Dr. Aparna Varde is a Tenure Track Assistant Professor in the Department of Computer Science at Montclair State University, NJ, USA. She obtained her Ph.D. and M.S. in Computer Science, from Worcester Polytechnic Institute, MA, USA; and her B.E. in Computer Engineering from the University of Bombay, India. She was awarded an associate membership of Sigma Xi, the Scientific Research Society in 2005 for excellence in multidisciplinary work. Her research spans data mining, databases and artificial intelligence with around 40 publications in these areas and 2 software trademarks. Dr. Varde has co-chaired Ph.D. workshops in ACM CIKM 2007, 2008 and 2010. She has served on the PC of various conferences, e.g., ACM's EDBT 2009, IEEE's ICDM 2008, SIAM's SDM 2008 and has been a reviewer for journals including IEEE's TDKE, Springer's DMKD and ACM's VLDB journal. She is the dissertation advisor for a Ph.D. student in Environmental Management as Doctoral Faculty in that program and the research advisor for M.S. and B.S. students in Computer Science. She has also been an external committee member for Ph.D. students from Queensland University of Technology, Australia. Dr. Varde has served as a panelist for NSF's Cyber-enabled Discovery and Innovations Program (CDI) through their division of Information and Intelligent Systems (IIS). Her prior academic experience includes being a Tenure Track Faculty Member in the Department of Math and Computer Science at Virginia State University, USA; and a Visiting Senior Researcher at the Max Planck Institute for Informatics, Germany. She also has industrial experience mostly in multi-national companies such as Lucent Technologies and Citicorp. Dr. Varde is classified as an outstanding researcher by the Citizenship and Immigration Services, USA. Her research is supported by grants from organizations such as PSE&G and NSF, USA.

Data Mining
Artificial Intelligence
Database Management
Multidisciplinary Research

Decision Support in Green Information Technology

This multidisciplinary research in data mining and environmental management is supported by a grant from PSE&G. It involves investigating greener solutions for data centers with the goals of energy efficiency and adequate performance. The role played by data mining techniques is significant here in the development of a decision support system that will assist IT managers to head towards green computing in their respective data centers. This grant is supporting a Ph.D. student Michael Pawlish in Environmental Management with Dr. Varde as the dissertation advisor in her capacity as Doctoral Faculty Member in that Program. It has led to publications on in IEEE's ICIAFS, ACM's CIKM and other muldisciplinary venues.

Terminology Evolution in Information Retrieval

This research in the overall area of Web and text mining started as joint work with Max Planck Institute, Germany where Dr. Varde was a Visiting Senior Researcher. The goal of this project is to detect evolving terminology in responding to user queries on the Web by mining existing text archives. This is in order to enhance information retrieval by incorporating historical information on terms contained in queries. This led to a Masters' Project by a CS graduate student Debjani Roychoudhury and a Masters' Thesis by a CS graduate student Amal Kalurachchi. It has been published in AAAI, ACM's EDBT and ACM's CIKM conferences.

Learning By Mining Nanoscale Images

This work is funded by a grant from NSF REU ans supports undergraduate students from the tri-state area during summers. The focus of this grant is in the area of image processing and my contribution is in the area of learning from image data at the nanoscale level. The work entails proposing and implementing techniques for discovering knowledge from image data useful in domain-specific decision-making. This project involves real data obtained from researchers in Nanotechnology, used for running experiments with the proposed techniques. It has real-world applications such as drawing conclusions from biological images based on automating comparisons between them by learning suitable notions of similarity. This has the broader impact of catering to areas such as health informatics. For example, the results of the learning process are useful in finding a cheaper material instead of a more expensive material to develop a human body implant, if both materials yield similar results as evident from image similarity search. This is given the fact that these images are generated from real experimental Publications from this work include a paper in SPIE 2010 conference, a presentation in ACM CCSC 2010 conference, a paper in ICML 2010 Workshops and two earlier papers in IEEE ICDE DBRank workshop and the ICDCIT conference.

Treatment of Article and Collocation Errors in L2 English Texts

This research in the general area of text mining and computational linguistics involves the detection, correction and causal analysis of errors in article usage and collocation that occur in texts written by L2 (non-native) English speakers. Article errors pertaining to entering articles where not needed, omitting articles where needed, entering the wrong article. Collocation errors involve using incorrect collocation such as powerful tea when the user actually means strong tea. We are proposing methods to detect such errors, analyze their causes and suggest correct responses to them. Mining the concerned text and deploying machine learning techniques such as classification and ensemble learning play an important role here. Related publications include a paper accepted in the AAAI based FLAIRS 2010 conference on classification of article errors and one more in submission to a suitable conference on collocation error correction. A grant is in submission in for this work.

XML-based Markup Langauges and Cloud Computing in EHR Management

This work is supported by a grant to fund Honors students in BS degree programs in various science disciplines who are expected to work with their respective faculty mentors to complete an undergraduate thesis in a concerned area. My role as faculty mentor is to work with a BS student in Information Technology on a specific research project, namely, XML-based markup languages and Cloud Computing in management of EHR (Electronic Health Records). We are investigating the use of the medical markup language MML for storing and exchanging health records, proposing techniques for knowledge discovery over such XML based standards and also investigating the use of cloud technology in storage, retrieval and knowledge discovery pertaining to healthcare taking into account issues such as cost-effectiveness, risk analysis and scalability. Related publications include a paper in the IEEE ICDM 2011 conference in their KDCloud workshop and one more in submission to a suitable journal.

Scientific Data Management and Mining with Cloud Computing

This project focuses on research in cloud computing with emphasis on managing and mining large scale scientific data. Besides a thorough investigation of existing methodologies, it addresses the design and implementation of novel techniques and the enhancement of existing approaches for scientific data analysis on the cloud. Risk assessment forms an important aspect of this work. The grant proposal also entails the development of objective metrics to evaluate the performance of cloud technologies with particular emphasis on scientific data, considering issues such as efficiency, accuracy, complexity and scalability. It also addresses the prospects of greening the environment through the use of cloud computing, considering the contributions that can be made by the cloud with respect to greenness and evaluating the same subjectively and objectively, with a broader impact on sustainability. This has led to a Masters' Project by the student Shireesha Chandra and is in submission to suitable venues. More work is in progress with the potential involvement of other students.