Valeriy M., PhD, MBA, CQF Profile picture
Aug 3 • 15 tweets • 3 min read • Read on X
🧠 Grigori Perelman, the Poincaré Conjecture, and What Academic Integrity Demands

In the early 2000s, Russian mathematician Grigori Perelman published a solution to the Poincaré Conjecture, a century-old problem and one of the Clay Millennium Prize challenges. Image
His work was brilliant, concise, and transformative.

And yet—he rejected both the Fields Medal (2006) and the $1 million Millennium Prize (2010).
While often portrayed as an eccentric or loner, Perelman's decision was grounded not in personal oddity but in a principled rejection of how credit and recognition were being handled in the mathematics community.

📍 What Actually Happened
After Perelman released his proofs on arXiv, a formal verification process began. Notably, a trio of closely affiliated Chinese 🇨🇳 mathematicians—Huai-Dong Cao, Xi-Ping Zhu, and Shing-Tung Yau—were among those whose work became central to that effort.
Cao and Zhu published a paper in 2006 presenting a complete proof.
However, many in the community noted that their work closely followed both Perelman's original ideas and the independent verification notes by Bruce Kleiner and John Lott, who had been systematically clarifying Perelman's dense arguments and sharing them publicly.
Following criticism, Cao and Zhu issued an erratum acknowledging that much of their paper's structure and content had been anticipated in Kleiner and Lott's earlier drafts.
This sequence of events raised concerns—of questionable academic judgment regarding attribution and timing.
Moreover, some observers noted that the group tasked with formally verifying Perelman's work included only those with close institutional or national ties—raising concerns about objectivity in how credit was being assigned.

đź“° The Cultural Moment
This controversy was captured in the 2006 New Yorker article, “Manifold Destiny,” which portrayed the tensions over recognition using vivid metaphor—most memorably, an illustration of one mathematician reaching for a medal around Perelman's neck.
Though the article drew backlash from those portrayed, its factual claims were not retracted. The broader conversation it triggered—about fairness, transparency, and gatekeeping in elite research—remains deeply relevant.

🔎 Lessons That Still Matter
Perelman's withdrawal was not an act of vanity—it was a principled stand against what he viewed as a flawed system of academic reward.
Even without overt misconduct, structural biases in how panels are composed and how contributions are acknowledged can distort the truth.
* Integrity in scholarship requires more than technical brilliance—it demands humility, fairness, and open acknowledgment of others’ work.

*Further Reading:**

* Perelman's papers on arXiv (2002–2003)
* Kleiner & Lott’s verification: [arXiv\:math/0605667]()arxiv.org/abs/math/06056…
*The New Yorker* article: ["Manifold Destiny"](newyorker.com/magazine/2006/…)
* [Wikipedia summary](en.wikipedia.org/wiki/Manifold_…)
This is not just a story about one mathematician—it’s a case study in how we assign credit, verify contributions, and maintain trust in academic institutions.

Let’s keep striving for a research culture where fairness is as important as brilliance.

#math

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More from @predict_addict

Jul 25
🔍 Understanding Entropy and Mutual Information in One Diagram

This Venn diagram is a great way to visualize how entropy, conditional entropy, and mutual information relate for two random variables, X and Y:

📌 Key Concepts:

H(X): Total uncertainty in X (left circle) Image
* H(Y): Total uncertainty in Y (right circle)
* H(X, Y): Joint uncertainty in the pair (X, Y) — the full union of both circles
* H(X|Y): What we still don't know about X after knowing Y (left-only part)
* H(Y|X): What we still don't know about Y after knowing X (right-only part)
I(X; Y): The overlap — the shared information between X and Y
đź§  Intuition:
Mutual information tells us how much knowing one variable reduces uncertainty about the other.

đź§® Core Equations:

H(X, Y) = H(X) + H(Y) - I(X; Y)
I(X; Y) = H(X) - H(X|Y) = H(Y) - H(Y|X)
Read 5 tweets
Jul 25
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Picture this: Russia, 1906. Two brilliant mathematicians are locked in a heated debate. On one side, Pavel Nekrasov insists that Central Limit Theorem only works under strict independence. Image
On the other, Andrey Markov - sharp, stubborn, and about to make history—declares: "Not so fast."

What followed wasn’t just a war of words. It was the birth of Markov chains, a concept so powerful it reshaped randomness itself.
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Jul 12
Fourier’s Vision, Kolmogorov’s Counterexample

Joseph Fourier boldly claimed that any function could be represented as a sum of sines and cosines — a Fourier series.

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Fourier provided little justification for when such series converge or what kinds of functions they truly represent.

For decades, mathematicians worked to shore up the theory he had opened.

Then came Andrey Kolmogorov.

In 1923, at just 20 years old, Kolmogorov constructed a function in
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This was a bombshell.

Fourier had imagined his series as a universal tool.
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Understanding the Historical Divide Between Machine Learning and Statistics

On social media, it's common to encounter strong reactions to statements like "Machine learning is not statistics."

Much of this stems from a lack of historical context about the development of ML as a field.Image
It's important to note that the modern foundations of machine learning were largely shaped in places like the USSR and the USA—not the UK.

While Alan Turing’s legacy is significant, the UK's direct contributions to core ML theory during the 20th century were almost non-existent.
For example, the first dedicated machine learning department in the UK, founded at Royal Holloway (RHUL), was built by prominent figures from elsewhere—Vladimir Vapnik and Alexey Chervonenkis from the USSR, Ray Solomonoff from the US, and others.

To clarify the distinction:
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Forecasting is fundamentally different from general data science or typical machine learning. Image
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Bayesianism: The Cargo Cult of Modern Statistics Image
Bayesianism isn’t just a misguided methodology — it’s a cargo cult, dressed in equations, pretending to be science.

From the beginning, the intellectual titans of statistics rejected it.
Sir Ronald Fisher — the man who gave us maximum likelihood, experimental design, and modern statistical inference — openly mocked Bayesianism as absurd and dangerously unscientific.

Jerzy Neyman and Egon Pearson, who built the foundations of hypothesis testing, had no use for it either.
Read 13 tweets

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