Anders Kiss

I have a Master of Science in Artificial Intelligence with technical skills in Python, fullstack app development, machine learning, deep learning (Keras and Tensorflow), natural language processing, recommendation systems, graph theory, SQL, and visualization technologies like Tableau and Microsoft Power BI. Background in healthcare, biotechnology, and medical devices.

Data Scientist 10/2022 to Present

Gemological Institute of America (GIA) – remote

• Developed deep learning and machine learning models for diamond grading purposes based on image classification and semantic segmentation algorithms
• Applied cloud based platforms (e.g. AWS Sagemaker and S3) for deployment of scalable solutions
• Utilized statistical programming tools to perform advanced analytics and visualizations
• Worked with gemologists and researchers on experiments and statistical analyses for root-cause investigations

Varian – remote

• Licensed the nanoparticles in the European Union (CE-Mark registration) collaborating alongside Engineering, Operations, Marketing, Product Management, and Sales departments
• Managed the technical data and product dossiers for embolization nanoparticles (oncology product)
• Led international registrations for the microspheres globally

Medtronic – remote

• Submitted European Medical Device Regulation (MDR) technical file (CE-Mark) dossiers for nerve-monitoring spinal product
• Provided regulatory support in New Product Development meetings pertaining to IEC 60601-1 Basic Safety and Essential Performance, IEC 60601-1-2 EMC, IEC 62366 Usability, IEC 62304 Software deliverables

Master of Science, Artificial Intelligence 09/2021 to 09/2022 

Dublin Business School - Dublin, Ireland

// Coursework

• Machine Learning & Pattern Recognition, Deep Learning, Reinforcement Learning, Recommender Systems, Natural Language Processing (NLP), Programming for Data Analysis, Graph Theory, Cognitive & Ethical Dimensions of Artificial Intelligence

• A comparison study of LSTM and GRU algorithms and a feature analysis of multivariate macroeconomic features pertaining to their capacity to improve accuracy of Bitcoin price 

• A data acquisition / preprocessing pipeline script to obtain stock market data, create technical indicator, with data persisting to a database (Microsoft SQL Server). 
• A Content-based recommender system (RS) script is for a content-based recommender system (RS) that I built. This basic system recommends an Airbnb listing to a User based on the description fields of a their historical stays. The code implementation compares recommendation accuracy of the BERT and TF-IDF algorithms 
• An NLP topic modeling application to better understand the major themes for that day’s news headlines by ingesting the Python API, NewsAPI. It can be found here: 
• A Long Short-Term Memory (LSTM) versus Gated Recurrent Unit (GRU) Neural Network predictive, financial model in Keras/Tensorflow that ingests Python’s yfinance API to predict tomorrow’s price.
• A Python script that utilizes that targets communities from the MovieLens database (containing ~38,000 movies) via the graph theory (machine learning) algorithms, Label Propagation (Community Detection Algorithm), and K-Nearest Neighbors (Similarity Algorithm). 
• A Tensorflow / Keras image classification model compared the accuracy of three image classification models: ResNet50, VGG16, and a custom deep learning, neural network. For each model above, first a baseline model was trained and evaluated. Afterwards, a dropout layer was added to each model and compared in terms of the baseline models.