Ameet SoniAssistant Professor
Computer Science Department
phone: (610) 957-6288
office: 253 Science Center
|Fall 2017 Schedule|
|Lecture||11:20am-12:35pm Tuesday, Thursday||181 Science Center|
|Labs||1:05-2:35pm and 2:45-4:15pm Thursday||256 Science Center|
|Office Hours||3:00-5:00pm Wednesday, and by appointment||253 Science Center|
|Research Hours||1:00pm-5:00pm, Friday|
Transcription factor binding: transcription factors govern the regulation of genes - that is, determine when a gene is on or off. Understanding which transcription factors bind to which areas of the genome and in which cells helps understand the function of target genes. Our lab utilizes deep neural networks to predict the binding affinity of specific transcription factors to a given portion of DNA to identify these relationships without the need for expensive in vivo experiments.
Brain image analysis: Recently, my group has been researching several different computational problems in the area of MRI braining imaging. Initial results in this area include improved approaches for performing image segmentation in brain images using probabilistic graphical models (conditional random fields). Ongoing work aims to apply deep learning approaches - including convolutional neural networks - to improve early diagnosis of Alzheimer's disease.
Statistical relational learning: Real world data is inherently noisy and relational (i.e., elements are dependent on one another). Traditional machine learning algorithms fail to account for these realities. In collaboration with Prof. Sriraam Natarajan at Indiana University, I have done work with Relational Dependency Networks to model problems in complex domains. Examples including relational extraction problems from text (e.g., identify the CEO of a company from a newswire article) and diagnosis of Parkinson's disease from medical records.
Protein Structure Prediction: 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 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: The result of our group's efforts is ACMI, a probabilistic technique for determining protein structures. Prior to ACMI, techniques failed when trying to interpret low-quality images. With ACMI, crystallographers can now obtain complete and accurate structures from these difficult proteins instead of scrapping the project or dedicating months of effort.
Asterick's (*) indicate supervised students.
Deep Residual Nets for Improved Alzheimer’s Diagnosis.
In Proceedings of the 8th ACM Conference on Bioinformatics, Computational Biology and Health Informatics (ACM-BCB), 2017. [poster]
Identifying Parkinson’s Patients: A Functional Gradient Boosting Approach.
In Artificial Intelligence in Medicine (AIME), 2017.
Learning relational dependency networks for relation extraction.
In Proceedings of the 26th International Conference on Inductive Logic Programming (ILP), 2016. [slides]
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]