Download Curriculum Vitae - Christoforos anagnostopoulos

Curriculum Vitae
Christoforos Anagnostopoulos
My career has been motivated by a singular and genuine curiosity into what it means to
learn from evidence, and in particular from evidence arising from measurements, i.e., data.
This has driven me to study a variety of subjects, including mathematical logic,
epistemology, probability theory, statistical modelling, machine learning and artificial
intelligence. From a technical standpoint I am focused on the analysis of learning from
streaming data in highly dynamic situations, ranging from cybersecurity to neuroimaging.
Following an academic career as a Research Fellow at Cambridge University and an
Associate Professor at Imperial College, I co-founded a startup in Machine Learning for data
streams, www.ment.at.
Personal Information
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Nationality: British
Phone: +44(0)790757[fourty]28
Email: canagnos[at]ment[dot]at
Employment
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Nov 12 - to date: Co-Founder, Chief Scientist and CTO, Mentat Innovations, UK
Oct 11 - Dec 2014: Lecturer (Associate Professor) in Statistics, Mathematics,
Imperial College London, UK
Jan 10 - Aug 2011: Research Fellow, Statistical Laboratory, Cambridge University, UK
Industrial Engagement Highlights
Consulting and Software Development Highlights
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Within www.ment.at (post-2012):
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Anomaly Detection and AI-driven Cyber-Intelligence, Barclays Group
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AI-driven Quality Control from photographic evidence, EDF
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AI-driven Change Detection, Ordnance Survey
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Predictive Maintenance via AI, Festo
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Situational Awareness and Fault Monitoring on POS terminals, Cardlink
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Anomaly Detection on Mobile Virtual Private Networks, Undisclosed Client
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Predictive Optimisation of Fuel Logistics, Sensile
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Quality Control and Fault Monitoring, Cisco
As an individual (pre-2014):
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Construction of credit scorecards, Undisclosed client
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Online advertising optimisation, Advance Media
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Sortino Optimisation, Integral Capital Management
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Price discovery in e-commerce, Variably Technologies
Modelling user-generated restaurant reviews, www.ask4food.gr
Network analysis, Barclays Group
Network analytics and plan detection, BAe Systems
Startup Accelerator Competitions
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TechFounders 2016: Winner
Cybersecurity London (CyLon) 2015: Winner
Cisco Enterpreneurs in Residence Program: Winner
EY Privacy Challenge 2014: Runner-up
External Advisory Positions
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Jan 12 - Dec 14: Scientific Advisor, Variably Technologies, Hong Kong
Media and Government Engagement
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Aug 2016: Alternative Models League Table, Google News
Dec 2013: POST Parliamentary Committee: contribution to Working Group on Big Data
Jan 2014: The Independent, "The Missing Girls", contribution to gender inequality
analysis
March 2013: Facts Are Sacred, Guardian e-book, video interview
Aug 2012: Alternative Medals League Table, Guardian Datablog
Speaking Engagements
Corporate/Industrial Engagement Highlights
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July 2015, Panel Speaker on Artificial Intelligence, CISCO Live, San Jose, United States
July 2013, Why Data Science is a Science, AVIVA HQ, London
July 2013, Bayesian updating in non-stationary settings, Google HQ, Mountain View,
California, USA
Nov 10, Handling temporal variation of unknown characteristics in streaming data
analysis. Microsoft Research Centre, Cambridge, UK
Technical/Academic Engagement Highlights
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Jan 2014, MCMSki IV, Fifth IMS-ISBA joint meeting, Information-theoretic data
discarding for Dynamic Trees on data streams, Chamonix, France
Aug 2013, Strategies for Handling the Risk of Obsolete Information in Scorecards,
Credit Scoring and Credit Control XIII, Edinburgh, UK
July 2013, Adaptive power priors for Bayesian updating in the presence of drift with
applications, Statistics Seminar, University of British Columbia, Canada
Nov 12, Simultaneous handling of outliers and drift using adaptive learning rates,
Statistics Section, University College London
Oct 12, Learning in the presence of drift. Computer Lab, Cambridge University, UK
