Chitta Baral

(Tu Th 4:30 PM - 5:45 PM 8/22/24 - 12/6/24 Tempe COOR 174 )

Past teaching

Current Research Interests and Selected Publications

• Recent Highlights
  • Swaroop Mishra, Daniel Khashabi, Chitta Baral, Hannaneh Hajishirzi. Cross-Task Generalization via Natural Language Crowdsourcing Instructions. ACL 2022. (Mentioned in the blog https://bit.ly/Instr-tuning from Edinburgh and in the collection https://bit.ly/instr-tuning2 as one of the first papers on Instruction Tuning, along with FLANv1, T0, and InstructGPT.)

• Books
  • Chitta Baral. Knowledge representation, reasoning and declarative problem solving, Cambridge University Press, 2003. Slides based on the book. (pdf) (ps)

  • Paulo Shakarian, Chitta Baral, Gerardo I. Simari, Bowen Xi, Lahari Pokala. Neuro Symbolic Reasoning and Learning. Springer, 2023

  • Man Luo, Tejas Gokhale, Neeraj Varshney, Yezhou Yang, Chitta Baral. Advances in Multimodal Information Retrieval and Generation. Springer. 2024.

  [Top] [Book] [NLP-IR-QA] [Multi-agent] [KR-NLP-IR-VQA-Vision] [Human-Robot-C] [Machine-Learning] [QA,Frames] [Bioinformatics] [Logic+Prob] [Goal Languages] [Action-Change] [Answer Set] [LP-Nonmon] [KBs]

• Natural Language Understanding, Information/Text Retrieval, and Knowledge Representation and Reasoning
  Logic and Actions [Logic-Actions] [Bias-Fairness] [Instructions;Prompts] [Data-Smart] [Robustness;Self-aware] [Task-focussed] [Symbolic-Semantic-Parsing] [Knowledge-Reasoning-NLI-NeuroSymbolic] [IR+QA;Knowledge Hunting] [Information Extraction; NER] [Datasets;Systems] [Application-Cybersecurity] [Application-BioMed] [Application-Robotics] [Other]
  • Nisarg Patel, Mohith Kulkarni, Mihir Parmar, Aashna Budhiraja, Mutsumi Nakamura, Neeraj Varshney, Chitta Baral. Multi-LogiEval: Towards Evaluating Multi-Step Logical Reasoning Ability of Large Language Models. Accepted to EMNLP 2024.

  • Nemika Tyagi, Mihir Parmar, Mohith Kulkarni, Aswin RRV, Nisarg Patel, Mutsumi Nakamura, Arindam Mitra, Chitta Baral. Step-by-Step Reasoning to Solve Grid Puzzles: Where do LLMs Falter? Accepted to EMNLP 2024.

  • Mutsumi Nakamura, Santosh Mashetty, Mihir Parmar, Neeraj Varshney, and Chitta Baral. LogicAttack: Adversarial Attacks for Evaluating Logical Consistency of Natural Language Inference. Findings of EMNLP 2023.

  • Mihir Parmar, Nisarg Patel, Neeraj Varshney, Mutsumi Nakamura, Man Luo, Santosh Mashetty, Arindam Mitra, Chitta Baral. Towards Systematic Evaluation of Logical Reasoning Ability of Large Language Models. ACL 2024.

    Bias and Fairness [Logic-Actions] [Bias-Fairness] [Instructions;Prompts] [Data-Smart] [Robustness;Self-aware] [Task-focussed] [Symbolic-Semantic-Parsing] [Knowledge-Reasoning-NLI-NeuroSymbolic] [IR+QA;Knowledge Hunting] [Information Extraction; NER] [Datasets;Systems] [Application-Cybersecurity] [Application-BioMed] [Application-Robotics] [Other]

  • Mihir Parmar, Swaroop Mishra, Mor Geva and Chitta Baral. Don't Blame the Annotator: Bias Already Starts in the Annotation Instructions. EACL 2023. (Outstanding paper award)

    Data Generation; Instructibility; Hallucination Mitigation; Knowledge Guidance; Instruction Engineering; Limitations of Transformer models [Logic-Actions] [Bias-Fairness] [Instructions;Prompts] [Data-Smart] [Robustness;Self-aware] [Task-focussed] [Symbolic-Semantic-Parsing] [Knowledge-Reasoning-NLI-NeuroSymbolic] [IR+QA;Knowledge Hunting] [Information Extraction; NER] [Datasets;Systems] [Application-Cybersecurity] [Application-BioMed] [Application-Robotics] [Other]

  • Himanshu Gupta, Kevin Scaria, Ujjwala Anantheswaran, Shreyas Verma, Mihir Parmar, Saurabh Arjun Sawant, Chitta Baral, Swaroop Mishra. TarGEN: Targeted Data Generation with Large Language Models. COLM 2024.

  • Aswin RRV, Nemika Tyagi, Md Nayem Uddin, Neeraj Varshney, Chitta Baral. Chaos with Keywords: Exposing Large Language Models Sycophancy to Misleading Keywords and Evaluating Defense Strategies. ACL 2024 (Findings).

  • Neeraj Varshney, Pavel Dolin, Agastya Seth, Chitta Baral. The Art of Defending: A Systematic Evaluation and Analysis of LLM Defense Strategies on Safety and Over-Defensiveness. ACL 2024 (Findings).

  • Kevin Scaria, Himanshu Gupta, Siddharth Goyal, Saurabh Arjun Sawant, Swaroop Mishra, Chitta Baral. InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis. NAACL 2024.

  • Neeraj Varshney, Agneet Chatterjee, Mihir Parmar, Chitta Baral. Investigating Acceleration of LLaMA Inference by Enabling Intermediate Layer Decoding via Instruction Tuning with 'LITE'.Findings of NAACL 2024. (paper with code)

  • ... Chitta Baral ... (hundreds of co-authors) Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models. (BIG BENCH) arXiv:2206.04615. Dataset, Accepted by TMLR (Transactions on Machine Learning Research), 2023.

  • Y. Wang, S. Mishra, P. Alipoormolabashi, Y. Kordi, A. Mirzaei, A. Naik, A. Ashok, A. S. Dhanasekaran, A. Arunkumar, D. Stap, E. Pathak, G. Karamanolakis, H. Lai, I. Purohit, Ishan; Mondal, J. Anderson, K. Kuznia, K. Doshi, K. K. Pal, M. Patel, M. Moradshahi, M. Parmar, M. Purohit, N. Varshney, P. R. Kaza, P. Verma, R. S. Puri, R. Karia, S. Doshi, S. K. Sampat, S. Mishra, S. Reddy, S. Patro, T. Dixit, X. Shen, C. Baral, Y. Choi, N. A. Smith, H. Hajishirzi, and D. Khashabi. Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ Tasks. EMNLP 2022.

  • Pruthvi Jayeshkumar Patel, Swaroop Mishra, Mihir Parmar and Chitta Baral. Is a Question Decomposition Unit All We Need? EMNLP 2022.

  • Kirby Kuznia, Swaroop Mishra, Mihir Parmar and Chitta Baral. Less is More: Summary of Long Instructions is Better for Program Synthesis. EMNLP 2022.

  • Mihir Parmar, Swaroop Mishra, Mirali Purohit, Man Luo, M. Hassan Murad, Chitta Baral. In-BoXBART: Get Instructions into Biomedical Multi-Task Learning. Findings of NAACL 2022.

  • Swaroop Mishra, Daniel Khashabi, Chitta Baral, Hannaneh Hajishirzi. Cross-Task Generalization via Natural Language Crowdsourcing Instructions. ACL 2022.

  • Swaroop Mishra, Daniel Khashabi, Chitta Baral, Yejin Choi, Hannaneh Hajishirzi. Reframing Instructional Prompts to GPTk's Language. Findings of ACL 2022.

  • Kuntal Pal, Chitta Baral. Investigating Numeracy Learning Ability of a Text-to-Text Transfer Model. Findings of EMNLP 2021.

  Efficiency issues; Judicious use of Data; Data Quality and Data Reduction (for training as well as evaluation); Curriculum learning [Logic-Actions] [Bias-Fairness] [Instructions;Prompts] [Data-Smart] [Robustness;Self-aware] [Task-focussed] [Symbolic-Semantic-Parsing] [Knowledge-Reasoning-NLI-NeuroSymbolic] [IR+QA;Knowledge Hunting] [Information Extraction; NER] [Datasets;Systems] [Application-Cybersecurity] [Application-BioMed] [Application-Robotics] [Other]
  • Ravsehaj Singh Puri, Swaroop Mishra, Mihir Parmar and Chitta Baral. How Many Data Samples is an Additional Instruction Worth? Findings of EACL 2023.

  • Neeraj Varshney and Chitta Baral. Model Cascading: Towards Jointly Improving Efficiency and Accuracy of NLP Systems. EMNLP 2022.

  • Neeraj Varshney, Swaroop Mishra, Chitta Baral. ILDAE: Instance-Level Difficulty Analysis of Evaluation Data. ACL 2022.

  • Neeraj Varshney, Swaroop Mishra, Chitta Baral. Let the Model Decide its Curriculum for Multitask Learning. NAACL 2022 Deep Learning for Low-Resource NLP Workshop. arXiv:2205.09898.

  • Swaroop Mishra, Anjana Arunkumar, Chris Bryan, Chitta Baral. Our Evaluation Metric Needs an Update to Encourage Generalization. ICML UDL 2020. arXiv:2007.06898.

  • Swaroop Mishra, Anjana Arunkumar, Bhavdeep Sachdeva, Chris Bryan, Chitta Baral. DQI: A Guide to Benchmark Evaluation. ICML UDL 2020. arXiv:2008.03964

  Generalizability and Robustness; Dealing with novelty; Open-worlds; Self-awareness; Selective Prediction [Logic-Actions] [Bias-Fairness] [Instructions;Prompts] [Data-Smart] [Robustness;Self-aware] [Task-focussed] [Symbolic-Semantic-Parsing] [Knowledge-Reasoning-NLI-NeuroSymbolic] [IR+QA;Knowledge Hunting] [Information Extraction; NER] [Datasets;Systems] [Application-Cybersecurity] [Application-BioMed] [Application-Robotics] [Other]
  • Neeraj Varshney and Chitta Baral. Post-Abstention: Towards Reliably Re-Attempting the Abstained Instances in QA. ACL 2023.

  • Neeraj Varshney, Himanshu Gupta, Eric Robertson, Bing Liu and Chitta Baral. A Unified Evaluation Framework for Novelty Detection and Accommodation in NLP with an Instantiation in Authorship Attribution. Findings of ACL 2023.

