Javed Aslam

Machine learning
Information retrieval
Applications of statistics and information theory

PhD in Computer Science, Massachusetts Institute of Technology
MS in Computer Science, Massachusetts Institute of Technology
BS in Electrical Engineering, University of Notre Dame

Javed Aslam is chief of artificial intelligence and a professor at the Khoury College of Computer Sciences at Northeastern University, based in Boston.

While most of Aslam's research delves into machine learning and information retrieval, he has experience in human computation, transportation, computer security, wireless networking, and medical informatics. In machine learning, Aslam has developed models and algorithms for multi-label classification and learning in the presence of noisy or erroneous training data. In information retrieval, he has applied techniques from machine learning, statistics, information theory, and social choice theory to develop algorithms for automatic information organization, metasearch, and efficient search engine training and evaluation.

Before joining Northeastern, Aslam was an assistant professor at Dartmouth College and a postdoctoral researcher at Harvard University. He served as the general co-chair for the 2009 ACM SIGIR Conference on Research and Development in Information Retrieval and as the program co-chair for SIGIR 2016.

Published: February 19th, 2016
Optimal Allocation of Crowdsourced Resources for Information Retrieval Evaluation

Lead PI: Javed Aslam

Published: August 2nd, 2024
Unbiased Identification of Broadly Appealing Content Using a Pure Exploration Infinitely Armed Bandit Strategy

Citation: Maryam Aziz, Jesse Anderton, Kevin G. Jamieson, Alice Wang, Hugues Bouchard, Javed A. Aslam. (2025). Unbiased Identification of Broadly Appealing Content Using a Pure Exploration Infinitely Armed Bandit Strategy Trans. Recomm. Syst., 3, 4:1-4:22. https://doi.org/10.1145/3626324
Published: January 1st, 2021
Improving Query Graph Generation for Complex Question Answering over Knowledge Base

Citation: Kechen Qin, Cheng Li, Virgil Pavlu, Javed A. Aslam. (2021). Improving Query Graph Generation for Complex Question Answering over Knowledge Base EMNLP (1), 4201-4207. https://doi.org/10.18653/v1/2021.emnlp-main.346
Published: June 23rd, 2019
Adapting RNN Sequence Prediction Model to Multi-label Set Prediction

Citation: Conference Proceedings Adapting RNN Sequence Prediction Model to Multi-label Set Prediction. Qin, Kechen; Li, Cheng; Pavlu, Virgil; Aslam, Javed. Proceedings of the 2019 NAACL-HLT, Volume 1 (Long and Short Papers), 2019 8 jun I Association for Computational Linguistics, Minneapolis, Minnesota
Published: June 15th, 2019
Scaling Up Ordinal Embedding: A Landmark Approach

Citation: Anderton, J. & Aslam, J.. (2019). Scaling Up Ordinal Embedding: A Landmark Approach. Proceedings of the 36th International Conference on Machine Learning, in PMLR 97:282-290
Published: March 24th, 2018
Pure Exploration in Infinitely-Armed Bandit Models with Fixed-Confidence

Citation: Aziz, M., Anderton, J., Kaufmann, E. and Aslam, J.. (2018). "Pure Exploration in Infinitely-Armed Bandit Models with Fixed-Confidence." Proceedings of Algorithmic Learning Theory, in PMLR 83:3-24. DOI: 0.48550/arXiv.1803.04665
Published: October 23rd, 2015
Aggregation of Crowdsourced Ordinal Assessments and Integration with Learning to Rank: A Latent Trait Model

Citation: P. Metrikov, V. Pavlu, J. A. Aslam. "Aggregation of Crowdsourced Ordinal Assessments and Integration with Learning to Rank: A Latent Trait Model". Proceedings of the 24th ACM Conference on Information and Knowledge Management, Melbourne, Australia (2015). DOI: 10.1145/2806416.2806492
Published: September 29th, 2013
A Modification of LambdaMART to Handle Noisy Crowdsourced Assessments

Citation: Pavel Metrikov, Jie Wu, Jesse Anderton, Virgil Pavlu, and Javed A. Aslam. 2013. A Modification of LambdaMART to Handle Noisy Crowdsourced Assessments. In Proceedings of the 2013 Conference on the Theory of Information Retrieval (ICTIR '13). Association for Computing Machinery, New York, NY, USA, 133–134. https://doi.org/10.1145/2499178.2499198
Published: March 24th, 2013
Optimizing nDCG Gains by Minimizing Effect of Label Inconsistency

Citation: P. Metrikov, V. Pavlu, J. A. Aslam, "Optimizing nDCG Gains by Minimizing Effect of Label Inconsistency", Advances in Information Retrieval: 35th European Conference on IR Research (ECIR), Moscow, Russia (2013). Best Poster Paper Award
Published: August 12th, 2012
Impact of Assessor Disagreement on Ranking Performance

Citation: P. Metrikov, V. Pavlu, J. A. Aslam, "Effect of Assessor Disagreement on Ranking Performance", Proceedings of the 35th international ACM SIGIR conference on Research and Development in Information Retrieval, Portland, USA (2012)
Published: July 24th, 2011
A Large-scale Study of the Effect of Training Set Characteristics over Learning-to-Rank Algorithms

Citation: E. Kanoulas, S. Savev, P. Metrikov, V. Pavlu, J. A. Aslam, "A Large-scale Study of the Effect of Training Set Characteristics over Learning-to-Rank Algorithms", Proceedings of the 34th international ACM SIGIR conference on Research and Development in Information Retrieval, Beijing, China (2011)