commit 7d7d34668c88759880bc8526bbd8319ff10669c3
parent a8df2a94b08694c3196d09f696ae923981e70d81
Author: Silas Brack <silasbrack@gmail.com>
Date: Tue, 7 Apr 2026 00:03:11 +0200
feat: new style
Diffstat:
| M | main.typ | | | 301 | ++++++++++++++++++------------------------------------------------------------- |
1 file changed, 67 insertions(+), 234 deletions(-)
diff --git a/main.typ b/main.typ
@@ -5,8 +5,6 @@
y: 1.0cm,
),
)
-#show heading.where(level: 2): set text(size: 12pt)
-#show heading.where(level: 3): set text(size: 12pt)
#set text(size: 12pt, font: "Cooper Hewitt")
#set par(
first-line-indent: 0pt,
@@ -18,6 +16,9 @@
#show heading.where(level: 1): set align(center)
#show heading.where(level: 1): set text(size: 20pt)
#show heading.where(level: 1): set block(below: 2em)
+#show heading.where(level: 2): set text(size: 12pt)
+#show heading.where(level: 2): set block(below: 1.5em, above: 0.5em)
+#show heading.where(level: 3): set text(size: 12pt)
#align(center)[
#grid(rows: (auto,auto),
@@ -30,8 +31,6 @@
columns: (
auto,
auto,
- auto,
- auto,
),
gutter: 1em,
[
@@ -40,12 +39,6 @@
[
#link("https://silasbrack.com")[silasbrack.com]
],
- [
- #link("https://github.com/silasbrack/")[github.com/silasbrack]
- ],
- [
- #link("https://linkedin.com/in/silasbrack/")[linkedin.com/in/silasbrack]
- ],
)
],
// [
@@ -76,10 +69,14 @@
// - Independent
+#line(length: 10%, stroke: 2pt)
+== EXPERIENCE
+
#grid(
- columns: (1fr, 5fr),
+ columns: (auto, auto),
column-gutter: 1em,
- [== EXPERIENCE],
+ row-gutter: 1.5em,
+ [2025--],
[
#grid(
rows: (auto, auto),
@@ -87,56 +84,60 @@
[
#grid(
columns: (
- 3.5fr,
- 1.5fr,
+ 1fr,
+ auto,
),
+ column-gutter: 1em,
align(left)[
- *AI Research Engineer* \
- _Teton.ai --- AI Research and Computer Vision_
+ *AI Research Engineer*
],
align(right)[
- *Nov 2025 -- Present* \
- _Copenhagen, Denmark_
+ _Teton.ai_
],
)
],
text(size: 10pt)[
- - Computer vision, transformers, forecasting, embedded machine learning
+ Computer vision, transformers, forecasting, embedded machine learning
]
)
+ ],
+ [2021--2025],
+ [
#grid(
rows: (auto, auto),
row-gutter: 0.8em,
[
#grid(
columns: (
- 3.5fr,
- 1.5fr,
+ 1fr,
+ auto,
),
+ column-gutter: 1em,
align(left)[
- *Machine Learning Engineer* \
- _Saxo Bank A/S --- Department of Predictive Models and AI_
+ *Machine Learning Engineer*
],
align(right)[
- *Sep 2021 -- Oct 2025* \
- // *Apr 2023 -- Present* \
- _Copenhagen, Denmark_
+ _Saxo Bank A/S_
],
)
],
text(size: 10pt)[
- - Designed, built and deployed a real-time recommendation system serving relevant financial news to clients; to do so, we learn user and content embeddings and perform efficient nearest neighbour retrieval followed by reranking.
+ - 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: (1fr, 5fr),
+ columns: (auto, auto),
column-gutter: 1em,
- [== EDUCATION],
+ row-gutter: 1.5em,
+ [2020--2023],
[
#grid(
rows: (auto, auto),
@@ -144,23 +145,21 @@
[
#grid(
columns: (
- 3.5fr,
- 1.5fr,
+ 1fr,
+ auto,
),
+ column-gutter: 1em,
align(left)[
- *M.Sc. Mathematical Modelling and Computation* \
- _Technical University of Denmark_
+ *M.Sc. Mathematical Modelling and Computation*
],
align(right)[
- *Sep 2020 -- Feb 2023* \
- _Copenhagen, Denmark_
+ _Technical University of Denmark_
],
)
],
text(size: 10pt)[
// - Specialisation in _machine learning and signal processing_.
