{"id":109,"date":"2024-12-10T10:27:14","date_gmt":"2024-12-10T15:27:14","guid":{"rendered":"https:\/\/www.golive.clarku.edu\/faculty\/profiles\/li-han\/"},"modified":"2026-04-05T10:42:03","modified_gmt":"2026-04-05T14:42:03","slug":"li-han","status":"publish","type":"cu_faculty","link":"https:\/\/www.clarku.edu\/faculty\/profiles\/li-han\/","title":{"rendered":"Li Han"},"content":{"rendered":"<p>Li Han obtained her Ph.D. in Computer Science from Texas A&amp;M University, College Station, in 2000. She arrived at Clark in 2002, after completing her postdoc at Carnegie Mellon University. She is a Professor of Computer Science and currently serves as the Director of the <a href=\"http:\/\/www.cs.clarku.edu\/~data-science\/\" target=\"_blank\" rel=\"noreferrer noopener\">Data Science Program<\/a>, one of the newest and growing interdisciplinary programs at Clark.<\/p>\n<p>Professor Han&#8217;s doctoral study was in robotics, specifically dexterous manipulation and motion planning. At Clark, she had done research on robotics and computational study of protein folding, in collaboration with faculty from Mathematics and Chemistry, and with funding support from NSF and NIH. Her current research interest focuses on machine learning, data science education, and Chem-Informatics.<\/p>\n<p>Professor Han has developed and taught a variety of courses at Clark, such as Introduction to Computing, Introduction to Data Science, Algorithms, Robotics, and Computer Graphics. She served as the faculty adviser of the Clark Competitive Computing Club (C4) and the coach of the Clark programming competition teams for over a decade. Prior to COVID, Clark teams regularly participated and excelled in inter-collegiate contests. She now advises the Data Science Collaboration club.<\/p>\n","protected":false},"author":0,"featured_media":1891,"parent":0,"template":"","meta":{"cu_faculty_f180_userid":"C16031338","cu_faculty_first_name":"Li","cu_faculty_last_name":"Han","cu_faculty_employment_status":"Full Time","cu_faculty_rank":"Professor","cu_faculty_position":"Professor","cu_faculty_phone":"","cu_faculty_email":"lhan@clarku.edu","cu_faculty_location":"","cu_faculty_about":"<p>Li Han obtained her Ph.D. in Computer Science from Texas A&amp;M University, College Station, in 2000. She arrived at Clark in 2002, after completing her postdoc at Carnegie Mellon University. She is a Professor of Computer Science and currently serves as the Director of the <a href=\"http:\/\/www.cs.clarku.edu\/~data-science\/\" target=\"_blank\" rel=\"noreferrer noopener\">Data Science Program<\/a>, one of the newest and growing interdisciplinary programs at Clark.<\/p>\n<p>Professor Han's doctoral study was in robotics, specifically dexterous manipulation and motion planning. At Clark, she had done research on robotics and computational study of protein folding, in collaboration with faculty from Mathematics and Chemistry, and with funding support from NSF and NIH. Her current research interest focuses on machine learning, data science education, and Chem-Informatics.<\/p>\n<p>Professor Han has developed and taught a variety of courses at Clark, such as Introduction to Computing, Introduction to Data Science, Algorithms, Robotics, and Computer Graphics. She served as the faculty adviser of the Clark Competitive Computing Club (C4) and the coach of the Clark programming competition teams for over a decade. Prior to COVID, Clark teams regularly participated and excelled in inter-collegiate contests. She now advises the Data Science Collaboration club.