  • Tejas Gokhale, Swaroop Mishra, Man Luo, Bhavdeep Singh Sachdeva, Chitta Baral. Generalized but not Robust? Understanding the Effects of Out-of-Domain Generalization Methods. Findings of ACL 2022.

  • Neeraj Varshney, Swaroop Mishra, Chitta Baral. Investigating Selective Prediction Approaches Across Several Tasks in IID, OOD, and Adversarial Settings. Findings of ACL 2022.

  • Neeraj Varshney, Swaroop Mishra, Chitta Baral. Towards Improving Selective Prediction Ability of NLP Systems. ACL 2022 RepL4NLP Workshop. arXiv:2008.09371.

  Task-specific self-supervision; Test-time learning [Logic-Actions] [Bias-Fairness] [Instructions;Prompts] [Data-Smart] [Robustness;Self-aware] [Task-focussed] [Symbolic-Semantic-Parsing] [Knowledge-Reasoning-NLI-NeuroSymbolic] [IR+QA;Knowledge Hunting] [Information Extraction; NER] [Datasets;Systems] [Application-Cybersecurity] [Application-BioMed] [Application-Robotics] [Other]
  • Neeraj Varshney, Pratyay Banerjee, Tejas Gokhale, Chitta Baral. Unsupervised Natural Language Inference Using PHL Triplet Generation. Findings of ACL 2022.

  • Ming Shen, Pratyay Banerjee and Chitta Baral. Unsupervised Pronoun Resolution via Masked Noun-Phrase Prediction. ACL 2021.

  • Pratyay Banerjee, Tejas Gokhale and Chitta Baral. Self-Supervised Test-Time Learning for Reading Comprehension. NAACL 2021.

  • Pratyay Banerjee and Chitta Baral. Self-Supervised Knowledge Triplet Learning for Zero-shot Question Answering. EMNLP 2020.

  Symbolic issues; Math; Puzzles; Semantic Parsing; Inverse Lambda [Logic-Actions] [Bias-Fairness] [Instructions;Prompts] [Data-Smart] [Robustness;Self-aware] [Task-focussed] [Symbolic-Semantic-Parsing] [Knowledge-Reasoning-NLI-NeuroSymbolic] [IR+QA;Knowledge Hunting] [Information Extraction; NER] [Datasets;Systems] [Application-Cybersecurity] [Application-BioMed] [Application-Robotics] [Other]
  • Swaroop Mishra, Matthew Finlayson, Pan Lu, Leonard Tang, Sean Welleck, Chitta Baral, Tanmay Rajpurohit, Oyvind Tafjord, Ashish Sabharwal, Peter Clark and Ashwin K. Kalyan. LILA: A Unified Benchmark for Mathematical Reasoning. EMNLP 2022.

  • Swaroop Mishra, Arindam Mitra, Neeraj Varshney, Bhavdeep Singh Sachdeva, Peter Clark, Chitta Baral, Ashwin Kalyan. NumGLUE: A Suite of Fundamental yet Challenging Mathematical Reasoning Tasks. ACL 2022.

  • Arindam Mitra and Chitta Baral. Learning to use formulas to solve simple arithmetic problems. ACL 2016.

  • Chitta Baral and Tran Cao Son. Add Another Blue Stack of the Same Height!: ASP Based Planning and Plan Failure Analysis. LPNMR 2015. (old longer version)

  • Arindam Mitra and Chitta Baral. Learning to automatically solve logic grid puzzles. EMNLP 2015.

  • Arpit Sharma, Somak Aditya, Vo Nguyen and Chitta Baral. Towards Addressing the Winograd Schema Challenge - Building and Using a Semantic Parser and a Knowledge Hunting Module. IJCAI 2015.

  • Vo Nguyen, Arindam Mitra and Chitta Baral. The NL2KR platform for building Natural Language Translation Systems. ACL 2015.

  • Shruti Gaur, Nguyen H. Vo, Kazuaki Kashihara, and Chitta Baral. Translating Simple Legal Text to Formal Representations. In New Frontiers in Artificial Intelligence (JSAI-isAI 2014 Workshops, LENLS, JURISIN, and GABA, Kanagawa, Japan, November 23-24, 2014, Revised Selected Papers). Edited by: Tsuyoshi Murata, Koji Mineshima, Daisuke Bekki. 2015.

  • Chitta Baral, Juraj Dzifcak, Marcos Alvarez Gonzalez, Aaron Gottesman. Typed answer set programming lambda calculus theories and correctness of inverse lambda algorithms with respect to them. (appendix) TPLP 12(4-5): 775-791 (2012)

  • Chitta Baral, Marcos Alvarez Gonzalez, Aaron Gottesman. The Inverse Lambda Calculus Algorithm for Typed First Order Logic Lambda Calculus and Its Application to Translating English to FOL. Correct Reasoning 2012: 40-56

  • Chitta Baral and Juraj Dizfcak. Solving puzzles described in English by automated translation to answer set programming and learning how to do that translation. AAAI Fall Symposium on Advances in Cognitive Systems. 2011. A revised but shorter version at KR 2012.

  • Chitta Baral. Lessons from Efforts to Automatically Translate English to Knowledge Representation Languages. Abstract of invited presentation at LPNMR 2011.

  • Chitta Baral, Juraj Dzifcak, Marcos Gonzalez and Jiayu Zhou. Using Inverse Lambda and Generalization to Translate English to Formal Languages. Proceedings of International Conference on Computational Semantics (IWCS) 2011, Oxford.

  • Jiayu Zhou, Jieping Ye, Juraj Dzifcak, Chitta Baral. Using Sparse Parameter Estimation for Semantic Parsing. Unpublished Draft. November 2010.

  • Juraj Dzifcak, Matthias Scheutz, Chitta Baral and Paul Schermerhorn (2009) What to do and how to do it: Translating natural language directives into temporal and dynamic logic representation for goal management and action execution. Proceedings of the 2009 IEEE international conference on robotics and automation (ICRA '09)

  • Chitta Baral, Juraj Dzifcak, Tran Cao Son. Using Answer Set Programming and Lambda Calculus to Characterize Natural Language Sentences with Normatives and Exceptions.AAAI 2008, pages 818-823.

  • Chitta Baral, Juraj Dzifcak, Luis Tari: Towards Overcoming the Knowledge Acquisition Bottleneck in Answer Set Prolog Applications: Embracing Natural Language Inputs. ICLP 2007: 1-21 (invited paper) (presentation)

  OpenbookQA; NLI; Use of Knowledge, Commonsense, and Reasoning; Neuro-symbolic; Actions and Planning [Logic-Actions] [Bias-Fairness] [Instructions;Prompts] [Data-Smart] [Robustness;Self-aware] [Task-focussed] [Symbolic-Semantic-Parsing] [Knowledge-Reasoning-NLI-NeuroSymbolic] [IR+QA;Knowledge Hunting] [Information Extraction; NER] [Datasets;Systems] [Application-Cybersecurity] [Application-BioMed] [Application-Robotics] [Other]
  • Neeraj Varshney, Pratyay Banerjee, Tejas Gokhale, Chitta Baral. Unsupervised Natural Language Inference Using PHL Triplet Generation. Findings of ACL 2022.

  • Pratyay Banerjee, Swaroop Mishra, Kuntal Kumar Pal, Arindam Mitra, Chitta Baral. Commonsense Reasoning with Implicit Knowledge in Natural Language. AKBC 2021.

  • Ming Shen, Pratyay Banerjee and Chitta Baral. Unsupervised Pronoun Resolution via Masked Noun-Phrase Prediction. ACL 2021.

  • Arindam Mitra, Ishan Shrivastava and Chitta Baral. Enhancing Natural Language Inference Using New and Expanded Training Data Sets and New Learning Models. AAAI 2020.

  • Ashok Prakash, Arpit Sharma, Arindam Mitra and Chitta Baral. Combining Knowledge Hunting and Neural Language Models to Solve the Winograd Schema Challenge. ACL 2019.

  • Pratyay Banerjee, Kuntal Kumar Pal, Arindam Mitra and Chitta Baral. Careful Selection of Knowledge to solve Open Book Question Answering. ACL 2019.

  • Arindam Mitra, Peter Clark, Oyvind Tafjord and Chitta Baral. Declarative Question Answering over Knowledge Bases containing Natural Language Text with Answer Set Programming. AAAI 2019.

  • Arindam Mitra and Chitta Baral. Learning to use formulas to solve simple arithmetic problems. ACL 2016.

  • Arindam Mitra and Chitta Baral. Addressing a Question Answering Challenge by Combining Statistical Methods with Inductive Rule Learning and Reasoning. AAAI 2016.

  • Arpit Sharma, Somak Aditya, Vo Nguyen and Chitta Baral. Towards Addressing the Winograd Schema Challenge - Building and Using a Semantic Parser and a Knowledge Hunting Module. IJCAI 2015.

  Information Retrieval (IR)+ Question Answering, Neural IR, Knowledge Hunting [Logic-Actions] [Bias-Fairness] [Instructions;Prompts] [Data-Smart] [Robustness;Self-aware] [Task-focussed] [Symbolic-Semantic-Parsing] [Knowledge-Reasoning-NLI-NeuroSymbolic] [IR+QA;Knowledge Hunting] [Information Extraction; NER] [Datasets;Systems] [Application-Cybersecurity] [Application-BioMed] [Application-Robotics] [Other]
  • Man Luo, Zhiyuan Fang, Tejas Gokhale, Yezhou Yang and Chitta Baral. End-to-end Knowledge Retrieval with Multi-modal Queries. ACL 2023

  • Man Luo, Shashank Jain, Anchit Gupta, Arash Einolghozati, Barlas Oguz, Debojeet Chatterjee, Xilun Chen, Chitta Baral and Peyman Heidari. A Study on the Efficiency and Generalization of Light Hybrid Retrievers. ACL 2023.

  • Man Luo, Arindam Mitra, Tejas Gokhale, and Chitta Baral. Improving Biomedical Information Retrieval with Neural Retrievers. AAAI 2022.

  • Ashok Prakash, Arpit Sharma, Arindam Mitra and Chitta Baral. Combining Knowledge Hunting and Neural Language Models to Solve the Winograd Schema Challenge. ACL 2019.

  • Arpit Sharma, Somak Aditya, Vo Nguyen and Chitta Baral. Towards Addressing the Winograd Schema Challenge - Building and Using a Semantic Parser and a Knowledge Hunting Module. IJCAI 2015.

