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Michael Mersinias

Data Scientist & AI Engineer

Based in Seattle, WA

About

I work on projects which turn data into insights and insights into high-value products or business solutions.

Skills

Projects

Experience

2023-2024

Data Scientist & AI Engineer at Incelligent

• Retrieval‑Augmented Generation: Led the end‑to‑end research and development of a RAG Chatbot for a large multinational bank, which combined novel AI and IR methods to effectively retrieve facts from a document corpus as knowledge base to ground LLMs on accurate answers. • Document Information Extraction: Contributed to the development of a document information extraction pipeline for a real estate company, which analyzed extensive real estate valuation reports and extracted several valuation metrics. • Microsoft Azure Form Recognizer: Fine‑tuned and improved classification and information extraction neural MS Azure models for a large multinational bank, which classified and extracted information from several official government‑issued documents.

2022-2023

Graduate Research Assistant at UT Austin NLP Lab

• LLM Optimization: Developed an effective method for LLMs (GPT‑4, GPT‑3.5) to detect and correct their own errors, by combining two differently prompted instances of the model in order to self‑reflect and emulate the human cognitive process associated with controlled reasoning. • Text Generation and NLI: Improved the performance of AI text generation models (GPT‑3, GPT‑J) by incorporating natural language inference in order to dynamically adjust the decoding strategy parameters and perform controlled text generation. • Data Augmentation and Contrastive Learning: Increased the effectiveness of AI natural language inference models (BERT, BART, ELECTRA) by proposing a novel method of data augmentation, combined with contrastive learning and a hybrid loss function.

2019-2021

Data Scientist at Pharmasept

• Recommendation System: Conceptualized and developed a recommendation algorithm, based on machine learning and time‑series forecasting, to automatically schedule the routes of sales consultants to their networks, thus minimizing risk and increasing annual revenue by 11%. • Budget Forecast: Designed the budget forecast for the sales department using predictive analytics, resulting in a sales target for each product. • Data Analysis: Utilized statistical techniques, such as a weighted ABC analysis and K-means clustering, for product and customer segmentation. • Stakeholder Advice: Communicated findings and visualizations to advise the stakeholders on strategic decisions for the company.

Education

2021 - 2023

MS in Computer Science (AI), The University of Texas at Austin

• GPA: 4.00, Honors: Phi Kappa Phi (Top 10%) • Major: Computer Science (AI), Minor: Data Science and Business Analytics • Thesis: Introducing controlled reasoning into autoregressive large language models.

2013 - 2019

BEng and MEng in Electrical and Computer Engineering,Technical University of Crete

• Grade: 8.02, Class Rank: 7th (Top 10%) • Thesis: Effective fake news detection using machine learning techniques.