Adam Teichert

About 

My mom was raised in Los Angelos, California, and my dad was raised in a ranching family in Cokeville, Wyoming. In the early '90s, at the beginning of my elementary school years, my family moved from California to Wyoming, and I loved the small-town education there: the small class sizes (16 in my graduating class) and the encouragement to work hard and to be involved. I had one especially influential teacher who coached wrestling, volunteered as an EMT, taught Spanish and Math at the high school, and took us on a field trip to Peru. His excitement at seeing us understand new ideas and bring together multiple concepts to solve real problems was contagious.

As I started college at BYU, I worried about how to turn my interests in music, language, teaching, math and computing into a viable career.  During elementary school, I'd been introduced to computer programming by my older brother when our family bought our first computer, and I tried to incorporate those skills into my math classes.  When I struggled with remembering guitar chords in the jazz band, a new TI C compiler let me write a program to help me. I eventually settled on Computer Science as a field that I could be good at and use to support a family and do some good in the world.

After a two-year mission in southern Brazil, the idea to become a college professor slowly began to settle in my mind.  I decided to take a "machine learning" course, looking for insights into how humans learn and teach, and I discovered the exciting impact of Math and Statistics on computing.  An advertisement for a "natural language processing" course opened up a channel between many of my interests as the methods tended to lean heavily on machine learning.  It was at about this time that I stopped selling back textbooks and started taking challenging, non-required courses in Artificial Intelligence, Bayesian Statistics, Data Mining, Linguistics, and advanced Natural Language Processing.  Meanwhile, I got involved in mentored research projects with Dr. Eric Ringger in NLP and with Dr. Quinn Snell that involved adding programming language ideas to an open-source scripting language for phylogenetic research.  Additional work during those final undergraduate years in the honors program and as a research assistant and teaching assistant left me excited to be a computer science professor.

Intrigued by a guest talk by Hal Daumé III about his NLP work at the University of Utah, I finished at BYU and then started a master's degree at the University of Utah, doing additional NLP research with Hal and others and also gaining experience with bioinformatics and working with natural language in medical records. My Algorithms class from Suresh Venkatasubramanian, the "Friends of Algorithms Lunch Bunch" that he organized,  a small semantics group led by Matt Might, and the colloquial talks in the computer science department all left a big impact on me and opened my eyes to the wide array of interesting approaches and applications in computers science and the connections to other fields.

After graduation, I left Utah with a new bride to pursue a PhD with the Center of Language and Speech Processing at Johns Hopkins University in Baltimore, Maryland.  It was a wonderful, cross-disciplinary environment where I was able to work closely with dozens of top-notch students, educators and researchers.  I'm particularly grateful for my advisors and mentors: Jason Eisner, Adam Lopez, Mark Dredze, and Benjamin Van Durme.  After eight years and only "some final work" left to do on my dissertation (a deceptively large task that ended up taking four more years!) , we moved to Ephraim for me to begin teaching in the new 4-year software engineering bachelor's program.

I have been loving the experience here at Snow.  To me, Snow College encapsulates so many of the great things from my childhood, college, and graduate school years.

I am the father of five inspiring children.

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.