- Graduate Students
-Recent Graduates
M.Sc and PhD Graduates
·
Anna Meng:
Thesis: A Data Warehouse View Selection Scheme to Accommodate Dimension Hierarchies (1998).
·
Jian Zheng: Thesis: Towards Making Object Horizontal Fragmentation
Dynamic. (1999).
·
Mei
Xu: Thesis: Maintaining Horizontally Partitioned
Warehouse Views (1999).
·
Sharon
Zhou : Incremental
Mining of Association Rules. (2000).
·
Yi
Liu: Code generator for integrating warehouse data sources (2001).
· Yue Su:
Mining incremental association rules with generalized FP-tree (2001).
·
Chungsheng Liu: Code Generator for Integrating
Warehouse XML Data Sources. (2001).
·
Timothy Ohanekwu:
A Pre and Post Warehouse Data Cleaning Technique. (2002).
·
Kashif Bhutta: Calculating Data Warehouse
Aggregates Using Range-Encoded Bitmap Index (2002).
·
Ajumobi Udechukwu: Domain-Independent De-duplication in
Data Warehouse Cleaning
. (2002).
·
Yi
Lu: Pre-Order Linkage WAP-tree Mining of Sequential
Patterns. (2002).
· Pinakpani Dey: Incremental Horizontal Fragmentation
of objects in object database systems (2002).
·
Malik Agyemang: Mining Outliers on Traditional and Web
data sources (2002).
·
Min
Chen: Incremental Mining of Web Log Patterns with Pre-Order Linked WAP tree
(2003).
·
Shariful Islam: Data Warehouse Stream View Update (2003).
· Monwar Mustafa:
Mining Frequent
Sequential Patterns in Data Streams using SSM Algorithm. (Fall
2005).
· Jing Li: Cleaning Web Pages for
Effective Web Content Mining. (Fall
2005).
· Jingyu Dong:
A Sensor with Misuse and Anomaly Based Data Mining Technique for Web
Intrusion Detection.(Fall 2003-Summer 2006) .
· Catherine Inibhunu: A Semantic Partition
Based Text Mining Model for Document Classification. (Winter 2005-Summer
2006).
· Maxwell Osita Ejelike: Data Mining of Sensor
Networks for Intrusion Detection. (Winter 2006-Fall 2007).
· Dr. Estella Annoni (Post-Doc): Modeling Web
Documents as Objects for AutomaticWeb Content Mining (PostDoc Aug. 2007 – Feb. 2008).
· Ahmedur S. Rahman:
Data Mining for Network Intrusion Detection. (Fall
2005-Summer 2008).
· Kashif Saeed: Mining Very Long
Sequences with PLWAPLong Algorithms (Fall 2005- Winter 2009).
· Zillur Rahman:
Data Mining Based Intrusion Detection System in Sensor Networks. (Fall 2005-Dec 2009).
·
Olalekan, Kadri: Mining Uncertain Web Log Sequences
with Access History Probabilities (Fall 2007-Winter 2010).
·
Mutsuddy, Titas: Towards
Comparative Web Content Mining using Object Oriented Model. (Fall 2006 – Fall 2010).
·
Dr. Mabroukeh, Nizar (PhD): SemAware: An Ontology-Based Web
Recommendation System (Fall 2007-Winter 2011).
·
Zhang, Dan: Object-oriented Mining in Multiple Data Sources (Fall 2007 – Fall 2011).
·
Sabbir Ahmed: Discovering Influential Nodes from Social Trust Network (Fall 2010-Summer 2012).
·
Harun-Or-Rashid M.: Mining Multiple Web Sources Using Non-Deterministic Finite State Automata (Fall 2009-Fall 2012).
·
Yanal Alahamad: Comparative Mining of Multiple Web Data Source Contents with
Object Oriented Model (Winter 2011-Fall 2012).
·
Gunjan Soni: An Automatic Email Mining Approach
Using Semantic Non-Parametric K-Means++ Clustering (Fall 2011 – Winter2013).
·
Tamanna Mumu:
Social Network Opinion and Posts Mining for Community Preference Discovery
(Fall 2011 – Winter 2013).
·
Joyce
Cao: Influence Maximization Mining for Competitive Social Networks (Fall 2013
– Winter 2015).
· Chukwuma Ejieh: Aspect-based Opinion Mining of Product
Reviews in Microblogs using most Relevant Clusters of Terms (Jan 2015-May
2016).
· Dr. Ritu Chaturvedi
(PhD): Task-based Example Miner for Intelligent Tutoring Systems (May
2012-May 2016).
·
Bindu Peravali: WebOMiner_Simple
for Comparative Mining of Web Data Sources (Jan 2015 –
Aug 2016).
·
Vignesh Aravindan: Mining Frequent
Sequential Patterns from Multiple Databases Using Transaction IDs (Sep
2015 – Dec 2016).
·
Vangala Sravya: Mining High Utility
Sequential Patterns from Uncertain Web Access Sequences using the PL-WAP (Sep
2014-May 2017).
