Hi! I’m Suf
I’m Suf, a data scientist and AI researcher with a deep background in experimental particle physics. My journey—from analyzing vast datasets at the Large Hadron Collider to developing cutting-edge machine learning models—has shaped my expertise in building intelligent systems that extract meaningful insights from complex data. On this site, I share my research, tools, and practical guides on machine learning, programming, and algorithm design to help others apply advanced techniques effectively.
Education
PhD in Experimental Particle Physics
University of Sussex | 2012 – 2016
Thesis: “Search for the electroweak production of supersymmetric particles in three-lepton events at the ATLAS detector with focus on compressed mass spectra”
View Thesis →MSci in Physics
University College London (UCL) | 2007 – 2011
Current Role – Senior Advisor, Data Science
Dell Technologies | 2023 – Present
- Providing technical leadership in optimizing log message classification services
- Leading exploration of Retrieval-Augmented Generation (RAG) with large language models
- Implementing novel anomaly detection algorithms for hardware and software metrics
Senior Research Scientist
Moogsoft Ltd. | 2021 – 2023
- Research on neural networks with multi-modal structures
- Implementation of high-performance sequence classification
- Leading research on periodicity analysis for real-time anomaly detection
Professional Profiles
IEEE Author Profile
Published research and contributions on IEEE Xplore
See my IEEE research contributions on →Research Publications
Efficient Periodicity Analysis for Real-Time Anomaly Detection
NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium
Citations: 1
View Publication →Enhancements to Language Modeling Techniques for Adaptable Log Message Classification
IEEE Transactions on Network and Service Management (Volume: 19, Issue: 4)
Citations: 1
View Publication →Exploring the Adaptability of Word Embeddings to Log Message Classification
2021 IFIP/IEEE International Symposium on Integrated Network Management (IM)
View Publication →Improved Fault Localization using Transfer Learning and Language Modeling
NOMS 2020 – 2020 IEEE/IFIP Network Operations and Management Symposium
Citations: 8
View Publication →Technical Skills
- Languages: Java, Python, R, SQL, C++, JavaScript
- ML Libraries: Keras, TensorFlow, PyTorch, DL4J
- Frameworks: Kafka, Kubernetes, Quarkus
- Developer Tools: Git, Jenkins, Docker, AWS
Research Focus
- Natural Language Processing
- Real-time Anomaly Detection
- Transfer Learning
- Deep Learning Architecture Design