Demetris Trihinas
Hello World!
I’m currently a tenured Assistant Professor at the Department of Computer Science, University of Nicosia with specialization in Big Data Management and Processing. I am also a Senior Member t the University’s Artificial Intelligence Lab overseeing all Data-Intensive Computing initiatives.
As a Researcher, I focus on designing and developing scalable and self-manageable data exploration and analysis systems by exploring the intersection between (big) data management, distributed systems and machine learning. Examples include performance and quality-aware optimization of continuous analytic jobs in edge computing settings, scalable emulation of data-intensive IoT services, benchmarking tools for geo-distributed ML applications, low-cost approximation techniques for self-adaptive monitoring, interoperable analytics over blockchains and recently I have also been working on energy- and carbon-aware ML.
Previously, I was an Adjunct Lecturer and Postdoctoral Fellow at the Department of Computer Science, University of Cyprus. As a Postdoc, I was associated with the Laboratory for Internet Computing. I additionally hold a MSc in Computer Science from the University of Cyprus and a Dipl-Ing in Electrical and Computer Engineering from the National Technical University of Athens. My PhD dissertation targeted developing low-cost probabilistic and adaptive learning models for approximate monitoring in order to improve energy- efficiency and reduce both the volume and velocity of data generated and consumed by IoT services. For the research conducted during my doctoral studies, I was selected by the Heidelberg Laureate Forum as one of the 100 young promising researchers in Computer Science for 2015.
I have extensive experience in European projects where I participated as a Work Package Leader and Senior Researcher in multiple projects (e.g., RAINBOW, Unicorn, PaaSport, CELAR) funded under the European Commission FP7 and H2020 grant schemes. Recently, I was the Project Coordinator for the FlockAI project funded by the UNIC SEED grant scheme (Oct. 2020 – Sept. 2022). FlockAI delivered a framework supporting Machine Learning testing and benchmarking for drone applications. I was also the WP Leader (Data Management for Fog Services) to the EC co-funded RAINBOW H2020 project. RAINBOW developed an open and trusted fog computing platform that facilitates the deployment of scalable and heterogeneous IoT services by pushing orchestration and data management intelligence to the network “edge”. Currently I contribute to the EC co-funded ALAMEDA H2020 project where I focus on MLOps for the design of AI-infused services aiding in the data collection of patient daily routine and medical interventions.
Our lab also features a number of other EU and National projects that I help out with. You may see a list here.
Recent News
Jul '23 Promoted to Assistant Professor!!!
May '23 The AILab successfuly hosted the first AI and Data Science Day at UNIC.
Mar '23 With Dr. Lauritz Thamsen (University of Glasgow) and Dr. David Bermbach (Technical University of Berlin), we are looking for papers for a special issue of Wiley's SPE journal on Benchmarking, Experimentation Tools, and Reproducible Practices for Data-Intensive Systems from Edge to Cloud. Submissions due March 31st.
Feb '23 Just got tenure!!!
Jun '22 Best Paper award at IEEE ISCC 2022 for our paper entitled "BenchPilot: Repeatable and Reproducible Benchmarking for Edge Micro-DCs".
Mar '22 Best Paper award at IEEE/ACM IoTDi 2022 for out paper entitled "5g-slicer: An emulator for mobile iot applications deployed over 5g network slices"
Researcher Openings
Open job posting for a Researcher in the area of Big Data processing for geo-distributed environments, Energy- and Carbon-Aware ML, as well as other aspects of Data-Intensive Computing. For more information please contact me.
Open job posting for a Reasearcher in the area of Machine Learning for Drone Swarms (Ref: CSAI06/20). For more information, see the detailed job posting here.
Open job posting for a Reasearcher in the area of Machine Learning for Smart Cities (Ref: CSAI11/19). For more information, see the detailed job posting here.