  Information extraction, NER [Logic-Actions] [Bias-Fairness] [Instructions;Prompts] [Data-Smart] [Robustness;Self-aware] [Task-focussed] [Symbolic-Semantic-Parsing] [Knowledge-Reasoning-NLI-NeuroSymbolic] [IR+QA;Knowledge Hunting] [Information Extraction; NER] [Datasets;Systems] [Application-Cybersecurity] [Application-BioMed] [Application-Robotics] [Other]

  • Pratyay Banerjee, Kuntal Kumar Pal, Murthy Devarakonda, Chitta Baral. Bio-Medical Named Entity Recognition via Knowledge Guidance and Question Answering. ACM Transactions on Computing for Healthcare. 2021.

  • Somak Aditya, Chitta Baral, Nguyen Ha Vo, Joohyung Lee, Jieping Ye, Zaw Naung, Barry Lumpkin, Jenny Hastings, Richard Scherl, Dawn M. Sweet, Daniela Inclezan. Recognizing Social Constructs from Textual Conversation. HLT-NAACL 2015.

  • Arpit Sharma, Nguyen H. Vo, Somak Aditya and Chitta Baral. Identifying Various Kinds of Event Mentions in K-Parser Output The 3rd Workshop on EVENTS: Definition, Detection, Coreference, and Representation. HLT-NAACL 2015.

  • Earlier work on information extraction the in Biomedical domain.

  Challenges & Datasets; Visualization; NLP Systems [Logic-Actions] [Bias-Fairness] [Instructions;Prompts] [Data-Smart] [Robustness;Self-aware] [Task-focussed] [Symbolic-Semantic-Parsing] [Knowledge-Reasoning-NLI-NeuroSymbolic] [IR+QA;Knowledge Hunting] [Information Extraction; NER] [Datasets;Systems] [Application-Cybersecurity] [Application-BioMed] [Application-Robotics] [Other]
  • Justin Payan, Swaroop Mishra, Mukul Singh, Carina Negreanu, Christian Poelitz, Chitta Baral, Subhro Roy, Rasika Chakravarthy, Benjamin Van Durme, Elnaz Nouri. InstructExcel: A Benchmark for Natural Language Instruction in Excel.

  • Himanshu Gupta, Neeraj Varshney, Swaroop Mishra, Kuntal Kumar Pal, Saurabh Arjun Sawant, Kevin Joseph Scaria, Siddharth Goyal and Chitta Baral. "John is 50 years old, can his son be 65?" Evaluating NLP Models' Understanding of Feasibility. EACL 2023.

  • Anjana Arunkumar, Swaroop Mishra, Bhavdeep Singh Sachdeva, Chitta Baral and Chris Bryan. Real-Time Visual Feedback to Guide Benchmark Creation: A Human-and-Metric-in-the-Loop Workflow. EACL 2023.

  • Swaroop Mishra, Matthew Finlayson, Pan Lu, Leonard Tang, Sean Welleck, Chitta Baral, Tanmay Rajpurohit, Oyvind Tafjord, Ashish Sabharwal, Peter Clark and Ashwin K. Kalyan. LILA: A Unified Benchmark for Mathematical Reasoning. EMNLP 2022.

  • Swaroop Mishra, Arindam Mitra, Neeraj Varshney, Bhavdeep Singh Sachdeva, Peter Clark, Chitta Baral, Ashwin Kalyan. NumGLUE: A Suite of Fundamental yet Challenging Mathematical Reasoning Tasks. ACL 2022.

  • Arpit Sharma, Somak Aditya, Vo Nguyen and Chitta Baral. Towards Addressing the Winograd Schema Challenge - Building and Using a Semantic Parser and a Knowledge Hunting Module. IJCAI 2015.

  • Vo Nguyen, Arindam Mitra and Chitta Baral. The NL2KR platform for building Natural Language Translation Systems. ACL 2015.

  • The Kparser System. About Kparser - from archive.

  • The NL2KR System . Archived NL2KR site; A short user manual type paper.

  NLP Applications to Cybersecurity [Logic-Actions] [Bias-Fairness] [Instructions;Prompts] [Data-Smart] [Robustness;Self-aware] [Task-focussed] [Symbolic-Semantic-Parsing] [Knowledge-Reasoning-NLI-NeuroSymbolic] [IR+QA;Knowledge Hunting] [Information Extraction; NER] [Datasets;Systems] [Application-Cybersecurity] [Application-BioMed] [Application-Robotics] [Other]
  • Kuntal Kumar Pal, Ati Priya Bajaj, Pratyay Banerjee, Audrey Dutcher, Mutsumi Nakamura, Zion Leonahenahe Basque, Himanshu Gupta, Saurabh Arjun Sawant, Ujjwala Anantheswaran, Yan Shoshitaishvili, Adam Doupe, Chitta Baral, Ruoyu Wang. Len or index or count, anything but v1: Predicting Variable Names in Decompilation Output with Transfer Learning. IEEE Symposium on Security and Privacy 2024.

  • Garima Agrawal, Kuntal Pal, Yuli Deng, Huan Liu, and Chitta Baral. AISecKG: Knowledge Graph Dataset for Cybersecurity Education. AAAI-MAKE 2023: Challenges Requiring the Combination of Machine Learning 2023 (2023).

  • Kuntal Kumar Pal, Kazuaki Kashihara, Ujjwala Anantheswaran, Kirby C. Kuznia, Siddhesh Jagtap and Chitta Baral. Exploring the Limits of Transfer Learning with Unified model in the Cybersecurity Domain. 2023.

  • Kuntal Kumar Pal, Kazuaki Kashihara, Pratyay Banerjee, Swaroop Mishra, Ruoyu Wang and Chitta Baral. Constructing Flow Graphs from Procedural Cyber Security Text. Findings of ACL. 2021.

  NLP Applications to Biomedical domains [Logic-Actions] [Bias-Fairness] [Instructions;Prompts] [Data-Smart] [Robustness;Self-aware] [Task-focussed] [Symbolic-Semantic-Parsing] [Knowledge-Reasoning-NLI-NeuroSymbolic] [IR+QA;Knowledge Hunting] [Information Extraction; NER] [Datasets;Systems] [Application-Cybersecurity] [Application-BioMed] [Application-Robotics] [Other]
  • Mihir Parmar, Swaroop Mishra, Mirali Purohit, Man Luo, M. Hassan Murad, Chitta Baral. In-BoXBART: Get Instructions into Biomedical Multi-Task Learning. Findings of NAACL 2022.

  • Man Luo, Arindam Mitra, Tejas Gokhale, and Chitta Baral. Improving Biomedical Information Retrieval with Neural Retrievers. AAAI 2022.

  • Pratyay Banerjee, Kuntal Kumar Pal, Murthy Devarakonda, Chitta Baral. Bio-Medical Named Entity Recognition via Knowledge Guidance and Question Answering. ACM Transactions on Computing for Healthcare. 2021.

  • Earlier work on NLP applications in Biology.

  NLP Applications to Robotics [Logic-Actions] [Bias-Fairness] [Instructions;Prompts] [Data-Smart] [Robustness;Self-aware] [Task-focussed] [Symbolic-Semantic-Parsing] [Knowledge-Reasoning-NLI-NeuroSymbolic] [IR+QA;Knowledge Hunting] [Information Extraction; NER] [Datasets;Systems] [Application-Cybersecurity] [Application-BioMed] [Application-Robotics] [Other]
  • Divyanshu Raj, Omkar Patil, Weiwei Gu, Chitta Baral, Nakul Gopalan. Learning Temporally Composable Task Segmentations with Language. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024.

  • Simon Stepputtis, Joseph Campbell, Mariano Phielipp, Stefan Lee, Chitta Baral and Heni Ben Amor. Language-Conditioned Imitation Learning for Robot Manipulation Tasks. NeuRIPS 2020.

  • Juraj Dzifcak, Matthias Scheutz, Chitta Baral and Paul Schermerhorn (2009) What to do and how to do it: Translating natural language directives into temporal and dynamic logic representation for goal management and action execution. Proceedings of the 2009 IEEE international conference on robotics and automation (ICRA '09)

  Other aspects of NLP [Logic-Actions] [Bias-Fairness] [Instructions;Prompts] [Data-Smart] [Robustness;Self-aware] [Task-focussed] [Symbolic-Semantic-Parsing] [Knowledge-Reasoning-NLI-NeuroSymbolic] [IR+QA;Knowledge Hunting] [Information Extraction; NER] [Datasets;Systems] [Application-Cybersecurity] [Application-BioMed] [Application-Robotics] [Other]
  • Papers on vision and language. [KR-NLP-IR-VQA-Vision]

  [Top] [Book] [NLP-IR-QA] [Multi-agent] [KR-NLP-IR-VQA-Vision] [Human-Robot-C] [Machine-Learning] [QA,Frames] [Bioinformatics] [Logic+Prob] [Goal Languages] [Action-Change] [Answer Set] [LP-Nonmon] [KBs]

• Multi-agent reasoning about actions and change; Impact on the real world, and on agents' knowledge and beliefs
  • Chitta Baral, Gregory Gelfond, Enrico Pontelli, Tran Cao Son. An Action Language for Multi-Agent Domains. AI journal. January 2022.

  • Chitta Baral, Thomas Bolander, Hans van Ditmarsch, and Sheila McIlraith. Epistemic Planning. Report from Dagstuhl Seminar 17231, June 5-9, 2017.

  • Chitta Baral, Gregory Gelfond, Enrico Pontelli and Tran Cao Son. Multi-Agent Action Modeling through Action Sequences and Perspective Fluents. CommonSense 2015, AAAI Spring Symposium 2015.

  • Tran Cao Son, Enrico Pontelli, Chitta Baral and Gregory Gelfond. Exploring the KD45n property of a Kripke model after the execution of an action sequence. AAAI 2015.

  • Tran Cao Son, Enrico Pontelli, Chitta Baral and Gregory Gelfond. Finitary S5-Theories. In JELIA 2014. pages 239-252.

  • Chitta Baral, Gregory Gelfond, Enrico Pontelli and Tran Cao Son. Reasoning About the Beliefs of Agents in Multi-Agent Domains in the Presence of State Constraints: The Action Language mAL. CLIMA 2013.

  • Chitta Baral, Gregory Gelfond, Enrico Pontelli and Tran Cao Son. An Action Language for Reasoning about Beliefs in Multi-Agent Domains. NMR 2012.

