Experienced researcher with a demonstrated history of working in the higher education industry. Skilled in machine learning, robotics, Python, C++, Pattern Recognition, Wireless Sensor Networks, and Data Science. Strong research professional with a Doctor of Philosophy (PhD) focused in Computer Science from Örebro University.
Machine learning consultant
Feb 2019 -
Victor’s duties is to help the sales and management team to establish a customer base. Victor’s work consists of performing data exploration, developing demonstrators and proof-of-concept systems. Victor’s duties also include presenting analysis and results to potential customers.
Data analysisData miningTensorflowArtificial intelligence PandasModel evaluationPythonMachine learningDeep learning
Nov 2015 -
During his Ph.D. studies, Victor’s main research work was as the lead scientist in the project Gasbot. The project developed a mobile robot for the monitoring of methane emissions in landfill sites and was developed in cooperation between Örebro University and the municipality of Örebro. During the project, Victor developed a new approach that combined remote gas sensing, 3D perception and machine learning to estimate statistical models of gas distribution that showed likely locations of methane leaks. Due to its positive environmental impact, the project generated considerable attention, not only in local media, but also in international outlets. Gasbot was the recipient of two international awards and raised interest on new robotics applications, such as emission monitoring.
Mobile robotsMachine learningData collectionData AnalysisStatistical modelingModel evaluationC/C++Optimization
Feb 2015 -
Victor worked in several EU and national projects such as Smokebot (support robots for fire brigades) and RAISE (Indoor pollution monitoring with mobile robots). During his employment as a researcher, Victor collaborated with several industrial partners as well as research institutes such as the German Aerospace Center (DLR), the Tokio University of Agriculture and Technology, the University of Warwick and the fluid mechanics lab at Cornell University and the University of Malaysia-Perlis. In 2017 Victor was one of the co-recipients of the best paper award at the 2017 ISOCS/IEEE International Symposium on Olfaction and Electronic Nose, Canada. In addition, Victor co-supervised two Ph.D. students that worked on problems related to unsupervised learning and sensor planning based on optimization techniques.
Mobile robotsData collectionMachine learningProbability theoryTime series analysisScientific writingPythonC/C++Git
Jan 2011 -
Victor was awarded a Ph.D. in computer science in 2015. His research topic focused on machine learning applied to environmental monitoring robots. In such area of research, Victor developed different statistical models for tasks such as prediction of pollution levels. This task requires, for example, the implementation and quantitative/qualitative analysis of different regression models and the selection of informative/useful variables. In addition, Victor worked with different machine learning techniques for classification, unsupervised learning and optimization.
Victor worked in projects related to Information and Entertainment radios for General Motors’ vehicles. Victor’s task included the implementing new functionality, conducting changes and testing.
Software engineeringC/C++Embedded systems
Aug 2005 -
Worked as a systems engineer for different HVAC projects for customers such as General Motors and Ford. As a systems engineer, Victor tasks include requirement management and definition, visiting the customer’s production site for in-vehicle testing and coordinating between the different competencies (e.g. electric, software and manufacturing engineering) during the project’s life cycle.
Systems engineeringRequirement management
Independent Test and Verification (IT&V) Engineer
Jan 2004 -
Victor developed and executed test procedures for embedded modules. In addition, he developed an automated testing tool for network communications. The tool allowed to perform functional test of network communications (e.g. CAN, LIN, J1850) and produced pass/fail reports. The tool was quickly adopted by several projects in Delphi.