Machine Learning researcher developed novel algorithms and solutions for various domains. Some of my projects are highlighted below:- Generative Adversarial Attack on Ensemble Clustering (WACV 2022)o Instigated the concept of exploiting vulnerabilities of ensemble clustering against adversarial attackso Designed and engineered coherent adversarial methodology and critically evaluated against various datasets to prove both its imperceptibility and effectiveness- Adversary for Social Good (AAAI 2020)o Conceived a novel idea of adapting adversarial attacks for social privacy protection through GNNo Effectively developed a defensive method to solve the problem by considering both graph nodes and edges and successfully presented the results both quantitatively and qualitatively on visual dataset- Cross-database Mammographic Image Analysis (IEEE BigData 201)o Explored different transfer learning techniques to overcome limited training data for mammogram image datasets and achieved a 20% increase in classification accuracy- Multi-modal Brain-Computer Interface Classificationo Collaborated with interdisciplinary team to investigate brain signal data, performed representation learning and implemented GNN for healthy vs. patients’ classification resulted in significant improved performance (JNP 2023)o Collaborated and mentored juniors to curate, analyze and develop graph structure and multi-modal analysis to characterize auditory vs. deviant noises using brain signals (IEEE EMBC 2023)- Data Collection and Action Recognition using Skeleton Data (Data Science Final Project)o Kinect V2 was used to collect both RGB and depth data and deep learning model was developed to perform action recognition using depth (skeleton) data

Taught multiple undergraduate courses labs, assessed assignments and exams and held weekly office hours to address students’ questions and concerns.– CIS 180: Introduction to Object Oriented Programming I– CIS 272: Introduction to Computing Systems– CIS 280: Software Specification and Design– CIS 180, 181: Object-Oriented Programming I, II– CIS 361: Models of Computation– CIS 550: Advanced Machine Learning

Designed and executed a time-bound pipeline for analyzing mass spectrometry data, including clustering spectra, creating similarity networks, and detecting molecules.

Worked with multidisciplinary team to research and design innovative molecule generation framework to enhance molecules properties by leveraging deep learning

• Carried out statistical analysis on Nursing School admissions and course enrollment & results data and used JavaScript and D3 to visualize different patterns and trends for students’ performance against the courses• These results effectively helped the school to maintain and redesign their curriculum to get most success rate• First time a machine learning model using R language is built for this Nursing School to predict successfulness for each candidate appearing in the upcoming NCLEX-RN (Nursing License Exam)