About

Computer Science Masters student at the Delft University of Technology. Enthusiastic about distributed systems from center to edge and Deep Learning (DL) applications. Passionate about learning and helping others to understand.

Education

MSc. in Computer Science

2020 - now
Delft University of Technology

Computer Science Master, following the Data Science Track.

  • Oriented at Distributed and Machine/Deep Learning Systems.
  • Master Thesis: Robust meta-learning.

BSc. in Computer Science and Engineering

2017 - 2020
Delft University of Technology

Bachelor education in Computer Science and Engineering, following the Intelligent Data Analysis track.

  • Context Project: Developed yaqc, a large-scale NCBI/SRA quality metric aggregation platform, using Kubernetes and Helm.
  • Minor: Minor Robotics at Delft University and Technology
  • Bachelor Thesis: “Multi-inference on the Edge: Scheduling Networks with Limited Available Memory”

Publications

MemA: Fast Inference of Multiple Deep Models
J. Galjaard, B. Cox, A. Ghiassi, L. Y. Chen, R. Birke
2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)
Masa: Responsive Multi-DNN Inference on the Edge
B. Cox, J. Galjaard, A. Ghiassi, R. Birke and L. Y. Chen
2021 IEEE International Conference on Pervasive Computing and Communications (PerCom)

Experiences

TA Seminar Distributed Machine Learning

2021 - now
Delft University of Technology

Teaching assistant at MSc. level course. Developed and maintained the Kuberentes FLTK testbed. Hosted a live tutorial session to introduce students to the testbed, and assisted students to work on their projects and resolved issues with the code base.

TA Quant. Performance Evaluation of Comp. Sys.

2021 - now
Delft University of Technology

Teaching assistant at MSc. level course. Developed the Kuberentes FLTK testbed. The testbed was used by MSc. students for their projects. Hosted live tutorial sessions to introduce students to the testbed, and assisted students to work on their projects.

  • Developed and maintained the FLTK testbed.
  • Hosted live tutorial sessions to introduce the FLTK testbed on campus.

TA Introduction to Quantum Computing

2020 - now
Delft University of Technology

Teaching assistant at BSc. level course. Assisted students with written and programming exercises to strenghten their understanding of core Quantum Computing concepts. Aided responsibl professor to convert and extend exercises into LaTeX and WebLab.

  • Created and maintained reference solutions for written assignments.
  • Developed and maintained Python programming assignments based on IBM’s qiskit package.

Research Assistant at Distributed Systems Group

2020 - 2021
Delft University of Technology
  • Performed research to improve inference of multiple Deep Neural Networks on edge computing devices.
  • Developed and extended edgecaffe for edge inference.
  • Developed the FLTK testbed.

Head TA Big-Data Processing (BSc.)

2018 - 2020
Delft University of Technology

Teaching assistant at MSc. level course. Supported responsible teacher and professor with the course organization. Supervised lab sessions, and provided support for technical and course-related student questions.

  • Developed and tested assignments to hone students Big Data processing skills.
  • Assisted students to implement BD exercises and understand core BD concepts.

Student Mentor

2018 - 2019
Delft University of Technology

Helped getting CSE freshmen students getting started at the Delft University of Technology. Organized weekly meetings to discuss study-related topics, and assisted students help them get familiar with the Delft University of Technology as a student.

Member Students Panels Computer Science (CRI)

2017 - 2020
Christiaan Huygens (Study Association CSE)

Provided feedback on courses and lecturers/professors during my Bachelor education to help improve the quality of the courses in the curriculum.

Projects

(Kubernetes) Federated Learning ToolKit (FLTK) - A Kubernetes cluster and Helm based toolkit for Deep Learning training and inference.
Project BeachBot (BB) - Developed ROS software stack for a litter collection robot prototype.

Skills

Python

Docker & Docker Compose

Java

Scala, Akka, Spark & Flink

Kubernetes & Helm

LaTeX, BibTex & TiKZ