• StanfordKPD

    A platform for kidney paired donation (KPD). It allows hospitals to store patient and donor information and find matches using an intelligent matching algorithm. It enables patients with kidney disease to find a donor for a kidney transplantation even though their donor may have an incompatible blood type.


    Stanford, CA


    • Itai Ashlagi, Associate Professor of Management Science and Engineering, Stanford University

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All Publications

  • Examining augmented reality in journalism: Presence, knowledge gain, and perceived visual authenticity New Media & Society Aitamurto, T., Aymerich-Franch, L., Saldivar, J., Kircos, C., Sadeghi, Y., Sakshuwong, S. 2020

    View details for DOI 10.1177/1461444820951925

  • Knapsack Voting for Participatory Budgeting ACM TRANSACTIONS ON ECONOMICS AND COMPUTATION Goel, A., Krishnaswamy, A. K., Sakshuwong, S., Aitamurto, T. 2019; 7 (2)

    View details for DOI 10.1145/3340230

    View details for Web of Science ID 000496754600004

  • Who Is in Your Top Three? Optimizing Learning in Elections with Many Candidates Garg, N., Gelauff, L., Sakshuwong, S., Goel, A. 2019: 22–31
  • Motivation to Select Point of View in Cinematic Virtual Reality Won, A., Aitamurto, T., Kim, B., Sakshuwong, S., Kircos, C., Sadeghi, Y., IEEE IEEE. 2019: 1233–34
  • Sense of Presence, Attitude Change, Perspective-Taking and Usability in First-Person Split-Sphere 360 degrees Video Aitamurto, T., Zhou, S., Sakshuwong, S., Saldivar, J., Sadeghi, Y., Tran, A., ACM ASSOC COMPUTING MACHINERY. 2018
  • Sequential Deliberation for Social Choice Fain, B., Goel, A., Munagala, K., Sakshuwong, S. 2017: 177–90
  • A genome-wide approach for detecting novel insertion-deletion variants of mid-range size. Nucleic acids research Xia, L. C., Sakshuwong, S., Hopmans, E. S., Bell, J. M., Grimes, S. M., Siegmund, D. O., Ji, H. P., Zhang, N. R. 2016; 44 (15)


    We present SWAN, a statistical framework for robust detection of genomic structural variants in next-generation sequencing data and an analysis of mid-range size insertion and deletions (<10 Kb) for whole genome analysis and DNA mixtures. To identify these mid-range size events, SWAN collectively uses information from read-pair, read-depth and one end mapped reads through statistical likelihoods based on Poisson field models. SWAN also uses soft-clip/split read remapping to supplement the likelihood analysis and determine variant boundaries. The accuracy of SWAN is demonstrated by in silico spike-ins and by identification of known variants in the NA12878 genome. We used SWAN to identify a series of novel set of mid-range insertion/deletion detection that were confirmed by targeted deep re-sequencing. An R package implementation of SWAN is open source and freely available.

    View details for DOI 10.1093/nar/gkw481

    View details for PubMedID 27325742

    View details for PubMedCentralID PMC5009736

  • Estimation of individual cumulative ultraviolet exposure using a geographically-adjusted, openly-accessible tool. BMC dermatology Zhu, G. A., Raber, I., Sakshuwong, S., Li, S., Li, A. S., Tan, C., Chang, A. L. 2016; 16 (1): 1-?


    Estimates of an individual's cumulative ultraviolet (UV) radiation exposure can be useful since ultraviolet radiation exposure increases skin cancer risk, but a comprehensive tool that is practical for use in the clinic does not currently exist. The objective of this study is to develop a geographically-adjusted tool to systematically estimate an individual's self-reported cumulative UV radiation exposure, investigate the association of these estimates with skin cancer diagnosis, and assess test reliability.A 12-item online questionnaire from validated survey items for UV exposure and skin cancer was administered to online volunteers across the United States and results cross-referenced with UV radiation indices. Cumulative UV exposure scores (CUES) were calculated and correlated with personal history of skin cancer in a case-control design. Reliability was assessed in a separate convenience sample.1,118 responses were included in the overall sample; the mean age of respondents was 46 (standard deviation 15, range 18 - 81) and 150 (13 %) reported a history of skin cancer. In bivariate analysis of 1:2 age-matched cases (n = 149) and controls (n = 298), skin cancer cases were associated with (1) greater CUES prior to first skin cancer diagnosis than controls without skin cancer history (242,074 vs. 205,379, p = 0.003) and (2) less engagement in UV protective behaviors (p < 0.01). In a multivariate analysis of age-matched data, individuals with CUES in the lowest quartile were less likely to develop skin cancer compared to those in the highest quartile. In reliability testing among 19 volunteers, the 2-week intra-class correlation coefficient for CUES was 0.94. We have provided the programming code for this tool as well as the tool itself via open access.CUES is a useable and comprehensive tool to better estimate lifetime ultraviolet exposure, so that individuals with higher levels of exposure may be identified for counseling on photo-protective measures.

    View details for DOI 10.1186/s12895-016-0038-1

    View details for PubMedID 26790927

    View details for PubMedCentralID PMC4721109