Huan Liu

Research summary

Two parallel research threads appear: feature selection methodology, and electrode materials for sodium-ion batteries. An IEEE TKDE article framed feature selection algorithms by search strategy, evaluation criteria and data-mining task, surveyed existing algorithms for classification and clustering, and proposed a unifying meta-algorithm platform [1]. A spectral feature-selection framework based on spectral graph theory unified supervised and unsupervised regimes, recovering ReliefF and Laplacian Score as special cases [3]. A 2014 review of classification feature selection addressed noise in collected data including medical imaging and social-media data [5]. An ACM Computing Surveys article reviewed advances in feature selection in the big-data era [2], with a companion arXiv survey on the same theme [8]. Relational learning via latent social dimensions extracted multi-dimensional connection types from network data for collective inference on social media [7]. On materials, SnO2 nanowires with tetragonal structure were grown by catalyst-free thermal evaporation, and their electrochemical performance in lithium-ion batteries was related to their high length-to-diameter ratio and absence of metal-catalyst contamination [4]. SnS/SnO2 ultrafine heterostructures were fabricated and used as sodium-ion-battery anodes; the built-in electric field at the SnS/SnO2 interface produced enhanced charge-transfer and high-rate capability [9]. A review of Prussian-blue-analogue cathodes for sodium-ion batteries covered history, parameters and rational design principles bridging laboratory work and commercialization [6]. A second sodium-ion review focused on factors limiting commercialization and evaluation of active-material and full-cell systems [10].

Recent publications

1. Toward integrating feature selection algorithms for classification and clustering2005 · IEEE Transactions on Knowledge and Data Engineering · 2669 citationsDOI
2. Feature Selection2017 · ACM Computing Surveys · 2268 citationsDOI
3. Spectral feature selection for supervised and unsupervised learning2007 · 901 citationsDOI
4. Preparation and Electrochemical Properties of SnO2 Nanowires for Application in Lithium‐Ion Batteries2006 · Angewandte Chemie International Edition · 859 citationsDOI
5. Feature selection for classification: A review2014 · 788 citationsDOI
6. Prussian Blue Analogues for Sodium‐Ion Batteries: Past, Present, and Future2021 · Advanced Materials · 783 citationsDOI
7. Relational learning via latent social dimensions2009 · 782 citationsDOI
8. Feature Selection: A Data Perspective2016 · arXiv (Cornell University) · 776 citationsDOI
9. Boosted Charge Transfer in SnS/SnO2 Heterostructures: Toward High Rate Capability for Sodium‐Ion Batteries2016 · Angewandte Chemie International Edition · 744 citationsDOI
10. Sodium‐Ion Batteries: From Academic Research to Practical Commercialization2017 · Advanced Energy Materials · 743 citationsDOI

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Email Huan Liu 6-12 months before your application deadline. Read several recent papers and reference specific work in your message. Use our how to email a Japanese professor guide for the proven email structure.

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External profiles

• ORCID: https://orcid.org/0000-0002-0253-647X
• OpenAlex: openalex.org

Profile compiled from public sources (Researchmap, OpenAlex, Nagoya University faculty directory). Last refreshed 2026-05. Report incorrect information.