About me

I am a Software Engineer, passionate about what I do. Over the years, I have taken different roles that complement each other and, in particular, cover my needs to keep learning new things at every opportunity. At Intel, I worked on the design of Intel processors while also being involved in path-finding that led to over 30 patents being filled in different areas, such as distributed accelerators and resources (monitoring, management, multi-tenancy, etc.).

Later, I decided to embark on a doctoral journey and pursue my PhD, where I had the opportunity to work on my passion: optimization of computer-intensive tasks for the cloud.

Today, I am back in production systems, leading a team of data scientists and software engineers to provide timeseries forecasting at scale.

Career

Technical Lead

2022 - Present
Encert Predict

Lecturer

2019 - Present
Universitat Oberta de Catalunya (UOC)

ML/AI Researcher

2021 - 2022
Enveda Biosciences

Doctoral Researcher

2017-2021
Barcelona Supercomputing Center (BSC)

Software Engineer

2014-2017
Intel Corporation

Multiprocessor Performance Engineer

2013-2014
Intel Corporation

Skills

Programming

Python, C/C++, Go, Shell/Bash

DevOps

Docker, Kubernetes, CI/CD, Virtualization

Deep Learning

Computer Vision, Timeseries Analysis, PyTorch

Parallel Programming

OpenMP, MPI, CUDA

Hardware Architecture

Cache Coherence, Memory Hierarchy, microarchitecture

Publications

  • Ensembles of knowledge graph embedding models improve predictions for drug discovery
  • Daniel Rivas, Daniel Domingo-Fernández, Yojana Gadiya, David Healey
    Briefings in Bioinformatics, 2022
  • Towards automatic model specialization for edge video analytics
  • Daniel Rivas, Francesc Guim, Jordà Polo, Pubudu M Silva, Josep Ll Berral, David Carrera
    Future Generation Computer Systems, 2022
  • Large-scale video analytics through object-level consolidation
  • Daniel Rivas, Francesc Guim, Jordà Polo, Josep Ll Berral, David Carrera
    7th EAI International Conference, SmartCity360°, 2022
  • Causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery
  • Daniel Domingo-Fernández, Yojana Gadiya, Abhishek Patel, Sarah Mubeen, Daniel Rivas, Chris W Diana, Biswapriya B Misra, David Healey, Joe Rokicki, Viswa Colluru
    PLOS Computational Biology, 2022
  • Performance characterization of video analytics workloads in heterogeneous edge infrastructures
  • Daniel Rivas, Francesc Guim, Jorda Polo, David Carrera
    Concurrency and Computation: Practice and Experience, 2021
  • Drug2ways: Reasoning over causal paths in biological networks for drug discovery
  • Daniel Rivas, Sarah Mubeen, Francesc Guim Bernat, Martin Hofmann-Apitius, Daniel Domingo-Fernández
    PLOS Computational Biology, 2021

    Patents

    I have been involved in 38 patents (over 20 already granted) in the areas of distributed systems, network resources, and disaggregated accelerators. Here, I list some of the most relevant ones. (full list in my scholar profile here)

  • Methods and apparatus for composite node creation and management through SDI partitions
  • Daniel Rivas, Francesc Guim Bernat, Susanne M Balle, John Chun Kwok Leung, Suraj Prabhakaran, Murugasamy K Nachimuthu, Slawomir Putyrski
    US Patent 11,444,866, 2022
  • Technologies for object-based data consistency in distributed architectures
  • Francesc Guim Bernat, Thomas Willhalm, Karthik Kumar, Raj K Ramanujan, Daniel Rivas
    US Patent 10,255,305, 2019
  • Persistent Remote Direct Memory Access
  • Karthik Kumar, Suleyman Sair, Francesc Guim Bernat, Thomas Willhalm, Daniel Rivas
    US Patent App. 15/435,886, 2018
  • Hardware-based shared data coherency
  • Kshitij A Doshi, Francesc Guim Bernat, Daniel Rivas
    US Patent App. 15/283,284, 2018