Massachusetts, Amherst
ABOUT ME
PhD student in the College of Information and Computer Science Department at University of Massachusetts Amherst.
Working on the intersection of machine learning, applied statistics and database systems.
For more information you can take a look at my Resume and/or read my Statement of purpose.
My Timeline
Poster Accepted at NEDB
I presented my poster for the latest work on Incremental Package Maintenance
2023Joined the DREAM Lab
at University of Massachusetts Amherst
“Pushing analytics close to database systems”
Working as Research Assistant with Prof Alexandra Meliou and Prof Peter J. Haas on dynamic query package maintenance. A work that extends PackageBuilder under evolving queries & data stream.
Received a UMass CICS scholarship of $4,000 for the Best Ph.D. applicant
Sept 2022Ph.D. Applications Accepted
I had the honor to receive a letter of acceptance both from Boston University and University of Massachusetts Amherst.
Feb 2022BsC & MEng in Electrical and Computer Engineering
at Technical University of Crete
“Online Machine Learning in Distributed Environments for Big Data”
I worked with Prof Antonios Deligiannakis on Random Forest Optimizations under Data Drifts
Class Ranking: Top 5%
GPA (Computer Science): 3.6/4
1st Place Entrepreneurship Initiative Start-up Pitch Greece Section
at IEEE Greek SB Conference
“Scan, Throw, Recycle”
Founded a start-up initiative, RεScan, a user-friendly app promoting correct recycling habits. By scanning the barcode of items, the integrated machine learning model determines and suggests the recyclability category (if any), guiding users to dispose of their waste properly.
2020Technical Skills
Frameworks
Programming Languages
Tools
My technical skills as Research Data Scientist
Online Material that I find useful
Online Material
- Machine Learning Specialization (Andrew Ng) DeepLearning.AI
- MIT 18.065: Matrix Methods in Data Analysis, Signal Processing and Machine Learning, Spring 2018
- MIT 6.041: Probabilistic Systems Analysis And Applied Probability 2010
- MIT 6.S191: Introduction to Deep Learning
- CMSC 828W: Foundations of Deep Learning
- MIT 6.438: Algorithms for Inference
- MIT 15.S12: Blockchain and Money