Konstantinos Karatsenidis
Computer Science PhD Researcher
Education
-
SEP 2022 – PRESENT
-
KAUST, Saudi Arabia – Computer Science MScAUG 2019 – DEC 2020
-
University of Athens, Greece – Computer Science BScJAN 2014 – FEB 2018
Work Experience
-
Meta, Menlo Park – Software Engineering InternMAY 2025 – AUG 2025Summer internship with Presto team at Meta.
-
Boston University, USA – Teaching AssistantJAN 2023 – MAY 2025Courses: Graduate Introduction to Database Systems (F23, F24, S25), Introduction to Database Systems (S23, S24)
-
ScaleTorch, Remote – Software EngineerAUG 2021 – OCT 2021Created features that automate integration with various cloud computing providers and backup checkpoints for cloud storage.
-
Atypon Systems, Greece – Software EngineerFEB 2019 – JUL 2019Developed features on the company's main product, and deployed progressively with feature flags. Resolved issues, fixed bugs, and participated in team meetings. Automated the procedure that creates SEO-friendly sitemaps for clients.
-
Hellenic Army, Greece – Backend Engineer (Java)MAY 2018 – FEB 2019Worked as a Java Backend Engineer at the Hellenic Ministry of National Defence. Developed web apps, maintained features, and implemented new ones used by the Army Control & Information Management System.
-
Interamerican, Greece – Android DeveloperFEB 2017 – MAY 2017Developed an application that downloads and manages company reports using SOAP web services.
-
University of Athens, Greece – Teaching AssistantSEP 2014 – FEB 2018Other Activities: Microsoft Student Partner 2015 – 2018
Publications
-
Designed and developed QuIT, a B+-tree with a fast leaf access optimization, that achieves up to 3.2× faster ingestion for near-sorted workloads. It also reduces the memory footprint by up to 28%, resulting in 1.36× faster range lookup queries. Implemented a concurrent version of the tree using atomic version locks.
-
Worked on a hardware-based solution that allows memory or storage components to perform on-the-fly transparent data transformation and improves performance by reducing data movement.
-
Created a live demo for Relational Memory using specialized hardware (Xilinx Zynq ZCU102) for the on-the-fly transformation of row tables into column groups at query time. This work won the Best Demo Paper Award at VLDB 2023.
-
Created an indexing performance benchmark for near sorted data.
-
Developed a Horovod extension that supports various compression methods for distributed data-parallel deep-learning training. GRACE integrates communication methods provided by MPI and NCCL. Implemented and compared various compression, communication, and memory compensation methods from literature using PyTorch and TensorFlow (v1/v2). The experiments were performed on NVIDIA V100 GPUs, and the results were visualized with WandB.