What We Do

We investigate human-centric AI systems with common sense that can team with people in intuitive and reliable ways.

We perform fundamental research on commonsense AI and investigate its application to challenging domains, informed by empirical insights and cognitive theories.

Research Topics

  • Commonsense Reasoning (e.g., modeling story procedures, tracking participant states, and performing counterfactual inference; decision-making in everyday situations)
  • Human-centric NLP (e.g., drawing situational analogies according to cognitive theories; lateral thinking in AI; studying and building robust and explainable models)
  • AI for Social Good (e.g., value-aware detection of fallacies in arguments and hate speech in memes; what-if reasoning in traffic)

PhD Students

Fabian Hoppe, VU Amsterdam, 2023-
Neuro-symbolic verification models

Zhivar Sourati, USC/ISI, 2022-
Robust and explainable commonsense reasoning with analogies

Jiarui Zhang, USC/ISI, 2022-
Multimodal complex reasoning

Master Students

Prateek Chhikara, USC/ISI, 2022-
Text-based games with commonsense reasoning

Darshan Girish Deshpande, USC/ISI, 2022-
Active learning


Peifeng Wang, PhD, 2020-2023
Commonsense reasoning with knowledge graphs and language models

Yifan Jiang, MS, 2022-2023
Procedural reasoning

Vishnu Priya Prasanna Venkatesh, MS, 2022-2023
Logical fallacy identification

Himanshu Rawlani, MS, 2022
Propaganda detection

Thiloshon Nagarajah, MS, 2022
Understanding affordances in procedures

Aravinda Kolla, MS, 2022
Slang representation learning

Abhinav Kumar Thakur, MS, 2022
Meme understanding

Ana Iglesias, PhD intern, 2022
Modeling Temporal Knowledge Graphs

Lucas Zhuang, BS intern, 2022-
Knowledge Graph Fact Verification

Harshit Manektalia, MS, 2022
Dimensional similarity of concepts

Shubham Akhilesh Singh, MS, 2022
Knowledge graph question answering

Jiasheng Gu, MS, 2022
Link prediction with critic language models

Dweepa Honnavali, MS, 2022
Commonsense story generation

Vaibhav Vats, MS, 2022
Entity linking in Wikidata

Sukavanan Nanjundan, MS, 2022
Dimensional similarity of concepts

Jiang Wang, MS, 2021-2022
Link prediction with augmented knowledge graphs

Jiarui Zhang, BS, 2021-2022
Zero-shot commonsense reasoning with knowledge graphs

Bohui Zhang, MS, 2021-2022
Enriching Wikidata with linked open data

Nicholas Klein, MS (USC/ISI Rising star program), 2020-2022
Profiling Wikidata entities and identification of surprising facts

Sara Melotte, Aditya Malte, Linglan Zhang, Namita Mutha, MS (CKIDS program), 2021
Biases in commonsense knowledge sources

Zaina Shaik, BS (NSF REU program), 2021
Biases in factual knowledge graphs

Pushkaraj Jitendra Sarnobat, MS, 2021
Scene graph generation

Kartik Shenoy, MS, 2021-2022
Wikidata quality and concept similarity

Ehsan Qasemi, PhD, 2020-2021
Preconditions of commonsense knowledge

Avijit Thawani, PhD, 2020-2021
Language model numeracy

Bin Zhang, MS, 2020-2021
Organizing commonsense knowledge, and integration with language models

Hanzhi Zhang, MS, 2020-2021
Commonsense story generation and explanation