<\/p>","cu_faculty_degrees":"<span>Ph.D. in Computer Science,<\/span> Texas A&amp;M University, 2000\n<span>M.S. in Biomedical Engineering,<\/span> Xi'an Jiaotong University, 1992\n<span>B.S. in Biomedical Engineering,<\/span> Xi'an Jiaotong University, 1989","cu_faculty_cv":"https:\/\/faculty180.interfolio.com\/public\/download.php?key=SDRwNCtxSUpsamxBQ213WS9ucHFuNnMwT0hzQU11b2RPQkJ2cWc3amxyUmNRdVVXTkF4MU1sQjlBTGVzd29jQnZTZWY5RmRIVkVFaXZERXM5d0pTUDNzdjRJdGExWmxUcXlsSkRvZkdkVHdla2hVQ1FlVXBraU5qSFB5MTZPS3Y%3D","cu_faculty_links":"[]","cu_faculty_scholarly_interests":"Data Science, Computational Protein Study","cu_faculty_scholarly_works":"[{\"activityid\":13626,\"fields\":{\"Type\":\"Papers Published - Conference Proceedings\",\"Title of Paper\":\"&lt;p&gt;Exploring Deep Learning and Data Representations for the Prediction of Erosion Channels&lt;\\\/p&gt;\",\"Title of Published Proceedings\":\"30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Undergraduate and Master\\u2019s Consortium\",\"Title of Conference\":\"\",\"Conference Location\":\"Toronto, Canada\",\"Month \\\/ Season\":\"August\",\"Year\":2025,\"Publisher\":\"\",\"Publisher City and State\":\"\",\"Volume\":\"\",\"Issue Number \\\/ Edition\":\"\",\"Page Numbers\":\"\",\"DOI\":\"\",\"CoAuthor\":null,\"URL\":\"\",\"Description\":\"&lt;p&gt;Accurately forecasting the evolution of erosion channels is essential for safeguarding buildings, dams, and other earth structures. In this paper, we consider a simplified channel system and two machine learning challenges based on thousands of high-fidelity simulations: single-frame prediction of a channel\\u2019s ultimate direction (left, right, or split) and long-horizon synthesis of its full trajectory from an early image or image sequence. For final direction prediction, a lightweight convolutional network and a hybrid Autoencoder-CatBoost pipeline are used, with the latter achieving better early-warning accuracy, the former remaining unexpectedly robust when inputs are aggressively down-sampled or binarized. For path prediction, a recurrent video model (PredRNN-V2) excels at intersection-over-union, whereas a latent-space autoencoder predicts finer path geometry deeper into the future, especially for binary images. These findings quantify how model architecture, training-set size, and image fidelity affect actionable lead time in erosion monitoring, and point to several promising research directions.&lt;\\\/p&gt;\",\"Include description in output citation\":0,\"Origin\":\"Manual\"},\"facultyid\":\"C16031338\",\"status\":[{\"id\":13626,\"status\":\"Completed\\\/Published\",\"term\":\"Summer\",\"year\":2025,\"termid\":\"2024\\\/05\",\"listingorder\":6,\"completionorder\":6},{\"id\":13626,\"status\":\"Submitted\",\"term\":\"Spring\",\"year\":2025,\"termid\":\"2024\\\/03\",\"listingorder\":2,\"completionorder\":2}],\"userid\":\"C16031338\",\"attachments\":[],\"coauthors_list\":[\"Alexander Vu\",\" Nishal Sukumar\",\"Arshad Kudrolli\",\"Li Han\"],\"sort_date\":\"2025-8-01\"},{\"activityid\":13628,\"fields\":{\"Type\":\"Papers Published - Conference Proceedings\",\"Title of Paper\":\"&lt;p&gt;Leveraging Machine Learning to Understand and Predict Student Transfer in Higher Education&lt;\\\/p&gt;\",\"Title of Published Proceedings\":\"30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Undergraduate and Master\\u2019s Consortium\",\"Title of Conference\":\"\",\"Conference Location\":\"Toronto, Canada\",\"Month \\\/ Season\":\"August\",\"Year\":2025,\"Publisher\":\"\",\"Publisher City and State\":\"\",\"Volume\":\"\",\"Issue Number \\\/ Edition\":\"\",\"Page Numbers\":\"\",\"DOI\":\"\",\"CoAuthor\":null,\"URL\":\"\",\"Description\":\"&lt;p&gt;Student transfer and attrition in higher education continue to pose significant global challenges, affecting institutional effectiveness, financial viability, and long-term student success. Global dropout rates are estimated to range between 20% and 40%, with some regions facing even higher levels of attrition. This study presents a comprehensive multi-institutional analysis of student transfer behavior by leveraging academic, behavioral, emotional, and social network features. Through rigorous evaluation of multiple models, we demonstrate that classifiers such as Logistic Regression and XGBoost effectively identify students at risk of transferring. In one university-specific case, our model achieved an AUC of 0.93, a true positive rate of 0.88, and an accuracy of 92\\\\%. Beyond predictive accuracy, we offer practical insights into the most influential 25 features and provide targeted recommendations for institutional interventions. Importantly, our findings show that even with a reduced subset of the top features, the model retained strong performance (AUC = 0.92), underscoring the potential for efficient, scalable early-warning systems.