Past Research Projects

The following are research projects that, for one reason or another, ended up falling down the priority list and are no longer being actively worked on. I list them here as a possible conversation starter with students looking for interesting work

  • [IN PREP] Cowen R, Mitchel MW, Hare-Harris A, King BR. Incorporation of Brown’s stages of syntactic and morphological development in a word prediction model of conversational speech from young children
  • [IN PREP] – Cowen R, Mitchel MW, Hare-Harris A, King BR. An adaptive n-gram based stochastic word prediction model for conversational speech.
  • [IN PREP]- Hare A, Essae E, King BR, Ledbetter DH, Martin CL. Determining the dosage effect of copy number variants in the human genome.
  • [IN PREP] – Ren C, King BR – Protein residue contact map prediction using bagged decision trees

Current Student Research

These are ongoing projects as of Summer 2019

Bhagawat Acharya ’20 – Using deep learning for handwriting text recognition.

  • This is a collaborative, interdisciplinary project with Katherine Faull (Comparative Humanities and German Studies) and Carrie Pirmann (Research Services Librarian). We are working together to develop an improved handwriting translation pipeline to increase the HTR throughput of 17-18th century Moravian handwritten literature that is part of the Moravian archives.
  • Funding – Bucknell Emerging Scholars Summer Research Program

Taehwan Kim ’20 – Using Deep Learning to Forecast Monthly Extreme Temperatures over the United States

  • Undoubtedly, climate change is one of most pressing, disconcerting issues of our time. Collaborating with atmospheric science and aerosol science expert Dabrina Dutcher, Assistant Prof. in Chemistry and Chemical Engineering, we are exploring the use of deep learning to develop advanced models that can improve future temperature predictions
  • Funding – Katherine Mabis McKenna Environmental Internship

Lily Romano ’20 – Software for Aerosol Analysis

  • We are developing a new software toolkit to aid in the aerosol research of my colleagues in Chemical Engineering, Dabrina Dutcher, PhD and Timothy Raymond, PhD. Lily is resuming work that was initiated by former student Khai Nguyen ’18 on the software, including advancing the data analysis tools available for aerosol researchers.
  • Funding – Clare Boothe Luce Research Scholars Program

Kartikeya Sharma ’20 – Trajectory Gaze Path Analysis on Eye Tracking Data for Autism Spectrum Disorder Studies

  • This is a collaborative project with my colleagues, Vanessa Troiani, PhD and Antoinette Sabatino DiCriscio, PhD at the Geisinger Autism Developmental Medicine Institute. The primary aim is to develop a toolkit for the eye tracking research community that incorporates my novel method for extracting scanpath trends from group-level eye tracking data.
  • Funding – Ciffolillo Healthcare Technology Inventors Program

Yili Wang ’21 – Using deep learning to identify discriminative features of images with high interest of autistic children

  • This is a collaborative project with my colleague Vanessa Troiani, PhD at Geisinger Autism and Developmental Medicine Institute. This is also a continuation of a project with former student Tongyu Yang `17, who is continuing to assist with the effort
  • Funding – Bucknell Program for Undergraduate Research (PUR)

These are projects that are unfinished for a variety of reasons:

Summer 2016

It has been quite some time since I’ve updated current events. Thanks to our students, we have had a pretty active summer…

  • Robert Cowen is continuing his work with me on word prediction models. We have good results and are writing our first paper. The first draft should be complete by the beginning of September.
  • Morgan Eckenroth has started work on the development of a virtual reality app (using Google Cardboard) that will be used by autistic children to help assess (and hopefully retrain) biases in their visual processing
  • Khai Nguyen is working on a collaborative project, funded together by the College of Engineering, Chemical Engineering, and Computer Science. The aim of the project is to develop a new application for aerosol researchers in Chem Eng.
  • Ryan Stecher is working on a collaborative project with Dr. Aaron Mitchel in Psychology to develop and finalize a web-based series of perception tests.
  • Tongyu Yang has been investigating the use of deep learning to help autism researchers better understand why autistic children have substantial interest in certain types of images

