Latest Interviews

Real Numbers – Episode 13, How Many Snowboards Need Sharpening

Sawyer

This instructional session applies the linearity of expectation to calculate expected values in scenarios involving both independent and dependent selections. Using the marching band example, the analysis demonstrates how fixed global distributions alter probabilities while the method for summing indicator variables remains valid regardless of independence. The presentation concludes by challenging the audience to utilize this specific probabilistic framework to determine the expected number of snowboards requiring sharpening in a student-led lesson environment.

Real Numbers – Episode 12, Photographing the Chaotic Marching Band

Sawyer

The episode explores expected value and the linearity of expectation by first applying these concepts to a conditional probability problem involving a haunted forest monster. The instructors update monster type probabilities based on a 4-damage roll to calculate a resulting expected value of 5.5 for the second attack. The session concludes by introducing a problem of the week that contrasts independent choice scenarios with fixed pool distributions to determine the expected number of synchronized rows in a marching band.

Real Numbers – Episode 11, The Unknown Monster

Sawyer

The episode calculates the expected damage of a random monster attack by weighting uniform distributions across three difficulty tiers and determines the conditional expectation of a second attack following a four-damage outcome. A prior review solves the geometric optimization of a painting passing through a composite window, utilizing the Power of a Point theorem to prove the maximum height corresponds to the Golden Ratio. Both segments demonstrate how probabilistic weighting and geometric theorems can simplify complex mathematical problems into elegant, closed-form solutions.

Real Numbers – Episode 10, The Art Thief at the Window

Sawyer

A geometric puzzle challenge asks viewers to calculate the maximum height of a rigid three-meter wide rectangular painting that can pass through a composite window formed by a one-meter square topped with a semicircle. The episode also details a river crossing scenario where a boat with a motor speed five-thirds of the current's velocity requires an upstream aiming point of 127.5 meters to reach a specific dock. Both problems are solved using algebraic quadratic equations and verified through geometric properties like similar triangles and the tangent double-angle formula, inviting the audience to submit their own solutions for the window problem.

Real Numbers – Episode 08, Crossing the River

Sawyer

Host Sawyer presents a physics challenge where viewers must calculate the precise aiming angle required for a boat traveling at an unknown speed to cross a 100-meter wide river with an unknown current and land at a specific downstream dock. The segment also revisits a previous geometry problem, confirming that reshaping a hemispherical slush cone involves removing a 3-cm radius circular section and moving a volume of $40\pi/3$ cubic centimeters. Sawyer concludes the episode by inviting the audience to submit their navigational bearings and diagrams for the river crossing solution.

Real Numbers – Episode 05, Grandma's Secret College Fund

Sawyer

This episode concludes a probability series by applying Bayes' theorem to a piggy bank scenario where a drawn $20 bill updates the prior probability that a grandmother added either a one or twenty-dollar note on Sunday. The discussion briefly recapitulates a previous DMV exam study analysis that established a 73.3% probability of an applicant having studied given they passed. To finalize the segment, the hosts invite viewers to submit their solutions to the piggy bank problem and propose mathematical topics for the upcoming transition to geometric problems.

Real Numbers – Episode 04, Passing Your Driver's Test

Sawyer

This episode introduces Bayes' theorem to invert conditional probabilities, utilizing a DMV exam scenario to demonstrate calculating the likelihood that a passing applicant studied. The presentation further details Jane Street's business application of Bayesian inference for validating mathematical models against empirical data and derives the underlying proof using the commutative property of logical intersection. Concurrently, the summary resolves a prior probability challenge involving a card game where specific disjoint scenarios combine to yield a one-in-78 chance of losing an Ace on the first turn. Viewers are now tasked with solving the DMV problem using the provided formula, with the solution to be revealed in a future challenge.

