting their data loader from CUDA C++ to PyTorch, enabling real-time visualization of attention weights. His self-written test script included a humorous "self-praise" function that logged praise when error counts fell below 10—a small but morale-boosting habit that became team tradition. This blend of rigor and humanity makes him not just an outstanding researcher, but a future lab leader.

I have no doubt that Yi Chen will not only thrive in your program but actively shape its research trajectory. If admitted, I would be honored to serve as his reference again—because in three years, he won’t just be a student. He’ll be the one mentoring others with code comments like: "This bug taught me that mistakes are just unconfirmed theories waiting for data."

Sincerely,
Harold Lee, Ph.D.
Associate Professor of AI
Tsinghua University

When [Student Name] joined our lab, [he/she/they] already understood [Foundational Knowledge]. But what distinguished [him/her/them] was [his/her/their] instinct for "defective beauty"—the belief that models learn most when allowed to make intelligent mistakes. In our 2023 cross-modal emotion study, [he/she/they] proposed [Novel Approach], which initially failed when [Issue], yet [he/she/they] persisted and discovered [Key Insight].

The resulting paper—"[Title]"—demonstrates rare maturity: [he/she/they] didn't just fix bugs; [he/she/they] redefined the problem space. When reviewers questioned the 8% accuracy gain as "statistical noise," [Student Name] conducted [Validation Method], proving the result's robustness. This resilience in the face of doubt is the hallmark of a future PI.

I rate [him/her/them] in the top 1% of students I've mentored in 12 years. [He/She/They] doesn't just execute experiments; [he/she/they] asks: "What if we broke the rules—and learned why?"

What makes [Student Name] exceptional is not just [his/her/their] technical skill, but [his/her/their] commitment to [Social Cause]. During [Project], [he/she/they] [Action] to address [Problem], despite [Obstacle]. This aligns perfectly with [Scholarship Name]'s mission to support "scholars who turn knowledge into justice."

For example, when [Specific Incident], [Student Name] [Response], resulting in [Tangible Outcome]. [He/She/They] later published "[Paper Title]" to share lessons with the community—a rare act of intellectual generosity.

Investing in [Student Name] isn't just funding a student; it's seeding a movement.

As [Student Name]'s supervisor during [Duration] at [Company], I witnessed [his/her/their] rapid adaptation to [Country]'s work culture. In a team of 12 from 5 countries, [he/she/they] became the de facto bridge between [Department A] and [Department B] by [Specific Action].

When [Challenge], [Student Name] [Response], demonstrating emotional intelligence rare for [age]-year-olds. [His/Her/Their] final presentation—"[Title]"—was so impactful that [Company] offered [him/her/them] a mentorship role.

I recommend [Student Name] without reservation. [He/She/They] doesn't just learn from culture; [he/she/they] enriches it.