Pavan Ravishankar
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I am a Computer Science PhD student at the Courant Institute of Mathematical Sciences, NYU, a member of ML4G lab. My PhD is fully funded by MacCracken fellowship. I am fortunate to have been advised by Prof. Daniel Neill, Prof. Balaraman Ravindran and Prof. Sudarsan Padmanabhan. My work is shaped through scientific writing, code, and dialogue, as practices of rigor, critique, and shared understanding, in no particular order.

I am interested in the following questions in the algorithm-human pipeline: 

• Model Anomalies: Learn hyperrectangular representations to detect cohesive anomalous multidimensional subsets
• Model Stability: Estimate influence of a set of points on predictions, without retraining, in linear/non-linear models
• Model Multiplicity: Optimize for equity within an (approximately) equally accurate exponential model search space
• Model Interpretability: Learn interpretable representations capturing disparities in neural network outputs

Machine learning, Representation learning, and Algorithm design

• PC Member: EAAMO 2026
• Reviewer: EAAMO 2026, MURE AAAI Workshop 2026, EAAMO 2025, CODS COMAD 2024, DAI Workshop AAAI 2023
• Organizer: DEAADIGS Workshop ACM Web Science Conference, 2021

Sigma Xi Full Membership

Prison Mathematics Project

If you're interested in working with me, please don't hesitate to get in touch!!

• Fairness Without Demographics in Training through Variance-Preserving Rashomon Set Sampling                       
  Dai, G.*, Ravishankar, P.*, Yuan, R., Black, E.⁺, & Neill, D.B.⁺                      
  Review                  
• Learning Representational Disparities                       
  Ravishankar, P.*, Shah, R., & Neill, D.B.                      
  Preprint [Paper]                   
• Be Intentional About Fairness!: Fairness, Size, and Multiplicity in the Rashomon Set                       
  Dai, G.*, Ravishankar, P.*, Yuan, R., Black, E.⁺, & Neill, D.B.⁺                      
  EAAMO 2025 [Paper] [Best Paper Honorable Mention]                   
• Provable Detection of Propagating Sampling Bias in Prediction Models                       
  Ravishankar, P.*, Mo, Q., McFowland III, E, & Neill, D.B.                      
  AAAI 2023 [Paper]                  
• Financial Exclusion of Internal Migrant Workers of India during COVID-19: Can Digital Financial Inclusion be facilitated by AI?                       
  Ravishankar, P.*, Padmanabhan, S., & Ravindran, R.                      
  JITCAR 2023 [Paper]                  
• A Causal Approach for Unfair Edge Prioritization and Discrimination Removal                       
  Ravishankar, R.*, Malviya, P.*, & Ravindran, B.                       
  ACML 2021 [Paper]                   
               

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