The field of academic research is witnessing significant developments in personalized search and peer review. Researchers are exploring innovative approaches to improve search effectiveness and reduce information overload, leveraging techniques such as knowledge graphs and neural language models. Meanwhile, the peer review process is being reexamined, with studies applying formal methods from argumentation theory to support transparent and unbiased dispute resolution. Noteworthy papers include PARK, which proposes a two-step approach to leveraging knowledge graphs for personalized academic search, and Dispute Resolution in Peer Review with Abstract Argumentation and OWL DL, which applies formal methods to support transparent and unbiased dispute resolution. Other notable studies focus on forecasting faculty placement from patterns in co-authorship networks, disparities in peer review tone, and citation recommendation using deep canonical correlation analysis.