Djellel Difallah Assistant Professor of Computer Science NYU Abu Dhabi

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Bio

Djellel Difallah received his Ph.D. from the University of Fribourg (Switzerland) and worked thereafter as a Postdoctoral Researcher at the eXascale Infolab. His main research interests revolve around human computation and its application to data management, data processing, machine learning, natural language processing and knowledge extraction. Specifically, he investigates novel active learning techniques, Bayesian models for truth inference and task scheduling optimization problems. Djellel worked for Schlumberger and Microsoft and is an ACM member and a Fulbright Alumni. He is currently a Moore-Sloan Fellow at the Center for Data Science at New York University.

News

• Latest news Paper accepted at ICDM'23: HyperKGQA: Question Answering over Knowledge Graphs using Hyperbolic Representation Learning.
• The Book on crowdsourcing applications in reserach is out! with Gianluca Demartini, Ujwal Gadiraju and Michele Catasta.
• Paper with Panos Ipeirotis and Elena Filatova accepted to WSDM 2018 "Demographics and Dynamics of Mechanical Turk Workers" Poster Available Here.
• Starting June 1st 2017 Djellel starts at NYU CDS as a Moore Sloan Data Science Fellow.
• Grant started! The FashionBrain project is a €2.8m three year project funded by the European Commission’s Horizon 2020 program! (2017-2020)

Research Interests

My main research interests revolve around human computation and its application to data management, data processing, machine learning, natural language processing and knowledge extraction. Specifically, I investigate novel human machine learning techniques, Bayesian models for truth inference, task scheduling optimization problems and analysis of crowdsourcing marketplaces.

More details can be found on my research statement for more information: Research Statement

Past projects

• OLTP-Bench

  OLTPBenchmark is a multi-threaded load generator. The framework is designed to be able to produce variable rate, variable mixture load against any JDBC-enabled relational database. The framework also provides data collection features, e.g., per-transaction-type latency and throughput logs.

• Implementing the SSDB Benchmark on SciDB

  SSDB is a benchmark modeled around an astronomy use-case with different levels of difficulty, mostly bound to the data size in order to test the scalability of the target systems. The input data is a collection of raw images randomly generated in a large space. The benchmark has a pre-processing phase--called ''Cooking''--that extracts notable features in the images, and a workload composed of nine different queries that manipulate both the extracted features and the raw imagery. The queries in SSDB reproduce common operations in science applications: spatial queries, aggregates, data processing, and geometry operations

Recent Professional Activities

• Program Committee, 27th ACM Conference on Information and Knowledge Management (CIKM 2018)
• Program Committee, IEEE International Conference on Data Mining (ICDM 2018)
• Program Committee, 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2018)
• Program Committee, 12th International AAAI Conference On WEB and Social Media (ICWSM 2018)
• Program Committee, 27th International World Wide Web Conference (WWW 2018)
• Program Committee, 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2017)
• Program Committee, 26th ACM Conference on Information and Knowledge Management (CIKM 2017)
• Program Committee, 26th International World Wide Web Conference (WWW 2017)
• Program Committee, 16th International Conference on Web Engineering (ICWE 2016)

Awards

• Best Computer Science PhD Thesis Award, University of Fribourg 2015
• Fulbright Scholarship, 2009-2011

Teaching

Big Data Systems (NYU Abu Dhabi - Spring 2023) - Faculty

Discrete Mathematics (NYU Abu Dhabi - Fall 2022) - Faculty

Discrete Mathematics (NYU Abu Dhabi - Fall 2021) - Faculty

Data Structures (NYU Abu Dhabi - Spring 2021) - Faculty

Data Mining (NYU Stern School of Business - Spring 2019) - Adjunct

Big Data Infrastructures (University of Fribourg - Fall 2016) - Lecturer

Data Science Seminar (University of Fribourg - Fall 2015) - Lecturer

Social computing (University of Fribourg - 2011/2014) - Teaching Assistant

My teaching statement is available here

Contact Information

• Email:
• Phone: (917) 696-4069
• Office: 622
• Mailing address
  Djellel Difallah
  NYU Abu Dhabi
  Saadiyat, Abu Dhabi