Alessandro Moschitti is a professor of the CS Department of the University of Trento, Italy. He is currently a Principal Research Scientist of the Qatar Computing Research Institute (QCRI), within the Hamad Bin Khalifa University.
He obtained his PhD in CS from the University of Rome in 2003. He has worked as (i) an research fellow for the University of Texas at Dallas, (ii) as a visiting professor for the University of Columbia (NY), Colorado and John Hopkins and (iii) as visiting researcher at the IBM Watson Research center (participating at the Jeopardy! Challenge) and at MIT-CSAIL.
His expertise concerns theoretical and applied machine learning (ML) in the areas of Natural Language Processing (NLP), Information Retrieval (IR) and Data Mining. He has devised innovative structural kernels and neural networks for advanced syntactic/semantic processing, documented by more than 260 scientific articles published in NLP, IR and ML communities.
He has been the General Chair of EMNLP 2014, a PC co-chair of CoNLL 2015, an action editor of TACL, and on the editorial board of MLJ, JAIR and JNLE. He has received four IBM Faculty awards, one Google Faculty award, five best paper awards and the best researcher award from Trento University. He has lead many projects, currently is the PI (QCRI side) of a large collaboration project between MIT CSAIL and QCRI.

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Research

The iKernels group led by Prof. Moschitti carries out advanced research on machine learning methods for syntactic and semantic processing of natural language. The group aims at improving the state of the art in Information Search and Retrieval by enriching language models (typically based on bag-of-words) with structural representations and semantics.

In the context of natural language, the group owns internationally recognized expertise in the following applications: Question Answering, FrameNet and PropBank Predicate Argument Extraction (Semantic Role Labeling), Relation Extraction, Syntactic and Semantic Parsing, Co-reference resolution, Text Categorization, Textual Entailment Recognition, Word Sense Disambiguation, Entity Recognition and Normalization, Opinion Mining, Speech and Noisy Text Processing, Text Similarity, Summarization and Link Open Data.

Regarding Machine Learning, the group has developed new theory and methods for Kernel Machines: Kernel Methods, Structural Kernels, Support Vector Machines, On-line Learning, Structured Output Spaces, Multi-label and Hierarchical Classification and Re-Ranking, and reverse kernel engineering for efficient processing and automatic feature engineering.
In the last two years, they have developed breakthrough models for Neural Networks in opinion mining, question answering and named entity recognition.

Theory and methods are also applied to broader ICT areas than natural language processing and Information Retrieval, such as Automatic Telecommunication Network Management, Electronic Failure detection, Anomaly Detection, Bioinformatics, Automatic Software Analysis (e.g., code classification).