&lt;\\\/p&gt;\",\"Include description in output citation\":0,\"Origin\":\"Manual\"},\"facultyid\":\"C16031338\",\"status\":[{\"id\":13628,\"status\":\"Completed\\\/Published\",\"term\":\"Summer\",\"year\":2025,\"termid\":\"2024\\\/05\",\"listingorder\":6,\"completionorder\":6},{\"id\":13628,\"status\":\"Submitted\",\"term\":\"Spring\",\"year\":2025,\"termid\":\"2024\\\/03\",\"listingorder\":2,\"completionorder\":2}],\"userid\":\"C16031338\",\"attachments\":[],\"coauthors_list\":[\"Keerthana Goka\",\"Kunal Malhan\",\"Vinod Nithin Kumar Rachakonda\",\"Li Han\"],\"sort_date\":\"2025-8-01\"},{\"activityid\":6404,\"fields\":{\"Type\":\"Presentations\",\"Title of Presentation\":\"Allosteric signal propagation pathways of unphosphorylated RING-type E3 ubiquitin ligase c-Cbl\",\"Conference \\\/ Meeting Name\":\"ACS (American Chemical Society) Annual Meeting, Fall 2022\",\"Location of Conference \\\/ Meeting\":\"Chicago, IL\",\"Month \\\/ Season\":\"August\",\"Year\":2022,\"Sponsoring Organization\":\"ACS\",\"CoAuthor\":null,\"URL\":\"https:\\\/\\\/doi.org\\\/10.1021\\\/scimeetings.2c00539\",\"Description\":\"\",\"Include description in output citation\":0,\"Origin\":\"Manual\"},\"facultyid\":\"C16031338\",\"status\":[{\"id\":6404,\"status\":\"Completed\\\/Published\",\"term\":\"Fall\",\"year\":2022,\"termid\":\"2022\\\/01\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C16031338\",\"attachments\":[],\"coauthors_list\":[\"Tianyi Yang\",\"Li Han\",\"Shuanghong Huo\"],\"sort_date\":\"2022-8-01\"},{\"activityid\":6405,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"Dynamics and Allosteric Information Pathways of Unphosphorylated c-Cbl\",\"Journal Title\":\"Journal of Chemical Information and Modeling\",\"Series Title\":\"\",\"Month \\\/ Season\":\"Nov. \",\"Year\":2022,\"Publisher\":\" American Chemical Society\",\"Publisher City and State\":\"\",\"Publisher Country\":\"\",\"Volume\":\"\",\"Issue Number \\\/ Edition\":\"62\",\"Page Number(s) or Number of Pages\":\"6148-6159\",\"ISSN\":\"\",\"DOI\":\"\",\"CoAuthor\":null,\"URL\":\"https:\\\/\\\/pubs.acs.org\\\/doi\\\/10.1021\\\/acs.jcim.2c01022\",\"Description\":\"\",\"Include description in output citation\":0,\"Origin\":\"Manual\"},\"facultyid\":\"C16031338\",\"status\":[{\"id\":6405,\"status\":\"Completed\\\/Published\",\"term\":\"Fall\",\"year\":2022,\"termid\":\"2022\\\/01\",\"listingorder\":6,\"completionorder\":6},{\"id\":6405,\"status\":\"Completed\\\/Published\",\"term\":\"Summer\",\"year\":2022,\"termid\":\"2021\\\/05\",\"listingorder\":6,\"completionorder\":6},{\"id\":6405,\"status\":\"In Progress\",\"term\":\"Spring\",\"year\":2022,\"termid\":\"2021\\\/03\",\"listingorder\":1,\"completionorder\":1}],\"userid\":\"C16031338\",\"attachments\":[],\"coauthors_list\":[\"Tianyi Yang\",\"Li Han\",\"Shuanghong Huo\"],\"sort_date\":\"2022-11-01\"},{\"activityid\":3819,\"fields\":{\"Type\":\"Presentations\",\"Title of Presentation\":\"Predicting Erosion Channel First Passage with Machine Learning\",\"Conference \\\/ Meeting Name\":\"\",\"Location of Conference \\\/ Meeting\":\"\",\"Month \\\/ Season\":\"March\",\"Year\":2021,\"Sponsoring Organization\":\"American Physical Society\",\"CoAuthor\":null,\"URL\":\"http:\\\/\\\/meetings.aps.org\\\/Meeting\\\/MAR21\\\/Session\\\/X05.2\",\"Description\":\"\",\"Include description in output citation\":0,\"Origin\":\"Manual\"},\"facultyid\":\"C16031338\",\"status\":[{\"id\":3819,\"status\":\"Completed\\\/Published\",\"term\":\"Spring\",\"year\":2021,\"termid\":\"2020\\\/03\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C16031338\",\"attachments\":[],\"coauthors_list\":[\"Isaac Khor\",\"Li Han\",\"Arshad