  • Enrico Pontelli, Tran Cao Son, Chitta Baral and Gregory Gelfond. Answer Set Programming and Planning with Knowledge and World-Altering Actions in Multiple Agent Domains. Correct Reasoning 2012: 509-526

  • Chitta Baral and Gregory Gelfond. On Representing Actions in Multi-Agent Domains. Lecture Notes in Computer Science, 2011, Volume 6565/2011, 213-232, Springer, 2011.

  • Chitta Baral, Gregory Gelfond, Tran Cao Son, Enrico Pontelli: Using answer set programming to model multi-agent scenarios involving agents' knowledge about other's knowledge. AAMAS 2010: 259-266.

  • Chitta Baral, Gregory Gelfond, Enrico Pontelli, Tran Cao Son: Logic programming for finding models in the logics of knowledge and its applications: A case study. Theory and Practice of Logic Programming. 10(4-6): 675-690 (2010)

  • Chitta Baral. Reasoning about Actions and Change: From Single Agent Actions to Multi-Agent Actions (Extended Abstract). Invited Talk at KR 2010

  • Chitta Baral, Tran Cao Son, and Enrico Pontelli. Reasoning about Multi-agent Domains Using Action Language C: A Preliminary Study. NRAC 2009 version. CLIMA 2010 version.

  [Top] [Book] [NLP-IR-QA] [Multi-agent] [KR-NLP-IR-VQA-Vision] [Human-Robot-C] [Machine-Learning] [QA,Frames] [Bioinformatics] [Logic+Prob] [Goal Languages] [Action-Change] [Answer Set] [LP-Nonmon] [KBs]

• Knowledge representation (and logic) meets Computer Vision meets NLP
  Text2Image; Compositionality [Text2Image] [VQA-Robustness] [V-n-L-Data-Smart] [VQA-Self-Supervision] [Image-Understanding-Knowledge-Reasoning-Commonsense-NeuroSymbolic] [VLQA-Inference]
  • Maitreya Patel, Naga Sai Abhiram kusumba, Sheng Cheng, Changhoon Kim, Tejas Gokhale, Chitta Baral, Yezhou Yang. TripletCLIP: Improving Compositional Reasoning of CLIP via Vision-Language Negatives. Accepted to NeuRIPs 2024.

  • Agneet Chatterjee, Gabriela Ben Melech Stan, Estelle Guez Aflalo, Sayak Paul, Dhruba Ghosh, Tejas Gokhale, Ludwig Schmidt, Hanna Hajishirzi, Vasudev Lal, Chitta Baral, Yezhou Yang. Getting it Right: Improving Spatial Consistency in Text-to-Image Models. ECCV 2024.

  • Agneet Chatterjee, Yiran Luo, Tejas Gokhale, Yezhou Yang, Chitta Baral. REVISION: Rendering Tools Enable Spatial Fidelity in Vision-Language Models. ECCV 2024.

  • Michael Saxon, Yiran Lawrence Luo, Sharon Levy, Chitta Baral, Yezhou Yang, William Yang Wang. Lost in Translation? Translation Errors and Challenges for Fair Assessment of Text-to-Image Models on Multilingual Concepts. NAACL 2024.

  • Maitreya Patel, Tejas Gokhale, Chitta Baral, Yezhou Yang.. ConceptBed: Evaluating Concept Learning Abilities of Text-to-Image Diffusion Models. AAAI 2024.

  • Maitreya Patel, Changhoon Kim, Sheng Cheng, Chitta Baral, Yezhou Yang. ECLIPSE: A Resource-Efficient Text-to-Image Prior for Image Generations. CVPR 2024

    Generalizability, Robustness, Adversarial training in VQA and other Vision and Language domains [Text2Image] [VQA-Robustness] [V-n-L-Data-Smart] [VQA-Self-Supervision] [Image-Understanding-Knowledge-Reasoning-Commonsense-NeuroSymbolic] [VLQA-Inference]

  • Agneet Chatterjee, Tejas Gokhale, Chitta Baral, Yezhou Yang. On the Robustness of Language Guidance for Low-Level Vision Tasks: Findings from Depth Estimation. CVPR 2024

  • Tejas Gokhale, Rushil Anirudh, Jayaraman J. Thiagarajan, Bhavya Kailkhura, Chitta Baral, Yezhou Yang; Improving Diversity With Adversarially Learned Transformations for Domain Generalization. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023, pp. 434-443

  • Tejas Gokhale, Abhishek Chaudhary, Pratyay Banerjee, Chitta Baral, Yezhou Yang. Semantically Distributed Robust Optimization for Vision-and-Language Inference. Findings of ACL 2022.

  • Tejas Gokhale, Rushil Anirudh, Bhavya Kailkhura, Jayaraman J. Thiagarajan, Chitta Baral and Yezhou Yang. Attribute-Guided Adversarial Training for Robustness to Natural Perturbations. AAAI 2021.

  • Tejas Gokhale, Pratyay Banerjee, Chitta Baral and Yezhou Yang. MUTANT: A Training Paradigm for Out-of-Distribution Generalization in Visual Question Answering. EMNLP 2020.

  • Tejas Gokhale, Pratyay Banerjee, Chitta Baral, Yezhou Yang. VQA-LOL: Visual Question Answering under the Lens of Logic. ECCV 2020.

  Judicious use of Data and Evaluation mechanisms in Vision and Language domains [Text2Image] [VQA-Robustness] [V-n-L-Data-Smart] [VQA-Self-Supervision] [Image-Understanding-Knowledge-Reasoning-Commonsense-NeuroSymbolic] [VLQA-Inference]
  • Yiran Luo, Pratyay Banerjee, Tejas Gokhale, Yezhou Yang, Chitta Baral. To Find Waldo You Need Contextual Cues: Debiasing Who's Waldo. ACL 2022.

  • Man Luo, Shailaja Sampat, Riley Tallman, Yankai Zeng, Manuha Vancha, Akarshan Sajja, and Chitta Baral. 'Just because you are right, doesn't mean I am wrong': Overcoming a Bottleneck in Development and Evaluation of Open-Ended Visual Question Answering (VQA) Tasks. EACL 2021.

  Task-specific self-supervision in VQA and other vision and language domains [Text2Image] [VQA-Robustness] [V-n-L-Data-Smart] [VQA-Self-Supervision] [Image-Understanding-Knowledge-Reasoning-Commonsense-NeuroSymbolic] [VLQA-Inference]
  • Pratyay Banerjee, Shweti Mahajan, Kushal Arora, Chitta Baral and Oriana Riva. Lexi: Self-Supervised Learning of the UI Language. Findings of EMNLP 2022.

  • Man Luo, Yankai Zeng, Pratyay Banerjee and Chitta Baral. Weakly-Supervised Visual-Retriever-Reader for Knowledge-based Question Answering. EMNLP 2021.

  • Pratyay Banerjee, Tejas Gokhale, Yezhou Yang and Chitta Baral. Weakly Supervised Relative Spatial Reasoning for Visual Question Answering. ICCV 2021.

  • Pratyay Banerjee, Tejas Gokhale, Yezhou Yang and Chitta Baral. WeaQA: Weak Supervision via Captions for Visual Question Answering. Findings of ACL. 2021.

  Knowledge representation, NLP, Retrieval and Reasoning in Computer Vision and Image Understanding; Visual common-sense for scene understanding; Semantic representation of images/videos; Neuro-symbolic approaches [Text2Image] [VQA-Robustness] [V-n-L-Data-Smart] [VQA-Self-Supervision] [Image-Understanding-Knowledge-Reasoning-Commonsense-NeuroSymbolic] [VLQA-Inference]
  • Zhiyuan Fang, Tejas Gokhale, Pratyay Banerjee, Chitta Baral and Yezhou Yang. Video2Commonsense: Generating Commonsense Descriptions to Enrich Video Captioning. EMNLP 2020.

  • Somak Aditya, Yezhou Yang, Chitta Baral. Integrating Knowledge and Reasoning in Image Understanding. IJCAI 2019.

  • Somak Aditya, Rudra Saha, Yezhou Yang and Chitta Baral. Spatial Knowledge Distillation to aid Visual Reasoning. WACV 2019.

  • Somak Aditya, Yezhou Yang, Chitta Baral. Explicit Reasoning over End-to-End Neural Architectures for Visual Question Answering. AAAI 2018.

  • Somak Aditya, Yezhou Yang, Chitta Baral and Yiannis Aloimonos. Combining Knowledge and Reasoning through Probabilistic Soft Logic for Image Puzzle Solving. UAI 2018.

  • Somak Aditya, Yezhou Yang, Chitta Baral, Yiannis Aloimonos and Cornelia Fermuller. Image Understanding using Vision and Reasoning through Scene Description Graph. Computer Vision and Image Understanding Journal. 2018.

  • Somak Aditya, Yezhou Yang, Chitta Baral and Yiannis Aloimonos. Answering Image Riddles using Vision and Reasoning through Probabilistic Soft Logic.. Arxiv version. 2016. Website with additional information on this work.

  • Somak Aditya, Chitta Baral, Yezhou Yang, Yiannnis Aloimonos and Cornelia Fermuller. DeepIU: An architecture for image understanding. Advances in Cognitive Systems. 2016.

  • Somak Aditya, Yezhou Yang, Chitta Baral, Cornelia Fermuller, Yiannis Aloimonos. From Images to Sentences through Scene Description Graphs using Commonsense Reasoning and Knowledge. Arxiv version.

  • Somak Aditya, Yiannis Aloimonos, Chitta Baral, Cornelia Fermuller and Yezhou Yang. Visual common-sense for scene understanding using perception, semantic parsing and reasoning. Common-sense 2015, AAAI 2015 Spring Symposium. (Appenidix with code.)

  Visio-Linguistic Question Answering (VLQA), Vision Language Inference (VLI) [Text2Image] [VQA-Robustness] [V-n-L-Data-Smart] [VQA-Self-Supervision] [Image-Understanding-Knowledge-Reasoning-Commonsense-NeuroSymbolic] [VLQA-Inference]
  • Pratyay Banerjee, Shweti Mahajan, Kushal Arora, Chitta Baral and Oriana Riva. Lexi: Self-Supervised Learning of the UI Language. Findings of EMNLP 2022.

  • Maitreya Patel, Tejas Gokhale, Chitta Baral and Yezhou Yang. CRIPP-VQA: Counterfactual Reasoning about Implicit Physical Properties via Video Question Answering. EMNLP 2022.

  • Shailaja Keyur Sampat, Pratyay Banerjee, Yezhou Yang and Chitta Baral. Learning Action-Effect Dynamics for Hypothetical Vision-Language Reasoning Task. Findings of EMNLP 2022.

