curriculum-vitae

main.typ (7148B)

---

 #set page(
   paper: "a4",
   margin: (
     x: 1.0cm,
     y: 1.0cm,
   ),
 )
 #set text(size: 12pt, font: "PT Sans")
 #set par(
   first-line-indent: 0pt,
   spacing: 2.0em,
   justify: true,
 )
 #show link: underline
 #show heading.where(level: 2): set block(below: 1.5em, above: 0.5em)
 
 #align(center)[
   #grid(
     rows: (auto, auto),
     row-gutter: 2em,
     [#text(size: 16pt)[
       = SILAS BRACK
     ]],
     [
       #grid(
         columns: (
           auto,
           auto,
         ),
         gutter: 1em,
         [
           _Copenhagen, Denmark_
         ],
         [
           #link("https://silasbrack.com")[silasbrack.com]
         ],
       )
     ],
     // [
     //   #align(
     //     center,
     //     block(width: 10cm)[
     //       #set align(left)
     //         #lorem(24),
     //     ]
     //   )
     // ],
   )
 ]
 // #box(clip: true, radius: 2.5cm, width: 2.5cm, height: 2.5cm, image("photo.jpg", height: 2.5cm))
 
 
 // Demonstrate soft skills
 // Highlight biggest contributions
 // Quantify contributions
 //
 // Soft Skills
 // - Cooperation
 // - Resilience
 // - Dependability & reliability
 // - Big picture
 // - Customer-oriented
 // - Interdisciplinary communication (technical & non-technical)
 // - Independent
 
 
 #line(length: 7%, stroke: 2pt)
 == EXPERIENCE
 
 #grid(
   columns: (auto, auto),
   column-gutter: 1em,
   row-gutter: 1.5em,
   [2025--],
   [
     #grid(
       rows: (auto, auto),
       row-gutter: 0.8em,
       [
         #grid(
           columns: (
             1fr,
             auto,
           ),
           column-gutter: 1em,
           align(left)[
             *AI Research Engineer*
           ],
           align(right)[
             _Teton.ai_
           ],
         )
       ],
       text(size: 10pt)[
         - Performed an in-depth analysis of thousands of falls to understand which factors are related to falls and how they can be better prevented. Preprocessed data to account for various biases and confounders.
         - Trained various models to predict falls, though ended up converging on a two-stage model with one brute-force optimisation step and one logistic regression to forecast fall likelihood across different time horizons. This approach was chosen to maximise interpretability and actionability for care staff.
         - Based on my analysis and modelling, I designed, implemented and rolled out a new notification system based on resident actions rather than whereabouts, along with an automatic fall risk prediction model which led to a notification volume drop of 56% (mitigating spammy alerts) and a fall rate reduction of 30%. First-hand user feedback on this feature has also been collected and has been consistently positive.
       ],
     )
   ],
 
   [2021--2025],
   [
     #grid(
       rows: (auto, auto),
       row-gutter: 0.8em,
       [
         #grid(
           columns: (
             1fr,
             auto,
           ),
           column-gutter: 1em,
           align(left)[
             *Machine Learning Engineer*
           ],
           align(right)[
             _Saxo Bank A/S_
           ],
         )
       ],
       text(size: 10pt)[
         - Designed, built and deployed a real-time recommendation system serving relevant financial news to clients; we learn user and content embeddings, retrieve with nearest neighbour retrieval and apply reranking.
         - Built a RAG-based chatbot yielding a 170\% improvement in question-answering accuracy and decreasing the yearly number of manual agent chats by 20k, saving around \$150k in yearly agent costs.
         - Built, trained and deployed an NLP-inspired embeddings model for finding similar stocks. As of February 2025, the tool sits at around 110k monthly interactions. // This was the first client-facing machine learning tool my team deployed. // [Gensim, Kubernetes, FastAPI, OpenAPI]
       ],
     )
   ],
 )
 
 #line(length: 7%, stroke: 2pt)
 == EDUCATION
 
 #grid(
   columns: (auto, auto),
   column-gutter: 1em,
   row-gutter: 1.5em,
   [2020--2023],
   [
     #grid(
       rows: (auto, auto),
       row-gutter: 0.8em,
       [
         #grid(
           columns: (
             1fr,
             auto,
           ),
           column-gutter: 1em,
           align(left)[
             *M.Sc. Mathematical Modelling and Computation*
           ],
           align(right)[
             _Technical University of Denmark_
           ],
         )
       ],
       text(size: 10pt)[
         // - Specialisation in _machine learning and signal processing_.
         "Effortless Bayesian Deep Learning: Tapping Into the Potential of Modern Optimizers," with Søren Hauberg.
       ],
     )
   ],
   // [2017--2020],
   // [
   //   #grid(
   //     rows: (auto, auto),
   //     row-gutter: 0.8em,
   //     [
   //       #grid(
   //         columns: (
   //           1fr,
   //           auto,
   //         ),
   //         align(left)[
   //           *B.Sc. General Engineering* \
   //
   //         ],
   //         align(right)[
   // 	    _Technical University of Denmark_
   //         ],
   //       )
   //     ],
   //     text(size: 10pt)[
   //       // - Specialisation in _cyber materials_.
   //       "Membrane Capacitive Desalination," with Johan Hjelm
   //     ]
   //   )
   // ],
 )
 
 #line(length: 7%, stroke: 2pt)
 == PROJECTS
 
 #grid(
   columns: (auto, auto),
   column-gutter: 1em,
   row-gutter: 1.5em,
   [2022--2023],
   [
     #grid(
       rows: (auto, auto),
       row-gutter: 0.8em,
       [
         #grid(
           columns: (
             1fr,
             auto,
           ),
           column-gutter: 1em,
           align(left)[
             *Marginal Likelihood Training of Linearized Laplace Approximations Without Hessian Reductions*
           ],
           align(right)[
             _DTU Compute_
           ],
         )
       ],
       text(size: 10pt)[
         Developed a novel method for computing the Laplace approximation using only Jacobian-vector products in JAX, implementing posterior sampling (inference) and the log-determinant (optimising the marginal likelihood during training) of the Laplace covariance without explicitly instantiating it. Supervised by Søren Hauberg.
       ],
     )
   ],
 
   [2022--2023],
   [
     #grid(
       rows: (auto, auto),
       row-gutter: 0.8em,
       [
         #grid(
           columns: (
             1fr,
             auto,
           ),
           column-gutter: 1em,
           align(left)[
             *Bayesian Metric Learning for Uncertainty Quantification in Image Retrieval*
           ],
           align(right)[
             _DTU Compute_
           ],
         )
       ],
       text(size: 10pt)[
         Developed a method for training Bayesian neural networks in metric learning and demonstrate its effectiveness on small- to large-scale image datasets in yielding well-calibrated uncertainty estimates. We proved that contrastive loss constitutes a valid log-likelihood in spherical space and present a novel decomposition of its Generalized Gauss-Newton (GGN) approximation. Our paper was accepted to NeurIPS 2023 @warburg2023bayesian in New Orleans, which I had the fortune of attending. Supervised by Søren Hauberg.
       ],
     )
   ],
 )
 
 
 #line(length: 7%, stroke: 2pt)
 == PAPERS
 
 #bibliography(
   "hayagriva.yaml",
   title: none,
 )