SMiLe CLiNiC research aims to develop flexible yet efficient probabilistic learning methods that can take into account diverse statistical features of real-world data. While machine learning methods have emerged as one of the most promising statistical frameworks for addressing real-world challenges, many of the current models and algorithms are too restrictive for capturing complex tasks, and are challenging for massive scale applications. In the latest study by IDC (J. Gantz and D. Reinsel. The Digital Universe in 2020: Big Data, Bigger Digital Shadows, and Biggest Growth in the Far East.), it is forecasted that 40 ZettaBytes of digital content were created or replicated by 2020, and as much as 33% of the digital universe will contain information that might be valuable if analysed.

Complex statistical features are commonplace for those data; SMiLe CLiNiC focuses on the heterogeneity or multi-modality of the data. Phrased differently data that has textual, imaging, and network relationship representations and comes from multiple sources. Being in a real-world setting, the multi-modality challenge turns into a multi-faceted problem containing the following three major sub-problems: i) dynamic time evolution (data is collected over a long time period); ii) rich interdependency structures (in text, not only words but also how words combine into a sentence carry relevant information); iii) output inconsistencies (information mismatch between or within data sources). Scientific and societal challenges of the 21st century are to draw insights and make predictions to support knowledge creation from this massive amount of data.

SMiLe CLiNiC is part of the Sussex Informatics department, affiliated with Data Science @ Sussex and based in the Sussex campus set on the edge of the beautiful South Downs National Park. Key facts about Sussex: 1) Top 10 in the UK - 60th in the world for research influence (Times Higher Education, World University Rankings 2013-2014), 2) The sunniest part of UK, 3) Less than half an hour by cycle to the Brighton Beach, 4) The 6th safest university cities and towns.

  • Chao Chen (City University of New York (CUNY) Queens College) and Novi have a paper accepted at ICML 2016!
  • We have 2 papers accepted at CVPR 2016. CVPR is the only conference in the top-100 of most cited sources by Google Scholar, which further consists only of journals. The lists starts with Nature followed by many major journals from other fields such as JAMA at 13, Nature Neuroscience at 61, and Astronomy and Astrophysics at 84.
  • Joe's LUFe (means love in a Dictionary of the Older Scottish Tongue (up to 1700)) paper was accepted at IJCAI 2016! Joe will be happy to share his lufe in oral and poster presentations in NYC.
  • Novi is an Associate Editor for IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI).
  • We are organising the 1st Workshop on Human is More Than a Labeler (co-located with IJCAI 2016 in New York City). Our invited speakers include Vladimir Vapnik, Alan Fern, Rogerio Feris, Michael Littman, and Rich Caruana. For more information:
  • Novi has served as an Area Chair for NIPS 2015.
  • We have received an NVIDIA Hardware Grant and an Amazon AWS Cloud Credits for Research (US$7,500).
  • We have hosted several research visitors: Kristian Kersting (TU Dortmund) and Sriraam Natarajan (Indiana University) from 13th-14th August 2015, and Anastasia Pentina (IST Austria) from 6th-13th September 2015.
  • SMiLe CLiNiC is offering consultancy services in the areas of big data analytics, time series forecasting, and deep learning methods.
  • For 2016 conferences, we are/were involved as a programme committee/reviewer for NIPS, ICML, AAAI, AISTATS, ECCV, ...
  • For 2015 conferences, we are/were involved as a programme committee/reviewer for ICML, KDD, IJCAI, AISTATS, CVPR, AAAI, ...
  • For 2014 conferences, we are/were involved as a programme committee/reviewer for NIPS, ICML, AISTATS, ACML, CVPR, ECCV, SIGGRAPH, CHI, SSPR, ICPR, AAAI.
  • Dr. Novi Quadrianto
    SMiLe Lecturer

    Novi is smiling about nonparametric machine learning methods for big data.

  • Dr. Viktoriia Sharmanska
    SMiLe Visiting Research Fellow

    Viktoriia is smiling about machine learning methods for computer vision.

  • Dr. Srinivas Sridharan
    SMiLe Visiting Research Fellow

    Sri is approximately smiling about time series forecasting.

  • Dr. Pietro Galliani
    SMiLe Research Fellow

    Pietro is smiling about scalable human-in-the-loop nonparametric Bayesian methods.

  • David Spence
    SMiLe PhD Student

    David is smiling about Twitter demographic profiling.

  • Joseph Gerard Taylor
    SMiLe PhD Student

    Joe is smiling about defining what privileged information is.

  • Thomas Bonam
    SMiLe SURA Scholar

    Tom is smiling about making Sussex Machine Learning Teaching Aid.

  • Dr. Rosemary Tate
    SMiLe Honorary Member

    Rosemary is smiling about applying machine learning and statistical techniques to large biomedical datasets.

Content by SMiLe CLiNic, website layout by Sebastian Riedel.