Update: I am on the industry and academic job market!
Welcome! :)
I am Nastaran, a final-year Ph.D student at the Max Planck Institute for Software Systems in Kaiserslautern, Germany, where I am advised by Manuel Gomez-Rodriguez. I recently submitted my PhD thesis on “Predictive Accuracy and Fairness in Human-AI Teams,” which contributes to a deeper understanding of opportunities and risks in AI-assisted decision-making.
During my PhD I interned at Meta, where I worked with Niek Tax and Daniel Haimovich on multicalibration of ML models, and I visited Amazon, where I worked with Dominik Janzing on root-cause analysis of outliers from a causal perspective. In 2023, I was selected to participate in the Machine Learning Summer School in Okinawa.
I did my undergrad in computer engineering at Ferdowsi University of Mashhad. During my undergrad I interned at IST Austria, working with Amir Goharshady and Krishnendu Chatterjee. Together with Amir, we won the IEEE best student paper award for our POPL paper on data packing. I also interned at the Institute of Computational Perception in JKU Linz, working with Hamid Eghbal-Zadeh and Gerhard Widmer. In 2017, I was awarded the Singapore International Pre-Graduate Award (SIPGA) to visit A*STAR Bioinformatics Institute in Singapore, where I worked with Cheng Li. During this time, we won the first place in iNTUition 2017, a hackathon competition at NTU Singapore, together with Mahdi Abolfazli. In 2018, I was selected to participate in the Heidelberg Laureate Forum.
Recent News
- December 2025: I will be giving a talk at the Social Foundations of Computation lab of MPI for Intelligent Systems!
- December 2025: Our workshop on Metacognition in Generative AI will be held on December 7 at EurIPS!
- December 2025: I will be presenting a poster of our NeurIPS paper on Root Cause Analysis of Outliers with Missing Structural Knowledge at EurIPS! This paper is a result of collaboration with the causality lab of Amazon AWS!
- November 2025: Our paper MCGRAD: Multicalibration at Web Scale is accepted at the KDD Applied Data Science Track! This paper is a result of collaboration with an amazing team from the Central Applied Science team at Meta.
Research Interests
The ultimate goal of my research is to ensure that Machine Learning (ML) models are efficient, reliable, and safe for those interacting with or affected by them. My current (broad) research interests include uncertainty quantification in ML models (for example, using techniques such as conformal prediction and calibration), LLM safety alignment, provenance (e.g., watermarking), efficiency (e.g., speculative decoding), and, more recently, social and economic aspects in generative-AI.
In my PhD research, my goal was to design a human-AI synergy that leverages the respective strengths of human and AI while mitigating their respective biases. I demonstrate in a line of work that for optimal joint performance in human-AI teams, we must shift our focus from model-centric optimization to team-aware learning. This perspective has inspired the line of research on human-AI complementarity within the human-centric ML literature. In another line of work, I bring attention to the fact that high-stakes decisions made by human-AI teams impact individuals—deciding who receives opportunities—and reshape the distribution of demographic groups in society over time. I identify potential biases in such decisions and propose strategies to mitigate them.
In my free time, I enjoy playing squash, cycling, running, and calisthenics!
MCGrad: Multicalibration at Web Scale
Lorenzo Perini, Daniel Heimovich, Fridolin Linder, Niek Tax, Dima Karamshuk, Nastaran Okati, Milan Vojnovic, Pavlos Apostolopoulos
All authors contributed equally
KDD ADS Track 2026Root Cause Analysis of Outliers with Missing Structural Knowledge
Nastaran Okati*, William Roy Orchard*, Sergio Hernan Garrido Mejia, Patrick Bloebaum, Dominik Janzing
*Equal contributions
NeurIPS 2025Towards Human-AI Complementarity with Prediction Sets
Giovanni De Toni, Nastaran Okati, Suhas Thejaswi, Eleni Straitouri, Manuel Gomez-Rodriguez
NeurIPS 2024On the Within-Group Fairness of Screening Classifiers
Nastaran Okati, Stratis Tsirtsis, and Manuel Gomez-Rodriguez
ICML 2023Improving Expert Predictions with Conformal Prediction
Eleni Straitouri, Lequng Wang, Nastaran Okati, Manuel Gomez-Rodriguez
ICML 2023Differentiable Learning Under Triage
Nastaran Okati, Abir De, and Manuel Gomez-Rodriguez
NeurIPS 2021Classification Under Human Assistance
Nastaran Okati*, Abir De*, Ali Zare-Zadeh and Manuel Gomez-Rodriguez
*Equal contribution
AAAI 2021Efficient Parameterized Algorithms for Data Packing
Krishnendu Chatterjee, Amir Goharshady, Nastaran Okati, and Andreas Pavlogiannis
Alphabetical Author Order
POPL 2019
IEEE Best Student Paper AwardComputational Approaches for Stochastic Shortest Path on Succinct MDPs
Krishnendu Chatterjee, Hongfei Fu, Amir Goharshady, and Nastaran Okati
Alphabetical Author Order
IJCAI 2018Is Your LLM Overcharging You? Tokenization, Transparency, and Incentives
Tokenization Workshop at ICML’25 and Information Economics and Large Language Models workshop at EC’25
Ander Artola, Stratis Tsirtsis, Nastaran Okati, and Manuel Gomez-RodriguezMeasuring Multicalibration
Ido Guy, Daniel Heimovich, Fridolin Linder, Nastaran Okati, Lorenzo Perini, Niek Tax, Mark Tygert
Alphabetical Author OrderRegression Under Human Assistance
Abir De, Nastaran Okati, Paramita Koley, Niloy Ganguly, and Manuel Gomez-Rodriguez