Clean Resource Innovation Network (CRIN) and Petroleum Technology Alliance Canada (PTAC) hosted a free hybrid event on January 26 from 9:00 am to 11:45 am, at the Energy Transition Centre, where Kris Palka from Akinê, Ilya Perederiy from NTWIST, Ling Bai and Yu Pang from VL Energy, and Nick Shaw from Highwood Emissions Management outlined strategies to increase the value of actionable outcomes from Artificial Intelligence and Machine Learning – Identifying the right kind of datasets for long term gains through Artificial Intelligence. Artificial Intelligence and Machine Learning require large amounts of data to extract unobvious learnings, identify trends and make actionable recommendations. The critical challenge is continuously accessing large data sets specific to a type of workflow or process. This hybrid in-person and webinar event focused on proving strategies that have worked for innovators in the field. Click below to watch a complete recording of the event.

Event Recording



Meet the Speakers

Akinê Inc

Founder and CEO of Akinê Inc, Krzysztof (Kris) Palka, is a serial entrepreneur with a focus on efficiency and process improvement and a pioneer in the digital oilfield.  Kris understands the need to create resiliency in the industry by embracing innovation and new practices. With over 20 years expertise in oil production optimization and technology development, an MSc in Mechanical Engineering, and a Harvard business degree, Kris sees automation combined with digital transformation as critical on the road to more efficient hydrocarbon production in the carbon constrained economy.




Ilya Perederiy has over 10 years of experience at the intersection of industrial research, technical consulting and business strategy. Ilya earned a doctorate in chemical engineering from the University of Toronto in 2011 and spent a number of years with mining companies becoming interested in industrial applications of machine learning and joining NTWIST’s Edmonton office in 2019. At NTWIST, Ilya combines his cross-disciplinary expertise to drive product innovation.




VL Energy

Ling helps energy companies with better use of data, create digital operations, reduce emissions, evaluate M&As and use of renewable energy to be competitive and sustainably profitable. Ling’s expertise includes product innovation and strategy, emission reduction, regulatory compliance, ESG and operational leadership. Ling Bai is currently a PhD candidate of Chemical and Petroleum Engineering- Major in Environmental Sustainability Engineering of University of Calgary. Ling holds a Duo- MBA from Queens University and Cornell University. Ling has a Master of Geomatics Engineering- major in Environmental Engineering, and a Bachelor of Environmental Science from the University of Calgary. Ling has a combination of technical and business background in the oil and gas industry with air emission specialties and business operations for 12 years. Ling has worked for both consulting and operational producing companies in the oil and gas industry. She has a deep understanding in industrial challenge in continuous monitoring system. Ling has developed profound operational, regulatory technical knowledge in the progress of pilot predictive emission monitoring system project.


Dr. Yu Pang joined VL Energy as a key leader of data science innovation; where he leads innovation on developing data-driven models to precisely predict, monitor gas emissions from combustion devices and optimizing the operational processes to mitigate hazard gases and greenhouse gases emissions. Dr. Pang has a unique background of Chemical Engineering and Machine Learning, specializing in innovating machine learning and deep learning models for forecasting oil and gas production and optimizing production operations. He has novel research and development in CO2 sequestration, CO2 enhanced oil and gas production, and fluid flow and storage in unconventional reservoirs. Dr. Pang obtained Ph.D. degree in petroleum engineering from Texas Tech University and is accomplishing Master of Computer Science with a focus in machine learning from Georgia Institution of Technology. He achieved more than 30 publications, and he has served as an editor or reviewer for several high impact journals.




Nick Shaw is the Chief Technology Officer at Highwood Emissions, where he leads the scaling of the impact of Highwood’s industry-leading Greenhouse Gas Emissions expertise using innovative technology. Nick has 10+ years in the energy industry, data, software development, and software leadership which he leverages to help Highwood achieve its lofty vision of massive reductions in greenhouse gases. Nicks’ focus is building a world-class software team and leveraging cutting-edge technologies to reduce the barriers to building and executing greenhouse-gas emission reduction programs.