Tabassum Kakar

Affiliated with:

Education:

Worcester Polytechnic Institute (WPI), Master of Science in Data Science, May 2016

Ghulam Ishaq Khan Institute Of Engineering Sciences And Technology (GIKI), Bachelor of Computer Software Engineering, June 2011

I am a PhD candidate in data science program at WPI. I completed my MS at WPI as a Fulbright scholar. After my BS, I have worked for three years in telecommunication industry as a BI developer and database administrator. My research interests include using machine learning techniques on big data and design visual analytical systems to help domain experts analyze their data effectively.

Office Location

Fuller Lab, 319

Contact

Phone:

917-861-9738

tkakar@wpi.edu

Awards

ORISE Fellowship for Ph.D. at WPI

Fulbright Scholarship for MS at WPI.

Data Science Leadership award at WPI (2016, 2017). ·

Third position at Graduate Research Innovation Exchange WPI, 2017.

GHC scholarship to attend Grace Hopper Conference (2016).

Lucky Cement Scholarship for BS at GIK Institute (GIKI) of Engineering Sciences and Technology, Pakistan.

Gold Medalist at GIKI in Computer Software Engineering Department.

Research Interests

Research Interests:

Visual Analytics

Human Computer Interaction

Machine Learning

Scholarly Work

Scholarly Work:

“MARAS: Signaling Multi-Drug Adverse Reactions”, ACM KDD. Xiao Qin, Tabassum Kakar, Susmitha Wunnava, Elke Rundensteiner, Lei Cao, August 2017.

“Towards Transforming FDA Adverse Event Narratives into Actionable Structured Data for Improved Pharmacovigilance”, ACM Symposium on Applied Computing (SAC) S. Wunnava, Xiao Qin, Tabassum Kakar, Vimig Socrates, Amber Wallace, E. Rundensteiner, April 2017.

[EuroVis’19] DIVA: Exploration and Validation of Hypothesized Drug-Drug Interactions. Tabassum Kakar, Xiao Qin, Elke A. Rundensteiner, Lane Harrison, Sanjay K. Sahoo and Suranjan De.

[IVAPP’19] MedViz: Visual Analytics for Medication Error Detection. Tabassum Kakar, Xiao Qin, Cory Tapply, Derek Murphy, Daniel Yun, Oliver Spring, Elke A. Rundensteiner, Lane Harrison, Thang La, Sanjay K. Sahoo, and Suranjan De.

[Drug Safety’19] Adverse Drug Event Detection from Electronic Health Records Using Hierarchical Recurrent Neural Networks with Dual-Level Embeddings. Susmitha Wunnava, Xiao Qin, Tabassum Kakar, Cansu Sen, Elke A. Rundensteiner and Xiangnan Kong.

[URTC’18] Drug-Drug Interaction Signal Detection from Drug Safety Reports. Brian Zylich, Brian McCarthy, Andrew Schade, Huy Quoc Tran, Xiao Qin, Tabassum Kakar and Elke A. Rundensteiner.

[CIKM’18 Demo] DEVES: Interactive Signal Analytics for Drug Safety. Tabassum Kakar, Xiao Qin, Susmitha Wunnava, Brian McCarthy, Andrew Schade, Huy Quoc Tran, Brian Zylich, Elke A. Rundensteiner, Lane Harrison, Sanjay K. Sahoo, and Suranjan De.

[AMIA’18 Poster] Deep Learning Strategies for the Automatic Detection of Medication and Adverse Drug Events from Electronic Health Records. Susmitha Wunnava, Xiao Qin, Tabassum Kakar, Elke A. Rundensteiner and Xiangnan Kong.

[MADE’18 Workshop] Bidirectional LSTM-CRF for Adverse Drug Event Tagging in Electronic Health Records. SusmithaWunnava, Xiao Qin, Tabassum Kakar, Elke A. Rundensteiner and Xiangnan Kong.

[ICDE’18 Demo] Multi-Drug Adverse Reactions Analytics. Xiao Qin, Tabassum Kakar, Susmitha Wunnava, Brian McCarthy, Andrew Schade, Huy Quoc Tran, Brian Zylich, Elke A. Rundensteiner, Lane Harrison, Sanjay K. Sahoo, and Suranjan De.

[HEALTHINF ’18]One Size Does Not Fit All: An Ensemble Approach Towards Information Extraction from Adverse Drug Event Narratives. Susmitha Wunnava, Xiao Qin, Tabassum Kakar, Xiangnan Kong, Elke Rundensteiner, Sanjay Sahoo and Suranjan De.

[KDD’17] MARAS: Signaling Multi-Drug Adverse Reactions. Xiao Qin, Tabassum Kakar, Susmitha Wunnava, Elke Rundensteiner, and Lei Cao.

[SAC’17] Towards Transforming FDA Adverse Event Narratives into Actionable Structured Data for Improved Pharmacovigilance. Susmitha Wunnava, Xiao Qin, Tabassum Kakar, Vimig Socrates, Amber Wallace, and Elke Rundensteiner.