·
Amrut Chachad: Implementation and Web
Mounting of the WebOMiner_S Recommendation System (Sep 2013 –
May 2017).
·
Ying
Xiao: Recommending
Best Products from E-Commerce Purchase History and User Click Behavior
Data (Sept 2016 – Apr. 2018).
·
Raj Bhatta: Discovering
E-commerce Sequential Data Sets and Sequential Patterns for Recommendation (Jan. 2017 – Apr. 2019).
·
Mehdi Naseri: E-Commerce
Recommendation by an Ensemble of Purchase Matrices with Sequential Patterns
(Jan. 2018 – Aug. 2019).
·
Hemni Sri Rajeswari Karlapalepu: A Taxonomy of Sequential Patterns Based
Recommendation Systems (Jan. 2019 – Aug. 2020).
· Hongyuan Wei : Using Large Language Models to Mine Multiple Variety
Big Web Data for Aspect Opinion Mining. (Since
Fall 2026).
·
Komal Virk: Improving
E-Commerce Recommendations using High Utility Sequential Patterns of Historical Purchase and Click Stream
Data (Jan. 2019 – Dec. 2020).
·
Manil Patel: Neural Network
Based Multi-Task Learning for Product Aspect Opinion Mining (Jan. 2020 –
Aug. 2021).
·
Priyanka Motwani: Discovering High
Profit Product Feature Groups by mining High Utility Sequential Patterns from
Feature-Based Opinions (Sept. 2019 – Aug.
2021).
·
Vinay Manjunath: Mining Twitter
Sequences of Product opinions with aspect terms. (Sept. 2019 – Aug. 2021).
·
Semwal, Mayank: Active Community
Opinion Network Mining and Maximization through Social Network Posts (Jan. 2019 – Aug. 2021).
·
Dr. Mahreen Nasir Butt
(PhD): Semantic
Embedded Sequential Recommendation for E-Commerce Products through Mining
Customers’ Historical Interactions and Products’ Data (Sept.
2018 – Apr. 2022).Burhan Uddin: Multi-Data Source Recommendations with
Derived Sequential Pattern Mining (Jan 1, 2020- Sep. 2022).
·
Emmanuel Jojoe Ainoo:
Mining User Facebook Likes for Cross Domain Product Recommendations across
E-commerce Platforms (Jan. 2022 – Jan. 15 2024).
·
Saadhika Bandreddy: Using Sequential
Multi-Behavior Product Features for E-Commerce Recommendation.
(Sept 2021 – Mar. 2024).
·
Dr. Abdulrauf, Gidado (PhD): Mining for Product
Recommendation on Document-Based NoSQL Big Data. (Jan.
2020 – Apr. 2024).
·
Ayomide Elijah Oduba: Enhancing
E-commerce Dataset Recommendations Using BERT and Named Entity Recognition. (Jan. 2022 – Apr. 2024).
·
Sudipta Dhar: Bert Based
Sequential Mining For Richer
Contextual Semantics E-Commerce Recommendation (BERT-SEMSREC). (Sep. 2023 – Dec. 2024).
·
Esther Mercy Umoh: High Utility
Sequential Pattern Based Recommendation Systems of Big Data Using Map Reduce. (Sep. 2023 – Apr. 2025).
·
Gurpartap Singh Ahluwalia:
Transformer-based Adaptive Tagging Framework for Aspect Sentiment Triplet
Extraction (ATF-ASTE). (Sep. 2023 – Apr. 2025).
·
Muhammad Zohaib Zeeshan:
Cross-Domain Recommendations Via Aspect Sentiment Feature Extraction using
Large Language Models (CRAS-LLM). (Sep. 2023 – Apr. 2025).
·
Behrad Ghiasi: CFs-SETRec: LLM-Based Generative Recommendation Capturing Short- to
Long-Term CF User-Item Dependencies. (Sept 2023 –
Jan. 2026).
·
Asmita
Prabhakar: A Disentangled Attention and Graph Neural Network Hybrid Model for
Multi-Aspect Sentiment Triplet Extraction (Jan. 2024 –
June 2026).
Working on Thesis and Course work
M.Sc.
Students
·
Rajalben Patel: Using Large Language Models to Mine Multiple Variety Web Data for Aspect
Opinion Mining.
(since Fall 2025).
·
Md Minhaz
Uddin: Mining Big data of Multiple
Variety Types with LLM for
Recommendation.
(since Winter 2026).
· Hongyuan Wei : Using Large Language Models to Mine Multiple Variety
Big Web Data for Aspect Opinion Mining. (Since
Fall 2026).
Ph.D Students
· Kruthika Shanta Murthy: Using Large Language Models to Mine Multiple Big
Variety Web Data (since Fall 2025).
·
Ganiyat Afolabi-Yusuf: Deep Sequential Mining of Semantics in Fast Changing Customer Purchase
Behavior Big Data for Recommendation.
(since Fall 2025).
· Zarka Khan: Using Large Language Models to Mine Multiple Big Variety Web Data (since
Winter 2026)
TBA: TBA
Page last updated: Saturday,
August 15, 2026.