Ameet SoniAssistant Professor
Computer Science Department
phone: (610) 957-6288
office: 283 North Parrish
|CS21 Introduction to Computer Science|
|Lecture||1:15-2:30pm Tuesday, Thursday||256 Sci Ctr|
|Office Hours||10:00am-noon Wednesday, and by appointment||253 Sci Ctr|
|Prep time (limited availability)||10pm-noon, Tuesday, Thursday||253 Sci Ctr|
|Research Hours||1:00pm-4pm Wednesday, Friday|
Asterick's (*) indicate supervised students.
Learning relational dependency networks for relation extraction.
In Proceedings of the 26th International Conference on Inductive Logic Programming (ILP), 2016. (To Appear September 2016)
A Comparison of weak supervision methods for knowledge base construction.
In 5th Workshop on Automated Knowledge Base Construction (AKBC) at NAACL, 2016. [poster]
A comprehensive analysis of classification algorithms for cancer prediction from gene expression.
In Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics (ACM-BCB), pp. 525–526, 2015. [poster]
A support program for introductory CS courses that improves student performance and retains students from underrepresented groups.
In Proceedings of the 45th ACM Technical Symposium on Computer Science Education (SIGCSE), pp. 433–438, 2014.
A graphical model approach to ATLAS-free mining of MRI images.
In Proceedings of the 2014 SIAM International Conference on Data Mining (SDM), pp. 974–982, 2014. [poster]
Probabilistic ensembles for improved inference in protein-structure determination.
In Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine (ACM-BCB), pp. 264–273, 2011. Invited for journal publication. [slides] [cached]
Guiding belief propagation using domain knowledge for protein-structure determination.
In Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology (ACM-BCB), pp. 285–294, 2010. Best Paper Award. [slides] [cached]
Creating protein models from electron-density maps using particle-filtering methods.
In Bioinformatics, Oxford Univ Press, vol. 23, no. 21, pp. 2851–2858, 2007. PMCID: PMC2567142
Improved methods for template-matching in electron-density maps using spherical harmonics.
In IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 258–265, 2007. Invited for journal publication. [code/data]
Most recently, I have begun researching several different computational problems in the area of MRI braining imaging. Specifically, I seek to apply probabilistic methods to improve the ability to diagnose onset of Alzheimer's disease and other cognitive impairments. Currently, my group is pursuing methods for segmenting anatomical regions of the brain using graph-based methods as well intuit anatomical relationships between patients in the larger ADNI study.
Previously, I worked on ACMI (Automated Crystallographic Map Interpretation). The task of determining protein structures has been a central one to the biological community for several decades. The structure allows biologists to extract information about the underlying biology of a protein, and has implications for various applications such as disease treatment, drug design, and protein design. The most popular method for producing protein structures is by interpreting an electron-density map - a three-dimensional image of a molecule produced through X-ray crystallography. This process, however, remains a resource- intensive and time-consuming task, stunting basic biological research. Thus, the main objective of the project is: