My main areas of expertise are machine learning, statistics, and data visualisation applied to real world data problems. I am a data scientist at Balderton Capital, an early stage venture firm in Europe. My vision is to modernise and scale the investment decision making process through the use of data. I make use of external data repositories, as well as the rich internal data and the expertise of our amazing investment team. I also help companies in our portfolio getting better at data. Previously I have lead research at PeerIndex, a venture-backed social data analysis and marketing tech startup in London. We built data products that allowed brands and individuals understand their influence and their audiences on twitter. We crunched through terabytes (if not more) of data using state-of-the-art technology, but what I’m most proud of are the delicate things we did with small data. During my PhD at Cambridge I worked mainly on machine learning theory with a focus on nonparametric Bayesian methods and kernel machines. I worked on applications as diverse as cognitive psychology and quantum physics experiments, and I generally did not appreciate how much free time I had :)

data scientist @ I am a data scientist at Balderton Capital, an early stage venture firm in Europe. My vision is to modernise and scale the investment decision making process through the use of data. I make use of external data repositories, as well as the rich internal data and the expertise of our amazing investment team. I also help companies in our portfolio getting better at data. From March 2014 to Present (1 year 10 months) London, United Kingdomsenior data scientist @ I oversaw the development of algorithms to analyse influence between people on social networks. Our team implemented proprietary algorithms then-state-of-the-art data platforms like Apache Hive and Amazon RedShift. I was part of the core team re-architecting our technology as the company transitioned from a B2C offering to a B2B dashboard product. From November 2011 to March 2014 (2 years 5 months) London, United Kingdomvisiting researcher @ Visiting Bernhard Schölkopf's Empirical Inference group working on the interface between probabilistic and kernel methods in machine learning. From October 2011 to November 2011 (2 months) Tübingen Area, Germanyresearch intern @ Worked on a product developed by a startup Indextools, acquired by Yahoo! to become Yahoo Web Analytics. In my internship I developed graphical model-based machine learning methods to improve click-through-rate prediction based on visitors' search and browsing history. From July 2009 to September 2009 (3 months) Visiting Researcher @ Computational and Biological Learning Lab From February 2009 to May 2009 (4 months) Cambridge, United KingdomResearcher, Sortware Engineer @ Embodied and Communicating Agents focus group. From February 2005 to August 2008 (3 years 7 months) summer vacation scholar @ Working under supervision of Professor Jotun Hein From June 2008 to July 2008 (2 months)

PhD, Machine Learning @ University of Cambridge From 2009 to 2013 MSc, Computer Science @ Technical University of Budapest From 2004 to 2009 Ferenc Huszár is skilled in: Machine Learning, Artificial Intelligence, Statistics, Bayesian statistics, Algorithms, Python, Matlab, Statistical Modeling, Data Mining, Hadoop, Unsupervised Learning, Data Analysis, LaTeX, Data Science, R, Pattern Recognition, Endorsements, Git