Son Pham, ’17

Project: Using Deep Learning to Automatically Learn Feature Representation and Build a Better Classification Model on Protein Sequential Data
Started: Summer 2015
Funding: Bucknell University PUR


In theory, deep learning is not new. However, it has recently become one of the most exciting directions that machine learning has witnessed in years. It has had a tremendous impact on image classification. However, there are very few methods that have investigated its use on strictly sequential data, such as those found in biological sequences. This study will aim to investigate the use of deep learning to induce a protein sequence classifier that can outperform existing methods.


  • Poster Presentation – Sigma Xi 2015 Summer Research Symposium
  • Poster Presentation – Fifth Annual Susquehanna Valley Undergraduate Research Symposium, SVURS 2015, August 4, Bucknell University, Lewisburg, PA
  • Poster Presentation – Presented at 15th Annual Kalman Research Symposium, April 2, 2016, Bucknell University, Lewisburg, PA


Son graduated with his degrees in Computer Science and Engineering, together with Digital Studio Arts. He went on to work for Amazon as an Software Engineering Intern, then took a position at Google working with machine learning. Son graduated with the aim of going back to graduate school in 1-2 years.

Chuqiao Ren, ’15

Project: A novel ensemble classifier for protein contact map prediction
Duration: Summer 2013 – Spring 2015
Funding: Bucknell University Program for Undergraduate Research, BRK Startup Fund, Geisinger BGRI Grant, CS Dept. Fund


One of the greatest challenges in bioinformatics is how to predict the 3-D structure of a protein by understanding the relationship between a sequence and its amino acid structure.  A protein contact map is a useful way of representing protein 3-D conformations. It is based on a distance matrix, which is a symmetric matrix that contains the Euclidean distance between each pair of C-alpha atoms in each residue in the folded protein.  

Our goal is to improve existing machine learning algorithms for predicting a protein contact map from protein sequence, and develop a novel algorithm that improves the performance of existing contact map predictors.


  • Honors Thesis – Successfully defended, April 2015
  • Short paper and poster – ACB BCB ’14 – ACM International Conference on Bioinformatics, Computational Biology and Biomedicine, Sept 20-23, Newport Beach, CA [link]
  • Poster Presentation – Fourth Annual Susquehanna Valley Undergraduate Research Symposium, SVURS 2014, August 5, Geisinger Research, Danville, PA
  • Poster – Kalman Research Symposium 2014, March 29, Bucknell University, Lewisburg, PA.


Chuqiao successfully defended her honors thesis in April, 2015. She is staying for a bit longer this summer to help finish a journal publication and submit before she departs us. She is currently planning on pursuing her graduate degree in computer science at Columbia University, starting Fall 2015. Congratulations, Chuqiao!

Summer 2015

We have an active summer in store. Three students are working on entirely different research projects, while Rachel Ren is wrapping up her work.

  • Son Pham is working on investigating the use of Deep Learning for protein sequence classification. Deep Learning has recently gained substantial recognition due to its success with automated image recognition and speech classification. Very few have examined its use in bioinformatics. Son will help me explore this untapped area in bioinformatics.
  • Jason Hammett will be applying data mining techniques to years of regional climate data, including local stats for the Susquehanna River, to develop explanatory and predictive models for anomalistic weather events around the Susquehanna River Valley.
  • Robert Cowen will be continuing the wonderful work that I started with Bucknell Student Stephanie Gonthier last year on word prediction. Robert will be collaborating with myself and speech pathologists at the Geisinger-Bucknell Autism and Developmental Medicine Institute (ADMI) to develop a preliminary version of a new augmentative and alternative communication (AAC) app that will utilize my word prediction model. This first version will be developed to run on Android tablets.
  • Rachel Ren is graciously staying for a month after graduating to help submit a paper based on her extensive work completed for her honors thesis. Stayed tuned!