- - Thesis: "Effortless Bayesian Deep Learning: Tapping Into the Potential of Modern Optimizers," with Søren Hauberg.
- // - Published a paper to NeurIPS 2023
+ "Effortless Bayesian Deep Learning: Tapping Into the Potential of Modern Optimizers," with Søren Hauberg.
]
)
// #grid(
@@ -190,10 +189,14 @@
]
)
+#line(length: 7%, stroke: 2pt)
+== PROJECTS
+
#grid(
- columns: (1fr, 5fr),
+ columns: (auto, auto),
column-gutter: 1em,
- [== PROJECTS],
+ row-gutter: 1.5em,
+ [2022--2023],
[
#grid(
rows: (auto, auto),
@@ -201,227 +204,57 @@
[
#grid(
columns: (
- 3.5fr,
- 1.5fr,
+ 1fr,
+ auto,
),
+ column-gutter: 1em,
align(left)[
- * Marginal Likelihood Training of Linearized Laplace Approximations Without Hessian Reductions* \
- _Supervised by Søren Hauberg --- DTU Compute_
+ *Marginal Likelihood Training of Linearized Laplace Approximations Without Hessian Reductions*
],
align(right)[
- * Sep 2022 -- Feb 2023* \
- _Copenhagen, Denmark_
+ _DTU Compute_ \
+ w/ Søren Hauberg
],
)
],
text(size: 10pt)[
- - Developed a novel method for computing the Laplace approximation using only Jacobian-vector products in JAX, implementing posterior sampling (for inference) and the log-determinant (for optimising the marginal likelihood during training) of the Laplace covariance without explicitly instantiating it.
+ Developed a novel method for computing the Laplace approximation using only Jacobian-vector products in JAX, implementing posterior sampling (for inference) and the log-determinant (for optimising the marginal likelihood during training) of the Laplace covariance without explicitly instantiating it.
]
)
+ ],
+ [2022--2023],
+ [
#grid(
rows: (auto, auto),
row-gutter: 0.8em,
[
#grid(
columns: (
- 3.5fr,
- 1.5fr,
+ 1fr,
+ auto,
),
+ column-gutter: 1em,
align(left)[
- * Bayesian Metric Learning for Uncertainty Quantification in Image Retrieval* \
- _Supervised by Søren Hauberg --- DTU Compute_
+ *Bayesian Metric Learning for Uncertainty Quantification in Image Retrieval*
],
align(right)[
- * Apr 2022 -- May 2023* \
- _Copenhagen, Denmark_
+ _DTU Compute_ \
+ w/ Søren Hauberg
],
)
],
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.
+ 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.
]
)
- ]
-)
-
-#grid(
- columns: (1fr, 5fr),
- column-gutter: 1em,
- [== SKILLS],
- [
- #grid(rows: (auto, auto, auto),
- row-gutter: 1em,
- [*Languages*: Python, SQL, C\#],
- [*Technology*: Spark, Databricks, Docker, Kubernetes, Airflow, Kafka, Terraform],
- [*Modelling*: Gradient boosting (LightGBM), deep learning (PyTorch, JAX), Word2Vec, embeddings, LLMs]
- )
- ]
-)
-
-#grid(
- columns: (1fr, 5fr),
- column-gutter: 1em,
- [== PAPERS],
- [
- #bibliography(
- "hayagriva.yaml",
- title: none,
- )
- ]
+ ],
)
-// == WORK EXPERIENCE
-// #line(length: 100%)
-
-// #grid(
-// columns: (
-// 7fr,
-// 2fr,
-// ),
-// align(left)[
-// === Machine Learning Engineer \
-// _Saxo Bank A/S --- Department of Predictive Models and AI_
-// ],
-// align(right)[
-// === Apr 2023 -- Aug 2025 \
-// _Copenhagen, Denmark_
-// ],
-// )
-// #text(size: 11pt)[
-// // - Developed a proof of concept of a news agent using RAG and MCP for interacting with financial news and other financial data sources. Automatically generated localised market update articles, saving strats team \~5000 hours a year.
