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.

More

ELECTRICAL GRID - OPTIMIZING ONE OF THE MOST COMPLEX SYSTEMS EVER CREATED

We use machine learning to improve the operational planning of electricity production and distribution, improving reliability, efficiency, transparency, and pollution.
problem
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.
implications
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.
impact
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

ELECTRICAL GRID CHALLANGES
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.
WASTE AND POLLUTION
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.
WHAT WE DO
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.
All
Leadership
Research
Development
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 now also the Managing Director and Chief Science Officer of Invenia Labs in Cambridge. 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 an assistant professor in computer science and statistics at the University of Toronto. He received his PhD in machine learning from Cambridge University. Before Invenia, he also worked at Google Research, the Max Planck Institute for Intelligent Systems, and the Harvard Intelligent Probabilistic Systems group.
Researcher
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's worked on numerous applications of ML, including one for a startup in the energy sector and another for risk models in fintech. In his spare time, Alex can often be found playing board games or occasionally hacking around on personal ML projects.
Researcher
Anton Isopoussu
Most recently, Anton has been interested in unsupervised learning using ideas from optimal transport. Before getting into machine learning, he studied mathematics, physics and computer science in Helsinki and then went on to complete a PhD in algebraic geometry in Cambridge.
Researcher
Astrid Dahl
After achieving a Masters in Economics and Econometrics at the University of Sydney, Astrid completed her PhD in machine learning at the University of New South Wales. She has previously worked as a professional econometrician in the energy and financial sectors but now, as a researcher for Invenia, she finds ways of improving the computational efficiency of multi-task Gaussian process models for solar power forecasting. Her main research interests are scalable nonparametric methods for spatiotemporal modeling, structured prediction and grid integration of distributed generation.
Senior Researcher
Bella Wu
Bella achieved her PhD in engineering at 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.
Developer
Brendan Curran-Johnson
Brendan is a developer and a documentarian at Invenia. Whether he's writing docs or infrastructure code, his work is integral for others to be able to do theirs.
Developer
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 the Delft University of Technology, and his work covers topics related to Energy, Sustainability, Linked Data, Machine Learning, Data Visualisation, 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 graduated from the University of Manitoba in 2019 with a Bachelor of Computer Science (Honours) degree. He started working at Invenia as a co-op student in 2018 and transitioned to a full-time position after graduation. Outside of work, Cole enjoys hiking, camping, reading, and gaming.
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.
Advisor
Doyne Farmer
Alongside his work with Invenia as a Research Advisor, Doyne is also a 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.
Content Writer
Emma Lunn
Emma is a Content Writer who has previously written for tech and biotech companies but has also done some pretty adventurous jobs before that - including zoo keeping! She likes to work on content creation and building a brand voice, but she also has experience running paid social campaigns, generating content strategies and much more in the way of general marketing too.
Senior Developer
Eric Davies
Eric received his Bachelor's Degree in Computer Science with a specialisation in Artificial Intelligence from the University of Manitoba. Now, he 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.
Researcher
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 looked into 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 people and systems. When he’s not messing with his Linux servers at home, Fernando likes to cook, make music, play games and learn new skills.
Research Advisor
Francesco Caravelli
Francesco's research focuses on statistical physics as well as 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 Bachelor's 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 Master's graduate in data science from the University of Edinburgh where he has previously completed a Bachelor's 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 a particular interest in probabilistic modelling, Bayesian learning, and graph theory.
Developer
James Hamblin
James graduated with a BSc in Mathematics from Loughborough University. Before 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 optimisation, 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 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 Management 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.
Researcher
Letif Mones
Letif received his PhD in computational chemistry from ELTE University, Hungary, where he worked on the methodological improvement of hybrid QM/MM approaches and free energy computations for complex systems. At the University of Cambridge and later at the University of Warwick, he developed an efficient sampling technique in combination with Gaussian process regression. In addition to this, he also 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 undergraduate degrees in Pure Mathematics and Computation and in Electrical and Electronic Engineering. His PhD 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.
Facilities Coordinator
Magda Karavatou
Magda was born in Thessaloniki, Greece, and moved to Cambridge in 2019. She has previously studied Economic Science at the University of Macedonia and has received an IT skills certificate from the University of Cambridge.
Researcher
Mahdi Jamei
Mahdi Jamei received a BSc degree in Science and Technology in 2013 and an MSc in Electrical and Computer Engineering from Florida International University in 2014. He then went on to achieve a Ph.D. in Electrical and Computer Engineering from Arizona State University in 2018. His research interests lie primarily in developing computational analytic tools for power systems employing mathematical and signal processing techniques. He has been studying the security challenges of interdependent critical infrastructures of power, cyber and gas networks over the past few years.
Developer
Mary Jo Ramos
Mary Jo graduated with a BSc in Genetics before realizing her true passion for Computer Science. Before 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
In 2017, Matt graduated from the University of Manitoba with a BSc in Computer Science. His experience so far has mainly been in 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, tinkering with his car, going to AutoCross and is slowly working towards his Time Attack license. He also loves to cook and learn about new cuisines (currently learning more Thai recipes).
Data Scientist
Max Lensvelt
Max has worked in power markets and renewable energy within the UK, Europe, and the United States for almost a decade. His experience encompasses analytical roles in private equity, utilities, and industry. Most recently, he has worked as a Data Scientist for the developer of the UK's first virtual power plant. He also 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 master's 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.
Developer
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 a 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.
Intern
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 everyday 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. At the moment, Rory is currently completing a graduate degree in Computational Neuroscience.
Developer
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 to plants, watching science fiction shows, and trying her hand at new hobbies and skills.
Developer
Sam Massinon
Sam is a recent graduate from the University of Manitoba with a Bachelor of Computer Science. He joined Invenia as a co-op during the summer of 2015 and started working full time in December that year. Since then, he has been involved in several 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 that drive revenue and service delivery within start-up and blue-chip businesses. His expertise includes designing enterprise architecture and delivering operational capability across people, processes and technology. In addition to this, he has masses of international experience in leading business development activity, contract negotiations to generate multi-million-pound revenue streams, and leading multiple teams within both matrix and direct management environments. His previous work has also included managing relationships at the CxO level with clients, suppliers, and strategic partners.
Researcher
Sean Lovett
Sean received his PhD in computational physics from the University of Cambridge, where he worked 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 Certified Public Accountant and Chartered Accountant who has previously 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
Timothy was born in Malaysia and has recently graduated from the University of Manitoba with a bachelor’s of science degree in Computer Science. His work at Invenia started as part of his first internship as a junior developer, and he spent most of his time working with the data feeds team to build Invenia’s data gathering framework. In his spare time, he loves to explore new places.
Researcher
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 researches machine learning and investigates applications thereof. His main 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.
Advisor
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 ten years. His current research interests include statistical machine learning, Bayesian nonparametrics, 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.
All
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FIlePaths
A type based approach to working with filesystem paths in julia.
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Holidays
Julia library for handling holidays.
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Arbiter
A concurrent task-runner that automatically resolves dependency issues.
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BayesianOptimization
A julia package for bayesian optimization of black box functions.
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Playground
A julialang environment builder (like python's virtualenv).
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ResultTypes
A Result type for Julia—it's like Nullables for Exceptions.
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DeferredFutures
Julia Futures which are initialized when written to.
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Mocking
Allows Julia function calls to be temporarily overloaded for purpose of testing.
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matpy
Call Python from MATLAB.
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FTPClient.jl
Julia FTP client using LibCURL.jl
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