Apurva Virkud

avirkud [@] umich.edu

I'm currently a research associate in Prof. Roya Ensafi's lab at the University of Michigan, where I help maintain the Censored Planet observatory. My research interests are in security and privacy.

I graduated from the University of Michigan in 2020 with a BSE in computer science. During undergrad, I also did research in computer aided diagnosis (Michigan Medicine: CAD-AI Lab), automotive security (UMTRI: ESG), and analysis of C. elegans (Life Sciences Institute: S. Xu Lab).

I also enjoy running, guitar, and chess! I have taught and competitively played chess for many years and am a National Master.

Research

Publications

A Large-scale Investigation into Geodifferences in Mobile Apps

conference USENIX Security Symposium, August 2022

Renuka Kumar Apurva Virkud Ram Sundara Raman Atul Prakash Roya Ensafi

PDF (preprint)

Recent studies on the web ecosystem have been raising alarms on the increasing geodifferences in access to Internet content and services due to Internet censorship and geoblocking. However, geodifferences in the mobile app ecosystem have received limited attention, even though apps are central to how mobile users communicate and consume Internet content. We present the first large-scale measurement study of geodifferences in the mobile app ecosystem. We design a semi-automatic, parallel measurement testbed that we use to collect 5,684 popular apps from Google Play in 26 countries. In all, we collected 117,233 apk files and 112,607 privacy policies for those apps. Our results show high amounts of geoblocking with 3,672 apps geoblocked in at least one of our countries. While our data corroborates anecdotal evidence of takedowns due to government requests, unlike common perception, we find that blocking by developers is significantly higher than takedowns in all our countries, and has the most influence on geoblocking in the mobile app ecosystem. We also find instances of developers releasing different app versions to different countries, some with weaker security settings or privacy disclosures that expose users to higher security and privacy risks. We provide recommendations for app market proprietors to address the issues discovered.

@inproceedings{kumar2022geodifferences,
title={A Large-scale Investigation into Geodifferences in Mobile Apps},
author={Renuka Kumar and Apurva Virkud and Ram {Sundara Raman} and Atul Prakash and Roya Ensafi},
booktitle={USENIX Security Symposium},
year={2022}
}

Prediction of Disease Free Survival in Laryngeal and Hypopharyngeal Cancers Using CT Perfusion and Radiomic Features: A Pilot Study

journal Tomography, February 2021

Sean Woolen Apurva Virkud Lubomir Hadjiiski Kenny Cha Heang-Ping Chan Paul Swiecicki Francis Worden Ashok Srinivasan

PDF

(1) Purpose: The objective was to evaluate CT perfusion and radiomic features for prediction of one year disease free survival in laryngeal and hypopharyngeal cancer. (2) Method and Materials: This retrospective study included pre and post therapy CT neck studies in 36 patients with laryngeal/hypopharyngeal cancer. Tumor contouring was performed semi-autonomously by the computer and manually by two radiologists. Twenty-six radiomic features including morphological and gray-level features were extracted by an internally developed and validated computer-aided image analysis system. The five perfusion features analyzed included permeability surface area product (PS), blood flow (flow), blood volume (BV), mean transit time (MTT), and time-to-maximum (Tmax). One year persistent/recurrent disease data were obtained following the final treatment of definitive chemoradiation or after total laryngectomy. We performed a two-loop leave-one-out feature selection and linear discriminant analysis classifier with generation of receiver operating characteristic (ROC) curves and confidence intervals (CI). (3) Results: 10 patients (28%) had recurrence/persistent disease at 1 year. For prediction, the change in blood flow demonstrated a training AUC of 0.68 (CI 0.47–0.85) and testing AUC of 0.66 (CI 0.47–0.85). The best features selected were a combination of perfusion and radiomic features including blood flow and computer-estimated percent volume changes-training AUC of 0.68 (CI 0.5–0.85) and testing AUC of 0.69 (CI 0.5–0.85). The laryngoscopic percent change in volume was a poor predictor with a testing AUC of 0.4 (CI 0.16–0.57). (4) Conclusions: A combination of CT perfusion and radiomic features are potential predictors of one-year disease free survival in laryngeal and hypopharyngeal cancer patients.

@Article{tomography7010002,
AUTHOR = {Woolen, Sean and Virkud, Apurva and Hadjiiski, Lubomir and Cha, Kenny and Chan, Heang-Ping and Swiecicki, Paul and Worden, Francis and Srinivasan, Ashok},
TITLE = {Prediction of Disease Free Survival in Laryngeal and Hypopharyngeal Cancers Using CT Perfusion and Radiomic Features: A Pilot Study},
JOURNAL = {Tomography},
VOLUME = {7},
YEAR = {2021},
NUMBER = {1},
PAGES = {10--19},
URL = {https://www.mdpi.com/2379-139X/7/1/2},
PubMedID = {33681460},
ISSN = {2379-139X},
DOI = {10.3390/tomography7010002}
}

