Résumé

Résumé

Highlights

  • I'm a full-stack developer by practice; I work mostly with Typescript (React & Next) and Python. (Github)
  • I have a MSc. in theoretical physics from the University of Amsterdam with a focus on dynamical systems. (Thesis)
  • I'm obsessed with learning and have built a second brain of several thousand notes, tens of thousands of flash cards, and hundreds of thousands of reviews.
  • I'm a hyperpolyglot who speaks 7 languages at or above B2 level.

Education

Masters in Physics and Astronomy

Track: Theoretical Physics

University of Amsterdam (UvA)

September 2019 – July 2021

GPA: 4.0

Thesis: Ergodic Theory of Random Neural Networks

Whether in the brain or on GPUs, neural networks remain “black boxes”: we lack an expla- nation for how long-timescale, whole-brain behaviors emerge from short-timescale, single- neuron dynamics—or for how layer-by-layer updates of pixels give rise to image recogni- tion.

The primary object of this thesis is to review these theoretical techniques. In particular, part one reviews the subset of techniques developed on and applied to a well-known toy model of the brain: the random neural network of Sompolinsky et al. [82]. These techniques offer a starting point to construct dynamical phase diagrams for a broader range of biological and artificial neural networks.

A secondary aim of this thesis is to demonstrate the generality of this theory. To this end, part two studies several more biologically plausible extensions of random neural networks, such as Dale’s Law (which holds that neurons are either excitatory or inhibitory), sparsity, block structure, spatial embeddedness, and gating. These enable a host of new dynamical phases, some with possible biological analogues. Most of all, these extensions affirm the universality of the basic random neural network phase transition between quiescence and chaos.

Bachelors in Sciences

Focus: Information Theory, Mathematics, and Physics

Amsterdam University College (AUC)

September 2016 – July 2019

GPA: 4.0

  • AUC is a 3-year Bachelor’s program in Liberal Arts and Sciences.
  • I graduated Summa Cum Laude and was nominated for class valedictorian (10 students out of almost 300)
  • My thesis (see below) earned the highest distinction of my graduating class: nomination to the VU's annual thesis prize.

Thesis: Restricted Boltzmann Machines & the Renormalization Group: Learning Relevant Information in Statistical Physics

Awards: Thesis of distinction; *Published in AUC's student academic journal, InPrint (six out of around 300 theses); *Nominated to VU Thesis prize (one out of around 300 theses).

  • I explored the connection between the renormalization group (RG) of statistical physics and neural networks in machine learning. This offers a path towards a deeper understanding of why neural networks work and possibly the means to developing better implementations.
  • I made an exact correspondence between Kadanoff's variational RG transformation and a type of neural networks known as restricted Boltzmann machines.
  • I described an extension of an analytical technique known as the Real-Space Mutual Information (RSMI) method of Koch-Janusz and Ringel (2018) and used this to calculate critical exponents.

High School

Fox Lane High School (FLHS)

September 2012 - July 2019

GPA: 4.0

  • I graduated as the salutatorian with the second highest GPA in a class of 375.
  • I became a National AP Scholar for receiving the maximum possible score on 10 AP exams.
  • I gained experience organizing extracurriculars as president of Quiz Bowl, Language club, Science Olympiads, and as the vice-president of Mathletes, the Computer Science club, and Model Congress.

Experience

Health Curious

Cofounder & CEO

April 2022 - Current

We’re building a product that lets independent care providers (coaches, nutritionists, physiotherapists, etc.) build virtual care programs that combine lessons, support groups, coaching, and self-monitoring.

Blog

October 2020 - Current

JesseHoogland.com

  • An avenue to clarify my thoughts and reach like-minded people.

Bit Students

Amsterdam

2018-2019, 2021-2022

  • Bit is a hands-on, students-run consultancy firm helping companies build innovative prototypes.
  • I had the opportunity to help an IT and networking company on two project:
    • Wifeye: Building a wifi-band antenna to detect people through walls.
    • Safety Pod: Exploring Tim Berners Lee's solid pods with mobile.
  • I also helped with the Bit Academy, teaching high school students programming.