  • Shailaja Keyur Sampat, Akshay Kumar, Yezhou Yang and Chitta Baral. CLEVR_HYP: A Dataset and Baselines for Visual Question Answering with Hypothetical Actions over Images. NAACL 2021.

  • Shailaja Keyur Sampat, Yezhou Yang and Chitta Baral. Visuo-Lingustic Question Answering (VLQA) Challenge. Findings of EMNLP 2020.

  • Tejas Gokhale, Abhishek Chaudhary, Pratyay Banerjee, Chitta Baral, Yezhou Yang. Semantically Distributed Robust Optimization for Vision-and-Language Inference. Findings of ACL 2022.

  [Top] [Book] [NLP-IR-QA] [Multi-agent] [KR-NLP-IR-VQA-Vision] [Human-Robot-C] [Machine-Learning] [QA,Frames] [Bioinformatics] [Logic+Prob] [Goal Languages] [Action-Change] [Answer Set] [LP-Nonmon] [KBs]

• Human Robot Collaboration; Human Robot Communication; Human Robot Interaction
  • Simon Stepputtis, Joseph Campbell, Mariano Phielipp, Stefan Lee, Chitta Baral and Heni Ben Amor. Language-Conditioned Imitation Learning for Robot Manipulation Tasks. NeuRIPS 2020.

  • Simon Stepputtis, Joseph Campbell, Mariano Phielipp, Chitta Baral, and Heni Ben Amor. Imitation Learning of Robot Policies by Combining Language, Vision and Demonstration. NeurIPS Workshop on Robot Learning 2019.

  • Simon Stepputtis, Chitta Baral, Heni Ben Amor. Speech Enhanced Imitation Learning and Task Abstraction for Human-Robot Interaction. IROS 2017 Workshop on SBLI (Synergies between learning and interaction.)

  • Chitta Baral, Barry Lumpkin, and Matthias Scheutz. A High Level Language for Human-Robot Interaction . Advances in Cognitive Systems, 2017.

  • Juraj Dzifcak, Matthias Scheutz, Chitta Baral and Paul Schermerhorn (2009) What to do and how to do it: Translating natural language directives into temporal and dynamic logic representation for goal management and action execution. Proceedings of the 2009 IEEE international conference on robotics and automation (ICRA '09)

  • Related Work [Goal Languages] : Specifying goals: Rich goal languages and planning with respect to such goals.

  • Related Work [Action-Change] - Action and Change: Representation, Reasoning and Mental Simulation : [Sensing actions] [Probabilistic effects] [Planning and Control] [Agents; Robots] [Diagnosis] [Action Languages] [Modalities: Intention] [Workflow]

  [Top] [Book] [NLP-IR-QA] [Multi-agent] [KR-NLP-IR-VQA-Vision] [Human-Robot-C] [Machine-Learning] [QA,Frames] [Bioinformatics] [Logic+Prob] [Goal Languages] [Action-Change] [Answer Set] [LP-Nonmon] [KBs]

• Machine Learning; Rule Learning; Inductive Logic Programming
  • Arindam Mitra and Chitta Baral. Incremental and Iterative Learning of Answer Set Programs from Mutually Distinct Examples. TPLP 18(3-4): 623-637 (2018).

  [Top] [Book] [NLP-IR-QA] [Multi-agent] [KR-NLP-IR-VQA-Vision] [Human-Robot-C] [Machine-Learning] [QA,Frames] [Bioinformatics] [Logic+Prob] [Goal Languages] [Action-Change] [Answer Set] [LP-Nonmon] [KBs]

• Knowledge representation, Frame based reasoning, and Question Answering (especially Why/How questions) involving deep reasoning
  • Chitta Baral, Nguyen Ha Vo. Event-Object Reasoning with Curated Knowledge Bases: Deriving Missing Information. LPNMR 2013.

  • Chitta Baral, Nguyen Ha Vo, Shanshan Liang. Answering Why and How questions with respect to a frame-based knowledge base: a preliminary report. ICLP (Technical Communications) 2012: 26-36

  • Chitta Baral, Shanshan Liang. From Knowledge Represented in Frame-Based Languages to Declarative Representation and Reasoning via ASP. KR 2012

  • Chitta Baral, Shanshan Liang and Vo Nguyen. Towards deep reasoning with respect to natural language text in scientific domains. Proceedings of Deep Knowledge Representation Challenge Workshop, 2011.

  • M. Balduccini, C. Baral and Y. Lierler. Knowledge representation and Question Answering. In Handbook of Knowledge Representation, editors Vladimir Lifschitz, Frank van Harmelen and Bruce Porter, 2008.

  • Luis Tari and Chitta Baral. Using AnsProlog with Link Grammar and WordNet for QA with deep reasoning. Proc. of AAAI'05 workshop on Inference for Textual Question Answering.

  • Chita Baral, Gregory Gelfond, Michael Gelfond and Richard Scherl. Textual Inference by combining multiple logic programming paradigms. Proc. of AAAI'05 workshop on Inference for Textual Question Answering.

  [Top] [Book] [NLP-IR-QA] [Multi-agent] [KR-NLP-IR-VQA-Vision] [Human-Robot-C] [Machine-Learning] [QA,Frames] [Bioinformatics] [Logic+Prob] [Goal Languages] [Action-Change] [Answer Set] [LP-Nonmon] [KBs]

• Application of Artificial Intelligence to Molecular Biology: Interaction Extraction, Collaborative Curation, Reasoning about bio-molecular pathways, Discovering Drug-Drug interactions, and Learning interactions from data.

  Automatic Extraction and Curation. [Automatic Extraction] [Knowledge Capture] [Pathway Modeling] [Learning;Causality] [Other]

  • Pratyay Banerjee, Kuntal Kumar Pal, Murthy Devarakonda, Chitta Baral. Bio-Medical Named Entity Recognition via Knowledge Guidance and Question Answering. ACM Transactions on Computing for Healthcare. 2021.

  • Joerg Hakenberg, Dmitry Voronov, Nguyen Ha Vo, Shanshan Liang, Saadat Anwar, Barry Lumpkin, Robert Leaman, Luis Tari, Chitta Baral. A SNPshot of PubMed to associate genetic variants with drugs, diseases, and adverse reactions. Journal of Biomedical Informatics 45(5): 842-850 (2012)

  • Luis Tari, Phan Huy Tu, Joerg Hakenberg, Yi Chen, Tran Cao Son, Graciela Gonzalez, Chitta Baral. Incremental Information Extraction Using Relational Databases. IEEE Trans. Knowl. Data Eng. 24(1): 86-99 (2012). Electronically published: vol. 22, issue 99, Oct 28 2010

  • Joerg Hakenberg, Illes Solt, Domonkos Tikk, Nguyen Ha Vo, Luis Tari, Quang Long Nguyen, Chitta Baral, Ulf Leser. Molecular Event Extraction from Link Grammar Parse Trees in the BioNLP'09 Shared Task. Computational Intelligence 27(4): 665-680 (2011)

  • Luis Tari, Phan Huy Tu, Joerg Hakenberg, Yi Chen, Tran Cao Son, Graciela Gonzalez, Chitta Baral: GenerIE: Information extraction using database queries. ICDE 2010: 1121-1124.

  • J. Hakenberg, Robert Leaman, Nguyen Ha Vo, Siddhartha Jonnalagadda, Ryan Sullivan, Christopher Miller, Luis Tari, Chitta Baral, Graciela Gonzalez: Efficient Extraction of Protein-Protein Interactions from Full-Text Articles. IEEE/ACM Trans. Comput. Biology Bioinformatics. 7(3): 481-494 (2010)

  • CBioC:: Annotate while you read: a short blurb on CBioC in June 23rd, 2006 issue of Science Magazine.

  • Lian Yu, Syed Toufeeq Ahmed, Graciela Gonzalez, Brandon Logsdon, Mutsumi Nakamura, Shawn Nikkila, Kalpesh Shah, Luis Tari, Ryan Wendt, Amanda Zeigler and Chitta Baral. Genomic information retrieval through seletive extraction and tagging by the ASU-BioAI Group. Proceedings of the 2005 TREC Genomics track.

  • Prabhdeep Singh, Ravi Bhimavarapu, Hasan Davulcu, Chitta Baral, Seungchan Kim, Huan Liu, Mike Bittner and I.V. Ramakrishnan. BioLog: A Browser Based Collaboration and Resource Navigation Assistant for BioMedical Researchers. Proc. of the 2nd International Workshop on Data Integration in the Life S ciences (DILS'05), San Diego, July 20-22, 2005. 19-30.

  • Chitta Baral, Hasan Davulcu, Mutsumi Nakamura, Prabhdeep Singh, Lian Yu and Luis Tari. Collaborative Curation of Data from Bio-medical Texts and Abstracts and its integration. Proc. of the 2nd International Workshop on Data Integration in the Life Sciences (DILS'05), San Diego, July 20�22, 2005. 309-312.

  • Syed Toufeeq Ahmed, Deepthi Chidambaram, Hasan Davulcu and Chitta Baral. IntEx: A Syntactic Role Driven Protein-Protein Interaction Extractor for Bio-Medical Text. Proc. of BioLINK SIG: Linking Literature, Information and Knowledge for Biology, a Joint Meeting of The ISMB BioLINK Special Interest Group on Text Data Mining and The ACL Workshop on Linking Biological Literature, Ontologies and Databases: Mining Biological Semantics (Biolink'2005), Detroit, Michigan, June 24, 2005.

    Knowledge Capture through Automatic Extraction and Reasoning [Automatic Extraction] [Knowledge Capture] [Pathway Modeling] [Learning;Causality] [Other]

  • Luis Tari, Nguyen Vo, Shanshan Liang, Jagruti Patel, Chitta Baral, James Cai. Identifying novel drug indications through automated reasoning. To appear in PLoS ONE.

  • L. Tari, S. Anwar, S. Liang, J. Cai and C. Baral. Discovering drug-drug interactions: a text mining and reasoning approach based on properties of drug metabolism. Bioinformatics 26(18):2010. (special issue of ECCB 2010.)

  • Luis Tari, Saadat Anwar, Shanshan Liang, Joerg Hakenberg, Chitta Baral. Synthesis of Pharmacokinetic Pathways through Knowledge Acquisition and Automated Reasoning. Pacific Symposium on Biocomputing 15:465-476(2010)

  • Luis Tari, Joerg Hakenberg, Graciela Gonzalez, Chitta Baral. Querying parse tree database of medline text to synthesize user-specific biomolecular networks. In PSB'09.

