Sebastiaan Buiting

Researcher, AIQ, G42

Joined

September 2024

Names

Sebastiaan Buiting (Preferred)

J.S. Buiting

Emails

****@gmail.com (Confirmed)

****@g42.ai (Confirmed)

Personal Links

Google Scholar

Career & Education History

Researcher

AIQ, G42 (g42.ai)

2022 – Present

Intern

HFT, Optiver (optiver.com)

2018 – 2018

MS student

Information Sciences, University of Amsterdam (uva.nl)

2017 – 2018

Advisors, Relations & Conflicts

Coworker

Abdallah Benzine

Present

Expertise

LLM

2024 – Present

Time Series

2017 – Present

HFT

2017 – Present

Deep Learning

2017 – Present

RNNs

2017 – 2020

Publications

LLMs as Judges for Domain-Specific Text: Evidence from Drilling Reports
Abdallah Benzine, Soumyadipta Sengupta, Sebastiaan Buiting, Imane Khaouja, Yahia Salaheldin Shaaban, Amine EL KHAIR
- NeurIPS 2025 LLM Evaluation Workshop Poster
- Readers: Everyone
LiveDrill: Multimodal Segment-Triggered Data-to-Text for Time Series Foundation Models
Soumyadipta Sengupta, Amine EL KHAIR, Sebastiaan Buiting, Imane Khaouja, Yahia Salaheldin Shaaban, Abdallah Benzine
- BERT2S
- Readers: Everyone
DriMM: Drilling Multimodal Model for Time-Series and Text in the Era of Large Models
Sebastiaan Buiting, Soumyadipta Sengupta, Abdallah Benzine, Amine EL KHAIR, Imane Khaouja, Youssef Tamaazousti
- FMSD @ ICML 2025
- Readers: Everyone
Do Large Foundation Models Improve Time Series Segmentation? An Industrial Case Study in Oil and Gas Drilling
Imane Khaouja, Amine EL KHAIR, Abdallah Benzine, Sebastiaan Buiting, Soumyadipta Sengupta, Youssef Tamaazousti
- FMSD @ ICML 2025
- Readers: Everyone
When Larger Isn’t Better: Lightweight CNNs Outperform Large Time-Series Models in Classification of Oil and Gas Drilling Data
abdallah benzine, J.S. Buiting, Soumyadipta Sengupta, Badal Gupta, Youssef Tamaazousti
- NeurIPS 2024 TSALM Workshop
- Readers: Everyone

Sebastiaan Buiting

Names

Emails

Personal Links

Career & Education History

Advisors, Relations & Conflicts

Expertise

Publications

LLMs as Judges for Domain-Specific Text: Evidence from Drilling Reports

LiveDrill: Multimodal Segment-Triggered Data-to-Text for Time Series Foundation Models

DriMM: Drilling Multimodal Model for Time-Series and Text in the Era of Large Models

Do Large Foundation Models Improve Time Series Segmentation? An Industrial Case Study in Oil and Gas Drilling

When Larger Isn’t Better: Lightweight CNNs Outperform Large Time-Series Models in Classification of Oil and Gas Drilling Data

Co-Authors