commit 271ea866a20fe457c6b22f4b15f6c982ba27581a
parent b605de9765c500f9e9bfeb37210f05d5f1adcb5d
Author: Silas Brack <silasbrack@Silass-MacBook-Pro.local>
Date: Sat, 14 Mar 2026 13:09:27 +0100
Clean up
Diffstat:
| M | main.tex | | | 42 | ++++++++++++++++-------------------------- |
1 file changed, 16 insertions(+), 26 deletions(-)
diff --git a/main.tex b/main.tex
@@ -9,10 +9,15 @@
\setlength{\parindent}{0pt}
\pagestyle{empty}
+\newcommand{\entry}[4]{%
+ \parbox[t]{\linewidth}{%
+ \parbox[t]{12cm}{\textbf{#1}}\hfill\parbox[t]{3.8cm}{\raggedleft\textbf{#2}}\\[0.2em]
+ \parbox[t]{12cm}{\textit{#3}}\hfill\parbox[t]{3.8cm}{\raggedleft\textit{#4}}}}
+
\begin{document}
\begin{center}
- {\Large\textbf{SILAS BRACK}}
+ {\huge\textbf{SILAS BRACK}}
\vspace{1em}
@@ -25,18 +30,12 @@
\vspace{1.5em}
\noindent
-\begin{tabular}{@{}p{2.5cm}p{16.5cm}@{}}
+\begin{tabular}{@{}p{2.0cm}p{16cm}@{}}
\textbf{\textsc{Experience}} &
- \begin{tabular}[t]{@{}p{12.5cm}@{\hfill}p{4cm}@{}}
- \textbf{AI Research Engineer} & \textbf{Nov 2025 -- Present} \\
- \textit{Teton.ai --- AI Research and Computer Vision} & \textit{Copenhagen, Denmark}
- \end{tabular} \\[0.3em]
+ \entry{AI Research Engineer}{Nov 2025--Present}{Teton.ai --- AI Research and Computer Vision}{Copenhagen, Denmark} \\[0.3em]
& {\small \textbullet\ Computer vision, transformers, forecasting, embedded machine learning} \\[0.8em]
- & \begin{tabular}[t]{@{}p{12.5cm}@{\hfill}p{4cm}@{}}
- \textbf{Machine Learning Engineer} & \textbf{Sep 2021 -- Oct 2025} \\
- \textit{Saxo Bank A/S --- Department of Predictive Models and AI} & \textit{Copenhagen, Denmark}
- \end{tabular} \\[0.3em]
+ & \entry{Machine Learning Engineer}{Sep 2021--Oct 2025}{Saxo Bank A/S --- Department of Predictive Models and AI}{Copenhagen, Denmark} \\[0.3em]
& {\small \textbullet\ 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.} \\[0.3em]
& {\small \textbullet\ 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.} \\[0.3em]
& {\small \textbullet\ 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.} \\
@@ -45,30 +44,21 @@
\vspace{1.0em}
\noindent
-\begin{tabular}{@{}p{2.5cm}p{16.5cm}@{}}
+\begin{tabular}{@{}p{2.0cm}p{16cm}@{}}
\textbf{\textsc{Education}} &
- \begin{tabular}[t]{@{}p{12.5cm}@{\hfill}p{4cm}@{}}
- \textbf{M.Sc. Mathematical Modelling and Computation} & \textbf{Sep 2020 -- Feb 2023} \\
- \textit{Technical University of Denmark} & \textit{Copenhagen, Denmark}
- \end{tabular} \\[0.3em]
+ \entry{M.Sc. Mathematical Modelling and Computation}{Sep 2020--Feb 2023}{Technical University of Denmark}{Copenhagen, Denmark} \\[0.3em]
& {\small \textbullet\ Thesis: ``Effortless Bayesian Deep Learning: Tapping Into the Potential of Modern Optimizers,'' with S{\o}ren Hauberg.} \\
\end{tabular}
\vspace{1.0em}
\noindent
-\begin{tabular}{@{}p{2.5cm}p{16.5cm}@{}}
+\begin{tabular}{@{}p{2.0cm}p{16cm}@{}}
\textbf{\textsc{Projects}} &
- \begin{tabular}[t]{@{}p{12.5cm}@{\hfill}p{4cm}@{}}
- \textbf{Marginal Likelihood Training of Linearized Laplace Approximations} & \textbf{Sep 2022 -- Feb 2023} \\
- \textit{Supervised by S{\o}ren Hauberg --- DTU Compute} & \textit{Copenhagen, Denmark}
- \end{tabular} \\[0.3em]
+ \entry{Marginal Likelihood Training of Linearized Laplace Approximations}{Sep 2022--Feb 2023}{Supervised by S{\o}ren Hauberg --- DTU Compute}{Copenhagen, Denmark} \\[0.3em]
& {\small \textbullet\ 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.} \\[0.8em]
- & \begin{tabular}[t]{@{}p{12.5cm}@{\hfill}p{4cm}@{}}
- \textbf{Bayesian Metric Learning for Uncertainty Quantification in Image Retrieval} & \textbf{Apr 2022 -- May 2023} \\
- \textit{Supervised by S{\o}ren Hauberg --- DTU Compute} & \textit{Copenhagen, Denmark}
- \end{tabular} \\[0.3em]
+ & \entry{Laplace Approximation in Bayesian Metric Learning}{Apr 2022--May 2023}{Supervised by S{\o}ren Hauberg --- DTU Compute}{Copenhagen, Denmark} \\[0.3em]
& {\small \textbullet\ 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.} \\[0.3em]
& {\small \textbullet\ Our paper was accepted to NeurIPS 2023 [1] in New Orleans, which I had the fortune of attending.} \\
\end{tabular}
@@ -76,7 +66,7 @@
\vspace{1.0em}
\noindent
-\begin{tabular}{@{}p{2.5cm}p{16.5cm}@{}}
+\begin{tabular}{@{}p{2.0cm}p{16cm}@{}}
\textbf{\textsc{Skills}} &
\textbf{Languages}: Python, SQL, C\# \\[0.3em]
& \textbf{Technology}: Spark, Databricks, Docker, Kubernetes, Airflow, Kafka, Terraform \\[0.3em]
@@ -86,7 +76,7 @@
\vspace{1.0em}
\noindent
-\begin{tabular}{@{}p{2.5cm}p{16.5cm}@{}}
+\begin{tabular}{@{}p{2.0cm}p{16cm}@{}}
\textbf{\textsc{Papers}} &
[1] F. Warburg, M. Miani, S. Brack, \& S. Hauberg. ``Bayesian Metric Learning for Uncertainty Quantification in Image Retrieval.'' \textit{NeurIPS 2023}. \\
\end{tabular}
