Lead AI Scientist · Poiro
CV
I lead Data Science & AI at Poiro. Previously a Research Consultant in Walmart’s AdTech team, and a Postdoctoral Research Fellow at the National University of Singapore working on outlier-robust optimization and multi-armed bandits. I completed my Ph.D. at the Indian Institute of Science as a Prime Minister’s Research Fellow, under Prof. Y. Narahari and Assoc. Prof. Siddharth Barman.
Anand Krishna
I lead Data Science & AI at Poiro. Previously a Research Consultant in Walmart’s AdTech team, and a Postdoctoral Research Fellow at the National University of Singapore working on outlier-robust optimization and multi-armed bandits. I completed my Ph.D. at the Indian Institute of Science as a Prime Minister’s Research Fellow, under Prof. Y. Narahari and Assoc. Prof. Siddharth Barman.
Experience
Lead AI ScientistatPoiro
Leading Data Science & AI. Poiro builds AI systems and agents that supercharge marketing workflows and bring brands closer to consumers.
- AI systems trained on a brand’s structured and unstructured marketing data — social content, marketplace data, first-party customer data — to build a knowledge representation of the brand and its category.
- Agents that run analytics and data-science workflows over that representation to produce actionable insight and guide marketing execution.
- Used by brands to identify content whitespaces, target creative and creator recommendations at ROI, and audit creator risk before commercial commitments.
Research Consultant, AdTechatWalmart
- Built scalable systems for real-time advertiser query processing to estimate impressions.
- Approximate query processing and high-performance data pipelines over large-scale systems.
Postdoctoral Research FellowatNational University of Singapore
School of Computing, working with Prof. Vincent Y. F. Tan.
- Outlier-robust optimization and multi-armed bandits, including the LEARN invex loss for outlier-oblivious online convex optimization (UAI 2026).
- Extended p-mean welfare objectives from social choice to stochastic bandits, unifying average and Nash regret in one algorithm (AAAI 2025).
- Sample-efficient alternating minimization for robust phase retrieval, published in IEEE Transactions on Information Theory.
Prime Minister’s Research Fellow (PMRF)atIndian Institute of Science
Ph.D. in the Department of Computer Science and Automation, advised by Prof. Y. Narahari and Assoc. Prof. Siddharth Barman.
- Approximation algorithms for fair division: Nash social welfare and p-mean welfare under subadditive, XOS, and dichotomous valuations.
- Results published at ESA 2020, IJCAI 2022, WINE 2022, and ITCS 2024.
Research InternatIBM Research India
- Developed a privacy-preserving framework for Discrete Preference Games with Dr. Ramasuri Narayanam and Dr. Rishi Saket. Presented the work at IBM and IISc.
Research and Development InternatAindra Systems
- Regression models predicting reagent dipping times in the Papanicolaou staining procedure, for Aindra’s auto-stainer.
Education
Direct Ph.D., Intelligent Systems
Indian Institute of Science
Department of Computer Science and Automation, as a Prime Minister’s Research Fellow. CGPA 8.9/10. Advised by Prof. Y. Narahari and Assoc. Prof. Siddharth Barman. Coursework included Game Theory, Reinforcement Learning, Linear & Nonlinear Optimization, Stochastic Modeling, Matrix Theory, Foundations of Data Science, and Topics in Pattern Recognition.
B.Tech, Computer Science & Engineering
Government Engineering College, Thrissur
CGPA 8.28/10.
Publications
Listed in full on the publications page.
- . Dynamic Regret in Outlier-Oblivious Online Optimization using Nonconvex Robust Losses. UAI 2026, 2026.
- . A Sample Efficient Alternating Minimization-based Algorithm for Robust Phase Retrieval. IEEE Transactions on Information Theory, 2025.
- . p-Mean Regret for Stochastic Bandits. AAAI 2025, 2025.
- . Sublinear Approximation Algorithm for Nash Social Welfare with XOS Valuations. ITCS 2024, 2024.
- . Nash Welfare Guarantees for Fair and Efficient Coverage. WINE 2022, 2022.
- . Achieving Envy-Freeness with Limited Subsidies under Dichotomous Valuations. IJCAI 2022, 2022.
- . Tight Approximation Algorithms for p-Mean Welfare Under Subadditive Valuations. ESA 2020, 2020.
Teaching
Probability Models
Computational Methods of OptimizationE0 230
Game TheoryE1 254
Randomized AlgorithmsE0 234
Linear Algebra and ProbabilityE0 226
Awards & honours
Best Presentation Award, EECS Symposium in Theoretical Computer Science
Best Poster Award in Computer Science & Engineering, Data Science and Mathematics — invited to the highlighted poster session
Invited talk on the IJCAI 2022 paper
ADFOCS 2020 summer school on Market Design & Computational Fair Division
Theory of Reinforcement Learning Boot Camp
EC’20 Global Outreach Program and EC Mentoring Workshop
Prime Minister’s Research Fellowship (PMRF)
Academic service
Subreviewer
- FOCS 2024
- APPROX 2023
- WWW 2023
- SOSA 2023
- WWW 2022
- SAGT 2021
- WINE 2020
- WINE 2019
Areas & tools
Research areas
- Game Theory
- Fair Division
- Online Learning
- Randomized Algorithms
- Reinforcement Learning
- Optimization
- Machine Learning
- Deep Learning
- Statistics
Programming
- Python
- SQL
- C++
- C
- LaTeX
Libraries
- PyTorch
- scikit-learn
- NumPy
- Pandas
- PySpark
- Matplotlib
- Seaborn
Languages
- MalayalamNative
- EnglishFluent
- TamilConversational
- HindiConversational