samit shivadekar
Assistant Teaching Professor
About
I am a full time regular lecturer in computer science and electrical engineering(CSEE) department at the University of Maryland Baltimore County, where my duties are teaching different computer science related subjects to undergraduate and graduate students. I work in the Center for Accelerated Real Time Analytics (CARTA) Lab under the supervision of Professor Milton Halem. My research interests include artificial intelligence (AI), machine learning, computer vision, natural language processing, and their applications to aerospace and earth science problems. I have a strong background in computer science, mathematics, and statistics. I hold a Ph.D. degree in Computer Science from University of Maryland Baltimore County, USA and Master of Science in Computer Science from the University of California State University, Fullerton, USA. I also hold a Bachelor of Engineering degree in Information Technology from Shivaji University, India.I have published several papers in top-tier conferences and journals in the field of AI and data science, such as ICPP, SPIE, IGARSS, AMS, AGU, Springer and IEEE Transactions on Streaming Tensor decomposition, Pattern Analysis and Machine Intelligence. I have also participated in several data science competitions and hackathons, such as Great-learning hackathon, Kaggle and Data Science Bowl. I have won several awards and recognitions for my work, such as: ✓ Won the First position in AIML Hackathon arranged by Great Learning in 2022 ✓ Intern of the month for Maryland Technology Internship Program 2019
Research interests
I intend to advance my research in cognitive artificial intelligence by developing sophisticated deep learning and probabilistic models tailored for medical diagnostics, imaging analysis, and weather forecasting in the United States. My research will focus on enhancing AI-driven medical imaging techniques to improve the early detection of diseases such as cancer and neurological disorders, enabling more accurate and timely interventions. Additionally, I will develop predictive models that analyze patient data to forecast health outcomes, aiding clinicians in personalized treatment planning and risk assessment. In the domain of meteorology, I aim to refine AI-powered weather forecasting models by integrating real-time satellite and sensor data, improving the accuracy of storm tracking, wildfire predictions, and disaster risk preparedness. Through collaborations with leading universities, hospitals, and space and weather agencies, I will conduct large-scale data analysis, optimize AI algorithms for real-world applications, and ensure ethical and transparent AI deployment. By bridging theoretical AI advancements with practical implementation, my goal is to create scalable, high-impact solutions that revolutionize healthcare and weather related disaster resilience. I am actively contributing to two high-impact AI-driven medical research initiatives at the Center for Accelerated Real-Time Analytics (CARTA) and the Institute for Data Science and Computing (IDSC) at the University of Miami . In addition, I am actively participating on a NASA grant to develop and implement an AI-based wildfire digital twin that can provide fire spread information to responders occurring anywhere over the US. The two former projects address critical gaps in AI-driven disease prediction and regulatory AI validation, aligning with U.S. government priorities in healthcare innovation, patient safety, and AI governance . The latter wildfire digital twin project involves establishing field instruments such as ceilometers at prescribed fire campaigns for edge computing detection of aerosol smoke dispersal from wildfire plumes. My contribution involves development and application of AI physics inferred calculations of aerosols from smoke entering the planetary boundary layer where it can affect human health.As a collaborator and key contributor on the medical projects, I am engaged in the development and application of advanced AI methodologies to enhance early-stage Parkinson’s disease detection[4] and progression modeling as well as establishing an AI-testbed for real-world monitoring of medical AI devices. These projects represent significant advancements in AI-based diagnostics and regulatory oversight, providing novel AI healthcare solutions that are not currently available in the U.S.
Teaching interests
As per departmental requirements, I am flexible enough to teach different subjects too. Because I successfully completed Post Graduate Program in Artificial Intelligence and Machine Learning: Business Applications from Texas McCombs, the University of Texas at Austin, McCombs School of Business, USA, in January 2023 and completed my Ph.D. in August 2023, I am also interested in developing and teaching new non-conventional courses such as a course that uses science fiction to teach Artificial Intelligence, Data Science or a course that uses e-commerce or world wide web as domains to introduce databases. I believe that these mixed type of courses would be more suitable at an undergrad level since they will be able to provide practical context to otherwise theoretical subjects and potentially inspire students to pursue these subjects at an advanced level.I can teach :--
CMSC 104 :-- Problem Solving and Computer Programming
CMSC 201 - Computer Science I
CMSC 341 - Data Structures
CMSC 421 - Principles of Operating Systems
CMSC 441 - Design and Analysis of Algorithms
CMSC 447 :-- Software Engineering-I
CMSC 462 - Introduction to Data Science
CMSC 621 - Advanced Operating Systems
Education
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Ph D, Computer Science
— UMBC (2023) A Streaming Tensor Decomposition Analysis for Earth Science Informatics