Song, Dongjin

Dongjin Song

Assistant Professor, School of Computing

Email dongjin.song@uconn.edu
Phone (860) 486-0662
Mailing Address University of Connecticut 371 Fairfield Way, Unit 4155 Storrs, CT 06269-4155
Campus Storrs
Link Affiliation Website
Google Scholar Link

Brief Bio

Dr. Song is an assistant professor in the Department of Computer Science and Engineering, University of Connecticut (UConn). Before that, he was a research staff member at NEC Labs America in Princeton, NJ, since July 2016. In June 2016, he received my Ph.D. degree in the Department of Electrical and Computer Engineering from University of California San Diego (UCSD) with the guidance of Prof. David A. Meyer. My thesis committee includes Prof. Lawrence Saul, Prof. Nuno Vasconcelos, Prof. Gert Lanckriet, and Prof. Julian McAuley. He also works closely with Prof. Dacheng Tao from the University of Sydney.

Dr. Song have broad research interests in machine learning, data mining, deep learning, time series analysis (e.g., representation, similarity search, prediction/forecasting, and anomaly detection), graph representation learning, and reinforcement learning. Recently, he is particular interested in (1) continual learning on graphs, with a focus on evolving graphs, dynamic systems, and physical world (e.g., IoT systems, environmental science, etc.) and (2) federated learning, trustworthy reinforcement learning with applications to healthcare and biomedical data. Two of my papers DARNN and HetGNN have been ranked as the most influential papers in IJCAI 2017 (2nd) and KDD 2019 (3rd) by paperdigest.org, respectively. He received the prestigious NSF Career Award in 2024 and UConn Research Excellence Program (REP) Award in 2021.

  • Computational Biology and Bioinformatics,
  • Epidemiology,
  • Machine Learning,
  • Algorithms and Optimization,
  • Graph and Network Theory
Eleven grand challenges in single-cell data science
D Lähnemann, J Köster, E Szczurek, DJ McCarthy, SC Hicks, ...
Genome biology 21 (1), 1-35
Good laboratory practice for clinical next-generation sequencing informatics pipelines
AS Gargis, L Kalman, DP Bick, C Da Silva, DP Dimmock, BH Funke, ...
Nature biotechnology 33 (7), 689-693
Technology dictates algorithms: recent developments in read alignment
M Alser, J Rotman, D Deshpande, K Taraszka, H Shi, PI Baykal, HT Yang, ...
Genome biology 22 (1), 249
Efficient error correction for next-generation sequencing of viral amplicons
P Skums, Z Dimitrova, DS Campo, G Vaughan, L Rossi, JC Forbi, ...
BMC Bioinformatics 13 (Suppl 10), S6
Accurate genetic detection of hepatitis C virus transmissions in outbreak settings
DS Campo, GL Xia, Z Dimitrova, Y Lin, JC Forbi, L Ganova-Raeva, ...
The Journal of infectious diseases 213 (6), 957-965
QUENTIN: reconstruction of disease transmissions from viral quasispecies genomic data
P Skums, A Zelikovsky, R Singh, W Gussler, Z Dimitrova, S Knyazev, ...
Bioinformatics 34 (1), 163-170
A large HCV transmission network enabled a fast-growing HIV outbreak in rural Indiana, 2015
S Ramachandran, H Thai, JC Forbi, RR Galang, Z Dimitrova, G Xia, Y Lin, ...
EBioMedicine 37, 374-381
Next-generation sequencing reveals large connected networks of intra-host HCV variants
DS Campo, Z Dimitrova, L Yamasaki, P Skums, DTY Lau, G Vaughan, ...
BMC genomics 15 (Suppl 5), S4
Epidemiological data analysis of viral quasispecies in the next-generation sequencing era
S Knyazev, L Hughes, P Skums, A Zelikovsky
Briefings in bioinformatics 22 (1), 96-108
Unlocking capacities of genomics for the COVID-19 response and future pandemics
S Knyazev, K Chhugani, V Sarwal, R Ayyala, H Singh, S Karthikeyan, ...
Nature Methods 19 (4), 374-380
Benchmarking of computational error-correction methods for next-generation sequencing data
K Mitchell, JJ Brito, I Mandric, Q Wu, S Knyazev, S Chang, LS Martin, ...
Genome biology 21 (1), 71
Accurate assembly of minority viral haplotypes from next-generation sequencing through efficient noise reduction
S Knyazev, V Tsyvina, A Shankar, A Melnyk, A Artyomenko, T Malygina, ...
Nucleic acids research 49 (17), e102-e102
Antigenic cooperation among intrahost HCV variants organized into a complex network of cross-immunoreactivity
P Skums, L Bunimovich, Y Khudyakov
Proceedings of the National Academy of Sciences 112 (21), 6653-6658
GHOST: global hepatitis outbreak and surveillance technology
AG Longmire, S Sims, I Rytsareva, DS Campo, P Skums, Z Dimitrova, ...
BMC genomics 18 (Suppl 10), 916
Reconstruction of viral population structure from next-generation sequencing data using multicommodity flows
P Skums, N Mancuso, A Artyomenko, B Tork, I Mandoiu, Y Khudyakov, ...
BMC bioinformatics 14 (Suppl 9), S2
From alpha to zeta: Identifying variants and subtypes of sars-cov-2 via clustering
A Melnyk, F Mohebbi, S Knyazev, B Sahoo, R Hosseini, P Skums, ...
Journal of Computational Biology 28 (11), 1113-1129
CliqueSNV: an efficient noise reduction technique for accurate assembly of viral variants from NGS data
S Knyazev, V Tsyvina, A Shankar, A Melnyk, A Artyomenko, T Malygina, ...
bioRxiv 264242
Analysis of the evolution and structure of a complex intrahost viral population in chronic hepatitis C virus mapped by ultradeep pyrosequencing
BA Palmer, Z Dimitrova, P Skums, O Crosbie, E Kenny-Walsh, LJ Fanning
Journal of virology 88 (23), 13709-13721
Inference of genetic relatedness between viral quasispecies from sequencing data
O Glebova, S Knyazev, A Melnyk, A Artyomenko, Y Khudyakov, ...
BMC genomics 18 (Suppl 10), 918
Graph fractal dimension and the structure of fractal networks
P Skums, L Bunimovich
Journal of complex networks 8 (4), cnaa037