Kudrolli\"],\"sort_date\":\"2021-3-01\"},{\"activityid\":3820,\"fields\":{\"Type\":\"Presentations\",\"Title of Presentation\":\"Statistical properties of ridge networks in crumpled sheets\",\"Conference \\\/ Meeting Name\":\"\",\"Location of Conference \\\/ Meeting\":\"\",\"Month \\\/ Season\":\"March\",\"Year\":2021,\"Sponsoring Organization\":\"American Physical Society\",\"CoAuthor\":null,\"URL\":\"http:\\\/\\\/meetings.aps.org\\\/Meeting\\\/MAR21\\\/Session\\\/X05.9\",\"Description\":\"\",\"Include description in output citation\":0,\"Origin\":\"Manual\"},\"facultyid\":\"C16031338\",\"status\":[{\"id\":3820,\"status\":\"Completed\\\/Published\",\"term\":\"Spring\",\"year\":2021,\"termid\":\"2020\\\/03\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C16031338\",\"attachments\":[],\"coauthors_list\":[\"Catalin Veghes\",\"Li Han\",\"Arshad Kudrolli\"],\"sort_date\":\"2021-3-01\"},{\"activityid\":3818,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"Approximating Dynamic Proximity with a Hybrid Geometry Energy-Based Kernel for Diffusion Maps\",\"Journal Title\":\"The Journal of Chemical Physics\",\"Series Title\":\"\",\"Month \\\/ Season\":\"September \",\"Year\":2019,\"Publisher\":\"The American Institute of Physics\",\"Publisher City and State\":\"\",\"Publisher Country\":\"U.S.A.\",\"Volume\":\"101\",\"Issue Number \\\/ Edition\":\"\",\"Page Number(s) or Number of Pages\":\"11 pages\",\"ISSN\":\"\",\"DOI\":\"10.1063\\\/1.5100968\",\"CoAuthor\":null,\"URL\":\"https:\\\/\\\/pubs.aip.org\\\/aip\\\/jcp\\\/article-abstract\\\/151\\\/10\\\/105101\\\/197761\\\/Approximating-dynamic-proximity-with-a-hybrid?redirectedFrom=fulltext\",\"Description\":\"\",\"Include description in output citation\":0,\"Origin\":\"Manual\"},\"facultyid\":\"C16031338\",\"status\":[{\"id\":3818,\"status\":\"Completed\\\/Published\",\"term\":\"Fall\",\"year\":2019,\"termid\":\"2019\\\/01\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C16031338\",\"attachments\":[{\"attachmentid\":2235,\"mimetype\":\"application\\\/pdf\",\"filename\":\"JCP_qtan_2019.pdf\",\"filesize\":4146682,\"downloadurl\":\"https:\\\/\\\/faculty180.interfolio.com\\\/public\\\/download.php?key=SDRwNCtxSUpsamxBQ213WS9ucHFuNnMwT0hzQU11b2RPQkJ2cWc3amxyUmNRdVVXTkF4MU1sQjlBTGVzd29jQkMzM1NZZmh0SzBHdFBjcWZyUXZvSkw2TXpsMW5xTXpqbW9LN3BpT3FsOTk4c1UzNWVONWJhZz09\"}],\"coauthors_list\":[\"Qingzhe Tan\",\"Mojie Duan\",\"Minghai Li\",\"Li Han\",\"Shuanghong Huo\"],\"sort_date\":\"2019-9-01\"}]","cu_faculty_awards_and_grants":"[{\"activityid\":4126,\"fields\":{\"Title\":\"CROSSROADS: Creating Robust Opportunities for Student Success through Real-world, Organized, and Applied Data Science \",\"Sponsor\":\"National Science Foundation\",\"Grant ID \\\/ Contract ID\":\"2436765\",\"Award Date\":\"2025-09-04\",\"Start Date\":\"2025-09-15\",\"End Date\":null,\"Period Length\":1,\"Period Unit\":\"Year\",\"Indirect Funding\":1,\"Indirect Cost Rate\":\"50.3\",\"Total Funding\":\"992671.09\",\"Total Direct Funding\":\"707312.77\",\"Currency Type\":\"USD\",\"Description\":\"<p>As Principal Investigator, I led the development of a major interdisciplinary research and education proposal in data science, with a total budget of $993k, submitted to the National Science Foundation's Data Science Corps Program. This innovative program brings together a strong team of faculty from multiple departments, exemplifying the deeply interdisciplinary nature of the data science field and our program.<\\\/p>\\n<p>The proposal was recommended for funding by the NSF Program Officer, a strong endorsement following rigorous review by subject-matter experts and review committees. Although the final funding decision is still pending amid broader uncertainties at the agency level, the positive recommendation itself stands as a strong validation of our vision, the collaborative strength of our team, and the institutional support behind this effort.