Publications
Publications
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- All HBS Web
(118,869)
- Faculty Publications (81)
- 12 Dec 2014
- Conference Presentation
Aspect Specific Sentiment Analysis Using Hierarchical Deep Learning
By: Himabindu Lakkaraju, Richard Socher and Chris Manning
- 2014
- Other Unpublished Work
Using Big Data to Improve Social Policy
By: Himabindu Lakkaraju, Jon Kleinberg, Jure Leskovec, Jens Ludwig and Sendhil Mullainathan
- Article
What's in a Name? Understanding the Interplay Between Titles, Content, and Communities in Social Media
By: Himabindu Lakkaraju, Julian McAuley and Jure Leskovec
- Article
Dynamic Multi-Relational Chinese Restaurant Process for Analyzing Influences on Users in Social Media
By: Himabindu Lakkaraju, Indrajit Bhattacharya and Chiranjib Bhattacharyya
- Article
TEM: A Novel Perspective to Modeling Content on Microblogs
By: Himabindu Lakkaraju and Hyung-Il Ahn
- 17 Dec 2011
- Conference Presentation
A Non Parametric Theme Event Topic Model for Characterizing Microblogs
By: Himabindu Lakkaraju and Hyung-Il Ahn
- 17 Dec 2011
- Conference Presentation
Unified Modeling of User Activities on Social Networking Sites
By: Himabindu Lakkaraju and Angshu Rai
- 2011
- Article
Exploiting Coherence for the Simultaneous Discovery of Latent Facets and Associated Sentiments
By: Himabindu Lakkaraju, Chiranjib Bhattacharyya, Indrajit Bhattacharya and Srujana Merugu
- 2011
- Article
Smart News Feeds for Social Networks Using Scalable Joint Latent Factor Models
By: Himabindu Lakkaraju, Angshu Rai and Srujana Merugu
- Research Summary
Adoption of Machine Learning Models in Real World Decision Making
The goal of this research is to assess the impact of deploying machine learning models in real world decision making in domains such as health care. View Details
- 2023
- Chapter
Analyzing Human Decisions and Machine Predictions in Bail Decision Making
By: Jon Kleinberg, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig and Sendhil Mullainathan
BOOK ABSTRACT: Oriented toward the introductory student, The Inequality Reader is the essential textbook for today's undergraduate courses. The editors have assembled the most important classic and contemporary readings about how poverty and inequality are... View Details
Keywords: Equality and Inequality
- Teaching Interest
Empirical Technology and Operations Management Course
I taught a set of lectures on "Introduction to Machine Learning for Social Scientists" as part of this required course for first year PhD students. This module familiarizes students with all the basic concepts in machine learning, their implementations, as well as the... View Details
- Teaching Interest
Interpretability and Explainability in Machine Learning
As machine learning models are increasingly being employed to aid decision makers in high-stakes settings such as healthcare and criminal justice, it is important to ensure that the decision makers correctly understand and consequent trust the functionality of these... View Details
- 14 Aug 2017
- Conference Presentation
Interpretable and Explorable Approximations of Black Box Models
By: Himabindu Lakkaraju, Ece Kamar, Rich Caruana and Jure Leskovec
- Research Summary
Making Machine Learning Models Fair
The goal of this research direction is to ensure that the machine learning models we build and deploy do not discriminate against individuals from minority groups. View Details
- Research Summary
Making Machine Learning Models Interpretable
I work on developing various tools and methodologies which can help decision makers (e.g., doctors, managers) to better understand the predictions of machine learning models. View Details
- Research Summary
Making Machine Learning Robust to Adversarial Attacks
The goal of this research is to ensure that machine learning models that we build and deploy are not easily susceptible to attacks by adversarial or malicious entities. View Details
- Research Summary
Overview
I develop machine learning tools and techniques which enable human decision makers to make better decisions. More specifically, my research addresses the following fundamental questions pertaining to human and algorithmic decision-making:
1. How to build... View Details
1. How to build... View Details
- Teaching Interest
Technology and Operations Management
This course enables students to develop the skills and concepts needed to ensure the ongoing contribution of a firm's operations to its competitive position. Topics include digital marketplaces, technology, and data science.
View Details