Our Vision

We are a team of scientists and engineers working together to solve key challenges that the world is facing. We focus on using machine learning to optimize complex decision making and reduce inefficiencies.

What We Do

At present we are successfully deploying our machine learning techniques to optimize the electricity grid by improving advance planning of electricity production and distribution, resulting in decreased economic waste and environmental damage.

Our Impact

Reduced CO2 emissions and pollution.
Improved reliability of the grid.
Less waste and increased economic efficiency.



We use machine learning to improve the operational planning of electricity production and distribution, improving reliability, efficiency, transparency, and pollution.
Electricity can not be efficiently stored. Power lines and transformers have limited capacity to carry power. Equipment on the grid can fail unexpectedly. Power generation is not scheduled accurately. Power use is not accurately anticipated. Electricity supply and demand must be balanced at all times.
Power lines get congested when nearing thermal limits, which means that power to serve some location must be sourced from more distant or expensive generators. Sourcing power from alternative sources to meet the unexpected demand, is expensive and can be operationally risky. Tapping into environmentally unfriendly sources leads to increased greenhouse gas emissions and pollution that is harmful to human health.
Increased reliability of the grid by helping create a better plan for one of the most complex systems ever created. Decreased greenhouse gas emissions and harmful pollution. Reduced waste and improved economic efficiency leading to lower electricity rates for those most in need.

What We Do

Electricity cannot be economically stored at utility scales. Supply and demand must be balanced at all times, and the structure of the transmission grid massive amounts of inefficiency. In order to deal with these issues, and to ensure reliable access to power, the global standard is to centralize control under System Operators, who coordinate the grid for electric utilities. The efficiency of the electricity grid is also of fundamental importance for achieving lower carbon emissions, and reducing the impact of coal and natural gas pollution on human health.
Electricity is the source of 25% of global CO2 emissions and billions of dollars in economic waste. The US electricity sector alone emits 2 billion tons of CO2 yearly, accounting for 38% of the country's total energy related CO2 emissions (2013)٭, power plants were responsible for 64% of SO2 emissions,16% of NOX emissions, 40% of CO2 emissions, and 68% of mercury air emissions in the US. The human health impacts are on par with traffic accidents.
Already successfully deployed from coast to coast in North American electricity grids, Invenia is actively growing and looking at expanding electrical grid optimization work globally. We interact directly with the grids, helping to plan for generation, flow and use of electricity in advance of real time operations. We help the system operators to optimize the power grid to ensure reliability, efficiency, transparency, while reducing harmful emissions.

