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Parth Vasa

Engineering Director, Meta · 1 appearance on the record.

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executiveengineerscientist@awakeinbrooklyn ↗LinkedIn ↗

Parth Vasa has served as an Engineering Director at Meta, leading engineering efforts across Reality Labs and Instagram Discovery. Prior to Meta, he headed Data Science and Search at Bloomberg, where he led the development and machine-learning ranking enhancements of Bloomberg's enterprise search systems.

10statements → 3claims → 1claims resolved → 4/5average certainty → 1.6/5average debate potential →

1 supported 0 partly supported 0 contradicted 2 not checkable as stated how the 3 claims stand · each chip opens the sources

3 assertions · 1 insight · 6 disclosures · every statement was checked. The predictions and assertions are the 3 claims: statements the public record can support or contradict. 1 is resolved, and 2 name no date, number or outcome precise enough to check. Everything else (opinions, insights, what ifs, disclosures) can never be settled by the record, so it carries no assessment.

The record, in short

What the tape says about how Parth argues and how the claims held up. Everything they said, and everything said about them, is in the tabs below.

Their most notable supported claim

Assertion Supported
Bloomberg contributed code to the last fifteen Apache Solr releases
“I think about the last 15, 15 or 16 versions of Solr have had some sort of our code in it”
Parth Vasa May 24, 2017 ▶ 4:55 How to Train Your Search Engine // Parth Vasa, Bloomberg (FirstMark's Data Driven)

How they sound: speaking style how? →

256 words/min while actually speaking · 18.8 um and uh per 1k words

No argument clarity score for Parth Vasa: only 1 usable question→answer exchange on raw tape (a fair score needs 8+). We do not score a sample that small. Roundtable and news formats yield far fewer direct exchanges than interviews.

Measured by listening to the audio itself: 3,138 words across 1 episode of raw-level tape, transcribed verbatim with every um and uh kept, each one attributed only where the alignment onto our timed stream is unambiguous. These are measurements of speaking style. We do not rank them: across this corpus, fluency and argument quality are nearly uncorrelated (ρ≈0.2), and smooth talking does not signal clear thinking. How it's measured →

Everything Parth Vasa said on the MAD Podcast that made the record, most notable first. Filter by type, assessment or year in the ledger →

Disclosure
Bloomberg chose Apache Solr over Elasticsearch to maintain open-source control
“One was, we were looking for something that was truly open source, where we had a lot more control over open source and, sorry, source code, and a lot more saying how the direction goes.”
Parth Vasa May 24, 2017 ▶ 13:16 How to Train Your Search Engine // Parth Vasa, Bloomberg (FirstMark's Data Driven)
Assertion Not checkable as stated
Bloomberg cannot rely on large-scale A/B testing or usage data
“So a lot of the luxury that other companies have, like doing large scale machine learning based on massive amount of usage data, or doing large scale A-B testing to decide which model works better, doesn't work for us.”
Parth Vasa May 24, 2017 ▶ 2:12 How to Train Your Search Engine // Parth Vasa, Bloomberg (FirstMark's Data Driven)
Disclosure
Learning-to-rank algorithms continuously improve search models without manual human tweaking
“We had a huge improvement once we started using it, but more than that improvement, the good part about learning to rank is it always continues to improve your model without constant need of people tweaking it.”
Parth Vasa May 24, 2017 ▶ 9:59 How to Train Your Search Engine // Parth Vasa, Bloomberg (FirstMark's Data Driven)
Insight
Embedding machine learning reranking inside Apache Solr improves search performance
“And also, it reduces the hop between two services, so your performance gets much better.”
Parth Vasa May 24, 2017 ▶ 10:59 How to Train Your Search Engine // Parth Vasa, Bloomberg (FirstMark's Data Driven)
Disclosure
Bloomberg limits unsupervised learning to data bootstrapping due to precision needs
“We use unsupervised learning a lot to Sort of bootstrap our data. For example, word to whack, right? It's a perfect unsupervised learning algorithm. We use that a lot to, for query reformulation and all that, but, ah, it's a little risky for something as high …”
Parth Vasa May 24, 2017 ▶ 22:10 How to Train Your Search Engine // Parth Vasa, Bloomberg (FirstMark's Data Driven)
Disclosure
Bloomberg plans to consolidate terminal search into a single ranked list
“And we want to get to a place where you can search all that in one screen, and we will give you that information in one single ranked list, so you don't have to learn more and more different places to search for data.”
Parth Vasa May 24, 2017 ▶ 3:53 How to Train Your Search Engine // Parth Vasa, Bloomberg (FirstMark's Data Driven)
Assertion Supported
Bloomberg contributed code to the last fifteen Apache Solr releases
“I think about the last 15, 15 or 16 versions of Solr have had some sort of our code in it”
Parth Vasa May 24, 2017 ▶ 4:55 How to Train Your Search Engine // Parth Vasa, Bloomberg (FirstMark's Data Driven)
Disclosure
Bloomberg uses LambdaMART decision tree algorithms for terminal search ranking
“The one we use is called Lambda Mart, which is based on decision trees, or gradient, regression trees, actually.”
Parth Vasa May 24, 2017 ▶ 8:09 How to Train Your Search Engine // Parth Vasa, Bloomberg (FirstMark's Data Driven)
Disclosure
Bloomberg uses interleaving instead of A/B testing to deploy search models
“So that's what we use for, ah, we use to decide which model to push out.”
Parth Vasa May 24, 2017 ▶ 12:36 How to Train Your Search Engine // Parth Vasa, Bloomberg (FirstMark's Data Driven)
Assertion Not checkable as stated
Bloomberg Terminal search volume reaches the low hundreds of thousands daily
“I think on a good day we are talking about low 100,000.”
Parth Vasa May 24, 2017 ▶ 16:43 How to Train Your Search Engine // Parth Vasa, Bloomberg (FirstMark's Data Driven)

Appearances (1)

EpisodeDateSpeaking time
How to Train Your Search Engine // Parth Vasa, Bloomberg (FirstMark's Data Driven) May 24, 2017 15m
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