Jesse Eickholt

34 papers A 2C 6Misc 1Journal 22Unranked 3

Year	Rank	Type	Title / Venue / Authors
2022	J	`jnl`	An Ornithologist's Guide for Including Machine Learning in a Workflow to Identify a Secretive Focal Species from Recorded Audio. Remote. Sens. Ming Liu, Qiyu Sun, Dustin E. Brewer, Thomas M. Gehring, Jesse Eickholt
2022	C	`conf`	Student-facing Learning Analytics Dashboard: Profiles of Student Use. FIE Jesse Eickholt, Jennifer L. Weible, Stephanie D. Teasley
2021	J	`jnl`	Practical Active Learning Stations to Transform Existing Learning Environments Into Flexible, Active Learning Classrooms. IEEE Trans. Educ. Jesse Eickholt, Matthew R. Johnson, Patrick Seeling
2020	J	`jnl`	LAGradebook: a tool for course-level comparative learning analytics. J. Comput. Sci. Coll. Chistopher Phillips, Jesse Eickholt
2020	A	`conf`	Pedagogy and Classroom: How Can I Do This in That Space or Does it Even Matter? SIGCSE Jesse Eickholt, Patrick Seeling
2020	C	`conf`	Supporting Instructor Reflection on Employed Teaching Techniques via Multimodal Instructor Analytics. FIE Jesse Eickholt
2020	J	`jnl`	TopQA: a topological representation for single-model protein quality assessment with machine learning. Int. J. Comput. Biol. Drug Des. John Smith, Matthew Conover, Natalie Stephenson, Jesse Eickholt, Dong Si, Miao Sun, Renzhi Cao
2019	C	`conf`	Advancing Adoption of Active Learning Pedagogy via New Avenues of Research and Training. FIE Jesse Eickholt, Lisa Gandy, Patrick Seeling, Matthew R. Johnson
2019	J	`jnl`	Creating a culture and environment for active learning success. J. Comput. Sci. Coll. Jesse Eickholt, Patrick Seeling, Lisa Gandy, Quentrese Cole, Matthew R. Johnson
2019	C	`conf`	Low-Cost Active Learning Benefits for Introductory Computer Science Courses. FIE Patrick Seeling, Jesse Eickholt, Quentrese Hinton, Matthew R. Johnson
2018	J	`jnl`	Barriers to Active Learning for Computer Science Faculty. CoRR Jesse Eickholt
2018	J	`jnl`	Confronting barriers to active learning in computer science through technology, community and culture: tutorial presentation. J. Comput. Sci. Coll. Jesse Eickholt, Patrick Seeling, Lisa Gandy, Quentrese Cole, Matthew R. Johnson
2017	—	`conf`	A distributed pipeline for DIDSON data processing. IEEE BigData Liling Li, Tyler Danner, Jesse Eickholt, Erin McCann, Kevin Pangle, Nicholas Johnson
2017	Misc	`conf`	Creating Economy Active Learning Classrooms for IT Students. SIGITE Quentrese Cole, Matthew R. Johnson, Jesse Eickholt
2017	C	`conf`	Levels of active learning in programming skill acquisition: From lecture to active learning rooms. FIE Patrick Seeling, Jesse Eickholt
2017	J	`jnl`	Supporting active learning in computer science through technology and community. J. Comput. Sci. Coll. Jesse Eickholt, Patrick Seeling, Matthew R. Johnson
2017	C	`conf`	Supporting active learning through commodity and open source solutions. FIE Jesse Eickholt, John Roush, Patrick Seeling, Thiruvarangan Vedantham, Matthew R. Johnson
2017	A	`conf`	Teaching Big Data and Cloud Computing with a Physical Cluster. SIGCSE Jesse Eickholt, Sharad Shrestha
2016	J	`jnl`	A bioinformatics approach for exploring text-processing: calculating protein weights J. Comput. Sci. Coll. Jesse Eickholt, Edward J. Mirielli
2015	J	`jnl`	A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction. IEEE ACM Trans. Comput. Biol. Bioinform. Matt Spencer, Jesse Eickholt, Jianlin Cheng
2015	J	`jnl`	Adding computing theory to a course on computer design and architecture. J. Comput. Sci. Coll. Jesse Eickholt
2015	J	`jnl`	Incorporating economy hardware into a computer design and architecture course. J. Comput. Sci. Coll. Jason Litchfield, Steven Ledsworth, Jesse Eickholt
2014	—	`conf`	Adopting the MapReduce framework to pre-train 1-D and 2-D protein structure predictors with large protein datasets. BIBM Jesse Eickholt, Suman Karki
2013	J	`jnl`	A study and benchmark of DNcon: a method for protein residue-residue contact prediction using deep networks. BMC Bioinform. Jesse Eickholt, Jianlin Cheng
2013	J	`jnl`	DNdisorder: predicting protein disorder using boosting and deep networks. BMC Bioinform. Jesse Eickholt, Jianlin Cheng
2012	J	`jnl`	Predicting protein residue-residue contacts using deep networks and boosting. Bioinform. Jesse Eickholt, Jianlin Cheng
2012	J	`jnl`	Recursive protein Modeling: a Divide and Conquer Strategy for protein Structure Prediction and its Case Study in CASP9. J. Bioinform. Comput. Biol. Jianlin Cheng, Jesse Eickholt, Zheng Wang, Xin Deng
2012	J	`jnl`	The MULTICOM toolbox for protein structure prediction. BMC Bioinform. Jianlin Cheng, Jilong Li, Zheng Wang, Jesse Eickholt, Xin Deng
2011	J	`jnl`	APOLLO: a quality assessment service for single and multiple protein models. Bioinform. Zheng Wang, Jesse Eickholt, Jianlin Cheng
2011	J	`jnl`	DoBo: Protein domain boundary prediction by integrating evolutionary signals and machine learning. BMC Bioinform. Jesse Eickholt, Xin Deng, Jianlin Cheng
2011	—	`conf`	Recursive protein modeling: A divide and conquer strategy for protein structure prediction and its case study in CASP9. BIBM Workshops Jianlin Cheng, Zheng Wang, Jesse Eickholt, Xin Deng
2010	J	`jnl`	MULTICOM: a multi-level combination approach to protein structure prediction and its assessments in CASP8. Bioinform. Zheng Wang, Jesse Eickholt, Jianlin Cheng
2009	J	`jnl`	NNcon: improved protein contact map prediction using 2D-recursive neural networks. Nucleic Acids Res. Allison N. Tegge, Zheng Wang, Jesse Eickholt, Jianlin Cheng
2009	J	`jnl`	PreDisorder: ab initio sequence-based prediction of protein disordered regions. BMC Bioinform. Xin Deng, Jesse Eickholt, Jianlin Cheng