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 where she studied Anthropology as a post-graduate. In her spare time, she also pursues research interests in machine learning and archaeology. In the past, she has excavated a 3,000-year-old settlement in Crete, Greece, and for her Master’s thesis, she applied machine learning to identify the ceramic manufacturing techniques of ancient potters.
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.
Abraham Alvarez-Bustos
Abraham Alvarez-Bustos was born in Mexico. He received a B.Sc. degree in Electrical Engineering from Instituto Politécnico Nacional (IPN) - ESIME, Mexico City, Mexico, in 2012. Then, a M.Sc. degree in Electrical Power Systems from the IPN - Sección de Estudios de Posgrado e Investigación (SEPI), Mexico City, Mexico, in 2015, where he got First Class Honours and an Honorific Mention. In 2016 he worked as a Transmission System Analyst in the National Control Centre of Energy (CENACE), Mexico. He obtained a Ph.D. degree at Durham University, Durham, UK in the area of Power Systems optimization and Control. His principal research interests lie in developing methods, models and software aimed at Power Systems Computational Analysis and Optimisation for Planning.
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.
Andrew Rosemberg
Andrew received a BSc in control engineering from PUC-RIO and a BSc in general engineering from École Centrale de Marseille. He also holds a master's in Electrical Engineering with an emphasis on Operation Research, focusing on Power Systems and Energy markets. Some of his previous projects revolve around energy economic dispatch analysis and simulation, financial data classification and portfolio optimization. His main interests are optimization, decisions under uncertainty and machine learning.
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.
Arnaud Henry
Arnaud graduated from the University of Edinburgh with an MSc in Artificial Intelligence in 2012. Since then, he's been working at various companies as a software engineer, notably in the music streaming industry and in the emerging space of autonomous vehicles. In his spare time, he enjoys travelling and tinkering with pet projects.
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.
Software Developer
Bailey Shirtliff
Bailey graduated in 2018 with a BSc in Computer Science from the University of Manitoba. He is an enthusiastic Python developer, with previous experience working on cloud-native software. His hobbies include beatboxing, painting miniatures, and playing board/card/video games.
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.
Research Engineer
Ben Cottier
Ben completed an MSc Artificial Intelligence at The University of Edinburgh in 2020. He likes to take a closer look at machine learning research and build software to make it better. When he’s not working, you might find him hiking or pondering the future of AI.
Office Coordinator
Bianca Felisbino
Bianca is originally from Brazil and moved to Canada in 2017. She has a bachelor’s degree in Chemical Engineering and a postgraduate diploma in Environmental Management and Sustainability. She has experience in technical and leadership roles in the manufacturing and hospitality industries. Throughout her career, she discovered her passion for sustainability, and for supporting and encouraging the core of every company: its people. She loves outside activities, nature, and getting to know different cultures and flavors.
Research Engineer
Branwen Snelling
Branwen did her PhD in Geophysics at Imperial College London, where she used an array of computational fluid dynamics models to research landslide-tsunami hazards. Along the way she explored uncertainty quantification and machine learning methods for natural hazard assessment. She is interested in building and using modelling tools to better understand the natural world and other complex systems.
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.
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.
Personal and Executive Assistant to the CEO
Camilla Regan
After graduating from Oxford Media and Business School, Camilla began her career working in London. Before joining Invenia she has worked as a Personal Assistant for various high profile individuals in different industries.
People Operations Associate
Caolán Jennings
Caolán has been a Cambridge local since 2007. With an academic background in philosophy and law, he is always ready for a friendly chat. Outside of work you can normally find him enjoying a good book or playing music with friends.
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.
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.
Chief Financial Officer
Dan Allen
Dan is an experienced financial leader who has held senior positions in several tech businesses. His corporate finance experience spans from raising debt and equity funds to leading on several mergers and acquisitions deals, including exits. Commercially he has experience of building scalable operational and financial infrastructure in global tech businesses and has led operational and data teams. He has vast experience of being a member of strategic management teams, including holding both exec and non-exec board seats.
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.
Senior Developer
Eric Davies
Eric received a Bachelor's Degree in Computer Science with a specialisation in Artificial Intelligence from the University of Manitoba. Now, 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 looked into High-Throughput Materials Science methods before joining Invenia.
Research Intern
Felix Opolka
Felix received his BSc in Computer Science from the Technical University of Munich and his MPhil in Computer Science from the University of Cambridge. Now he is working towards his PhD in Machine Learning at the Computer Lab of the University of Cambridge. He is interested in probabilistic modelling of network data, especially using Gaussian processes, and geometric machine learning.
