Pavithra Harsha

835 total citations
20 papers, 568 citations indexed

About

Pavithra Harsha is a scholar working on Marketing, Management Science and Operations Research and Management Information Systems. According to data from OpenAlex, Pavithra Harsha has authored 20 papers receiving a total of 568 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Marketing, 9 papers in Management Science and Operations Research and 7 papers in Management Information Systems. Recurrent topics in Pavithra Harsha's work include Consumer Market Behavior and Pricing (9 papers), Supply Chain and Inventory Management (7 papers) and Auction Theory and Applications (6 papers). Pavithra Harsha is often cited by papers focused on Consumer Market Behavior and Pricing (9 papers), Supply Chain and Inventory Management (7 papers) and Auction Theory and Applications (6 papers). Pavithra Harsha collaborates with scholars based in United States, India and Canada. Pavithra Harsha's co-authors include Munther A. Dahleh, Shivaram Subramanian, Joline Uichanco, Markus Ettl, Georgia Perakis, Maxime C. Cohen, Kay‐Yut Chen, Tad Hogg, Divya Singhvi and Ramesh Natarajan and has published in prestigious journals such as SHILAP Revista de lepidopterología, Management Science and IEEE Transactions on Power Systems.

In The Last Decade

Pavithra Harsha

18 papers receiving 540 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Pavithra Harsha United States 12 237 166 161 139 89 20 568
Yuhan Zhang China 7 179 0.8× 64 0.4× 90 0.6× 117 0.8× 138 1.6× 10 449
Jicai Li China 8 211 0.9× 64 0.4× 393 2.4× 220 1.6× 141 1.6× 19 738
Jian-Ya Ding China 12 95 0.4× 103 0.6× 128 0.8× 64 0.5× 80 0.9× 22 884
Fredrik Ygge Sweden 10 89 0.4× 145 0.9× 54 0.3× 125 0.9× 352 4.0× 32 601
Ana Radovanović United States 12 132 0.6× 29 0.2× 38 0.2× 76 0.5× 48 0.5× 33 462
David Dauer Germany 4 304 1.3× 15 0.1× 157 1.0× 73 0.5× 19 0.2× 9 621
Wang Chi Cheung Singapore 9 330 1.4× 153 0.9× 10 0.1× 165 1.2× 206 2.3× 23 681
Qiao‐Chu He China 12 31 0.1× 111 0.7× 33 0.2× 71 0.5× 41 0.5× 43 399
Joline Uichanco United States 12 28 0.1× 180 1.1× 48 0.3× 362 2.6× 287 3.2× 24 644
Arne Meeuw Switzerland 10 216 0.9× 28 0.2× 93 0.6× 50 0.4× 17 0.2× 12 418

Countries citing papers authored by Pavithra Harsha

Since Specialization
Citations

This map shows the geographic impact of Pavithra Harsha's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Pavithra Harsha with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pavithra Harsha more than expected).

Fields of papers citing papers by Pavithra Harsha

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Pavithra Harsha. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Pavithra Harsha. The network helps show where Pavithra Harsha may publish in the future.

