Research
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Yujun Zhou, Jingdong Yang, Kehan Guo, Pin-Yu Chen, Tian Gao, Werner Geyer, Nuno Moniz, Nitesh V Chawla, Xiangliang Zhang. Benchmarking LLMs on Safety Issues in Scientific Labs. ICLR'25: Thirteenth International Conference on Learning Representations}. 2025 [under review]
Jiayi Ye, Yanbo Wang, Yue Huang, Dongping Chen, Qihui Zhang, Nuno Moniz, Tian Gao, Werner Geyer, Pin-Yu Chen, Nitesh V. Chawla, Xiangliang Zhang. Justice or Prejudice? Quantifying Biases in LLM-as-a-Judge. ICLR'25: Thirteenth International Conference on Learning Representations. 2025 [under review]
Deng Pan, Nuno Moniz, Nitesh Chawla. Fast Explainability via Feasible Concept Sets Generator. AAAI'25: Thirty-Ninth AAAI Conference on Artificial Intelligence. 2025 [under review]
Joe Germino, Nuno Moniz, Nitesh Chawla. Intersectional Divergence: Measuring Fairness in Regression. KDD'25: 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, ACM. 2025 [revise and resubmit]
Brenda Nogueira, Nuno Moniz, Nitesh Chawla. The Impact of Operating Ranges in Rethinking Evaluation of Compound Potency Prediction. Nature Machine Intelligence, Springer. 2024 [in preparation]
Doheon Han, Nuno Moniz, Nitesh Chawla. AnyLoss: Transforming Classification Metrics into Loss Functions. KDD'24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining}, ACM. 2024
Yihong Ma, Xiaobao Huang, Bozhao Nan, Nuno Moniz, Xiangliang Zhang, Olaf Wiest, Nitesh V. Chawla. Are we making much progress? Revisiting chemical reaction yield prediction from an imbalanced regression perspective. WWW'2024: The ACM Web Conference 2024, ACM. 2024
Joe Germino, Nuno Moniz, Nitesh Chawla. FairMOE: Counterfactually-Fair Mixture of Experts with Levels of Interpretability. Machine Learning Journal, Springer. 2024
Tânia Carvalho, Nuno Moniz, Luís Antunes. A Three-Way Knot: Privacy, Fairness, and Predictive Performance Dynamics. EPIA'23: 22nd Portuguese Conference on Artificial Intelligence, Springer. 2023 [Best Paper Award]
Yihong Ma, Yijun Tian, Nuno Moniz, Nitesh Chawla. Class-Imbalanced Learning on Graphs: A Survey. ACM Computing Surveys, 2023 [under review]
Tânia Carvalho, Nuno Moniz, Pedro Faria, and Luís Antunes. Survey on Privacy-Preserving Techniques for Data Publishing. ACM Computing Surveys, 2023
Tânia Carvalho, Nuno Moniz, Pedro Faria, Luís Antunes. Towards a Data Privacy-Predictive Performance Trade-off}. Expert Systems with Applications, Elsevier, 2023
Aníbal Silva, Rita P. Ribeiro, Nuno Moniz. Model Optimization in Imbalanced Regression. DS'22: 25th International Conference on Discovery Science, Springer. 2022
Vitor Cerqueira, Nuno Moniz, Carlos Soares. VEST: automatic feature engineering for forecasting. Machine Learning, Springer, 2021
Nuno Moniz, Hugo Monteiro. No Free Lunch in Imbalanced Learning. Knowledge-Based Systems, Elsevier, 2021
Nuno Moniz, Vitor Cerqueira. Automated Imbalanced Classification via Meta-learning, Expert Systems with Applications, Elsevier, 2021
Rita P. Ribeiro, Nuno Moniz. Imbalanced regression and extreme value prediction. Machine Learning, Springer. 2020
Mariana Oliveira, Nuno Moniz, Luís Torgo, Vítor Santos Costa. Biased Resampling Strategies for Imbalanced Spatio-Temporal Forecasting. DSAA'19: 6th IEEE International Conference on Data Science and Advanced Analytics, IEEE. 2019
Vítor Cerqueira, Fábio Pinto, Luís Torgo, Carlos Soares, Nuno Moniz. Constructive Aggregation and its Application to Forecasting with Dynamic Ensembles. ECML'2018: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2018, Springer. 2018
Nuno Moniz, Rita P. Ribeiro, Vítor Cerqueira, Nitesh Chawla. SMOTEBoost for Regression: Improving the Prediction of Extreme Values. DSAA'18: 5th IEEE International Conference on Data Science and Advanced Analytics, IEEE. 2018
Nuno Moniz, Paula Branco, Luís Torgo. Resampling Strategies for Imbalanced Time Series Forecasting. International Journal of Data Science and Analytics. Springer, 2017
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National Science Foundation
Graph Imbalanced Regression (2024) with Nitesh Chawla (University of Notre Dame). PI, $600k (submitted)
Notre Dame-IBM Tech Ethics Lab
Mitigating Ethical Risks in Large Language Models through Localized Unlearning (2024) with Josep Domingo-Ferrer (Universitat Rovira i Virgili, Tarragona, Catalonia). ND PI, $8k
How LLMs Modulate our Collective Memory and its Ethical Implications (2024) with Jasna Curkovic Nimac (Catholic University of Croatia, Zagreb, Croatia). ND PI, $10k
Ethical LLM-based Approach to Improve Early Childhood Development in Children with Cancer in LMICs (2024) with Horacio Márquez-González (Hospital Infantil de México Federico Gómez). Co-PI, $12k
Governance and Risk Assessment of Large Language Models (2024) with Michael Hind and Elizabeth Daly (IBM Research}. PI, $32k
Explainability and Interpretability in Large Language Models (2024) with Keerthiram Murugesan (IBM Research). Co-PI, $32k
Energy-Efficient Large Language Models (2024) with Payel Das and Youssef Mroueh (IBM Research). PI, $32k
University of Notre Dame
20W Experiment: Machine Ability vs Human Intelligence (2023) with Don Brower (University of Notre Dame). PI, $10k
AI for Humanity: Overcoming the Evaluation and Monitoring Gap (2023) with Nitesh Chawla (University of Notre Dame). Co-PI, $15k
TALE: Trustworthy AI Lab for Education (2023) with Alison Cheng (University of Notre Dame). Co-I, $15k
European Space Agency
PORT XXI: Space Enabled Sustainable Port Services (2020-2021). WP Leader, €200k
European Structural and Investment Funds
CONTINENTAL: Factory of the Future (2022-2024). Co-I, €10.25M
AIDA: Adaptive, Intelligent, and Distributed Assurance Platform (2020-2022). Co-I, €1.2M
Industry R&D Services and Consulting
RUTE: Randtech Update and Test Environment (2018-2020). Co-PI, €50k
UbiRider: Underground Railways Activities Recognition Using Smartphone Data (2018-2019). Co-PI, €50k
Portuguese Foundation for Science and Technology
LUCCA: AI-based Models for Lung Cancer Characterization (2023) with Tânia Pereira (Universidade de Coimbra). WP Leader, €50k
For more updated list check out Google Scholar.