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 Engineer
Frames Catherine White
Frames is passionate about building the tools to do research better. Her current specific interests are around tooling for machine learning libraries, in particular automatic differentiation. Frames completed her PhD on Natural Language Processing via Machine Learning (Titled “On the surprising capacity of linear combinations of embeddings”) at the University of Western Australia in 2018. She has been programming Julia since 2014, and is currently a maintainer of, and contributor to, some unreasonable number of packages.
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.
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.
People Operations Associate
Jackie Diab
Jackie graduated in 2021 with a CIPD certified BSc in Business and Human Resource Management from Anglia Ruskin University. Before joining Invenia she has worked in various people management roles within the retail industry and has experience with HR administration, recruitment and delivering training. She’s passionate about fostering a healthy culture in the workplace, and is excited by the emergent field of people analytics.
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.
Talent Specialist
Jasmin Geddes-Rainbow
After gaining a degree in languages from the University of Cambridge, Jasmin spent a number of years working in business operations management at the University before choosing to specialize in Talent Acquisition. Outside of work, she can usually be found singing with her band, travelling, or in the gym.
People Operations Manager
João 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.
Data Scientist
Josh Chadney
Josh is a space and atmospheric physicist with a PhD in planetary aeronomy from Imperial College London, where he worked on building physical models of the upper atmosphere of extrasolar planets. He has since performed measurements of the northern lights from Svalbard, in the high Arctic, to infer the temperature of Earth’s upper atmosphere as a postdoctoral research fellow at the University of Southampton. More recently he has built systems to measure and monitor gas flaring and pollutant emissions produced by the energy industry for an environmental services start-up.
Accounting Associate
Kristan Aho
Kristan moved to Winnipeg to obtain her bachelor's degree in Environmental Studies. Soon after accomplishing that, she found herself on a long journey pursuing her CPA designation. Kristan has a wide range of hobbies, from gardening to camping to playing D&D with friends. When she isn't crunching numbers or crushing fictional mythical beasts, she can be found with her two boys, husband and furry family members.
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.
People Operations Associate
Lisa Morris
Lisa was born and raised in Calgary, Alberta, and has lived in Winnipeg since 2005. She has a diploma in Human Resource Management from the University of Winnipeg and an undergraduate degree in Criminology (Sociology/Psychology) from the University of Manitoba. In her spare time, Lisa volunteers with a local animal rescue, enjoys going to concerts, Jets games, Goldeyes games, and spending time with her partner and their two dogs.
Data Engineer
Lukas Timmerman
Lukas graduated from the University of Manitoba in 2020 with a BSc in Computer Science, specializing in Theoretical Computer Science, and Networks and Security. Outside of work, Lukas spends time producing music, creating art, and developing video games.
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.
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.
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.
Data Scientist
Meas Meng
Meas was previously a postdoctoral researcher at the University of California, Santa Barbara. She received her PhD in environmental engineering from the University of Southern California. She also holds an MS in mechanical engineering from USC, and a BS in mechanical engineering from California State University, Los Angeles.
Research Engineer
Miha Zgubič
Miha, originally from Slovenia, is a theoretical physicist by training and holds an MSci degree from Imperial College London. After completing his Masters, he moved to the countryside to pursue a PhD at the University of Oxford where he analysed the data from the Large Hadron Collider at CERN searching for a rare decay of the Higgs boson. He likes python, boosted decision trees, and Federer's one-handed backhand.
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.
Head of Development
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.
Raphael Saavedra
Raphael received his MSc and BSc degrees in Electrical Engineering with an emphasis in Operations Research from the Pontifical Catholic University of Rio de Janeiro. He previously worked in projects with major electricity distribution companies with the goal of forecasting demands and optimizing contracts. He is also a Julia programmer and contributes to open- source projects. His main interests are optimization, time series models, and power system operation.
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.
Talent Specialist
Robbie Smith
Robbie graduated from the University of Southampton with a Bachelor’s degree in Music, where he specialised in composition. Since then, he has worked in technical recruitment and a variety of administrative roles. Robbie loves learning about new technologies and computing. He also enjoys writing music and comedy in his spare time.
Research Engineer
Rory Finnegan
Rory Finnegan joined Invenia as a Computer Science co-op and Linux enthusiast with a background in Bioinformatics and Human-Computer Interactions. In 2016, Rory completed a Master’s degree in Computational Neuroscience and is now working towards his PhD.