Standardization in Quantitative Imaging: A Multicenter Comparison of Radiomic Features from Different Software Packages on Digital Reference Objects and Patient Data Sets

journal Tomography, June 2020

M. McNitt-Gray S. Napel A. Jaggi S.A. Mattonen L. Hadjiiski M. Muzi D. Goldgof Y. Balagurunathan L.A. Pierce P.E. Kinahan E.F. Jones A. Nguyen A. Virkud H.P. Chan N. Emaminejad M. Wahi-Anwar M. Daly M. Abdalah H. Yang L. Lu W. Lv A. Rahmim A. Gastounioti S. Pati S. Bakas D. Kontos B. Zhao J. Kalpathy-Cramer K. Farahani

PDF

Radiomic features are being increasingly studied for clinical applications. We aimed to assess the agreement among radiomic features when computed by several groups by using different software packages under very tightly controlled conditions, which included standardized feature definitions and common image data sets. Ten sites (9 from the NCI's Quantitative Imaging Network] positron emission tomography–computed tomography working group plus one site from outside that group) participated in this project. Nine common quantitative imaging features were selected for comparison including features that describe morphology, intensity, shape, and texture. The common image data sets were: three 3D digital reference objects (DROs) and 10 patient image scans from the Lung Image Database Consortium data set using a specific lesion in each scan. Each object (DRO or lesion) was accompanied by an already-defined volume of interest, from which the features were calculated. Feature values for each object (DRO or lesion) were reported. The coefficient of variation (CV), expressed as a percentage, was calculated across software packages for each feature on each object. Thirteen sets of results were obtained for the DROs and patient data sets. Five of the 9 features showed excellent agreement with CV < 1%; 1 feature had moderate agreement (CV < 10%), and 3 features had larger variations (CV ≥ 10%) even after attempts at harmonization of feature calculations. This work highlights the value of feature definition standardization as well as the need to further clarify definitions for some features.

@Article{j.tom.2019.00031,
AUTHOR = {McNitt-Gray, M. and Napel, S. and Jaggi, A. and Mattonen, S.A. and Hadjiiski, L. and Muzi, M. and Goldgof, D. and Balagurunathan, Y. and Pierce, L.A. and Kinahan, P.E. and Jones, E.F. and Nguyen, A. and Virkud, A. and Chan, H.P. and Emaminejad, N. and Wahi-Anwar, M. and Daly, M. and Abdalah, M. and Yang, H. and Lu, L. and Lv, W. and Rahmim, A. and Gastounioti, A. and Pati, S. and Bakas, S. and Kontos, D. and Zhao, B. and Kalpathy-Cramer, J. and Farahani, K.},
TITLE = {Standardization in Quantitative Imaging: A Multicenter Comparison of Radiomic Features from Different Software Packages on Digital Reference Objects and Patient Data Sets},
JOURNAL = {Tomography},
VOLUME = {6},
YEAR = {2020},
NUMBER = {2},
PAGES = {118--128},
URL = {https://www.mdpi.com/2379-139X/6/2/118},
ISSN = {2379-139X},
DOI = {10.18383/j.tom.2019.00031}
}


Posters and Abstracts

Standardization in Quantitative Imaging: A Multi-Center Comparison of Radiomics Feature Values Obtained by Different Software Packages on Digital Reference Objects and Patient Datasets

abstract Radiological Society of North America Meeting (RSNA), December 2019

Michael F. McNitt-Gray Sandy Napel Jayashree Kalpathy-Cramer Akshay Jaggi Nastaran Emaminejad Mark Muzi Dmitry Goldgof Hao Yang Ella F. Jones Muhammad W. Wahi-Anwar Yoganand Balagurunathan Mahmoud Abdalah Binsheng Zhao Lubomir M. Hadjiiski Apurva Virkud Heang-Ping Chan Larry A. Pierce II Keyvan Farahani

PDF

Exploratory Study for Identifying Predictors for Persistent Disease and Tumor Reoccurrence After Treatment of Head and Neck Cancers

abstract Radiological Society of North America Meeting (RSNA), December 2019

Sean Woolen Lubomir Hadjiiski Apurva Virkud Heang-Ping Chan Francis Worden Paul Swiecicki Ashok Srinivasan

PDF

Security Analysis of ADAS and Automated Driving Systems

poster MI ITS Meeting, September 2019

Apurva Virkud Sam Lauzon

2019 MI ITS Student Poster Winner

A low cost bio-imaging system incorporating machine learning algorithms for automatic analysis of animal behavior

abstract International C. elegans Conference, June 2017

poster UM UROP Symposium, April 2017

Adam Iliff Apurva Virkud Shawn Xu

PDF

Recent Projects

Censored Planet    September 2020 - present

I am a developer for Censored Planet, a remote measurement platform for Internet censorship. I help maintain the observatory codebase and documentation, and am working on an open-source pipeline (in collaboration with Jigsaw) for large-scale analysis of the data.
Observatory   raw data   readthedocs   github (docs) 
Analysis Pipeline   github 

Geodifferences in Mobile Apps    September 2019 - June 2021

I wrote measurement code for this project, which we used to collect Google Play metadata, APKs, and privacy policies for 5000+ apps from vantage points in 26 countries. We've made the code (with documentation) and data available for download. See our USENIX '22 paper for more on the results!
Resources   github   data