Assistant Professor, Director - BS in Data Science Program
Boston
Assistant Professor, Director - BS in Data Science Program
Boston
Assistant Professor, Director - BS in Data Science Program
Boston
Assistant Professor, Director - BS in Data Science Program
Boston
Byron Wallace is an assistant professor in the Khoury College of Computer Sciences at Northeastern University. He earned his PhD from Tufts University in 2012, after which he taught at Brown University as research faculty. He joined Northeastern from the University of Texas at Austin, where he was an assistant professor in the School of Information from 2014-2016.
Wallace’s research areas include artificial intelligence, data science, machine learning, natural language processing, and information retrieval, with emphasis on applications in health informatics. Byron is a member of the applied machine learning group and the Data Science and Analytics Lab at Northeastern.
Wallace develops machine learning and natural language processing methods that make synthesizing the vast biomedical evidence-base more efficient. He also works on core machine learning and natural language processing methods, with his more of his recent work concerning Convolutional Neural Network (CNN) architectures for text. Wallace has recently been developing hybrid, interactive human/machine learning systems that aim to robustly combine human and machine intelligence.
His work has been supported by grants from the Army Research Office, the NIH, and the NSF. He won the Tufts University 2012 Outstanding Graduate Researcher award and his thesis work was recognized as The Runner Up for the 2013 ACM Special Interest Group on Knowledge Discovery and Data Mining (SIG KDD) Dissertation Award. He recently co-authored the winning submission for the Health Care Data Analytics Challenge at the 2015 IEEE International Conference on Healthcare Informatics.
Leyden, Massachusetts
Outside of research, I do a fair amount of reading (both fiction and non-fiction). I’m a consistent (if mediocre) runner, and I like to ski. I also enjoy coffee and craft beer.
I’d hope that my work contributes to allowing us to realize better healthcare by processing and making sense of the vast amounts of health-related data that currently exists in unstructured formats like text. For example, one of my major points of focus has been building machine learning systems that enable researchers to make sense of the torrential volume of biomedical evidence now being published in biomedical journals. A lot of my work on methods in machine learning and natural language processing is therefore motivated by this aim.
More broadly, I hope my research contributes to continued progress toward teaching machines to make sense of language and text.
Working closely with domain experts on interdisciplinary problems is hugely interesting to me because it exposes a whole new set of questions and perspectives. I find that it also leads to novel work that could not be pursued within a single, narrowly defined discipline.
On a similar note, I personally find it most compelling when real-world problems directly motivate interesting methodological work (as opposed to developing new models or methods that address theoretical or abstract problems). I therefore tend to approach research from the perspective of addressing practical challenges. This comes with its own set of challenges, but I find it very rewarding because it means the research has an immediate impact.
Byron Wallace is an assistant professor in the Khoury College of Computer Sciences at Northeastern University. He earned his PhD from Tufts University in 2012, after which he taught at Brown University as research faculty. He joined Northeastern from the University of Texas at Austin, where he was an assistant professor in the School of Information from 2014-2016.
Wallace’s research areas include artificial intelligence, data science, machine learning, natural language processing, and information retrieval, with emphasis on applications in health informatics. Byron is a member of the applied machine learning group and the Data Science and Analytics Lab at Northeastern.
Wallace develops machine learning and natural language processing methods that make synthesizing the vast biomedical evidence-base more efficient. He also works on core machine learning and natural language processing methods, with his more of his recent work concerning Convolutional Neural Network (CNN) architectures for text. Wallace has recently been developing hybrid, interactive human/machine learning systems that aim to robustly combine human and machine intelligence.
His work has been supported by grants from the Army Research Office, the NIH, and the NSF. He won the Tufts University 2012 Outstanding Graduate Researcher award and his thesis work was recognized as The Runner Up for the 2013 ACM Special Interest Group on Knowledge Discovery and Data Mining (SIG KDD) Dissertation Award. He recently co-authored the winning submission for the Health Care Data Analytics Challenge at the 2015 IEEE International Conference on Healthcare Informatics.
Leyden, Massachusetts
Outside of research, I do a fair amount of reading (both fiction and non-fiction). I’m a consistent (if mediocre) runner, and I like to ski. I also enjoy coffee and craft beer.
I’d hope that my work contributes to allowing us to realize better healthcare by processing and making sense of the vast amounts of health-related data that currently exists in unstructured formats like text. For example, one of my major points of focus has been building machine learning systems that enable researchers to make sense of the torrential volume of biomedical evidence now being published in biomedical journals. A lot of my work on methods in machine learning and natural language processing is therefore motivated by this aim.
More broadly, I hope my research contributes to continued progress toward teaching machines to make sense of language and text.
Working closely with domain experts on interdisciplinary problems is hugely interesting to me because it exposes a whole new set of questions and perspectives. I find that it also leads to novel work that could not be pursued within a single, narrowly defined discipline.
On a similar note, I personally find it most compelling when real-world problems directly motivate interesting methodological work (as opposed to developing new models or methods that address theoretical or abstract problems). I therefore tend to approach research from the perspective of addressing practical challenges. This comes with its own set of challenges, but I find it very rewarding because it means the research has an immediate impact.