Data Engineer
Ryan Froese
After originally joining Invenia as a co-op student, Ryan achieved his Bachelor’s Degree in Computer Science at the University of Manitoba. He spends his free time exploring new technologies, bike riding and playing video games.
Chief Analytics Officer
Sam Hancock
Sam is a strategic analytical leader who has worked at companies ranging from 5-person startups to $100B+ multinationals. Most recently he guided Waymo’s Engineering Operations organization, the team that supports software development for Alphabet’s self-driving car project. At Invenia he is responsible for analytically connecting the company’s activities to its overarching objectives. Outside of work Sam is a semi-professional drummer, performing on the West End multiple times, and competes in triathlons.
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.
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.
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.
People Operations Associate
Shenya Wickramasinghe
Shenya originally hails from Sri Lanka and spent her early years growing up in Dubai. She completed her bachelor’s degree in Switzerland at the Cesar Ritz Colleges where she graduated with a Bachelor of Arts in Hospitality Business Management. After working within various roles in the hospitality industry, Shenya moved to Winnipeg in 2019 to pursue her passion for human resources. She has recently obtained a postgraduate diploma in Human Resources Management from the University of Winnipeg. In her spare time, Shenya enjoys watching documentaries, painting and baking.
Machine Learning Researcher
Simon Nakach
Simon Nakach has a PhD in Theoretical physics from Imperial College, where he studied how symmetry can lead to the next theory of particle Physics. After his PhD he spent two and a half years working on a range of Machine Learning projects ranging from Computer Vision to time series forecasting as a Data Scientist. He is interested in leveraging physically-motivated machine learning techniques to better understand and model real world systems
Finance Senior Manager
Stephen Maharaj
Stephen is a Chartered Professional Accountant (CPA), Chartered Accountant (CA) with experience in audit, financial reporting, operations, and tax compliance. After starting his career at Deloitte, Stephen transitioned to industry and held Controller roles with Uncharted Software and Harvest One. Outside of the office you can find Stephen travelling the world, checking out live events, art galleries, or exploring the latest lounges and restaurants with family and friends. 
Director of Finance
Steve Marr
Steve is a 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.
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.
Machine Learning Researcher
Tom Gillam
Tom has a broad interest in the application of statistical learning to real-world problems. He has a PhD in High Energy Physics from Cambridge, working with the ATLAS experiment at CERN. Prior to joining Invenia, Tom worked for a quantitative hedge fund, specializing in portfolio optimization, risk modelling, and strategy development. His research interests include Bayesian methods, optimization, and the incorporation of symmetries, causality, and other physical structure into models.
Machine Learning Research Intern
Tomisin Dada
Tomisin recently completed the MPhil in Advanced Computer Science at the University of Cambridge. He has previously worked as a Project Engineer in Nigeria and completed his Bachelors in Electrical Engineering at Imperial College. Tomisin believes Machine Learning has a large role to play in building an equitable, secure and sustainable power grid. In his spare time Tomisin enjoys music, reading and writing poetry.
Junior Developer
Tyler Loewen
Tyler completed his BSc in Computer Science at the University of Manitoba in 2021 with a focus in Software Engineering, Databases, and HCI & Graphics. In his free time he enjoys photography, riding motorcycles, hiking, and the outdoors.
Wessel Bruinsma
Wessel is currently a PhD student at the University of Cambridge. He holds an MPhil in Machine Learning and Machine Intelligence 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 in the Machine Learning Group at the University of Cambridge. He is interested in probabilistic models for spatio-temporal phenomena, in particular Gaussian processes. He occasionally advises on specific machine learning problems at Invenia. When not working, he can be found playing the guitar, or listening to people play it well.
Machine Learning Researcher
Will Wilkinson
Will received his PhD in Computer Science from Queen Mary University of London in 2019, after which, he spent 2.5 years as a postdoctoral researcher in Machine Learning at Aalto University, Finland. His research interests lie at the intersection of machine learning and signal processing, with a focus on statistical inference for spatio and spectro-temporal data using Gaussian processes.
Software Engineer
Wynand Badenhorst
Wynand graduated in 2019 with a BSc in Computer Science, specializing in Computer Systems, Human-Computer Interaction and Graphics. Before joining Invenia, his experience has been in full stack development of web and desktop applications, as well as development of hybrid cloud applications. In his free time he likes to watch sci-fi movies, play video games, build things, cook things, and make things in general.
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.
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.