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Hamed Nilforoshan

I am a Computer Science PhD student at Stanford University, advised by Prof. Jure Leskovec. Previously, I was a Machine Learning Data Scientist at Airbnb. I received my Bachelors in Computer Science at Columbia University, where I was advised by Prof. Eugene Wu.

I am broadly interested in developing data science and machine learning methods to quantify, understand, and mitigate structural inequalities. I am grateful to be supported by the Knight-Hennessy and NSF Graduate Research Fellowships.

2020

Large-Scale Diet Tracking Data Reveal Disparate Impacts of Food Environment
Tim Althoff, Hamed Nilforoshan, Jenna Hua, Jure Leskovec
Under Review
pdf

2019

SliceNDice: Mining Suspicious Multi-attribute Entity Groups with Multi-view Graphs
Hamed Nilforoshan, Neil Shah
IEEE DSAA 2019
pdf slides code

2018

Leveraging Quality Prediction Models for Automatic Writing Feedback
Hamed Nilforoshan, Eugene Wu
ICWSM 2018
pdf slides code

2017

PreCog: Improving Crowdsourced Data Quality Before Acquisition
Hamed Nilforoshan, Jiannan Wang, Eugene Wu
Arxiv 2017
pdf

Segment-Predict-Explain for Automatic Writing Feedback
Hamed Nilforoshan, James Sands, Rahul Khanna, Eugene Wu
Collective Intelligence 2017
pdf

Before starting my PhD, I worked as a Machine Learning Data Scientist at Airbnb. I designed several algorithms, currently in production, to mitigate gun violence, money-laundering, and credit card theft on the platform.

Prior to Airbnb, I worked at Snapchat, where I built SliceNDice, a graph-based method used to catch botnets and rings of fraudulent advertisers. Two years later, SliceNDice is still in production, and has resulted in two patents in addition to the publication.

If you are a college or high-school student seeking advice with any of the below, AND come from an under-represented background*, please send me and email and I am happy to help.

• CS PhD, graduate fellowship, or college applications

• Getting involved with research, or transitioning from industry to academia

• Career advice relating to data science or software engineering

• Reconciling religious faith with a secular career

* While under-representatation is typically defined along the lines of income, race/ethnicity, and gender (e.g. BIPOC, first-generation), there are also many other "invisible" forms of under-representation (e.g. along ideological lines of religion or politics) which manifest in the field of computer science and elite academic institutions. I am equally committed to increasing diversity across all of the above, so when in doubt, please reach out!

PhD Student
Computer Science
Stanford University

Gates CS Building
353 Jane Stanford Way
Stanford, CA 94305

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