Spring 2015

Rachel Ren successfully defended her honors thesis, titled, “Predicting Protein Contact Maps by Bagging Decision Trees”. Congratulations, Rachel! Additionally, Rachel will be attending graduate school starting in the fall at Columbia University, where she will pursue a Masters in Computer Science. Rachel intends to focus on research in machine learning.

Congratulations, Rachel! Bucknell is proud of you! We wish you the very best as you pursue your graduate work.

Stephanie Gonthier, ’15

Project: Using statistical learning to improve word prediction for augmentative and alternative communication
Duration: Summer 2014
Funding: Bucknell University Program for Undergraduate Research, Geisinger BGRI Grant


There are a multitude of reasons why people may be unable to communicate effectively through verbal speech, including disorders like ALS, MS, Cerebral Palsy and Autism. Some people use augmentative and alternative communication (AAC), which is simply any mode of communication besides verbal speech, including gestures, writing, facial expressions, pointing to pictures and so on. In recent decades, the field of AAC has been flooded by electronic devices which generate speech for these people based on combinations of pictures, symbols and/or words that are stored on the device. Unfortunately, these devices do present problems; notably, the communication rate with a device is reduced to a fraction of the communication rate of normal speakers. The average user of a device is only able to communicate 10 words per minute, compared to the 130-200 words per minute of an average speaker [ref]. This stark contrast can leave users frustrated, reducing the utility of such devices. The aim of this research is to develop a novel algorithm that would increase the communication rate for users of AAC devices.


  • Oral PresentationFourth Annual Susquehanna Valley Undergraduate Research Symposium, SVURS 2014, August 5, Geisinger Research, Danville, PA
    Winner for oral presentation – One of three chosen out of 86 submissions!
  • Poster Presentation – 2014 Sigma Xi Summer Student Research Symposium, July 24, Bucknell University, Lewisburg, PA


Elizabeth Dwornik, ’14

Project: Named-Entity Recognition
Duration: Summer 2013 – Spring 2014
Funding: Bucknell University Program for Undergraduate Research


Liz is working on a system that can annotate all of the named entities within a text. There are good systems that can identify named entities, however, identifying the type of named entity is a more challenging problem. Many successful systems use simple database lookup techniques and identify entities from a master gazetteer. We are working on a system that can distinguish among different types of named entities without a gazetteer. Our initial efforts will focus on distinguishing entities between location, organization, or person. We plan to start by developing a large set of regular expressions that can be used to classify the different types of entities.


  • Poster: Kalman Research Symposium 2013, April 13, Bucknell University, Lewisburg, PA


Liz pursued graduate school studies at Carnegie Mellon University, starting Fall 2014. She enrolled in the Software Management program in the Information Networking Institute. Congratulations, Liz!

Matthew Rogge, ’17

Project: Analysis of Spike Timing Dependent Neural Networks for More Efficient Starting State Learning
Duration: Summer 2014
Funding: Bucknell University Program for Undergraduate Research


Artifical Neural Networks (ANN) have been a popular machine learning method for decades. They aim to simulate the behavior of the neurons in the biological brain. One particular type of ANN that is an especially accurate representation of biological neurons is the Spike-Timing Dependent ANN. These ANNs differ from traditional back propagation ANNs in that they rely on the timing and frequency of signals, rather than their strength, to learn and process information. This type of ANN of often ignored for many reasons, mostly due to the computational complexity of learning using these models. On substantial challenge lies in the difficulty of determining the initial configuration of the network. The time required to train the network is also a formidable challenge. My research seeks to eliminate one of these hurdles by deriving an efficient algorithm that can determine the proper starting configuration for the ANN.


  • Poster: 2014 Sigma Xi Summer Student Research Symposium, July 24, Bucknell University, Lewisburg, PA