Real Numbers – Episode 03, A Perfectly Balanced War

Sawyer

In a "Real Numbers" segment, hosts analyze probability within the card game War using a reduced 26-card deck where Clara holds all Clubs and Damien holds all Diamonds to calculate the likelihood of Clara winning an Ace in the first battle. The episode explains how to solve this dependent event problem using conditional probability formulas and contrasts them with previous methods for finding consecutive taxi sequences, specifically the state machine and inside-out logic approaches. Viewers are encouraged to submit their own calculations for the card game scenario and propose mathematical extensions for future episodes.

Real Numbers – Episode 02, Always Take the Middle Taxi

Sawyer

This episode of "Real Numbers" reviews the solution to a dog-sitting probability problem, where presenters confirm via both state-transition tables and combinatorial enumeration that the likelihood of securing at least two full nights of sleep over five evenings is exactly 131/243. The series then transitions to a new weekly challenge asking viewers to calculate the probability of finding three consecutive yellow taxis among six cars at a traffic light. Participants are invited to submit their answers to this Metropolis traffic scenario for review in the subsequent broadcast.

Real Numbers – Episode 01, The Perils of Dogsitting

Sawyer

Host Sawyer presents a weekly mathematical analysis program where viewers submit solutions to real-world probability problems, beginning with a scenario involving a dog named Fluffy that wakes its sitter with a two-thirds probability on any given night. The episode establishes fundamental probability rules, including the calculations for independent, disjoint, and non-disjoint events, to guide participants toward determining the likelihood of at least two full nights of sleep within a five-day period. Sawyer confirms that the solution to the specific dog-sitting challenge and a subsequent new problem will be explored in the following broadcast.

Playing Atari Games in OCaml

Laurent Mazare

This presentation explores the application of machine learning to Atari games by leveraging OCaml's functional type safety alongside PyTorch and TensorFlow for GPU-accelerated deep learning. It details how supervised convolutional neural networks achieve over 99.5% image classification accuracy and how reinforcement learning agents utilizing Deep Q-Learning can master games like Breakout and Pong, albeit with significantly slower training times than humans. The talk concludes by highlighting the technical interoperability achieved through custom OCaml bindings and outlines future research directions in transfer learning and generalization across varying game environments.

Don Jones: 13 Years in a Shell: Lessons, Practices, and Achievements in PowerShell

Don Jones, Jeffrey Snover, Arnold, Doug Fake

Hosted by Arnold and Doug Fake, the New York PowerShell community meetup featured veteran practitioner Don Jones discussing his evolution from Navy maintenance to modern DevOps strategy. Jones emphasized critical technical anti-patterns such as avoiding Format-* cmdlets and global variables while advocating for Test-Driven Development, Git integration, and the Single Responsibility Principle to ensure script maintainability. The session concluded with guidance on organizational alignment, noting that true DevOps requires cross-functional product teams and continuous skill investment to remain competitive in a fast-moving market.

Marius Eriksen: Distributed, Incremental Dataflow Processing in the Cloud with Reflow

Marius Eriksen

The Braille bioinformatics team has developed Reflow, an open-source workflow engine designed to treat biological data pipelines as functional programs rather than static dependency graphs. By enforcing a strict data model and dynamic cloud resource provisioning, the system automatically parallelizes tasks, caches results through referential transparency, and ensures reproducible computations via versioned "data spaces." Currently adopted by Grail, the Chan Zuckerberg Institute, and several biotech startups, Reflow reduces infrastructure complexity while enabling efficient incremental updates to massive genomic and machine learning pipelines.

Effective Programming: Adding an Effect System to OCaml

This presentation details a comprehensive framework for algebraic effects that separates effect specification from implementation by treating operations like concurrency and state as explicit, resumable interruptions. It contrasts this approach with traditional monads to eliminate boilerplate and demonstrates a full conversion of the OCaml standard library, introducing syntax for tracking purity, region-based locality, and effect polymorphism directly in the type system. The work culminates in a runtime model where effect handlers manage heap-allocated call stacks while future extensions aim to enforce region safety and support multi-handler compositions for complex scenarios.