Han‐I Wang

492 citations
23 papers · 217 · h-index 9

Impact in

    • Vaccine Coverage and Hesitancy
    • Multiple Myeloma Research and Treatments
    • Acute Myeloid Leukemia Research

Papers in

Han‐I Wang

21 papers receiving 208 citations

Peers

Han‐I Wang
Comparison fields: 5 of 59
  • Health 32
  • Hematology 25
  • Modeling and Simulation 7
  • Public Health, Environmental and Occupational Health 31
  • Genetics 11
Replace Kelly Gavigan with:
Kelly Gavigan United States
Veerle Stouten Belgium
Faraz M Ali United Kingdom
María Merino Spain
Saeed Barzegari Iran
Jennifer Blase United States
Amy P Worrall Ireland
Sonam Shah United States
Farnaz Khatami Iran
Connie Ziegler Denmark
Han‐I Wang relative to Kelly Gavigan United States Kelly Gavigan's profile →
Citations per field
00.5×
Kelly Gavigan · 1×
Citations per year

Countries citing papers authored by Han‐I Wang

Since Specialization
Citations

This map shows the geographic impact of Han‐I Wang's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Han‐I Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Han‐I Wang more than expected).

Fields of papers citing papers by Han‐I Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Han‐I Wang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Han‐I Wang. The network helps show where Han‐I Wang may publish in the future.

Co-authors

The 25 scholars most cited alongside Han‐I Wang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Han‐I Wang Line = papers co-authored together Han‐I Wang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 23 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201836
2 201334
3 202133
4 201418
5 202115
6 202210
7 202410
8 200910
9 201710
10 20218
11 20226
12 20136
13 20216
14 20203
15 20213
16 20222
17 20222
18 20222
19 20211
20 20221

About Han‐I Wang

Han‐I Wang is a scholar working on Artificial Intelligence, Physiology, Neurology, Pulmonary and Respiratory Medicine and Cognitive Neuroscience, having authored 23 papers that have together received 217 indexed citations. Recurring topics across this work include Smoking Behavior and Cessation (3 papers), Autism Spectrum Disorder Research (2 papers), Long-Term Effects of COVID-19 (2 papers), Computational Physics and Python Applications (2 papers), Acute Myeloid Leukemia Research (1 paper), Vaccine Coverage and Hesitancy (1 paper), Cancer Genomics and Diagnostics (1 paper) and Respiratory Support and Mechanisms (1 paper). The work is most often cited by research in Health (32 citations), Hematology (25 citations), Modeling and Simulation (7 citations), Public Health, Environmental and Occupational Health (31 citations) and Genetics (11 citations). Han‐I Wang has collaborated with scholars based in United Kingdom, Taiwan and Australia. Frequent co-authors include Debra Howell, Russell Patmore, Eve Roman, Alexandra Smith, Martin Howard, Andrew Jack, Eline Aas, Sally E. Kinsey, Cathy Burton and Simon Appleton. Their work appears in journals such as Value in Health, The Lancet Global Health, International Journal of Epidemiology, BMC Psychiatry and Blood.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

Explore authors with similar magnitude of impact