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Oct 11, Dynamic trees for streaming and massive data contexts. Imperial College
London, UK
Dec 10, Handling temporal variation of unknown characteristics in streaming data
analysis. Imperial College London, UK
Nov 10, Online, temporally adaptive parameter estimation with applications to
streaming data analysis. Biostatistics and Medical Informatics, University of
Wisconsin-Madison, USA
Nov 10, Online, temporally adaptive parameter estimation with applications to
streaming data analysis. Engineering Department, Signal Processing Seminar,
University of Cambridge
Sep 10, Temporally adaptive online EM. Stochastic Approximation Workshop, Uni. of
Bristol
Aug 10, Online EM in the presence of drift. Greek stochastics 𝛽′, Lefkada, Greece
July 09, Online statistical inference and temporal adaptivity. Business Intelligence Lab
seminar, Telecom ParisTech ENST, Paris, France
Mar 09, Adaptive querying and temporally adaptive estimation of graphical model
structure in distributed sensor networks. ''Streaming Data Mining for Sensor
Networks'' Workshop of the International Federation of Classification Societies 2009
Conference, Dresden
Nov 08, Adaptive forgetting for streaming data. Statistics seminar, Lancaster
University
June 08, Online optimisation for streaming variable selection. Mobile Systems Group
seminar, Department of Computer Science, UCL
Apr 08, Simulating dynamic covariance structures for testing the adaptive behaviour
of variable selection algorithms. UKSIM/EUROSIM 2008, Cambridge, UK
Academic Engagement Highlights
Grants
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Sep 14 - Dec 15: Principal Investigator, Data Exploration and Predictive Analytics for
Music Publishing
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Total grant value of 330K GBP
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Industrial partner: Sentric Music
March 13: EPSRC Small Research Equipment Grant
Student Supervision
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PhD project on Principles of Active Learning (completed)
PhD project on Covariance Selection in Hetereogeneous Data for Neuroimaging (viva
pending)
PhD project on Wavelet Analysis on Data Streams (completed)
several MSc student projects
Teaching
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Oct 12 - Dec 14: Official Statistics MSc course, Imperial College
Oct 12 - Dec 14: Graphical Modelling MSc course, Imperial College
Oct 12 - Dec 14: Advanced Statistical Modelling final undergraduate course, Imperial
College
June 13, First Year Undergraduate Statistics
Workshops organised
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Feb 2014: Big Data -- Challenges and Applications, Imperial College London
Dec 2013: Stochastic Approximation for Big Data, ERCIM 2013, London
May 2013: Big Data -- Bridging the Gap between Theory and Practice, Imperial College
London
Education
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PhD Mathematics, Statistics Section, Imperial College, Nov 06 - Jan 10
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"A statistical framework for Streaming Data Analysis" (Prof. D.J. Hand and N.M.
Adams)
MSc Logic and Algorithms (distinction), Athens University, Greece, Oct 04 - June 06
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"Bertrand Paradoxes and Kolmogorov's Foundations of Probability" (Prof. Y.N.
Moschovakis)
MSc Machine Learning (distinction), Edinburgh University, UK, Sep 03 - Sep 04
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"Learning Probabilistic Relational Models for Genetic Networks" (Dr. D.
Husmeier)
BAHons Mathematics (2:1), Cambridge University, Pembroke College, UK, Sep 03 Sep 04
Scholarships and Awards
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Cambridge European Trust Undergraduate Fellowship on Academic Merit
Memberships: Royal Statistical Society, SIAM, IEEE, ACM
Skills
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Languages: Greek (native), English (fluent), French (advanced), Spanish (basic)
Programming: Python, R, Shiny/D3, Matlab, C, Java/Scala, Mathematica, IBM
Infosphere Streams, Spark, Latex
Databases: columnar, relational and document-stores
Stacks: Apache Storm, Kafka/RabbitMQ, Spark Streaming, Splunk, ELK (Logstash,
Elastic, Kibana) Stack
Publications / Open-Source Software Projects
Refereed Journal Publications
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Monti, R.P. and Hellyer, P. and Sharp, D. and Leech, R. and Anagnostopoulos, C. and
Montana, G. Estimating time-varying brain connectivity networks from functional MRI
time series.Neuroimage, 2014, in print.
Hand, D.J. and Anagnostopoulos, C. A better Beta for the 𝐻 measure of classification
performance. Pattern Recognition Letters, 2014, 40, 41--46.
Anagnostopoulos, C., and Hand, D.J. Information-theoretic data discarding for dynamic
trees on data streams. Entropy, 2013, 15(12), 5510--5535.
