Adam Teichert

About 

While I was in elementary school, an older sibling got me interested in writing computer programs for our family's first computer. In high school, I used programming to explore number patterns and to help keep track of complicated guitar chords for our school jazz band. I struggled to decide what to pursue for a career, but I ultimately went for a degree in Computer Science — it seemed like something I could do well, support a family with, and use for good.

I worked as a web developer for a university campus office that supported internships for Manufacturing Engineering students. A faculty-mentored research experience turned into a job as a research assistant developing a scripting language for phylogenetic researchers in a bioinformatics lab. The work was exciting; it was remarkable to me that I could be paid to do such enjoyable work!

At this point, I had already taken a course on compilers and had been exposed to a variety of programming languages and paradigms. I was also fascinated by human languages, and I slowly became aware of the work that was being done to bring computing to bear on human language. Natural Language Processing (NLP) became the focus of my remaining undergraduate and graduate schooling, including another mentored research project at Brigham Young University and various courses in machine learning, artificial intelligence, natural language processing, linguistics, and statistics from multiple schools. Before taking my current teaching position at Snow College, I held research assistant positions or internships related to NLP at the University of Utah, the Department of Veterans Affairs in Salt Lake City, Johns Hopkins University, the Army Research Lab in Adelphi, and the Human Language Technology Center of Excellence in Baltimore.

Coming to the Software Engineering program from a Computer Science background has been a stretch and a leap for me, but it has also been rewarding. I'm seeing the computer science ideas that have academic significance being leveraged to solve real problems faced by real people. I continue to learn so much each semester, and I love helping students become enthusiastic learners.

I enjoy doing many things outside of work as well (list is subject to change and does not imply competence in these activities): making music, spending time with my family (teaching, sports, outdoors, cooking, reading, games), and learning many things that it seems like I should have already known by now. Although I don't consider myself to be an animal person, since moving to Ephraim, we have acquired chickens, an angora rabbit, and a dog.

Education

• Ph.D.; Computer Science, Johns Hopkins University (August 2022) 
• Dissertation: Graded Decompositional Semantic Prediction
• M.S.; Computer Science, Johns Hopkins University (2016)
• Masters Project: Learning More-Flexible Hard Constraints For Translation Reordering
• M.S.; Computing, University of Utah (2010)
• Masters Project: Unsupervised Part of Speech Tagging Without a Lexicon
• B.S.; Computer Science, Brigham Young University (2008)
• Cum Laude with University Honors and Phi Kappa Phi Membership
  Honors Thesis: Psodascript: Applying Advanced Language Constructs to Open-Source Phylogenetic Search

Publications and Presentations

Publications

Journals

1. Hyrum Carroll, Adam R. Teichert, Jonathan Krein, Kenneth Sundberg, Quinn Snell, and Mark J. Clement. 2009. “An Open Source Phylogenetic Search and Alignment Package.” IJBRA 5 (3): 349–64.
2. Adam Lopez, Matt Post, Chris Callison-Burch, Jonathan Weese, Juri Ganitkevitch, Narges Ahmidi, Olivia Buzek, Leah Hanson, Beenish Jamil, Matthias Lee, Ya-Ting Lin, Henry Pao, Fatima Rivera, Leili Shahriyari, Debu Sinha, Adam Teichert, Stephen Wampler, Michael Weinberger, Daguang Xu, Lin Yang, and Shang Zhao. 2013. “Learning to Translate with Products of Novices: A Suite of Open-Ended Challenge Problems for Teaching MT.” TACL 1: 165–78.

Conferences

1. Jonathan L. Krein, Adam R. Teichert, Hyrum D Carroll, Mark J Clement, and Quinn O Snell. 2007. “PsodaScript: Applying Advanced Language Constructs to Open-Source Phylogenetic Search.” In Proceedings of the 4th Biotechnology and Bioinformatics Symposium (Biot-07), 89–94.
2. Jiarong Jiang, Adam R. Teichert, Hal Daumé III, and Jason Eisner. 2012. “Learned Prioritiza- tion for Trading Off Accuracy and Speed.” In Advances in Neural Information Processing Systems.
3. Jiarong Jiang, Adam Teichert, Hal Daumé III, and Jason Eisner. 2012. “Learned Prioritization for Trading Off Accuracy and Speed.” In ICML Workshop on Inferning: Interactions Between Inference and Learning.
4. Adam Teichert, Adam Poliak, Benjamin Van Durme, and Matthew R. Gormley. 2017. “Semantic Proto-Role Labeling.” In Proceedings of AAAI.
5. Rachel Rudinger, Adam Teichert, Ryan Culkin, Sheng Zhang, and Benjamin Van Durme. 2018. “Neural Davidsonian Semantic Proto-role Labeling.” In Empirical Methods in Natural Language Processing (EMNLP).

Workshops

1. Adam R. Teichert, and Hal Daumé III. 2009. “Unsupervised Part of Speech Tagging Without a Lexicon.” In NIPS Workshop on Grammar Induction, Representation of Language and Language Learning (Girlll).
2. Adam R. Teichert, Jagadeesh Jagarlamudi, Hal Daumé III. “Translating Part-of-Speech Tags via Dependency Structure.” 2010. In Proceedings of The Snowbird Learning Workshop.
3. Adrian Benton, Jay Deyoung, Adam Teichert, Mark Dredze, Benjamin Van Durme, Stephen Mayhew, and Max Thomas. 2014. “Faster (and Better) Entity Linking with Cascades.” In NIPS Workshop on Automated Knowledge Base Construction.

Presentations

1. “Clustering Vowel Sounds in Recorded Speech.” 15 Mar 2008. Spring Research Conference, BYU College of Physical & Mathematical Sciences.
2. “Learning Time-Sensitive Structured Prediction.” 9 Nov 2012. CLSP Student Seminar, Johns Hopkins University.
3. “Loss-informed Dynamic Schedules via Adjoint-Belief Propagation.” 7 Mar 2014. CLSP Student Seminar, Johns Hopkins University.
4. “An Adventure in Learning to Prioritize Message Passing.” 12 Feb 2016. CLSP Student Seminar, Johns Hopkins University.
5. “Another Look at Ordinal Annotation and Prediction.” 7 Dec 2017. CLSP Student Seminar, Johns Hopkins University.
6. “Graded Decompositional Semantic Prediction.” 19 Aug 2019. Computer Science Department Seminar, Johns Hopkins University.
7. “An Important Trick for Heading Upward Faster, Without Bugs, and Without Remembering Calculus Rules.” 19 Aug 2019. Science Division Seminar, Snow College.
8. “An Introduction to Computational Models of Language (and how they impact texting, translation, mp3s, and more).” 11 Nov 2021. Science Division Seminar, Snow College.
9. “Coding My Story: Interdisciplinary GE as Part of FYE.” David Allred, Lindsay Chaney, Adam Teichert. 4 Feb 2023. 42nd Annual Conference on the First-Year Experience, Los Angeles, CA.
10. “Transformers: Dissecting the AI.” 16 Mar 2023. Science Division Seminar, Snow College.