Maya Balakrishnan
PhD Candidate in Technology and Operations Management
Contact: maya@hbs.edu
I am currently on the 2023-2024 job market.
I am a fifth year doctoral candidate at Harvard Business School in the Technology and Operations Management program. I am fortunate to be advised by Professors Kris Ferreira (chair) and Ryan Buell at HBS and Jordan Tong at Wisconsin School of Business. In my research, I aim to design, improve, and evaluate trustworthy operations. This includes two streams of work: calibrating employee trust in algorithms for more effective human-algorithm collaboration and inspiring consumer trust in company operations through more effective corporate social responsibility initiatives. My interdisciplinary undergraduate and doctoral training as well as my work experience has given me a diverse skillset to tackle these important questions. As a result, my research uses a wide range of methods including sophisticated lab experiments, analytical models, econometrics, text analysis, and machine learning.
I previously earned a BS in Computer Science specializing in Human-Computer Interaction and a minor in Philosophy from Stanford University. Before entering graduate school, I previously worked at Microsoft and Goldman Sachs, and founded and ran a VC-funded company which created software for packaged food manufacturers to digitize their operations, get detailed analytics around process optimization opportunities, and comply with food safety regulations.
Outside of academia, I enjoy making art (I paint and sew clothes), reading science fiction and fantasy, cooking and hosting dinner parties (I once auditioned for the Food Network Show Chopped), and biking around the Cambridge/Boston area with my husband.
INFORMS Talks
I will be presenting two of my projects in three different sessions at INFORMS 2023 in Phoenix, Arizona.
On Sunday, October 15th from 2:15-3:30pm I will be presenting my job market paper "Improving Human-Algorithm Collaboration: Causes and Mitigation of Over- and Under-Adherence" in a session on Humans and Machines with Applications in Operations in CC-North 127A.
On Monday, October 16th from 8:00-9:15am I will be presenting my paper "Differentiating on Diversity: How Disclosing Workforce Diversity Influences Consumer Choice" in an awards session for the INFORMS Service Science Best Diversity, Equity, Inclusion, and Justice (DEIJ) Paper in CC-West 102B.
On Monday, October 16th from 4:00-5:15pm I will be presenting my job market paper "Improving Human-Algorithm Collaboration: Causes and Mitigation of Over- and Under-Adherence" in an awards session for the INFORMS Behavioral Operations Management Best Working Paper in CC-North 127A.
Please reach out to me at maya@hbs.edu if you would like to meet with me at INFORMS!
10/2023: Our paper "Improving Human-Algorithm Collaboration: Causes and Mitigation of Over- and Under-Adherence" won second place in the 2023 INFORMS Behavioral Operations Management Best Working Paper Competition!
10/2023: Our paper "Differentiating on Diversity: How Disclosing Workforce Diversity Influences Consumer Choice" was accepted at Production & Operations Management and will soon be forthcoming!
9/2023: Our paper "Improving Human-Algorithm Collaboration: Causes and Mitigation of Over- and Under-Adherence" was selected as a finalist in the 2023 INFORMS Behavioral Operations Management Best Working Paper Competition! We will be presenting this paper at INFORMS on Monday, October 16th from 4:00-5:15pm as part of the finalists presentation session.
8/2023: Our paper "Differentiating on Diversity: How Disclosing Workforce Diversity Influences Consumer Choice" was selected as a finalist for the INFORMS 2023 Service Science Best Diversity, Equity, Inclusion, and Justice (DEIJ) Paper Competition! We will be presenting this paper at INFORMS on Monday, October 16th from 8:00-9:15am as part of the finalists presentation session.
7/2023: Our paper "Speedy activists: Firm response time to sociopolitical events influences consumer behavior" was published as part of the Journal of Consumer Psychology's special issue on Consumer Insights from Text Analysis!