Our Team

We are a team of scientists, researchers and developers that come from machine learning, engineering, computer science, economics, theoretical physics, mathematics and management.
Chief Executive Officer / Co-Founder
Matt Hudson
Matt co-founded Invenia, and has been CEO from the start. He started Invenia while at Microsoft, after majoring in political science and economics with additional studies in computer science and engineering. He has developed a deep knowledge of the electrical grid, complex networks, and machine learning.
Chief Technology Officer / Co-Founder
Christian Steinruecken
Christian completed his PhD under the supervision of Prof Sir David MacKay at the University of Cambridge (UK), and is a specialist in machine learning. He has led engineering projects in artificial intelligence, data compression and probabilistic programming. Christian believes that building intelligent technology is our best hope for making the world a better place.
Chief People Operations Officer / Co-Founder
Oksana Koval
Oksana is a co-founder of Invenia and is currently the Chief People Operations Officer, overseeing operations in Canada and the UK. She attended the University of Manitoba and Red River College, where she studied Business Administration, and Anthropology as a post-graduate. In her spare time, she also pursues research interests in Machine Learning and Archaeology.
Managing Director Invenia Labs/ Chief Science Officer / Co-Founder
Cozmin Ududec
Cozmin is a co-founder of Invenia, and is currently Managing Director of Invenia Labs in Cambridge and Chief Science Officer. He received his PhD in the foundations of quantum theory from the University of Waterloo, and is still puzzling over the quantum world in his spare time.
Scientific Advisor / Co-founder
David Duvenaud
David is a co-founder of Invenia, and assistant professor in computer science and statistics at the University of Toronto. He received his Ph.D. in machine learning from Cambridge University. He has also worked at Google Research, the Max Planck Institute for Intelligent Systems, and the Harvard Intelligent Probabilistic Systems group.
Alex Robson
Alex has a PhD in Biophysics from Oxford University, where he worked on applying machine learning techniques to model biological data. Since then he has worked on different applications of ML, one for a startup in the energy sector and most recently risk models in fintech. In his spare time, he can be found playing board games, and occasionally hacking around on personal ML projects.
Astrid Dahl
Astrid completed her Ph.D. in machine learning at the University of New South Wales. Her research is focused on improving the computational efficiency of multi-task Gaussian process models for solar power forecasting. Prior to her Ph.D., Astrid worked as a professional econometrician in the areas of energy and financial modeling and completed her Master of Economics and Econometrics at the University of Sydney. Her research interests are primarily in the areas of scalable nonparametric methods for spatiotemporal modeling, structured prediction and grid integration of distributed generation.
Senior Researcher
Bella Wu
Bella got her PhD in engineering from the University of Cambridge, where she developed advanced signal processing techniques, including many based on Bayesian inference, for magnetic resonance applications. Before joining Invenia, Bella worked at a startup on building energy models that provide forecast and analysis for use in hedging, trading and investments. She is interested in combining mathematical modelling and machine learning with fundamental theories in fields such as engineering and economics to gain unique insights into complicated systems that have a significant social impact.
Brendan Curran-Johnson
Brendan is a developer and a documentarian at Invenia. Whether writing docs or infrastructure code, his goal is to make it easier for others to be able to do their work.
Cameron Ditchfield
Cameron originally joined Invenia as a co-op student from the University of Manitoba. An enthusiastic reader, Cameron enjoys a wide range of topics. From the sagas to The Guns of August, he likes to spend his free time with a good book.
Senior Researcher
Chris Davis
Chris was formerly an Assistant Professor in Energy Informatics and Modelling at the University of Groningen. He received his PhD at Delft University of Technology, and his work covers topics related to Energy, Sustainability, Linked Data, Machine Learning, Data Visualization and Agent Based Modelling. Here are a few papers published by Chris: 1. Secondary Resources in the Bio-Based Economy: A Computer Assisted Survey of Value Pathways in Academic Literature 2. Electric vehicle charging in China’s power system: Energy, economic and environmental trade-offs and policy implications 3. The state of the states: Data-driven analysis of the US Clean Power Plan For the rest of Chris' published work, please refer to his Google Scholar profile.
Developer Intern
Cole Peters
Cole Peters and is a 4th year Computer Science Student at the University of Manitoba. He is currently a Co-op at Invenia, planning to graduate in April 2019. He was born and raised in  Winnipeg. Some of his favourite pastimes include swimming, fishing, nature walks, and watching sports, specifically the Winnipeg Jets and Blue Bombers.