<\\\/p>\\n<p>We remain steadfast in our commitment to advancing data science research, education, and service in collaboration with our partner departments and faculty across campus. Through this work, we aim to establish Clark University\\u2019s leadership in data science education and research among small universities, while contributing to the institution\\u2019s profile and enhancing student recruitment, retention, and success.<\\\/p>\\n<p>***** Excerpt of the Positive Notification Email from the NSF Program Officer *****<\\\/p>\\n<p><span>Dear Dr. Han et al.,<\\\/span><span><\\\/span><\\\/p>\\n<p><span>\\u00a0<\\\/span><span><\\\/span><\\\/p>\\n<p><span><span>Congratulations<\\\/span>!\\u00a0I am pleased to inform you that we intend to move forward with recommending funding for your pending\\u00a0Data Science Corps (DSC) proposal, DSC: CROSSROADS: Creating Robust Opportunities for Student Success through Real-world, Organized, and Applied Data Science (DRL \\u2013 2436765)\\u00a0at the requested duration and budget level.<\\\/span><span><\\\/span><\\\/p>\\n<p><span>\\u00a0<\\\/span><span><\\\/span><\\\/p>\\n<p><b><span>Please note that this is only an initial notice and that no award or start date is official until you and your institution receives notification from our Division of Grants and Agreements (DGA)<\\\/span><\\\/b><\\\/p>\\n<p><b><span>....<\\\/span><\\\/b><\\\/p>\\n<p><span>Please inform us when you have prominent papers or any innovative results that arise from this project. We can often provide press releases for papers, and produce research highlights from our program, which are used by the Foundation in agency-wide and congressional reports. Let me know if you have any questions regarding this process. I wish you great success with your project.<\\\/span><\\\/p>\\n<p><\\\/p>\\n<p>\\u00a0<\\\/p>\",\"Abstract\":\"&lt;p&gt;&lt;span&gt;Data Science (DS) is a transformative field that has come to profoundly impact every aspect of society. As such, providing widespread DS education is essential for cultivating a skilled and engaged workforce while ensuring national competitiveness in this rapidly growing domain. Currently much of the focus in undergraduate DS education has been on creating courses and programs for majors and minors, yet this approach leaves a significant gap: students outside DS programs often lack opportunities to develop essential data skills and fail to recognize valuable and innovative connections to DS. However, data literacy is critical for college students across all disciplines, enabling them to thrive in both their personal and professional lives. Therefore, connecting DS to diverse fields and real-world challenges while fostering collaborations with domain experts and community partners will inspire, prepare, and engage students with a data-driven world.\\u00a0\\u00a0&lt;\\\/span&gt;&lt;\\\/p&gt;\\n&lt;p&gt;&lt;span&gt;CROSSROADS (Creating Robust Opportunities for Student Success through Real-world, Organized, and Applied Data Science)\\u00a0is an innovative program designed to create flexible pathways into, through, and beyond DS education for a broad spectrum of students while promoting engagement with real-world DS applications. This project aims to establish an inclusive and interdisciplinary DS education ecosystem through three key components: (a) Domain Exploration Modules (DEMs) that integrate DS into diverse disciplines to enhance data literacy among general students and enrich DS education for majors and minors; (b) Data Science Studio (DS Studio), as the core of a practicum system that provides hands-on learning through student collaborations with community partners; and (c) Data Science Collaborative (DSCO) Workshops, fostering partnerships among educators, students, and community organizations to drive curriculum innovation and impactful community service. Additionally, the resources and findings developed by the project will be freely available online, enabling other institutions to adopt and adapt these creative approaches and thoughtfully designed materials to strengthen their DS curricula. 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