Hello! I'm a first year PhD student in the School of Computer Science at Carnegie Mellon University, funded by an NSF Graduate Research Fellowship.
I previously obtained my MPhil in Advanced Computer Science on a Churchill Scholarship, where I worked with Mateja Jamnik to understand explainability through concept learning models.
I received my BS in Computer Science and Math from the University of Maryland, during which I worked with John Dickerson on fairness in rideshare, and with Jordan Boyd-Graber on entity linking algorithms.
During my summers, I worked at MIT Lincoln Labs and Facebook.
My research focuses on improving machine learning for decision making, with a particular focus on multi-agent systems.
This includes work on fairness in rideshare platforms [IJCAI'21], learning-to-defer algorithms [NeurIPS'21 WHMD], and my current work on bandits for food rescue platforms.
I'm interested in tackling the following questions:
Most recent publications on Google Scholar.
‡ indicates equal contribution.
Do Concept Bottleneck Models Obey Locality?
Naveen Raman, Mateo Espinosa, Juyeon Heo, Mateja Jamnik
NeurIPS '23 XAI: Past, Present, and Future Workshop
Human Uncertainty in Concept-Based AI Systems
Katherine M. Collins, Matthew Barker, Mateo Espinosa Zarlenga, Naveen Raman, Umang Bhatt, Mateja Jamnik, Ilia Sucholutsky, Adrian Weller, Krishnamurthy Dvijotham
AIES '23
Improving Learning-to-Defer Algorithms Through Fine-Tuning
Naveen Raman, Michael Yee
NeurIPS '21 Human and Machine Decisions Workshop
Data-Driven Methods for Balancing Fairness and Efficiency in Ride-Pooling
Naveen Raman, Sanket Shah, John Dickerson
IJCAI'21: International Joint Conference on Artificial Intelligence. 2021.
Stress and Burnout in Open Source: Toward Finding, Understanding, and Mitigating Unhealthy Interactions.
Naveen Raman, Minxuan Cao, Yulia Tsvetkov, Christian Kaestner, Bogdan Vasilescu
ICSE '20: International Conference on Software Engineering. 2020 (NIER Track)
Do Concept Bottleneck Models Obey Locality?
Naveen Raman, Mateo Espinosa, Juyeon Heo, Mateja Jamnik
NeurIPS '23 XAI: Past, Present, and Future Workshop
Human Uncertainty in Concept-Based AI Systems
Katherine M. Collins, Matthew Barker, Mateo Espinosa Zarlenga, Naveen Raman, Umang Bhatt, Mateja Jamnik, Ilia Sucholutsky, Adrian Weller, Krishnamurthy Dvijotham
AIES '23
Improving Learning-to-Defer Algorithms Through Fine-Tuning
Naveen Raman, Michael Yee
NeurIPS '21 Human and Machine Decisions Workshop
Eliciting Bias in Question Answering Models through Ambiguity
Andrew Mao‡, Naveen Raman‡, Matthew Shu, Eric Li, Franklin Yang, Jordan Boyd-Graber
EMNLP '21 Machine Reading for Question Answering Workshop
Data-Driven Methods for Balancing Fairness and Efficiency in Ride-Pooling
Naveen Raman, Sanket Shah, John Dickerson
IJCAI'21: International Joint Conference on Artificial Intelligence. 2021.
What more can Entity Linking do for Question Answering
Naveen Raman, Pedro Rodriguez, Jordan Boyd-Graber
NeurIPS '20: Human And Machine in-the-Loop Evaluation and Learning Strategies Workshop
Stress and Burnout in Open Source: Toward Finding, Understanding, and Mitigating Unhealthy Interactions.
Naveen Raman, Minxuan Cao, Yulia Tsvetkov, Christian Kaestner, Bogdan Vasilescu
ICSE '20: International Conference on Software Engineering. 2020 (NIER Track)