Amsterdam, Netherlands • dinos.ppk [at] gmail [dot] com
I'm a highly motivated AI professional, and a proud alumnus with an MSc degree in Artificial Intelligence from the University of Amsterdam. Currently, I work at Zeta Alpha as a Research Engineer, specializing on dense embedding models and generative language models. My interests lie in Explainable AI, Neural Information Retrieval, and NLP.
Collaborating closely with enterprise clients to develop custom embedding models tailored to their proprietary data. My expertise centers on seamlessly incorporating these models into question-answering and conversational systems, boosting efficacy with domain-specific fine-tuning on the retrieval part.
Worked on my thesis titled Domain Adaptation for Dense Retrieval: Generating Diverse Synthetic Data with Large Language Models, under the supervision of Dr. Andrew Yates and Jakub Zavrel.
Distinction: Cum Laude
Notable Classes: Deep Learning 1 & 2, Information Retrieval 1 & 2, Deep Learning for NLPWorked as a Teaching Assistant during my second year for DL 1, FACT in AI, IR 1, and ATCS.
Highest Grade of Class of '21
Thesis: Machine Learning for Web Robot Detection (Scalper Bots)Minors: (1) Artificial Intelligence, (2) Data & Web Management
Applied a two-step knowledge distillation technique for cross-encoders using soft labels, achieving comparable performance between models of different sizes despite having an order of magnitude fewer parameters.
Published in the Findings of EMNLP 2023.
Studied the information-sharing mechanisms in BERT-based models, demonstrating the presence of joint encoding across languages, linguistic entities, and tasks.
Published in the Findings of ACL 2023.
Investigated the modeling aspects that influence the answer quality of retrieval-augmented models in long-from ambiguous QA, along with the alignment of common evaluation metrics with human judgment.
Published in the XAI4CV workshop at CVPR 2023.
Proposed an interpretation method for the Vision Transformer, which learns to mask out the subset of the input with the least impact on the output distribution, in order to analyze its decision-making process.
Published in the ReScience Journal, presented as a poster at NeurIPS 2022.
Reproducibility study of a paper on a collection of adversarial attacks that degrade a model’s performance with regard to fairness metrics.
Served as the chairperson of a student team for 2.5 years, during which I helped coordinate various administrative subteams and organize community events on technology topics.
Worked on a plethora of group projects as a student, many of which lead to scientific publications and/or open-source contributions.
As a Computer Science graduate, I've tackled many analytical problems regarding data structures and algorithms, while delivering computationally efficient solutions.
Participated in a collection of engaging activities, aiming to (i) understand the fairness issues in existing AI models and datasets, and (ii) propose ways to address these problems.
Selected for the Greek delegation for the Seeds For the Future program, during which we attended lectures and got awarded a certification on AI, 5G, IoT, and Cloud Computing.
Worked with a small team to design a robot to play football, using LEGO MINDSTORMS EV3, and develop a custom Java framework to handle the robot's state and actions.
Created a prototype website that used the municipality's open data to provide an interface for tourists to navigate through archaeological monuments, both on foot and by car.
Coordinated an administrative team of over 25 members and helped in the organization of 15 community events about Computer Science and Career Development.