Head of Development
Curtis Vogt
Curtis works on managing, architecting, and developing the next generation of Invenia's EIS. He also is a contributor and advocate for the Julia programming language.
Doyne Farmer
Doyne works with Invenia as a research advisor. Professor in the Mathematical Institute at the University of Oxford, and an External Professor at the Santa Fe Institute. His current research is in economics, including agent-based modeling, financial instability and technological progress. He was a founder of Prediction Company, a quantitative automated trading firm that was sold to the United Bank of Switzerland in 2006. His past research includes complex systems, dynamical systems theory, time series analysis and theoretical biology.
Senior Developer
Eric Davies
Eric received their Bachelor's Degree in Computer Science (Honours) with specialization in Artificial Intelligence from the University of Manitoba. Eric architects and develops improvements to Invenia's data processing and machine learning pipeline while pushing for a faster, more capable EIS. Eric also contributes to the Julia community and helps lead the charge for new technologies at Invenia.
Eric Perim Martins
Eric received his PhD in Physics from the University of Campinas, where he worked on Nanotechnology problems using Computational Physical-Chemistry techniques. He then moved to Duke University where he worked on High-Throughput Materials Science methods before joining Invenia.
Senior Developer
Fernando Chorney
Fernando is a huge fan of trying to make things easier and more efficient for all peoples and systems. When he’s not messing with his Linux servers at home, Fernando likes to cook, make music, play games, or work on learning new skills.
Research Advisor
Francesco Caravelli
Francesco's research focuses on statistical physics and complex systems, in particular complex networks, memristive circuits, econophysics and agent-based modelling. He is a theoretical physicist, interested in quantum and classical systems and the application of techniques of statistical physics and complexity to other disciplines such as economics, engineering and finance. He has been a Senior Researcher at Invenia Labs in Cambridge and a researcher at the London Institute for Mathematical Sciences, before moving as an Oppenheimer Fellow to Los Alamos National Laboratory.
Research Engineer
Glenn Moynihan
Glenn Moynihan is native of county Cork in Ireland but upped sticks to study Theoretical Physics at Trinity College Dublin where he received his Bachelors in 2013 and his PhD in 2018. His research focused on improving the accuracy and scope of linear-scaling density-functional theory applied to large-scale calculations of materials exhibiting strongly-correlated electrons.
Junior Researcher
Ian Goddard
Ian is a recent Masters graduate in data science from the University of Edinburgh where he has previously completed a Bachelors degree in Physics. His main area of interest is how we can use data science and machine learning to help in the transition of the energy sector to a more renewable mix. His interests in machine learning are broad, with particular interest in probabilistic modelling, Bayesian learning and graph
James Hamblin
James graduated with a BSc in Mathematics from Loughborough University. Prior to working at
Invenia he worked as a DevOps engineer for a startup developing artificial intelligence software
for the legal profession.
Senior Researcher
James Requeima
James is a senior researcher at Invenia and a PhD student studying machine learning at the University of Cambridge in the Computational and Biological Learning Lab under the supervision of Dr. Richard Turner. His interests include Bayesian optimization, learning, approximate inference methods, and deep generative models. He previously completed a master’s in machine learning, speech and language technology at the University of Cambridge under the supervision of Dr. Zoubin Ghahramani and a master’s of mathematics from the McGill University under Daniel Wise. He is also a tenured member of the Department of Mathematics at Dawson College in Montréal
People Operations Manager
Joao Moraes
After completing a law degree Joao went into business through a variety of roles, ultimately completing an MBA at the University of Cambridge. His previous background includes managing all aspects of a restaurant chain, brand strategy consultancy, and leadership development.
People Relations Manager
Kajal Bansal
Born and raised in Winnipeg, Kajal has moved around quite a bit having had the opportunity to live in Singapore, Ottawa and Toronto before eventually permanently settling back in Winnipeg. As a designated CPA, Kajal has worked in accounting, loan portfolio management, business development and recruitment. She will also be working toward her Human Resource Management Certification over the next 12 months.
People Operations Associate
Lisa Morris
Lisa was born and raised in Calgary, Alberta, and has lived in Winnipeg since 2005. She has an undergraduate degree in Criminology (Sociology/Psychology) from the University of Manitoba, and is currently working towards her diploma in Human Resource Manage
