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Aparna Varde

Associate Professor, Computer Science

Richardson Hall
BE, University of Bombay
MS, Worcester Polytechnic Institute
PhD, Worcester Polytechnic Institute
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Dr. Aparna Varde is a Tenured Associate 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 over 60 publications and 2 software trademarks. Dr. Varde has co-chaired Ph.D. workshops/forums in ACM CIKM 2007, 2008, 2010, 2012 and 2014 and IEEE ICDM 2013. She has served on the PC of various conferences, e.g., ACM's CIKM & EDBT, IEEE's ICDM, SIAM's SDM, Springer's DEXA and has been a reviewer for journals including IEEE's TDKE, ACM's TKDD, Elsevier's DKE, Springer's DMKD and ACM's VLDB journal. She has been the dissertation advisor for two Ph.D. students 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 two 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 research is supported by grants from organizations such as PSE&G and NSF, USA. Her prior academic experience includes being a Tenure Track Assistant Professor 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.


Data Mining - Scientific Data Mining, Text Mining, Cloud Mining, Big Data Analytics
Artificial Intelligence - Machine Learning, Decision Support, Cloud Intelligence
Database Systems - Web Databases, Spatial Data Management, Cloud Data Processing
Environmental Management (Doctoral Program) - Green IT, Sustainability, Geo-informatics


Research Projects

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 GreenDSS that will assist IT managers to head towards green computing in their respective data centers. This grant has supported 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 in ACM's SIGMOD Record Journal, IJCAC journal, IEEE's ICIAFS, ACM's CIKM workshops, IEEE's ICDM workshops and various other multi-disciplinary venues.
PhD Student: Michael Pawlish (Graduated: May 2014)
Funding: PSEG Research Grant (2011 to 2013)

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.
M.S. Thesis Student: Amal Kaluarachchi (Graduated: May 2010)
M.S. Project Student: Debjani Roychoudhury (Graduated: May 2009)
Funding: Faculty Research Visit at Max Planck Institute, Germany (2008)

Learning By Mining Nanoscale Images

This work is funded by a grant from NSF REU and 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, and a paper in ICML 2010 Workshops.
Summer Research Student: Gregory Roughton (Completed: July 2009)
Summer Research Student: Daniel Jackowitz (Completed: July 2010)
Funding: NSF REU Grant (2009 to 2010)

Article Errors, Odd Collocations and Preposition Prediction in L2 English Text

This research in the area of text mining and computational linguistics. It involves the classification of article errors, correction of odd collocations and prediction of preposition usage in texts written by L2 (non-native) English speakers. Article errors pertain to entering articles where not needed, omitting articles where needed and entering the wrong article. Odd collocations involve using incorrect combination of terms such as powerful tea when the user actually means strong tea. Preposition prediction involves suggesting an appropriate preposition to the user in writing aids typically designed for ESL learners. Mining the concerned text and deploying machine learning techniques such as classification and ensemble learning play an important role here. Related publications include conference papers in AAAI's FLAIRS 2010, IEEE's ICICS 2013 and a journal paper in ACM SIGKDD Explorations journal 2015. More work is in submission.
M.S. Thesis Student: Alan Varghese (Graduated May 2013)
M.S. Project Student: Aliva Pradhan (Graduated May 2011)
M.S. Project Student: Pooja Bhagat (Graduated May 2014)

XML-based Markup Languages and Cloud Computing in EHR Management

This work is supported by a SHIP grant through Roche and Merck 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 another one in IEEE's ICIAFS conference.
B.S. Honors Student: Jonathan Tancer (Graduated May 2012)
Funding: Science Honors Innovation Program (2010 onwards)

Cloud Computing in Big Data and Social Media

This project focuses on research in cloud computing with emphasis on managing and mining big data. Besides a thorough investigation of existing methodologies, it addresses the design and implementation of novel techniques and the enhancement of existing approaches for big data management and mining on the cloud. The project involves exploratory research with cloud technologies such as Hadoop, Hive and Mahout for big data. Various real world data sets are used in the context of areas such as scientific data management. Predictive analysis on the cloud is also conducted deploying machine learning algorithms in Mahout with specific reference to text classification, recommender systems and decision support. This project also involves opinion mining over cloud-based social media such as Twitter, where results of sentiment analysis are useful in applications such as recommenders. This has led to publications in the NJBDA Symposium, ACM CIKM's CloudDB 2013 and IEEE ICDM's KDCloud 2013. More work is in submission to suitable venues.
M.S. Project Student: Klavdiya Hammond (Graduated May 2013)
M.S. Project Student: Shireesha Chandra (Graduated May 2012)
M.S. Project Student: Ketaki Gandhe (Graduated May 2015)