  • Graciela Gonzalez, Juan C. Uribe, Luis Tari, Colleen Brophy, Chitta Baral. Mining Gene-Disease Relationships from Biomedical Literature: Weighting Protein-protein Interactions and Connectivity. Pacific Symposium on Biocomputing 12:28-39(2007)

    Modeling Pathways and Networks and Reasoning [Automatic Extraction] [Knowledge Capture] [Pathway Modeling] [Learning;Causality] [Other]

  • Deep QA - A Deep Reasoning Question Answering System.

  • Saadat Anwar and Chitta Baral. Pathway Specification and Comparative Queries: A High Level Language with Petri Net Semantics. in AAAI 2014.

  • Saadat Anwar. Representing, reasoning and answering questions about biological pathways - various applications Ph.D thesis.

  • Saadat Anwar, Chitta Baral, Katsumi Inoue. Encoding Petri Nets in Answer Set Programming for Simulation Based Reasoning. ICLP/TPLP Supplement 2013.

  • Saadat Anwar, Chitta Baral, Katsumi Inoue. Encoding Higher Level Extensions of Petri Nets in Answer Set Programming. LPNMR 2013.

  • N. Tran, C. Baral. (2007) Hypothesizing and reasoning about signaling networks. Journal of Applied Logic (In press).

  • N. Tran, C. Baral. (2007) Reasoning about non-immediate triggers in biochemical networks. Annals of Mathematics and Artificial Intelligence.

  • Nam Tran, Chitta Baral, Vinay Nagaraj and Lokesh Joshi. Knowledge-Based Framework for Hypothesis Formation in Biochemical Networks: application to the p53 network. Proc. of the European Conference on Computational Biology (ECCB'2005), Bioinforamtics, 21: ii213-ii219. (A preliminary version in the DILS'05 workshop.)

  • Nam Tran, Chitta Baral and Carron Shankland. Issues in reasoning about cellular interactions: necessity of event ordering knowledge. Proc. of AAAI'05, 676-681.

  • Carron Shankland, Nam Tran, Chitta Baral, and Walter Kolch. Reasoning about the ERK signal transduction pathway using BioSigNet-RR. In Computational Methods in Systems Biology (CMSB'05) 2005. 3-5 April 2005, Edinburgh, Scotland.

  • C. Baral, K. Chancellor, Nam Tran, Nhan Tran, A. Joy, and M. Berens. A knowledge based approach for representing and reasoning about cell signaling networks. (Abstract) In ISMB/ECCB'04.

  • N. Tran and C. Baral. Reasoning about Triggered Actions in AnsProlog and its Application to Molecular Interactions in Cells. (Abstract) In KR'2004.

    Learning Interactions from Data; Causal Learning [Automatic Extraction] [Knowledge Capture] [Pathway Modeling] [Learning;Causality] [Other]

  • Xin Zhang, Seungchan Kim, Tie Wang and Chitta Baral. Joint learning of logic relationships for studying protein function using phylogenetic profiles and the Rosetta Stone method. In IEEE Transactions on Signal Processing.

  • Xin Zhang, Chitta Baral, Seungchan Kim. An algorithm to learn causal connection between genes from steady state data: simulation and its application to melanoma dataset. Proc. of 10th Conference on Artificial Intelligence in Medicine (AIME 05) 23 - 27 July 2005 Aberdeen, Scotland. pages 524-534.

    Other [Automatic Extraction] [Knowledge Capture] [Pathway Modeling] [Learning;Causality] [Other]

  • L. Tari, C. Baral and Partha Dasgupta. Understanding the global properties of functionally related gene networks using the gene ontology. In Proceedings of Pacific Symposium on Biocomputing 2005. (PSB'05), pages 209-220.

  [Top] [Book] [NLP-IR-QA] [Multi-agent] [KR-NLP-IR-VQA-Vision] [Human-Robot-C] [Machine-Learning] [QA,Frames] [Bioinformatics] [Logic+Prob] [Goal Languages] [Action-Change] [Answer Set] [LP-Nonmon] [KBs]

• Combining logical and probabilistic knowledge representation
  • Chitta Baral. Logic programming and uncertainty. Invited paper. Fifth International Conference on Sacalable Uncertainty Management. 2011.

  • Chitta Baral, Michael Gelfond and Nelson Rushton. Probabilistic Reasoning with Answer Sets. In Theory and Practice of Logic Programming. 2009

  • Chitta Baral, Matt Hunsaker. Using the Probabilistic Logic Programming Language P-log for Causal and Counterfactual Reasoning and Non-Naive Conditioning. In Proc. of IJCAI 2007. (abstract) pages 243-249.

  • C. Baral, M. Gelfond and N. Rushton. Probabilistic reasoning with answer sets. ( Abstract) In Proceedings of LPNMR7, 2004. (A slightly revised version.)

  • See also the papers here.

  [Top] [Book] [NLP-IR-QA] [Multi-agent] [KR-NLP-IR-VQA-Vision] [Human-Robot-C] [Machine-Learning] [QA,Frames] [Bioinformatics] [Logic+Prob] [Goal Languages] [Action-Change] [Answer Set] [LP-Nonmon] [KBs]

• Specifying goals: Rich goal languages and planning with respect to such goals.
  • Tran Son, Enrico Pontelli and Chitta Baral. A Non-Monotonic Goal Specification Language for Planning with Preferences. In Proc. of 6th Multidisciplinary workshop on advances in preference handling.

  • Chitta Baral, Jicheng Zhao. Non-monotonic Temporal Logics that Facilitate Elaboration Tolerant Revision of Goals. AAAI 2008, pages 406-411.

  • C. Baral, T. Eiter, M. Bjaereland and M. Nakamura. Maintenance goals of agents in a dynamic environment: formulation and policy construction. AI Journal. 172 (12-13), pages 1429-1469. 2008.

  • Chitta Baral, Jicheng Zhao. Non-monotonic Temporal Logics for Goal Specification. In Proc. of IJCAI 2007. (abstract) pages 236-242.

  • Chitta Baral and Jicheng Zhao. Goal specification, non-determinism and quantifying over policies. In AAAI'06.

  • Chitta Baral, Thomas Eiter and Jicheng Zhao. Using SAT and Logic Programming to Design Polynomial-Time Algorithms for Planning in Non-deterministic Domains. Proc. of AAAI'05, 575-583.

  • C. Baral and Jicheng Zhao. Goal specification in presence of non-deterministic actions. ( Abstract ), In proceedings of ECAI'04, pages 273-277.

  • C. Baral and T. Eiter. A Polynomial time algorithm for constructing k-maintainable policies. (Abstract), (Slides of a talk at UT Austin) In ICAPS'2004.

  • C. Baral, V. Kreinovich and R. Trejo. Computational Complexity of Planning with Temporal Goals. (Abstract) Version in IJCAI 2001 , 509--514. Extended version 1. (S. Sarkar is a co-author in this version.) A further revised and extended version. (S. Sarkar, X. Zhang, and N. Tran are additional co-authors in this version.) 5/31/03

  • Mutsumi Nakamura, Chitta Baral and Marcus Bjareland. Maintainability: a weaker stabilizability like notion for high level control. ( Abstract. ) In AAAI 2000, pgs 62-67. (postscript). An extended version.

  [Top] [Book] [NLP-IR-QA] [Multi-agent] [KR-NLP-IR-VQA-Vision] [Human-Robot-C] [Machine-Learning] [QA,Frames] [Bioinformatics] [Logic+Prob] [Goal Languages] [Action-Change] [Answer Set] [LP-Nonmon] [KBs]

• Action and Change: Representation, Reasoning and Mental Simulation

Sensing Actions and Knowledge Change/Updates: Reasoning and Planning with them. [Sensing actions] [Probabilistic effects] [Planning and Control] [Agents; Robots] [Diagnosis] [Action Languages] [Modalities: Intention] [Workflow]

• Phan Huy Tu, Tran Cao Son, and Chitta Baral. Reasoning and Planning with Sensing Actions, Incomplete Information, and Static Causal Laws using Answer Set Programming. Theory and Practice of Logic Programming. Volume 7, Issue 4, July 2007.

• Tuan Le, Chitta Baral, Son Tran. A State-Based Regression Formulation for Domains with Sensing Actions and Incomplete Information. In Logical Methods in Computer Science (Electronic journal) Volume 2, Issue 4, 2006. (no page numbers)

• C. Baral and Y. Zhang. Knowledge updates: Semantics and complexity issues. Artificial Intelligence, 164(1-2): 209-243 (2005)

• Le-Chi Tuan, C. Baral, Xin Zhang, Tran Son. Regression With Respect to Sensing Actions and Partial States. (Abstract) In AAAI'04.

• T. Son, P. Huy and C. Baral. Planning with Sensing Actions and Incomplete Information using Logic Programming. (Abstract) In Proceedings of LPNMR7, 2004.

• C. Baral and Y. Zhang. On the Semantics of Knowledge Update. (Abstract) IJCAI 2001, 97--102.

• C. Baral, V. Kreinovich, and R. Trejo. Computational complexity of planning and approximate planning in presence of incompleteness. Artificial Intelligence Journal, 122(1-2),241-267, 2000. (Abstract.) Initial version appeared in IJCAI 99, pgs 948-953. Version in Artificial Intelligence Journal (postscript)

• C. Baral and T. Son. Formalizing sensing actions -- a transition function based approach. Artificial Intelligence Journal, 125 (1-2), pgs 19-91, Jan 2001. (Abstract.) Version in Artificial Intelligence Journal (postscript), Technical report with all the proofs. A subset of it that appears in the International logic programming Symposium (ILPS), pgs 387-401, 1997. (postscript)

  Actions with probabilistic effects [Sensing actions] [Probabilistic effects] [Planning and Control] [Agents; Robots] [Diagnosis] [Action Languages] [Modalities: Intention] [Workflow]

• Chitta Baral, Matt Hunsaker. Using the Probabilistic Logic Programming Language P-log for Causal and Counterfactual Reasoning and Non-Naive Conditioning. In Proc. of IJCAI 2007. (abstract) pages 243-249.

• Nam Tran and Chitta Baral. Encoding probabilistic causal models in probabilistic action language PAL. (Abstract) In AAAI'04.

• C. Baral, Nam Tran and L. Tuan. Reasoning about actions in a probabilistic setting. (Abstract) Version in AAAI'02, pages 507-512.