ment at the University of Winnipeg. Lisa plans to obtain her CPHR in the future. In her spare time, Lisa volunteers with a local animal rescue, enjoys going to concerts, Jets games, Goldeyes games, and spending time with her two dogs.
Letif Mones
I received my PhD in computational chemistry from ELTE University, Hungary, where I was working on the methodological improvement of hybrid QM/MM approaches and free energy computations for complex systems. At University of Cambridge and later at University of Warwick I developed an efficient sampling technique in combination with Gaussian process regression, introduced a protocol for constructing high dimensional quantum surfaces of organic molecules using machine learning and developed universal preconditioners to enhance the performance of optimisation techniques.
Research Engineer
Lyndon White
Lyndon grew up in the small town of Busselton in Western Australia. Until recently he was
studying at the University of Western Australia in Perth. He has undergrad degrees in Pure
Mathematics and Computation and in Electrical and Electronic Engineering. His Ph.D. on
Natural Language Processing via Machine Learning (Titled "On the surprising capacity of linear
combinations of embeddings") is currently under examination. He has been programming Julia
since late 2014 (v0.3.0 days) and is currently a maintainer/contributor to some unreasonable
number of packages.
Mahdi Jamei
Mahdi Jamei received the B.Sc. degree in Science and Technology in 2013, M.Sc. in Electrical and Computer Engineering from Florida International University (FIU), Miami, FL in 2014 and Ph.D. in Electrical and Computer Engineering from Arizona State University (ASU), Tempe, AZ in 2018. His research interests lie primarily in developing computational analytic tools for power system employing mathematical and signal processing techniques. He has been studying the security challenges of interdependent critical infrastructures mainly power, cyber and gas networks over the past few years.
Mary Jo Ramos
Mary Jo graduated with a BSc in Genetics before realizing her true passion for Computer Science. Prior to joining Invenia, she gained experience in Web Development and Bioinformatics. In her spare time, she loves to explore restaurants and shops around the city, discover the latest in technology and fashion, and play the Sims.
Software Developer
Matthew Brzezinski
Matt graduated from the University of Manitoba with a BSc Computer Science in 2017. His past experiences have mainly been around WebDev, DevOps, and systems architecture. He is currently very interested in data analytics, learning more about systems design, and contributing to StackOverflow. Outside of software development, he loves playing video games, mainly Dota2. He enjoys tinkering with his car, going to AutoCross and is slowly working towards my Time Attack license. He also loves to cook and learn about new cuisines (currently learning more Thai recipes).
Data Scientist
Max Lensvelt
For nearly a decade Max has worked in power markets and renewable energy in the UK, Europe and the United States.  His experience encompasses analytical roles in private equity, utilities and industry and most recently as a Data Scientist for the developer of the UK's first "virtual power plant". He holds a BSc in Physics and an MSc in Data Science.
Senior Data Scientist
Mike de Denus
While working on his Master's degree, Mike developed a robotics system for maintaining formation movement with varying numbers of robots without the use of a centralized controller. His teams have won awards at numerous international robotics competitions. At Invenia, he focuses on the analysis and exploration of nodal and spot electricity markets.
Research Engineer
Nick Robinson
Nick recently completed a masters in AI at Edinburgh University, having previously studied analytic philosophy at Cambridge. In between, he worked at ASI building machine learning models for all sorts of different companies.
Nicole Epp
Nicole graduated from the University of Manitoba with a Computer Science degree specializing in Theoretical Computer Science, Networks and Security, and Web-Based Systems. She also completed a minor in Chemistry. Nicole worked at Invenia for two co-op student work terms before joining full time in 2018.
Scientific Developer
Nick Thiessen
After completing BSc in computer science, Nick came to Invenia to work on building and maintaining machine learning systems and simulations. During his spare time, he can be found either developing, playing, or discussing games of all sorts.
Pavel Berkovitch
Pavel is a graduate student in Computational Statistics and Machine Learning at University College
London who is working on his research thesis in collaboration with Invenia. His areas of interest
include forecasting in stochastic dynamical systems, deep Bayesian learning, reinforcement learning
and information theory. Before pursuing graduate studies, Pavel obtained a BA in Computer Science
from St John’s College, University of Cambridge.
People Operations Manager
Reena Varshney
As a CPHR professional, with over 10 years of HR experience, Reena is known for her strong
skills in advisory HR, employee relations and legal compliance. Her mandate is to help businesses find HR solutions to every day employment issues.
Senior Developer
Rory Finnegan
