About
I am currently a graduate student at University of Pennsylvania (UPenn) in the Electrical Engineering department pursuing research in Natural Language Processing (NLP) and Computer Vision. My ongoing Master’s thesis in the Computer Science department at University of Pennsylvania is focused on leveraging natural language processing to study temporal shifts in language markers indicative of cardiovascular health behaviors (diet, smoking, substance use, physical activity) during public health events such as COVID-19. My research is being supervised by Professor Lyle Ungar and Professor Sharath Chandra Guntuku. Professor Lyle Ungar’s research group develops explainable machine learning, deep learning, and natural language processing methods for psychology and medical research. I am also working in collaboration with Dr. Raina Merchant, Director of Center for Digital Health at Penn Medicine to build predictive language models for uncovering insight into health outcomes and psychological states of individuals and communities.
I am also working with Professor Kostas Daniilidis and Dr. Eleni Miltsakaki on vision and language navigation (VLN). More details about the project can be found here. In my summer I began working with Professor A.T Charlie Johnson to develop an autonomous, unbiased and reliable deep learning Application Programming Interface (API) that could detect COVID-19 and other medical conditions such as Ovarian Cancer, Pancreatic Cancer, Influenza, etc., by differentiating between human body odors. More details about the project can be found here.
Prior to enrolling in the Master’s program at UPenn I had worked with several of world’s leading Technology, Telecom, Retail and Manufacturing leaders, specializing in building analytical and machine learning solutions to help the clientele achieve their business goals smoothly. With five years of industry expertise working with multi million global businesses across USA, Latin-America, South-East Asia & Europe geographies my charter was to work closely with the client CXOs & BU leaders to help them create, incubate, launch & monetize successful investments in new digital technologies.
Research Interest
I am interested in developing explainable deep learning solutions, in particular applications of natural language processing alone or in collaboration with computer vision, robotics and Human-Computer Interaction. My ambition is to bring the dream of intelligent and autonomous systems closer to reality by leveraging multi-disciplinary research.
I am intrigued by those challenging research problems whose applications revolve around physical and mental well-being of the society. In my ongoing master’s thesis I aim to compare the health and lifestyle of people before and after the pandemic hit the world with special focus on risks associated with cardio-vascular disease using Natural Language Processing. More details about my ongoing Master’s Thesis can be found here.
I would love to develop systems that belong in:
- The intersection of natural language and computer vision:
- For instance creating a framework that can intelligently comprehend and interpret the driving environment visuals, recognize hazardous or abnormal scene and forecast the outcome in order to respond with utmost efficiency. This is equivalent to envisioning a smart machine utilizing image recognition and semantics to prevent locomotive crashes in real time
- Developing scalable computational language models utilizing large-scale user generated text on social media platforms:
- I believe that social media platforms such as Reddit and Twitter that are a rich ecological source of human interactions can offer large volumes of unbiased conversations that can be summarized using NLP. They can help form targeted messaging and mitigation strategies especially during crises and public health emergencies
Affiliations
- World Well Being Project/Positive Psychology Center
- GRASP Laboratory
- David Rittenhouse Laboratory
- Penn Medicine Center for Digital Health
- Penn Research in Machine Learning
- IEEE Robotics and Automation Society
- IEEE Computational Intelligence Society
- IEEE Engineering in Medicine and Biology Society