Tag: deep learning

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:

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

ABSTRACT

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.

ACHIEVEMENTS

  • 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

POST GRADUATION UPDATES

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.