Rory Finnegan joined Invenia as a Computer Science Co-op and Linux enthusiast with a background in Bioinformatics and Human Computer Interactions. Rory is currently completing a graduate degree in Computational Neuroscience.
Sam Morrison
Sam has recently finished her Bachelor of Computer Science (Honours) degree at the University of Manitoba. When not at work or studying, she can be found tending plants, watching science fiction shows, and attempting to learn useless skills.
Sam Massinon
Sam is a recent graduate from the University of Manitoba with a Bachelor of Computer Science. He started at Invenia as a co-op during the summer of 2015 and started working full time beginning in December of that year. He has been involved in a number of projects ranging from development to researching.
Head of Architecture and Operations
Sascha McDonald
Sascha is a senior executive with a track record of developing and implementing strategies to drive revenues and service delivery within start-up and blue-chip businesses. He comes with extensive international experience, key expertise includes: designing enterprise architecture, delivering operational capability across people, processes and technology; leading business development activity and contract negotiations to generate £multi-million revenue streams; leading multiple teams within both matrix and direct management environments; and managing relationships at CxO level with clients, suppliers and strategic partners.
Sean Lovett
Sean received his PhD in computational physics from the University of Cambridge, working on adaptive meshing methods in computational fluid mechanics. Since then he has worked as a research scientist in R&D for the oil and gas industry, where his research interests included complex fluids, reduced-order models, and the application of statistical modelling to physical systems. He has also been involved in several academic collaborations and physics outreach projects.
Director of Finance
Steve Marr
Steve is a CPA, CA who has worked at Deloitte, Great-West Life and most recently, as the Corporate Controller for the International Institute for Sustainable Development. He strives for continuous improvement and enjoys problem-solving collaboratively.
Developer Intern
Timothy Levins
I’m from Malaysia and I’m currently in the 3rd year of my CS degree at the University of Manitoba. This is my first internship as a junior developer and I am working with the data feeds team building Invenia’s data gathering framework. I drum a little in my spare time, work on mobile apps on the side, and I am always down to explore new places.
Wessel Bruinsma
Wessel is currently a PhD student at the University of Cambridge. He holds an MPhil in Machine Learning, Speech, and Language Technology also from the University of Cambridge. At Invenia, Wessel conducts research in the field of machine learning and investigates applications thereof. Research interests include probabilistic modelling, Bayesian nonparametrics, approximate inference, and signal processing.
Research Associate
Will Tebbutt
Will is currently a PhD student at the University of Cambridge, and occasionally advises on specific Machine Learning related matters at Invenia Labs. When not working he can be found playing the guitar, or listening to people play it well.
Zoubin Ghahramani
Zoubin works with Invenia as an advisor. He is also a professor of Information Engineering at the University of Cambridge, where he leads the Machine Learning Group consisting of about 30 researchers, and the Cambridge Liaison Director of the Alan Turing Institute, the UK's national institute for Data Science. His academic career includes concurrent appointments as one of the founding members of the Gatsby Computational Neuroscience Unit in London, and as a faculty member of CMU's Machine Learning Department for over 10 years. His current research interests include statistical machine learning, Bayesian non-parametrics, scalable inference, probabilistic programming, and building an automatic statistician. He has published over 250 papers, receiving over 30,000 citations (an h-index of 74).

Join Us

Being a part of the Invenia Labs team presents an opportunity to work with and learn from amazing people with expertise in machine learning, theoretical physics, mathematics, complex systems, and computer science while contributing to research that has a positive impact on our society and the environment.

We find great purpose in our ability to change the world for the better. It's what drives us to to work hard and continuously improve. If our vision resonates with you and you are interested in joining us, please visit our career page at www.joininvenia.com to apply.

Open Source

Our projects using Julia, Python and MATLAB languages.
A type based approach to working with filesystem paths in julia.
Julia library for handling holidays.
A concurrent task-runner that automatically resolves dependency issues.
A julia package for bayesian optimization of black box functions.
A julialang environment builder (like python's virtualenv).
A Result type for Julia—it's like Nullables for Exceptions.
Julia Futures which are initialized when written to.
Allows Julia function calls to be temporarily overloaded for purpose of testing.
Call Python from MATLAB.
Julia FTP client using LibCURL.jl
This is us, drop by sometime.