CASPR

Our vision is to build a socialbot that can really “understand” the conversation as well as the context in which the conversation is being carried out, just like humans do.

University of Texas at Dallas - Caspr.jpg
Location: Dallas, TX, USA
Faculty advisor: Gopal Gupta

Our team CASPR, consisting of computer science Ph.D. and master’s students from The University of Texas at Dallas, brings together the experiences in question-answering, conversational agents, commonsense reasoning, logic programming, and deep learning. Our vision is to build a socialbot that can really “understand” the conversation as well as the context in which the conversation is being carried out, just like humans do. The novelty of our approach is that our work mostly relies on automated commonsense reasoning. To achieve human intelligence that includes learning and reasoning, we believe that ML and commonsense reasoning should work in tandem.

Kinjal B. - Team leader

Kinjal is a computer science PhD student working under Prof. Gopal Gupta on closed domain question-answering and commonsensereasoning. His research interest lies in Logic Programming, Natural Language Understanding and Machine Learning, and my recent papers are in the area of textual and visual question answering. Kinjal also gathered real world data science project experience while working as an intern at Intuit.During his Bachelor's in Information Technology, he worked on several NLP projects (e.g. Text Similarity) and also interned at IIT Kharagpur for NLP research. Kinjal looks forward to contributing to the team with his knowledge and experience.

Huaduo W.

Huaduo is a computer engineering PhD student in ALPS lab led by Prof. Gopal Gupta. Her current research focuses on visual and textual based QA, commonsense reasoning. Huaduo's research interest lies in logic programming, natural language processing, and understanding. Her past work and research experience were about computer systems and infrastructure systems. In her last master's, she worked on a system kernel project for my research and interned as a backend SDE at Baidu. After that, she worked as a project manager of several infrastructure systems in China CITIC bank for 3 years.

Fang L.

Fang is a Computer Science PhD candidate under Dr. Gopal Gupta. Fang's research interests are explainable AI, commonsense reasoning, answer set programming, logic programming. Fang got their Master' in Applied Mathematics & Computer Science and graduated with honor from University of Central Oklahoma. In the past few years, they got hands-on experiences on researches such as human computer interaction, IoT, autonomous vehicle, AI planning, answer set programming solver, etc. Fang has been an instructor of Programming Languages course at UTD. They participated in LOVE'S ENTREPRENEUR'S CUP (2017) as the cofounder (CTO) of company Turning Systems and won the third place prize.

Sarat Chandra V.

Sarat Chandra is a PhD student in the CS Department at UT Dallas. Sarat Chandra's research work involves automatically synthesizing concurrent programs using commonsense knowledge about concurrency. Given a sequential program for a pointer data structure (e.g., insert a node, delete a node), the goal of this research is to automatically synthesize its concurrent version, just like a human would. To accomplish this, we need to have knowledge to model concurrency, shared memory, and how pointer data structures behave. All this knowledge is represented as axioms in answer set programming and reasoning performed in the s(CASP) system to synthesize the concurrent program.

Nancy D.

Nancy is a student at UTD working towards a Master's in Computer Science with a focus on Intelligent Systems and Data Science. Nancy started developing Alexa skills in 2018 while working as a research assistant for a professor at the school of Arts & Humanities and has since published two Alexa skills. Outside of school, Nancy enjoys playing Pokemon Go and spending time with her family and pets.

Xiangci L.

Xiangci is a first year Ph.D. student at UT Dallas. His research interest is natural language processing, particularly scientific document information extraction and summarization. He obtained his master’s degree from University of Southern California in computer science, and bachelor’s degree from New York University Shanghai in computer science and neuroscience. He has industry experiences at Baidu USA and Chan Zuckerburg Initiative, as well as a year of neuroscience research experience. He has a few publications on natural language processing.

Nancy D.

I’m a student at UTD working towards my Master's in Computer Science with a focus on Intelligent Systems and Data Science. I started developing Alexa skills in 2018 while I was working as a research assistant for a professor at the school of Arts & Humanities and since have published two Alexa skills. Outside of school, I enjoy playing PokemonGo and spending time with my family & pets.

Gopal Gupta - Faculty advisor

Gopal Gupta has been a faculty member in computer science since 1992 (currently hold the Erik Jonsson endowed chair). All his degrees are in computer science (BS CS from IIT Kanpur, MS & PhD from UNC Chapel Hill). His research over his whole career is focused on building practical automated reasoning and logic programming systems. For the last 5 years, his group has extensively researched automating commonsense reasoning--key to building chatbots that can respond by "understanding" humans. Have published more than 160 papers, founded 2 companies, and built many practical software systems related to automated reasoning, many of which are available publicly through Gopal's homepage.

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Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert livestreams to artist merch, Amazon Music is innovating at some of the most exciting intersections of music and culture. We offer experiences that serve all listeners with our different tiers of service: Prime members get access to all the music in shuffle mode, and top ad-free podcasts, included with their membership; customers can upgrade to Amazon Music Unlimited for unlimited, on-demand access to 100 million songs, including millions in HD, Ultra HD, and spatial audio; and anyone can listen for free by downloading the Amazon Music app or via Alexa-enabled devices. Join us for the opportunity to influence how Amazon Music engages fans, artists, and creators on a global scale. Learn more at https://www.amazon.com/music. The Music Catalog Quality team at Amazon Music serves a key role in developing solutions to ensure and improve the quality of catalog metadata and content across the music streaming experience. We create solutions that detect, measure, and remediate quality issues in music metadata - including artist information, track attributes, versions, content tags, and provide actionable insights that enable continuous improvement of the catalog. We leverage a host of scientific and engineering technologies to accomplish this mission, including Generative AI, classical ML, Natural Language Processing, Computer Vision, and automated data validation pipelines. Key job responsibilities As an Applied Scientist, you will own the design and development of end-to-end systems. You’ll have the opportunity to create technical roadmaps, and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. Other responsibilities include: - Collaborate with scientists, engineers, and product managers to define and frame business problems as ML or optimization tasks. - Use machine learning, deep learning, LLMs and Agentic AI techniques to create scalable solutions for business problems - Analyze and extract relevant information from large amounts of Amazon's data to help automate and optimize key processes - Design, development and evaluation of AI models for predictive learning - Research and implement novel machine learning and statistical approaches - Implement scalable data pipelines and model-serving systems. - Analyze experimental results, draw insights, and refine models to improve accuracy and robustness. - Communicate findings and recommendations to technical and non-technical audiences.