World's Best Scientists 2026 revealed!
Award Badge
Rising Stars
2025

D-Index & Metrics

Rising Stars

D-Index
35
Citations
7385
World Ranking
822
National Ranking
134

Computer Science

D-Index
34
Citations
6754
World Ranking
11994
National Ranking
4897

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Adam Trischler is a researcher affiliated with Microsoft in the United States, specializing primarily in Computer Science. Their work encompasses a broad range of topics, with a significant emphasis on Artificial Intelligence, as reflected in 33 publications within this subfield. Additional areas of research include Computer Vision and Pattern Recognition, Information Systems, and applications of Artificial Intelligence in healthcare and education.

Trischler's research contributions are frequently disseminated through several publication venues. The majority of their work appears in arXiv (Cornell University), with 14 publications, complemented by papers in the Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies and Proceedings of the ACM on Human-Computer Interaction.

Their research topics cover a wide array of subjects:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Multimodal Machine Learning Applications
  • Software Engineering Research
  • Interpreting and Communication in Healthcare
  • Artificial Intelligence in Healthcare and Education
  • Academic Writing and Publishing

Among the recent papers authored or co-authored by Trischler are:

  • "Learning Dynamic Belief Graphs to Generalize on Text-Based Games," 2020, arXiv (Cornell University)
  • "Assessing Factoid Question-Answer Generation for Portuguese (Short Paper)," 2020, arXiv (Cornell University)
  • "ALFWorld: Aligning Text and Embodied Environments for Interactive Learning," 2020, arXiv (Cornell University)
  • "Role-Wise Data Augmentation for Knowledge Distillation," 2020, arXiv (Cornell University)
  • "Deconstructing NLG Evaluation: Evaluation Practices, Assumptions, and Their Implications," 2022, Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Trischler has collaborated frequently with several researchers, including Alexandra Olteanu, Xingdi Yuan, Su Lin Blodgett, Kaheer Suleman, and Alessandro Sordoni. Their collaborative efforts have contributed to multiple published works, indicating active engagement in research networks and interdisciplinary projects.

Best Publications

  • Learning deep representations by mutual information estimation and maximization

    R. Devon Hjelm;Alex Fedorov;Samuel Lavoie-Marchildon;Karan Grewal

  • NewsQA: A Machine Comprehension Dataset

    Adam Trischler;Tong Wang;Xingdi Yuan;Justin Harris

  • An Empirical Study of Example Forgetting during Deep Neural Network Learning

    Mariya Toneva;Alessandro Sordoni;Remi Tachet des Combes;Adam Trischler

  • Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning

    Sandeep Subramanian;Adam Trischler;Yoshua Bengio;Christopher J Pal

  • Rapid Adaptation with Conditionally Shifted Neurons.

    Tsendsuren Munkhdalai;Xingdi Yuan;Soroush Mehri;Adam Trischler

  • Machine Comprehension by Text-to-Text Neural Question Generation

    Xingdi Yuan;Tong Wang;Caglar Gulcehre;Alessandro Sordoni

  • TextWorld: A Learning Environment for Text-Based Games

    Marc-Alexandre Côté;Ákos Kádár;Xingdi Yuan;Ben Kybartas

  • Boundary-Seeking Generative Adversarial Networks

    R Devon Hjelm;Athul Paul Jacob;Tong Che;Adam Trischler

  • TextWorld: A Learning Environment for Text-based Games

    Marc-Alexandre Côté;Ákos Kádár;Xingdi Yuan;Ben Kybartas

  • Neural models for key phrase detection and question generation

    Sandeep Subramanian;Tong Wang;Xingdi Yuan;Saizheng Zhang

  • FigureQA: An Annotated Figure Dataset for Visual Reasoning

    Samira Ebrahimi Kahou;Vincent Michalski;Adam Atkinson;Akos Kadar

  • Iterative alternating neural attention for machine reading

    Alessandro Sordoni;Philip Bachman;Adam Peter Trischler

  • Learning algorithms for active learning

    Philip Bachman;Alessandro Sordoni;Adam Trischler

  • Exploring and Predicting Transferability across NLP Tasks

    Tu Vu;Tong Wang;Tsendsuren Munkhdalai;Alessandro Sordoni

  • FigureQA: An Annotated Figure Dataset for Visual Reasoning

    Samira Ebrahimi Kahou;Adam Atkinson;Vincent Michalski;Ákos Kádár

  • Natural language comprehension with the epireader

    Adam Trischler;Zheng Ye;Xingdi Yuan;Philip Bachman

  • Synthesis of recurrent neural networks for dynamical system simulation

    Adam P. Trischler;Gabriele M.T. D'Eleuterio

  • A Joint Model for Question Answering and Question Generation

    Tong Wang;Xingdi (Eric) Yuan;Adam Trischler

  • Machine Comprehension by Text-to-Text Neural Question Generation

    Xingdi Yuan;Tong Wang;Caglar Gulcehre;Alessandro Sordoni

  • Building Dynamic Knowledge Graphs from Text using Machine Reading Comprehension.

    Rajarshi Das;Tsendsuren Munkhdalai;Xingdi Yuan;Adam Trischler

  • One Size Does Not Fit All: Generating and Evaluating Variable Number of Keyphrases.

    Xingdi Yuan;Tong Wang;Rui Meng;Khushboo Thaker

Frequent Co-Authors

Yoshua Bengio
Yoshua Bengio University of Montreal
Chris Pal
Chris Pal Polytechnique Montréal
Caglar Gulcehre
Caglar Gulcehre DeepMind (United Kingdom)
Pascal Poupart
Pascal Poupart University of Waterloo
Yi Tay
Yi Tay Google (United States)
Peter Brusilovsky
Peter Brusilovsky University of Pittsburgh
Joelle Pineau
Joelle Pineau McGill University
Fernando Diaz
Fernando Diaz Microsoft (United States)
Kyunghyun Cho
Kyunghyun Cho New York University
Geoffrey J. Gordon
Geoffrey J. Gordon Carnegie Mellon University

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Studying Computer Science in the USA opens the door to numerous flexible career options. Online degrees have become especially popular, offering students the chance to advance their education while balancing work or family commitments.

Many students interested in technology and analysis look beyond traditional computer science to fields like data analysis or business. For example, pursuing one of the top data science programs can help you specialize in big data and AI, while the cheapest accredited online accounting degree is ideal for those looking to combine tech skills with finance.

Additionally, some students shift toward more applied fields. Consider exploring the online construction management program for a technology-driven career in project management, or the best online criminal justice degree for those interested in cybersecurity and law enforcement technology.

No matter your interests, online degree options provide diverse pathways to build a rewarding and future-proof career in today’s digital world.

Best Scientists Citing Adam Trischler

Trending Scientists

Recently Published Articles