Aldo is currently a Lecturer (Asst. Prof.) in Machine Learning at the University College London (UCL) in the SpaceTimeLab led by Prof. Tao Cheng. His research broadly focuses on Information Retrieval, Natural Language Processing and Machine Learning.
Jarana’s research area is recommendation system. In particular, he aims to generate personalised venue recommendation to users based on their historical feedback and their current context (e.g. time of the day and their current location). We aim to exploit various techniques such as MF and BPR, a collaborative filtering technique that is widely used in previous literature, to effectively model the user’s preferences and to generate high quality of venue suggest. Recent, he explored the effectiveness of Deep Neural Networks (e.g. multi-layer perceptron and RNN) to capture the short-term preference of users based on their sequential order of checkins.
Bhaskar is a Principal Applied Scientist at Microsoft Artificial Intelligence and Research group in Montreal, Canada. His research interests include machine learning and information retrieval, and in particular the topic of neural information retrieval. Over the years, he has worked on several research problems related to document ranking, entity ranking, query formulation, and evaluation. Bhaskar has co-organized multiple workshops and tutorials, served as a guest editor for the special issue of the Information Retrieval Journal, and co-authored a book on the topic of neural information retrieval.
Qiang’s research interests lie in the areas of statistical machine learning and deep learning with applications to sequential data, such as natual lanuages, human behaviour and physiological signal. He is also also interested in multimodal data fusion and integration. Curretly he focuses on machine learning methods for automatic verification of textual facts on the web.
Andrew Burnie’s research examines the linkage between events and concerns expressed in social media posts and phasic shifts in the cryptocurrency price movement across time. This applies Natural Language Processing, Neural Networks (word2vec), Non-Parametric Statistical Hypothesis Testing, Time Series Analysis, Social Media Analysis, Causal Inference and Sentiment Analysis. He has an MA in Economics and Management Studies (Cambridge University) and an MSc in Finance (GGSB).
Sahan’s interests lie on the theme: “Improving Recommendations of Educational Contents to Lifelong Learners”. He is a recipient of multiple research fellowships including UCL Advances (twice), Cisco CIIP and UCL Departmental Scholarship. Before joining UCL, he worked in several research roles in the industry in cybersecurity and personalised advertising domains where he gained experience in user state modelling in a big data landscape. Sahan currently contributes to the X5GON project. His current research is motivated towards developing scalable, automatic quality assurance models for educational resources and modelling the knowledge state of lifelong learners to identify the most helpful educational resources that will enable them to achieve their learning goals.
Fanghua’s primary research interests include information retrieval, graph mining and machine learning. He has a strong passion in developing machine learning algorithms for mining deep knowledge from graph/relational structured data.
Sebastin Santy, Research intern (Summer 2018)
Rishabh Mehrotra, PhD student (February 2014 – 2018, now a research scientist in Spotify)
Manisha Verma, PhD student (November 2013 – 2017, now a research scientist in Yahoo)
Shangsong Liang, Postdoc researcher (May 2015 – 2017, now a professor in Sun Yat-sen University)
Mengdie Zhuang, Research intern, Turing Institute (Summer 2017, now a research fellow in UCL)
Jiyin He, Visiting researcher (February 2015 – 2016, now a senior data scientist in Signal Media)