Ameet Soni

Ameet Soni

Assistant Professor
Computer Science Department
Swarthmore College

phone: (610) 957-6288
office: 283 North Parrish

I am currently an Assistant Professor of Computer Science at Swarthmore College. I received my Ph.D. in Computer Science in August 2011 from the University of Wisconsin where I was advised by Professor Jude Shavlik. My general research interests are in the areas of machine learning and computational biology and medicine.

Current Semester

I am on leave for the 2015-16 academic year

Past Courses Taught:

Research Interests


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:

Given the electron-density map (3D image) and a primary sequence of a target protein, produce a three- dimensional, physically-feasible, all-atom model of the target protein's structure.

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.


Supervised students are underlined

Learning Relational Dependency Networks for Relation Extraction.
Dileep Viswanathan, Ameet Soni, Jude Shavlik, Sriraam Natarajan.
In the International Workshop on Statistical Relational AI, 2016.
arXiv:1607.00424 [cs.AI]

A Comparison of Weak Supervision Methods for Knowledge Base Construction.
Ameet Soni, Dileep Viswanathan, Niranjan Pachaiyappan, Sriraam Natarajan.
In the 5th Workshop on Automated Knowledge Base Construction (AKBC) at NAACL, 2016.

Relational Learning with Expert Advice.
Dileep Viswanathan, Anurag Wazalwar, Ameet Soni, Jude Shavlik, Sriraam Natarajan.
In the Proceedings of Text Analysis Conference (TAC) 2015 Workshop, 2015.

A Comprehensive Analysis of Classification Methods for Cancer Predication.
Raehoon Jeong, Ameet Soni.
In the Proceedings of the 6th Annual ACM Conference on Bioinformatics and Computational Biology (ACM-BCB '15), 2015. Poster

A Graphical Model Approach to ATLAS-free Mining of MRI Images.
Chris S. Magnano, Ameet Soni, Sriraam Natarajan, Gautam Kunapuli.
In the Proceedings of the 2014 SIAM International Conference on Data Mining (SDM '14), 2014.

Conditional Random Fields for Brain Tissue Segmentation.
Chris S. Magnano, Ameet Soni, Sriraam Natarajan, Gautam Kunapuli.
Presented at the Workshop on Machine Learning and Interpretation in NeuroImaging (MLINI) at NIPS, 2013.
This is a shortened version of the SDM 2014 paper.

A Support Program for Introductory CS Courses that Improves Student Performance and Retains Students from Underrepresented Groups.
Tia Newhall, Lisa Meeden, Andrew Danner, Ameet Soni, Frances Ruiz, Richard Wicentowski.
In the Proceedings of the ACM Technical Symposium on Computer Science Education (SIGCSE), 2014.

Probabilistic Ensembles for Improved Inference in Protein-Structure Determination.
Ameet Soni, Jude Shavlik.
Journal of Bioinformatics and Computational Biology, 10(1), 2012.
An extended version of this paper can be found here.

Techniques for Improved Probabilistic Inference In Protein-Structure Determination via X-Ray Crystallography
Ameet Soni
PhD Thesis, Department of Computer Sciences, University of Wisconsin-Madison, 2011.
Also appears as UW Technical Report CS-TR-11-1703
Slides (PPT/PPTX).

ACM DL Author-ize service Probabilistic ensembles for improved inference in protein-structure determination
Ameet Soni, Jude Shavlik
In the Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine (ACM-BCB '11), 2011
Slides (PPT/PPTX). Abstract.

Structural characterization of human Uch37.
E. Sethe Burgie, Craig Bingman, S. Leigh Grundhoefer, Ameet Soni, George Phillips
Proteins: Structure, Function, and Bioinformatics, 80: 649-654, 2011.

ACM DL Author-ize service Guiding belief propagation using domain knowledge for protein-structure determination
Ameet Soni, Craig Bingman, Jude Shavlik
In the Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology (ACM-BCB '10), 2010
Slides (PPT/PPTX). Abstract.

Spherical-Harmonic Decomposition for Molecular Recognition in Electron-Density Maps.
Frank DiMaio, Ameet Soni, George Phillips, Jude Shavlik.
International Journal of Data Mining and Bioinformatics, 3: 205-227, 2009.
doi: 10.1504/IJDMB.2009.024852; NIHMSID: NIHMS68171; PMCID: PMC2696052
(The paper is in pre-publication form. It is an extension to: DiMaio et al. (BIBM 2007))

Machine Learning in Structural Biology: Interpreting 3D Protein Images.
Frank DiMaio, Ameet Soni, Jude Shavlik.
In S. Mitra, S. Datta, T. Perkins & G. Michailidis, editors,
Introduction to Machine Learning and Bioinformatics, 237-276, 2008. Chapman & Hall/CRC Press

Creating Protein Models from Electron-Density Maps using Particle-Filtering Methods.
Frank DiMaio, Dmitry Kondrashov, Eduard Bitto, Ameet Soni, Craig Bingman, George Phillips, Jude Shavlik.
Bioinformatics, 23: 2851-2858, 2007.
doi: 10.1093/bioinformatics/btm480; PMCID: PMC2567142

Improved Methods for Template-Matching in Electron-Density Maps Using Spherical Harmonics.
Frank DiMaio, Ameet Soni, George Phillips, and Jude Shavlik.
In the Proceedings of the First IEEE International Conference on Bioinformatics and Biomedicine (BIBM '07), 2007
Code. Data. Abstract.

Research Students

Previous Current