Co-authorship network of co-authors of Pavithra Harsha

This figure shows the co-authorship network connecting the top 25 collaborators of Pavithra Harsha. A scholar is included among the top collaborators of Pavithra Harsha based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Pavithra Harsha. Pavithra Harsha is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Harsha, Pavithra, Ashish Jagmohan, Jayant Kalagnanam, Brian Quanz, & Divya Singhvi. (2025). Deep Policy Iteration with Integer Programming for Inventory Management. Manufacturing & Service Operations Management. 27(2). 369–388. 1 indexed citations
2.
Hussain, Saddam, et al.. (2024). Millet Crop Yield Variation through Feature Extraction Using XGBoost. SHILAP Revista de lepidopterología. 591. 8002–8002.
3.
Harsha, Pavithra, et al.. (2023). End-to-End Learning for Optimization via Constraint-Enforcing Approximators. Proceedings of the AAAI Conference on Artificial Intelligence. 37(6). 7253–7260. 5 indexed citations
4.
Jati, Arindam, et al.. (2023). Hierarchical Proxy Modeling for Improved HPO in Time Series Forecasting. 891–900. 3 indexed citations
5.
Bastani, Hamsa, Pavithra Harsha, Georgia Perakis, & Divya Singhvi. (2021). Learning Personalized Product Recommendations with Customer Disengagement. Manufacturing & Service Operations Management. 24(4). 2010–2028. 17 indexed citations
6.
Harsha, Pavithra, Ramesh Natarajan, & Dharmashankar Subramanian. (2021). A Prescriptive Machine-Learning Framework to the Price-Setting Newsvendor Problem. RePEc: Research Papers in Economics. 3(3). 227–253. 12 indexed citations
7.
Harsha, Pavithra, Ashish Jagmohan, Jayant Kalagnanam, Brian Quanz, & Divya Singhvi. (2021). Math Programming based Reinforcement Learningfor Multi-Echelon Inventory Management. SSRN Electronic Journal. 3 indexed citations
8.
Subramanian, Shivaram & Pavithra Harsha. (2020). Demand Modeling in the Presence of Unobserved Lost Sales. Management Science. 67(6). 3803–3833. 16 indexed citations
9.
Cohen, Maxime C. & Pavithra Harsha. (2019). Designing Price Incentives in a Network with Social Interactions. Manufacturing & Service Operations Management. 22(2). 292–309. 16 indexed citations
10.
Harsha, Pavithra, Shivaram Subramanian, & Markus Ettl. (2019). A Practical Price Optimization Approach for Omnichannel Retailing. 1(3). 241–264. 28 indexed citations
11.
Harsha, Pavithra, Shivaram Subramanian, & Joline Uichanco. (2019). Dynamic Pricing of Omnichannel Inventories. Manufacturing & Service Operations Management. 21(1). 47–65. 80 indexed citations
12.
Ettl, Markus, et al.. (2019). A Data-Driven Approach to Personalized Bundle Pricing and Recommendation. Manufacturing & Service Operations Management. 22(3). 461–480. 62 indexed citations
13.
Bastani, Hamsa, Pavithra Harsha, Georgia Perakis, & Divya Singhvi. (2018). Sequential Learning of Product Recommendations With Customer Disengagement. SSRN Electronic Journal. 6 indexed citations
14.
Harsha, Pavithra & Munther A. Dahleh. (2014). Optimal Management and Sizing of Energy Storage Under Dynamic Pricing for the Efficient Integration of Renewable Energy. IEEE Transactions on Power Systems. 30(3). 1164–1181. 203 indexed citations
15.
Cohen, Maxime C. & Pavithra Harsha. (2013). Designing Price Incentives in a Network with Social Interactions. SSRN Electronic Journal. 11 indexed citations
16.
Harsha, Pavithra, Mayank Sharma, Ramesh Natarajan, & Soumyadip Ghosh. (2013). A Framework for the Analysis of Probabilistic Demand Response Schemes. IEEE Transactions on Smart Grid. 4(4). 2274–2284. 13 indexed citations
17.
Harsha, Pavithra & Munther A. Dahleh. (2011). Optimal sizing of energy storage for efficient integration of renewable energy. 5813–5819. 45 indexed citations
18.
Harsha, Pavithra, Cynthia Barnhart, David C. Parkes, & Haoqi Zhang. (2009). Strong activity rules for iterative combinatorial auctions. Digital Access to Scholarship at Harvard (DASH) (Harvard University).
19.
Harsha, Pavithra, Cynthia Barnhart, David C. Parkes, & Haoqi Zhang. (2009). Strong activity rules for iterative combinatorial auctions. Computers & Operations Research. 37(7). 1271–1284. 16 indexed citations
20.
Hogg, Tad, Pavithra Harsha, & Kay‐Yut Chen. (2007). QUANTUM AUCTIONS. International Journal of Quantum Information. 5(5). 751–780. 31 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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