• Raul Trejo, Vladik Kreinovich and Chitta Baral. Towards feasible approach to plan checking under probabilistic uncertainty. (Abstract.) In AAAI 2000, pgs 545-550. (postscript)

  Planning and Control; Use of domain knowledge; Cognitive Robotics [Sensing actions] [Probabilistic effects] [Planning and Control] [Agents; Robots] [Diagnosis] [Action Languages] [Modalities: Intention] [Workflow]

• Tran Cao Son, Chitta Baral, Sheila McIlraith, and Nam Tran. Domain-Dependent Knowledge in Answer Set Planning. ACM Transactions on Computational Logic. Volume 7, Number 4 (October 2006), pages 1-70.

• Tran Son, Chitta Baral, and Le-Chi Tuan. Adding Time and Intervals to Procedural and Hierarchical Control Specifications. (Abstract) In AAAI'04.

• C. Baral and Tran Son. Extending ConGolog to allow partial ordering. (Abstract.) In Proceedings of ATAL (Agent theories, architectures and Languages) 99, pgs 188-204. (Awarded one of the two best paper awards.)

• C. Baral, L. Tuan, R. Trejo and V. Kreinovich. Computational Complexity of Planning Based on Partial Information About The System's Present and Past States. (Abstract.) In First International Conference on Computational Logic (KR track) CL'2000, pgs 882-896. (postscript)

  Agent control and Robotics [Sensing actions] [Probabilistic effects] [Planning and Control] [Agents; Robots] [Diagnosis] [Action Languages] [Modalities: Intention] [Workflow]

• C. Baral and M. Gelfond. Reasoning agents in dynamic domains. (Abstract.) In ``Logic based AI'' (postscript). Editor J. Minker. Kluwer Academic Publishers. Pages 257-279, 2000.

• C. Baral and T. Son. Relating theories of actions and reactive control In ETAI (Electronic transactions of AI), 2(3-4):211-271, 1998. (Abstract.) The initial version. (postscript) Shortened and revised version (postscript) The ETAI version.

• C. Baral, L. Floriano, A. Hardesty, D. Morales, M. Nogueira, and T.C. Son. From theory to practice: the UTEP robot in AAAI 96 and 97 robot contests. (Abstract. ) In Proc. of the second international conference on automated agents (Agents 98), 32-38. (in postscript)

  Diagnosis [Sensing actions] [Probabilistic effects] [Planning and Control] [Agents; Robots] [Diagnosis] [Action Languages] [Modalities: Intention] [Workflow]

• Chitta Baral, Sheila McIlraith, and Tran Cao Son. Formulating diagnostic problem solving using an action language with narratives and sensing. ( Abstract.) In KR 2000, pgs 311-322. (postscript)

  Action Languages and reasoning about actions [Sensing actions] [Probabilistic effects] [Planning and Control] [Agents; Robots] [Diagnosis] [Action Languages] [Modalities: Intention] [Workflow]

• Chitta Baral, Juraj Dzifcak, Nam Tran and Jicheng Zhao. Reasoning about Actions in Biophysical Systems. AAAI 2006 Workshop on Cognitive Robotics.

• C. Baral and M. Gelfond. Logic Programming and Reasoning about actions. In Handbook of Temporal reasoning in AI. Michael Fisher, Dov Gabbay, Lluis Vila editor. Elsevier Publications, 2005. pages 389-428.

• C. Baral, T. Son and L. Tuan. A transition function based characterization of actions with delayed and continuous effects. (Abstract) In Proc. of KR'02, pgs 291-302.

• C. Baral, A. Gabaldon and A. Provetti. Formalizing narratives using nested circumscription. In Artificial Intelligence journal, 104/1-2, pages 107-164, Sept 1998. (Abstract.) Version that appears in the Artificial Intelligence journal. The AAAI 96 version, pgs 652-657.

• Chitta Baral. Embedding revision programs in logic programming situation calculus. In Journal of Logic Programming, vol 30(1), pgs 83-97, Jan 1997. (Abstract.) The version in Journal of Logic Programming. (postscript)

• Chitta Baral and Jorge Lobo. Defeasible specifications in action theories. (Abstract.) IJCAI 97, pgs 1441-1446. (postscript)

• C. Baral, M. Gelfond, and A. Provetti. Representing Actions: Laws, Observation and Hypothesis. In Journal of Logic Programming, Vol 31(1-3), 201-243, 1997. (Abstract.) The version in Journal of Logic Programming. (postscript)

• C. Baral, and M. Gelfond. Reasoning about Effects of Concurrent Actions. In Journal of Logic Programming, vol 31(1-3), pgs 85-117, 1997. (Abstract.) The version in Journal of Logic Programming. (postscript) (A thoroughly revised version of an IJCAI 93 paper.)

• Reasoning about actions: non-deterministic effects, constraints, and qualification. Chitta Baral. (Abstract.) IJCAI 95, pgs 2017-2023. (postscript)

  Other Modalities: Intentions [Sensing actions] [Probabilistic effects] [Planning and Control] [Agents; Robots] [Diagnosis] [Action Languages] [Modalities: Intention] [Workflow]

• Chitta Baral and Michael Gelfond. Reasoning about intended actions. Proc. of AAAI'05, 689-694.

  Other applications: Workflows, active databases [Sensing actions] [Probabilistic effects] [Planning and Control] [Agents; Robots] [Diagnosis] [Action Languages] [Modalities: Intention] [Workflow]

• Goce Trajcevski, Chitta Baral and Jorge Lobo. Formalizing (and Reasoning About) the Specifications of Workflows. (Abstract.) In Proceedings of the Fifth IFCIS International conference on Cooperative Information Systems (CoopIS'2000). Awarded one of the best paper awards.

• M. Nakamura and C. Baral. Invariance, Maintenance and other declarative objectives of triggers -- a formal characterization of active databases. (Abstract.) In First International Conference on Computational Logic (DOOD track) CL'2000, pgs 1210-1224. (postscript)

• C. Baral, and J. Lobo. Formalizing Active Databases. (Abstract.) The version in LIDS (Logic in Databases) 96, pgs 175-195, LNCS 1154. (postscript)

[Top] [Book] [NLP-IR-QA] [Multi-agent] [KR-NLP-IR-VQA-Vision] [Human-Robot-C] [Machine-Learning] [QA,Frames] [Bioinformatics] [Logic+Prob] [Goal Languages] [Action-Change] [Answer Set] [LP-Nonmon] [KBs]

• Answer Set Programming; Logic Programming; Rules based languages

• Enrico Pontelli, Chitta Baral and Tran Son. A Framework for Composition and Interoperation of Rules in the Semantic Web. In Proceedings of Rules and Rule Markup languages for the Semantic Web. (RuleML 2006). pages 39-50. (extended version with additional co-author Omar Elkhatib)

• Chitta Baral, Juraj Dzifcak and Hiro Takahashi. Macros, Macro calls and use of ensembles in modular answer set programming. In ICLP'06.

• C. Baral and M. Gelfond. Logic Programming and Reasoning about actions. In Handbook of Temporal reasoning in AI. Michael Fisher, Dov Gabbay, Lluis Vila editor. Elsevier Publications, 2005. pages 389-428.

• Guray Alsac and Chitta Baral. Reasoning in description logics using declarative logic programming. (Abstract) ASU Technical Report 2001-02. (Some of it appears as chapter 6.6.7 in my book )

• Chitta Baral and Cenk Uyan. Declarative specification and solution of combinatorial auctions using logic programming (Abstract) In Proc. of LPNMR'01, pgs 186-199.

• Chitta Baral, Michael Gelfond and Olga Kosheleva. Expanding queries to incomplete databases by interpolating general logic programs. In Journal of Logic programming, vol 35, pgs 195-230, 1998. (Abstract.) The version in Journal of Logic programming. (postscript)

• Chitta Baral and Michael Gelfond. Logic programming and knowledge representation. In Journal of Logic Programming, 19,20:73-148, 1994. (Abstract.) The version in Journal of Logic Programming. (postscript)

[Top] [Book] [NLP-IR-QA] [Multi-agent] [KR-NLP-IR-VQA-Vision] [Human-Robot-C] [Machine-Learning] [QA,Frames] [Bioinformatics] [Logic+Prob] [Goal Languages] [Action-Change] [Answer Set] [LP-Nonmon] [KBs]

• Non-monotonic reasoning; Abductive reasoning; Semantics of negation in Logic Programming

• C. Baral. Abductive reasoning through filtering. Artificial Intelligence Journal, 120 (1), 1-28, 2000. (Abstract.) Version in Artificial Intelligence Journal. (postscript)

• C. Baral, A. Gabaldon and A. Provetti. Value minimization in nested circumscription. In Artificial Intelligence journal, 102/2, 163-186, July 1998. (Abstract.) The version in Artificial Intelligence journal. The initial KR 96 version, pgs 474-481.

• C. Baral. Varying Selection Function to Relate Conditional Logics and Preferential Models , Fundamenta Informaticae, 21-4: 307-320, 1994.

• Chitta Baral and V. S. Subrahmanian. Duality between alternative semantics of logic programs and nonmonotonic formalisms. In Journal of automated reasoning, 10:399-420, 1993. (Abstract.) The version in Journal of automated reasoning. (postscript)

• Chitta Baral and V. S. Subrahmanian. Stable and extension class theory for logic programs and default logics. In Journal of automated reasoning, 8: 345-366, 1992. (Abstract.) The version in Journal of automated reasoning. (postscript)

[Top] [Book] [NLP-IR-QA] [Multi-agent] [KR-NLP-IR-VQA-Vision] [Human-Robot-C] [Machine-Learning] [QA,Frames] [Bioinformatics] [Logic+Prob] [Goal Languages] [Action-Change] [Answer Set] [LP-Nonmon] [KBs]

• Multiple Knowledge Bases : Combining, Merging, Communicating

• C. Baral, S. Kraus, J. Minker and V. S. Subrahmanian. Combining Default Logic Databases , International Journal of Intelligent and Cooperative Information Systems, 3,3: 319-348, 1994.

• C. Baral, S. Kraus, J. Minker and V. S. Subrahmanian. Combining Knowledge Bases Consisting of First Order Theories , Computational Intelligence, 8, 1, (1992), 45-71.

• C. Baral, J. Minker and S. Kraus. Combining Multiple Knowledge Bases , IEEE Transactions on Knowledge and Data Engineering, June 1991, volume 3, number 2, pages 208-221.

• Chitta Baral, Sarit Kraus and Jack Minker. Communicating between multiple knowledge base systems with different languages. In Working Conference on Cooperating Knowledge Based Systems, pages 121--124, England, October 1990.
  [p1], [p2], [p3], [p4], [p5], [p6], [p7], [p8], [p9], [p10], [p11], [p12], [p13], [p14], [p15], [p16], [p17], [p18].

