Mark Szulyovszky
Co-Founder - Order Flow, Liquidity & Market Microstructure Metrics at Heii OU
Sliema, Central Region, Malta
Summary
👤
Senior🎓
Top SchoolMark Szulyovszky is a quant portfolio manager, researcher and engineer with 11 years of experience building production-grade trading infrastructure and consumer products. As Co-Founder of Unravel/Aperiodic he designs institutional-grade, market-neutral crypto factor portfolios and order-flow analytics, while running a family-office quant desk deploying long-short strategies. He previously co-founded and served as CTO/CRO of Drops, scaling the language app to 25 million users before exit to Kahoot!, and built high-performance systems with very small teams. Mark blends functional-programming-driven engineering, machine learning and product design, and uniquely pairs academic-style research with pragmatic deployment. Based in Malta, he’s equally comfortable coding low-level market microstructure metrics as he is shaping product and growth strategy.
12 years of coding experience
9 years of employment as a software developer
Intro to Machine Learning with Pytorch Nanodegree, Intro to Machine Learning with Pytorch Nanodegree at UdacityArtificial Intelligence, Computer Science, Artificial Intelligence, Computer Science at University of LeedsBachelor's, Communication Science (Specialization in Marketing & PR), Bachelor's, Communication Science (Specialization in Marketing & PR) at Szegedi TudományegyetemCertificate, Sound Engineering, Certificate, Sound Engineering at Oktopus Multimedia InstituteCertificate, Graphic & Media Design, Certificate, Graphic & Media Design at International People's College
Stackoverflow
313reputation
34kreached
2answers
1question
Github Skills (200)
onesignal10
in-app-purchase10
certificate-pinning10
swiftui10
urlrequest10
oauth2-client10
watchos10
forecasting10
time-series-forecasting10
gear-vr10
urlsession10
alamofire10
oauth10
react-native-module10
apple-tv10
Programming languages (14)
Github contributions (5)
dream-faster/laplace-gnn-recommendation
Mar 2022 - May 2022
⛓️ laplace is an end-to-end ML framework to train and predict on neurally-enhanced graphs for recommendation. The pipeline is designed for self-supervised edge prediction on heterogenous graphs.
Contributions:117 reviews, 97 commits, 117 PRs in 1 month
machine-learninggraph-neural-networksrecommendation-system
almostintuitive/almostintuitive.github.io
Aug 2015 - May 2026
Contributions:75 pushes, 6 branches in 10 years 10 months
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