-// - Designed, built and deployed a real-time recommendation system serving relevant financial news to clients; to do so, we learn user and content embeddings and perform efficient nearest neighbour retrieval followed by reranking. Built data pipelines combining batch and streaming jobs and optimized inference latency. Ran A/B tests to improve model. // [Databricks, SynapseML, Onnx, quantization, two-tower, Postgres, OpenAI, HNSW, A/B testing]
-// - Built a RAG-based chatbot yielding a 170\% improvement in question-answering accuracy over an existing solution 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]
-// ]
-
-// // #grid(
-// // columns: (
-// // 7fr,
-// // 2fr,
-// // ),
-// // align(left)[
-// // === Software Developer, Student Assistant \
-// // _MAN Energy Solutions --- Department of Master Data and Management_
-// // ],
-// // align(right)[
-// // === Apr 2018 -- Sep 2021 \
-// // _Copenhagen, Denmark_
-// // ],
-// // )
-// // #text(size: 11pt)[
-// // - Automated data engineering tasks and data pipelines with ETL jobs using SSIS and Microsoft SQL Server, being responsible for 15--20 jobs.
-// // - Communicated with data cleansing stakeholders to identify and automate the repetitive workflows which led to the most unnecessary time consumption, saving an estimated 1000 person-hours of manual cleansing work.
-// // - Increased fraction of cleansed data from 25% to 50%. Created and maintained over 20 tools for data manipulation and visualisation using C\#.
-// // - Participated in business trips to Greece in order to manage the team's relationship with various stakeholders and align on various projects.
-// // ]
-
-// == PROJECTS
-// #line(length: 100%)
+#line(length: 7%, stroke: 2pt)
+== PAPERS
-// #grid(
-// columns: (
-// 7fr,
-// 2fr,
-// ),
-// align(left)[
-// === Marginal Likelihood Training of Linearized Laplace Approximations Without Hessian Reductions \
-// _Supervised by Søren Hauberg --- DTU Compute_
-// ],
-// align(right)[
-// === Sep 2022 -- Feb 2023 \
-// _Copenhagen, Denmark_
-// ],
-// )
-// #text(size: 11pt)[
-// - Developed a novel method for computing the Laplace approximation using only Jacobian-vector products in JAX, implementing posterior sampling and the log-determinant of the Laplace covariance without explicitly instantiating it.
-// ]
-
-// #grid(
-// columns: (
-// 7fr,
-// 2fr,
-// ),
-// align(left)[
-// === Bayesian Metric Learning for Uncertainty Quantification in Image Retrieval \
-// _Supervised by Søren Hauberg --- DTU Compute_
-// ],
-// align(right)[
-// === Apr 2022 -- May 2023 \
-// _Copenhagen, Denmark_
-// ],
-// )
-// #text(size: 11pt)[
-// - Developed a method for training Bayesian 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.
-// ]
-
-
-// == EDUCATION
-// #line(length: 100%)
-
-// #grid(
-// columns: (
-// 7fr,
-// 2fr,
-// ),
-// align(left)[
-// === M.Sc. Mathematical Modelling and Computation \
-// _Technical University of Denmark_
-// ],
-// align(right)[
-// === Sep 2020 -- Feb 2023 \
-// _Copenhagen, Denmark_
-// ],
-// )
-// // #text(size: 11pt)[
-// // - Specialisation in _machine learning and signal processing_.
-// // - Thesis: "Effortless Bayesian Deep Learning: Tapping Into the Potential of Modern Optimizers," with Søren Hauberg.
-// // ]
-
-// // #grid(
-// // columns: (7fr, 2fr),
-// // align(left)[
-// // === B.Sc. General Engineering \
-// // _Technical University of Denmark_
-// // ],
-// // align(right)[
-// // === Sep 2017 -- Aug 2020 \
-// // _Copenhagen, Denmark_
-// // ],
-// // )
-// // #text(size: 11pt)[
-// // - Thesis: "Membrane Capacitive Desalination," with Johan Hjelm
-// // ]
-
-
-// == SKILLS
-// #line(length: 100%)
-
-// #grid(
-// columns: (
-// auto,
-// auto,
-// ),
-// gutter: 10pt,
-// align(left)[
-// *Languages* \
-// *Technology* \
-// *Modelling*
-// ],
-// align(left)[
-// Python, SQL, C\#, Scala, Rust, Erlang / Elixir / Gleam \ // , Clojure / Lisp
-// Spark, Databricks, Docker, Kubernetes, Airflow, Kafka, Terraform \
-// Gradient boosting (LightGBM), neural networks (PyTorch, JAX), Word2Vec, embeddings, LLMs
-// ],
-// )
-
-
-// == PUBLICATIONS
-// #line(length: 100%)
-
-// #bibliography(
-// "hayagriva.yaml",
-// title: none,
-// )
+#bibliography(
+ "hayagriva.yaml",
+ title: none,
+)
