Fred Gruber

Fred Gruber

Senior Principal Scientist



Fred K. Gruber is a senior principal scientist at Aitia. At Aitia he applies statistical and machine learning techniques for the development of models from biological data (genomic and clinical) with special attention to interpretation of the resulting models. His expertise goes beyond the usual machine learning topics (logistic regression, Neural Networks, Support Vector Machine, Genetic Algorithms) to include other techniques like statistical signal processing (random processes, optimal filters, Bayesian estimation), inverse problem theory (regularized least square solutions, pseudoinverse), sparse coding (sparse reconstruction, dictionary learning), and simulation and modeling (discrete event simulation, statistical modeling).

  • Artificial Intelligence
  • Machine Learning
  • Computational Biology - Bayesian Networks - Causal Inference - Causal Discovery - Generative Models - Unsupervised Learning
  • Target Trial Emulation, 2022

    Harvard University

  • Applied Deep Learning Boot Camp, 2020

    Massachusetts Institute of Technology

  • An Introduction to Causal Inference, 2018

    Harvard University

  • Data Science: Data to Insights, 2016

    Massachusetts Institute of Technology

  • PhD in Electrical Engineering, 2009

    Northeastern University

  • BSc in Electrical and Electronic Engineering, 2003

    Universidad Tecnologica de Panama








Causal Inference


Causal Discovery


Machine Learning



Senior Principal Scientist
Aug 2018 – Present Somerville, MA

My focus is in applying causal inference and causal discovery methods on multiple modalities of biological data including RNAseq (single and bulk), single nucleotides variants (germline and somatic), structural variants, copy number variants as well as clinically relevant variables for the purpose of discovering biomarkers and drug targets of various diseases. More recently I have worked with multiple myeloma clinical studies as well as the TCGA cancer datasets. While I mainly work with our proprietary causal Bayesian network learning and predictive tools as part of my job I also investigate other open source machine learning tools that have potential usage for the type of datasets that we deal with.


  • Work on various research projects involving causal inference, data integration, and biological data.
  • Provide scientific support to other scientists in the company as well as external customers
  • Present our work to clients
Principal Scientist
Oct 2016 – Aug 2018 Somerville, MA
Senior Systems Biologist
Sep 2013 – Oct 2016 Somerville, MA
Postdoctoral Scientist
May 2009 – Jun 2013 Cambridge, MA

I designed, implemented and tested novel algorithms for the analysis of Nuclear Magnetic Resonance measurements of rocks and fluids that are of interest in oil exploration. I use Matlab extensively for the research and Python in order to interface Matlab code with other company software.

Responsibilities included:

  • Designing mathematical models
  • Implementing Matlab and Python algorithms
  • Testing and validating algorithms
Research Assistant
Jan 2005 – May 2009 Boston, MA
Worked at Northeastern University’s Communications and Digital Signal Processing Center where I investigated various inverse problems in remote sensing and fundamental limits in wireless communication.
Research Assistant
Jan 2003 – Dec 2004 Orlando, FL

Worked at the Center for NASA Simulation Research Group where I was involved in several projects including:

  • Simulating the probability of failure during a space shuttle liftoff
  • Investigating the integration of genetic algorithms and support vector machine for a classification problem.

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