About me

Hola!

My name is Bernardo Gonzalez. I’m a Machine Learning Scientist from Mexico with 7+ years of academic and industrial experience.

Main interests

Clustering algorithms, Generative models, Learning from Contradictions.

Research

• Robust greedy algorithms for the Max-Cut relaxation. Bernardo Gonzalez, Angel Rodriguez, Ricardo Menchaca. Working paper.

• Generating universum samples from latent space. Bernardo Gonzalez, Ricardo Menchaca, Karina Torres. Working paper.

• DOC3: Deep One Class Classification using Contradictions. Sauptik Dhar, Bernardo Gonzalez. Machine Learning, 2023.

• Clustering Improves the Goemans–Williamson Approximation for the Max-Cut Problem. Angel Rodriguez, Bernardo Gonzalez, Ricardo Menchaca, Peter Stadler. Computation, 2020.

All papers in Google scholar.

Experience

• 2021-2022. Machine Learning Scientist at Intuition Machines Inc..
• 2020-2021. Machine Learning Consultant at Mexican Secretariat of the Civil Service (SFP).
• 2016-2020. Graduate Student Researcher at UCSC.
• Summers 2017, 2018. Machine Learning Intern at Bosch Center for Artificial Intelligence.

Some code releases

• MVTec data loader. Data loader for the MVTec dataset for benchmarking anomaly detection methods with a focus on industrial inspection.
• DOC3. Code for the DOC3 paper published in the Machine Learning journal in 2023.

Education

• PhD. in Computer Science. UCSC, Santa Cruz, CA, transferred to CIC-IPN, Mexico City (in progress).
• Msc. in Computer Science. UCSC, Santa Cruz, CA.
• MSc. in Computer Science. CIC-IPN, Mexico City.
• BSc. in Mechatronics. UPIITA-IPN, Mexico City.

Teaching

• Introduction to Data Structures and Algorithms. Spring 2020. Teaching Assistant for Prof. Patrick Tantalo
• Foundations of Data Science. Spring 2019. Graduate Teaching Assistant for Prof. Yang Liu
• Introduction to Data Structures and Algorithms. Fall 2018. Teaching Assistant for Prof. Nina Bhatti

Talks

• Advice for early-career data scientists. Tec de Monterrey CEM, Fall 2023
• Analyzing model’s performance through VC-dimension. Intuition Machines, Spring 2022
• Robust loss functions for large scale Graph SLAM. Bosch Center for Artificial Intelligence, Summer 2017
• Clustering pre-processing for SVM. Machine Learning Lab Seminar, Fall 2016