Hand, D.J., and Anagnostopoulos, C. When is the area under the receiver operating
characteristic curve an appropriate measure of classifier performance? Pattern
Recognition Letters, 2012, 34, pp 492--495.
Anagnostopoulos, C.,and Tasoulis, D.,and Adams, N.M.,and Hand, D.J. Online Linear and
Quadratic Discriminant Analysis with adaptive forgetting for streaming classification.
Statistical Analysis and Data Mining, 2012, 5(2), pp 139--166.
Anagnostopoulos, C.and Adams, N.M.and Hand, D.J., Streaming covariance selection
with applications to adaptive querying in sensor networks, The Computer Journal,
2010, doi: 10.1093/comjnl/bxp123
Adams, N.M.and Anagnostopoulos, C.and Hand, D.J.,and Tasoulis, D., Temporally
adaptive estimation of logistic classifiers on data streams, Journal of Advances in Data
Analysis and Classification (ADAC)}, 2009, doi:10.1007/s11634-009-0051-x
Refereed Conference Publications
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Evans, L. P.G. and Anagnostopoulos, C. and Adams, N.M., When does active learning
work? IDA 2013, Lecture Notes in Computer Science 8207, 174--185
Anagnostopoulos, C.,and Adams, N.M.and Hand, D.J.,and Tasoulis, D., Online
optimisation for variable selection in data streams, Proc. of the 18th Europ. Conference
on Artificial Intelligence, pp 132 -- 136, ECAI 2008
Anagnostopoulos, C.,and Adams, N.M., Simulating dynamic covariance structures for
testing the adaptive behaviour of variable selection algorithms, Proc. of the 10th Int.
Conference on Computer Modelling and Simulation, pp 52--57, UKSIM/EUROSIM 2008
Anagnostopoulos, C., Adams, N.M.and Hand, D.J., Deciding what to observe next:
adaptive variable selection for regression in multivariate data streams, Proc. of ACM
SAC, Vol. 2, pp 961--965, 2008
Open Source Packages
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The H-measure for measuring classification performance: www.hmeasure.org
Dynamic Trees for Classification and Regression
Conference Abstracts
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Anagnostopoulos, C., and Gramacy, R., Information-theoretic data discarding for
Dynamic Trees on data streams, MCMSki IV, 5th IMS-ISBA joint meeting, Chamonix,
France, Jan 2014
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Anagnostopoulos, C., Strategies for Handling the Risk of Obsolete Information in
Scorecards, Credit Scoring and Credit Control XIII, Edinburgh, UK
Anagnostopoulos, C., and Gramacy, R., Dynamic trees for massive data contexts, NIPS
workshop on Bayesian optimization, experimental design and bandits, Spain, Dec 2011
Anagnostopoulos, C., Online Expectation-Maximization in the presence of drift, Greek
stochastics 𝛽′, Lefkada, Greece, August 2010
Anagnostopoulos, C., Temporally adaptive, online Expectation-Maximization,
Stochastic Approximation Workshop, University of Bristol, September 2010
Ehrlich, E., Adams, N.M., Anagnostopoulos, C., and Tasoulis, D.K., Adaptive Filtering for
State-Space Identification and State Estimation, RSS 2010 Conference
Adams, N.M.and Tasoulis, D.K.and Anagnostopoulos, C., and Hand, D.J., TemporallyAdaptive Linear Classification for Handling Population Drift in Credit Scoring, in
Lechevallier, Y. And Saporta. (eds), Proceedings of the 19th International Conference
on Computational Statistics, 2010, Springer, 167-176, COMPSTAT2010
Publications: other
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Anagnostopoulos, C., Contribution to the Discussion of ''Maximum Likelihood
Estimation of a multi-dimensional log-concave density'' by M. Cule, R. Samworth and
M. Stewart, to appear in Journal of the Royal Statistical Society, Series B, 2010
Anagnostopoulos, C. and Tasoulis, D., Contribution to the Discussion of ''Sure
Independence Screening for ultrahigh dimensional feature space'' by Fan, J. and Lv, J.,
Journal of the Royal Statistical Society, Series B (Methodological), Vol 70, Issue 5, p
890, 2008
Anagnostopoulos, C.and Turnbull, M.C., A Note on Learning Linear Gaussian StateSpace Models via Expectation -- Maximization, Technical Report
http://www.ma.imperial.ac.uk/statistics/techreports/, Imperial College, 2007