[Top] [Book] [NLP-IR-QA] [Multi-agent] [KR-NLP-IR-VQA-Vision] [Human-Robot-C] [Machine-Learning] [QA,Frames] [Bioinformatics] [Logic+Prob] [Goal Languages] [Action-Change] [Answer Set] [LP-Nonmon] [KBs]

• Other lists of my papers.
  • Selected research results and papers (1992-2005)
  • An older list of my reports, publications -- by research areas
  • My Google Scholar listing; DBLP listing ; Computer Science Bibliography Listing
  • An old home page.

• Lecture notes and tutorial slides.
• Research Genealogy starting from thesis advisor .

A Sample of Current and Recent Sponsored Projects

• A systematic approach to reasoning about actions and change. NSF CAREER award. 1995-2001
• Reasoning and planning with sensing actions and their applications. NSF. 4/1/00 -- 3/31/05.
• Agent development and control verification using dual characterizations. NASA 01-04.
• Answering complex questions and performing deep reasoning in advance question answering systems , AQUAINT program , ARDA 04-06.
• Integrating knowledge based reasoning, common sense reasoning and natural language semantics in a QA system, DTO/IARPA (AQUAINT program). 10/1/06-12/31/07.

• Generalized Text Extraction form Life Science and Biomedicine Abstracts: empowering the CBioC Mass Collaborative Curation and Reasoning Systems. Science Foundation of Arizona, 03/01/07 - 08/28/08.

• Developing a state of the art biological interaction extraction system. Science Foundation Arizona, 06/01/08 - 05/31/09.

• Knowledge representation, reasoning, and problem solving in a cellular domain, NSF 8/1/04-7/31/09
• Integrating Machine Learning and Knowledge Representation for Discovery of Social Goals of groups. IARPA (SCIL program). 08/24/09-10/23/11.

• EAGER: Enabling collaboration in the creation of scientific databases from the published literature. NSF. 09/01/09 - 08/31/12

• Human-Robot Interaction in Littoral and Urban Military Domains: Human-Unmanned Systems Interactions. MURI award from ONR with Indiana University as the lead, 7/1/2007-12/30/2012.

• Natural Language Interaction With Systems and Agents: Acquiring Knowledge, Understanding Text, Reasoning and Responding. ONR. 01/01/2013 - 12/31/2015.

• Postdoc Best Practices in Computer Science and Engineering. CRA/NSF. $892,350 4/1/2014 - 6/30/2018

• Cognitive Processing of Combined Visual and Textual Inputs for Hard and Explainable QA. (coPI- Yezhou Yang) NSF $499,999 8/01/2018 - 7/31/2023

• CAREER: Visual Recognition with Knowledge. PI-Yezhou Yang. My role: Mentor and Senior Researcher. NSF $550,000 8/15/2018 - 7/31/2023.

• Cognitive Human Enhancements For Cyber Reasoning Systems (CHECRS). PI - Fish Wang. DARPA (CHESS program) 11/29/2018 - 5/29/2022.

• ACT-NOW: Autonomous Cognitive Technologies for Novelty in Open Worlds. DARPA (SAIL-ON program; through Tufts University) 11/15/2019 - 6/30/2023

• Doc-In-a-Box, Office of Naval Research, 5/1/2020-6/30/2021.

• Human-Assisted Cyber Reasoning Systems and Oppositional Human Factors DOD 1/1/2022-12/31/2023 PI - Yan Shoshitaishvili.

• An Active Approach for Data Engineering to Improve Vision-Language Tasks, NSF. 4/1/2022 - 3/31/2026. PI - Yezhou Yang.

Student Co-authors with thesis/project

• Tran Cao Son (Ph.D 2000), Professor, New Mexico State University. Selected Publications
• Graciela Gonzalez , (Ph.D 2000), Associate Professor, and Vice Chair, Cedars Sinai. Selected Publications.
• Raul Trejo, (Ph.D 2001; Co-advised by Vladik Kreinovich), CTO MegHabildades, Selected Publications
• Le-Chi Tuan, (Ph.D Dec 2004), Selected Publications
• Nam Tran, (Ph.D, October 2006) Selected Publications , GE Research
• Xin Zhang (Ph.D, July 2008), Google. Selected Publications
• Luis Ng Tari (Ph.D, August 2009), UpToDate. Selected Publications
• Jicheng Zhao (Ph.D, 2011), Baidu. Selected Publications
• Bob Leaman (Ph.D, December 2012, main advisor - Graciela Gonzalez), NIH. Selected Publications
• Saadat Anwar (Ph.D, May 2014). ASU. Selected Publications
• Nguyen, Vo (Ph.D, August 2015), Google. Selected Publications
• Gregory Gelfond (Ph.D, May 2018). Selected Publications ,University of Dayton Research Institute
• Somak Aditya (Ph.D, June 2018; Co-advised by Yezhou Yang), Assistant Professor, IIT Kharagpur.
• Arpit Sharma (Finished M.S in 2014, Ph.D, July 2019), Walmart Global Tech
• Arindam Mitra (Ph.D, September 2019), Microsoft
• Pratyay Banerjee (Ph.D, March 2022; GPC voted to award one of the Oustanding Graduating Doctoral Student in Computer Science Award in Spring 2022), Amazon
• Kazuaki Kashihara (Ph.D, November 2022), Design Pickle
• Swaroop Mishra (Ph.D, February 2023; CEN-CS PhD Outstanding Student Award for Spring 2023; one of the 2022-23 Dean’s Dissertation Award), Google-DeepMind
• Man Luo (Ph.D, April 2023), Mayo
• Tejas Gokhale (Ph.D, Co-advised by Yezhou Yang, April 2023), University of Maryland, Baltimore County
• Kuntal Pal (Ph.D, June 2023), JP Morgan
• Shailaja Sampat (Ph.D defended in August 2023), Fujitsu
• Neeraj Varshney (Ph.D, April 2024; SCAI Outstanding CS PhD Graduating student for the 2023-2024), Amazon
• Yiran Luo (Continuing Ph.D, co-advised with Yezhou Yang)
• Ming Shen (Continuing Ph.D)
• Mihir Parmar (Continuing Ph.D)
• Agneet Chaterjee (Continuing Ph.D, co-advised with Yezhou Yang)
• Maitreya Patel (Continuing Ph.D, co-advised with Yezhou Yang)
• Md Nayem Uddin (Continuing Ph.D, co-advised with Eduardo Blanco)
• Divij Handa (Continuing Ph.D)
• Amir Saeidi (Continuing Ph.D, co-advised with Irbaz Bin Riaz of Mayo Clinic)
• Shivam Singh (Continuing Ph.D)
• Shri Kumbhar (Continuing Ph.D)
• Venkatesh Mishra (Continuing Ph.D)
• Shanshan Liang (MS 2012). Selected Publications
• Arron Hardesty (MS 2000), Selected Publications
• David Morales (MS 1998), Selected Publications
• Amarendra Nandigam (MS 1998), Selected Publications
• Alfredo Gabaldon , (MS 1996, Ph.D at U of Toronto 2004), GE Research, Selected Publications

Curriculum Vitae (pdf)

Honors, Awards, and Professional Service

• President, KR Inc. May 2016 - October 2018.
• Mihir Parmar, Swaroop Mishra, Mor Geva and Chitta Baral. Don't Blame the Annotator: Bias Already Starts in the Annotation Instructions. EACL 2023. (Outstanding paper award)
• Associate Editor, Artificial Intelligence, 2015-2022.
• Area Editor (Non-monotonic reasoning and answer sets), ACM Transactions on Computational Logic, 2005-present.
• Tutorial at AAAI 2023 on "Advances in Neuro Symbolic Reasoning" with Paulo Shakarian, Gerardo I. Simari, and Alvaro Velasquez.
• Editorial Advisor, Theory and Practice of Logic Programming, a Cambridge University Press Journal, 2005-2019
• Associate Editor, 2007-2010 Journal of AI Research.
• Invited Plenary Panelist on the topic "The Place of Linguistics and Symbolic Structures" at NAACL 2022. Link. (With Dan Roth - Moderator, Emily M. Bender, Dilek Hakkani-Tür, and Christopher D. Manning)
• Invited Tutorial at ICLP 2019: Knowledge Representation and Reasoning Issues in Natural Language Question Answering.
• Tutorial at IJCAI 2019 (with Tran Son): The AI Universe of Actions: Agency, Causality, Commonsense and Deception
• Invited Participant, NSF Workshop on Research Challenges and Opportunities in Knowledge Representation.
• Invited Speaker/Panelist, NIH workshop on Natural language processing: State of the Art, Future Directions and Applications for Enhancing Clinical Decision-Making.
• Invited Speaker, LPNMR 2011.
• Invited Speaker, KR 2010. (slides)
• Invited Speaker, ICLP 2007.
• Invited Speaker, AAAI'05. (slides)
• Best paper award, CooPIS 2000.
• Best paper award, ATAL (Agents, theories and languages) 99.
• Research Initiation Award, National Science Foundation, 1992-1995
• CAREER Award, National Science Foundation, 1995-2001
• 3rd place finish in the AAAI 96 robot contest for office navigation.
• 1st place finish in the AAAI 97 robot contest for home vacuuming.

Highlights of contibution to Odisha and India (pdf)

Editorials and Op-eds in Indian Express: with references

• January 20th 2007: Looking for the lost keys under the street lamp: Why central universities in bac kward/tribal district clusters make sense? See also here.
• December 21st 2006: Will the HRD ministry and planning commission take steps to correct the existing regional imbalance in higher education opportunities? See also here.
• Nov 17th 2006: The Gold that keeps Orissa poor. See also here.
• July 11th and July 1st 2006: Inequity and favouritism in India's Human Resource Developement(HRD) budget allocations
  • http://www.indianexpress.com/story/7592.html
  • http://www.indianexpress.com/story/8266.html

Articles in other Indian Newspapers

• Jan 23, 2012, The Telegraph: Unknown History: Few people know about Buddhist sites in Jajpur district

Other Interests

Member, Director's Advisory Board, Indian Institute of Technology, Bhubaneswar.

Member, Higher Education Task Force, Orissa, India. (Thoughts and documents)

Blog/Compilation on Education in Orissa and India.

Blog/Compilation on infrastructure development of Orissa.

Tweets on Odisha; Facebook page on Odisha; Ornet archive.

Travel pictures: [Yellowstone, May27-29 2005]