[{"data":1,"prerenderedAt":164},["ShallowReactive",2],{"page-\u002Fresearch":3},{"id":4,"title":5,"body":6,"date":156,"description":153,"extension":157,"meta":158,"navigation":159,"path":160,"seo":161,"stem":5,"subtitle":162,"tags":156,"__hash__":163},"content\u002Fresearch.md","research",{"type":7,"value":8,"toc":152},"minimark",[9,13,17,40,43,73,76,115,118,149],[10,11],"terminal-prompt",{"cmd":12},"ls -la .\u002Fresearch",[14,15,16],"p",{},"total 3",[18,19,20,28,34],"ul",{},[21,22,23,27],"li",{},[24,25,26],"code",{},"thesis.md","    data valuation for label error detection in ML pipelines · ongoing",[21,29,30,33],{},[24,31,32],{},"ccce23.md","   interpretable hybrid recommender · CCCE'23, Stockholm",[21,35,36,39],{},[24,37,38],{},"isda22.md","   RePI: research-paper impact analysis · ISDA'22",[10,41],{"cmd":42},"cat thesis.md",[44,45,49,62],"terminal-callout",{"id":46,"label":47,"tone":48},"thesis","m.s. thesis · in progress","note",[14,50,51,55,59],{},[52,53,54],"strong",{},"Data Valuation for Label Error Detection in ML Pipelines",[56,57,58],"em",{},"Responsible Data Science (RDS) Lab, Purdue University · Aug 2024 – Present",[56,60,61],{},"Advised by Dr. Romila Pradhan",[14,63,64,65,68,69,72],{},"Developing ",[52,66,67],{},"Shapley-value-based data-valuation methods"," to detect and repair mislabeled training data in ML pipelines, with a focus on improving model ",[52,70,71],{},"fairness, reliability, and explainability",".",[10,74],{"cmd":75},"cat ccce23.md",[44,77,81,97,100],{"id":78,"label":79,"tone":80},"ccce23","ccce'23 · stockholm","info",[14,82,83,86,89,90],{},[52,84,85],{},"An Interpretable Hybrid Recommender Based on Graph Convolution to Address Serendipity",[56,87,88],{},"Published at CCCE'23 · Stockholm, March 2023"," — ",[91,92,96],"a",{"href":93,"rel":94},"https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-27440-4_20",[95],"nofollow","Springer link",[14,98,99],{},"Two novel contributions built on top of a 4-model hybrid graph-convolutional recommender:",[18,101,102,109],{},[21,103,104,105,108],{},"A new ",[52,106,107],{},"distance-based metric"," for quantifying recommendation serendipity, going beyond standard diversity\u002Fnovelty proxies",[21,110,111,114],{},[52,112,113],{},"KNN feature-importance analysis"," layered on the hybrid to make its recommendations interpretable to end users",[10,116],{"cmd":117},"cat isda22.md",[44,119,122,134,141],{"id":120,"label":121,"tone":80},"isda22","isda'22",[14,123,124,127,89,130],{},[52,125,126],{},"RePI: Research Paper Impact Analysis",[56,128,129],{},"Published at ISDA'22 · December 2022",[91,131,96],{"href":132,"rel":133},"https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-35299-7_3",[95],[14,135,136,137,140],{},"A web application for analyzing research-paper impact, built around a novel ",[52,138,139],{},"impact-factor ratio"," — a new metric for publication influence that goes beyond raw citation counts.",[18,142,143,146],{},[21,144,145],{},"Implemented the metric and pipeline in Python on top of the Semantic Scholar API",[21,147,148],{},"Built an interactive interface in Streamlit for exploring per-paper and per-author impact",[10,150],{"cmd":151},"exit",{"title":153,"searchDepth":154,"depth":154,"links":155},"",2,[],null,"md",{},true,"\u002Fresearch",{"title":5,"description":153},"thesis · publications","lM8tR_T1HqJ9_AZp2paCwY4f1i5u5z7HhFeyTfxT8x0",1782496776466]