ELLIS Honours Programme

Coursework: Machine Learning, Deep Learning, Reinforcement Learning, Natural Language Processing, Information Retrieval, Computer Vision, Recommender Systems, Causality, Human-in-the-Loop ML
GPA: 8.5/10

Thesis: Adapting to Dynamic User Preferences in Recommendation Systems via Deep Reinforcement Learning (link), supervised by Prof. Frans A. Oliehoek.
Minor: Electrical Sustainable Energy Systems.
GPA: 7.9/10

Working on Reinforcement Learning-Finetuned Temporal Graph Neural Networks for COVID-19 Contact Tracing, supervised by Rob Romijnders, Dr. Yuki M. Asano, QUVA Lab, University of Amsterdam and Prof. Pascal Frossard, EPFL, Switzerland.

Assisted in teaching graduate-level courses by making sure students understood the material, answering their questions, creating assignments, giving feedback, and grading exams.
Courses:

• Computer Vision 1
• Information Retrieval 1
• Fundamentals of Data Science
• Fairness, Accountability, Confidentiality & Transparency in AI

Developed a system for data capturing using Boston Dynamics SPOT robot within the ImpactLab Transformative Mobility Team, serving clients like NS and VolkerWessels. Involved in all stages of the project lifecycle, from concept to deployment.

Led a team of 5 students in high-profile Data Science and AI competitions. Top 10 finish in AWS DeepRacer Challenge 2021 (300+ participants).

Developed an application improve corporate entity matching, increasing efficiency by 40% with affinity propagation clustering algorithms and asynchronous processing

Luca Pantea and Andrei Blahovici (2023) ‘[Re] CrossWalk: Fairness-enhanced Node Representation Learning’, ReScience C, 9(2), p. 39. doi: 10.5281/zenodo.8173749. Journal Track at the Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS), 2023 (link).

Milena Kapralova, Luca Pantea and Andrei Blahovici (2023) ‘LightGCN: Evaluated and Enhanced’, New in ML Workshop, Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS), 2023 (link).

Programming Languages

Proficient in Python, SQL, Java, R, Scala, Spark, C/C++, Haskell, JavaScript. Capable of applying these languages to solve complex problems, develop software, and analyze data.

Libraries & Frameworks

Experienced with PyTorch (Torch, Geometric), Tensorflow, Keras, JAX, NumPy, Pandas, SciPy, scikit-learn, FastAI.

Big Data Technologies

Knowledgeable in Apache Hadoop, Spark, Kafka, Flink. Implements big data solutions for processing and analyzing large datasets.

Databases

Experienced with both SQL (MySQL, PostgreSQL) and NoSQL (Neo4j, MongoDB) databases.

Tooling

Proficient in Git, Jupyter, Docker, Kubernetes, Slurm, GCP and Linux.

Oxford Machine Learning Summer School (OxML)

Participant • 2023

Organised by AI for Global Goals and in partnership with CIFAR and the University of Oxford’s Deep Medicine Program. Covered 41 hours of lectures on advanced topics in ML theory and its applications in Finance & NLP. Certificate

Eastern European Machine Learning Summer School (EEML)

Participant • 2021

A week-long program covering advanced topics in Deep Learning and Reinforcement Learning, with seminars and tutorials about JAX, RL, causal effect estimation, explainability in ML and graph representation learning. Certificate

• Running (half-marathon ✅, marathon 🏗️)
• (Mountain) Bike touring (Europe 🏗️)
• Avid coffee bean juice extractor ☕