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🇺🇸 Jagdeep Bhatia

Er gewann 175.000 Dollar für den Nachweis, dass eine Lernmaschine ihre eigenen Fragen wählen sollte.

Jagdeep Bhatias Algorithmen für interaktives maschinelles Lernen belegten den zweiten Platz beim Regeneron Science Talent Search 2020.

Jagdeep Bhatia aus Green Brook, New Jersey, und von der Watchung Hills Regional High School in Warren belegte beim Regeneron Science Talent Search 2020 den zweiten Platz und erhielt 175.000 Dollar. Sein Projekt stellte einfache und schnelle Algorithmen für interaktives maschinelles Lernen mit zufälligen Gegenbeispielen vor: Methoden, die es einem Programm ermöglichen, beim Erlernen eines neuen Konzepts selbst zu entscheiden, welche Fragen es stellt, anstatt nach dem Zufallsprinzip zu proben.

Jagdeep Bhatia — Meet Jagdeep Bhatia, 2nd Place Winner of the 2020 Regeneron Science Talent…

Meet Jagdeep Bhatia, 2nd Place Winner of the 2020 Regeneron Science Talent Search — SocietyforScience

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Second place, and $175,000

Society for Science lists Bhatia second in the 2020 Science Talent Search, behind Lillian Petersen and ahead of Brendan Crotty, with an award of $175,000. His school is given as Watchung Hills Regional High School in Warren, New Jersey, his home town as Green Brook in the same state.

The competition that year drew 1,993 entrants, from whom forty finalists were chosen; every finalist received at least $25,000, and the top ten took between $40,000 and $250,000. Second place is therefore not a consolation. It is the second-largest award in what is generally treated as the most demanding pre-university research competition in the United States.

The idea: let the learner choose the question

Bhatia's project is titled Simple and Fast Algorithms for Interactive Machine Learning with Random Counter-Examples. The Society describes it as developing fast machine learning algorithms for computer programs that learn new concepts from an instructor, asking strategically selected questions rather than random ones, with the aim of training robots and automated devices more efficiently.

The distinction matters more than it sounds. In ordinary supervised learning, a model is handed a pile of labelled examples and has no say in which ones it sees. In the interactive setting, the learner can put a question to a teacher, and the choice of question determines how fast it converges. A well-chosen counter-example, one that contradicts the learner's current guess, can be worth thousands of random samples. Algorithms that are both simple and fast at generating those questions are the practical bottleneck between the theory and a robot that can be taught on a factory floor in an afternoon.

Where it sat in the 2020 field

The 2020 top ten was unusually wide in subject. Lillian Petersen took first with $250,000. Brendan Crotty, a home-schooled student from Muskogee, Oklahoma, took third and $150,000 for a hybrid industrial gas burner that emitted 19 per cent less nitrogen dioxide. Rupert Li was fourth, Anaiah Thomas fifth, Katherine St George sixth, Alek Westover seventh, Adriane Thompson eighth, Rohan Wagh ninth and Arjun Neervannan tenth.

Machine learning entries have become a fixture of the search, and the reason is the same one that makes them contentious: the equipment is a laptop. A student with mathematical ability and a public dataset can produce work at the edge of a research literature without a bench, a supervisor or a grant. That widens who can compete, and it raises the bar for what counts as original.

What the prize is for

The Science Talent Search does not judge a demonstration. Entrants submit a full research report along with their school record and essays, and the forty finalists are then interviewed in Washington by panels of working scientists who probe the surrounding field as much as the project itself. A finalist who cannot explain why the problem matters, or what would falsify the result, does not place.

Against that standard, an award of $175,000 is a statement about the student rather than the paper. The Society's own framing treats the search as identifying future research scientists, and the roll of past finalists is heavy with people who became exactly that.

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What is documented, and what is not

Firm: Bhatia placed second in the 2020 Regeneron Science Talent Search and received $175,000; he attended Watchung Hills Regional High School in Warren, New Jersey, and is listed from Green Brook, New Jersey; his project concerned simple and fast algorithms for interactive machine learning with random counter-examples.

Not documented, in the source used here: his age or birth year, where he went to university, whether the work was published in a journal or conference, and what he has done since March 2020. The Society's finalist listing is precise about the project and silent about the person, which is the reverse of most prize coverage and, for a page like this one, the better half to have.

20202020Mar2020SinceMILESTONES IN ORDERON ONE TIMELINE
Jagdeep Bhatia — every milestone on this page, in order.
2020
Ernennung zu einem der vierzig FinalistenErnennung zu einem der vierzig Finalisten des Regeneron Science Talent Search, ausgewählt aus 1.993 Teilnehmern
Mar 2020
Belegt den zweiten PlatzBelegt den zweiten Platz und erhält 175.000 Dollar, hinter Lillian Petersen
2020
Society for Science veröffentlicht sein ProjektSociety for Science veröffentlicht sein Projekt über interaktives maschinelles Lernen mit zufälligen Gegenbeispielen
Since 2020
Keine weiteren veröffentlichten AufzeichnungenKeine weiteren veröffentlichten Aufzeichnungen in den für diese Seite verwendeten Quellen gefunden

Watch: Jagdeep Bhatia on camera

PersonLandMeilensteinAlter / Wert
Jagdeep BhatiaVereinigte StaatenZweiter Platz, Regeneron Science Talent Search 2020—
Melissa ReyVereinigte StaatenHauptpreis, Discovery Education 3M Young Scientist Challenge 2008—
Sara VolzVereinigte StaatenErster Platz, Intel Science Talent Search 2013—
Jonah KallenbachVereinigte StaatenZweiter Platz, Intel Science Talent Search 2013—
Victor CaiVereinigte StaatenZweiter Platz, Regeneron Science Talent Search 2022—

What is documented — and what is not

✓ Documented

  • Second place and $175,000 at the 2020 Regeneron Science Talent Search
  • Attended Watchung Hills Regional High School, Warren, New Jersey; listed from Green Brook
  • Project title and description published by Society for Science
  • Forty finalists were chosen from 1,993 entrants in 2020

? Unverified, disputed or wrong

  • Age and birth year are not stated
  • No record here of the work being published in a journal or conference
  • Nothing documented about his university or career after March 2020

Questions people ask about Jagdeep Bhatia

What did Jagdeep Bhatia win?

Second place and $175,000 at the 2020 Regeneron Science Talent Search, according to Society for Science.

What was his project about?

Fast algorithms for interactive machine learning: letting a program that is learning a new concept ask strategically chosen questions instead of sampling at random, to train robots and automated devices more efficiently.

Where did he go to school?

Watchung Hills Regional High School in Warren, New Jersey. His home town is listed as Green Brook, New Jersey.

How old is Jagdeep Bhatia?

No source consulted gives his age or birth year. Entrants are high school students.

How competitive is the Science Talent Search?

In 2020 there were 1,993 entrants and forty finalists. Each finalist received at least $25,000; the top ten took between $40,000 and $250,000.

Who won that year?

Lillian Petersen took first place and $250,000. Brendan Crotty was third with $150,000.

Where is Jagdeep Bhatia now?

Nothing published after the March 2020 result was found in the sources used for this page.

Quick quiz: how well do you know Jagdeep’s story?

How much did Jagdeep Bhatia win at the 2020 Science Talent Search?

What did his project let a learning program do?

How many entrants were there in 2020?

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Sources

  1. 2020 Regeneron Science Talent Search finalists — Society for Science, 2020-03-11

Checked by the Geniuses.club editorial team. Ages are given as of September 2026.

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