CAD Seminar Series: Leveraging Machine Learning to Analyze Stochastic Models of Intracellular Networ
This event is in the past.
Speaker: Ankit Gupta, Ph.D., ETH Zurich
Permanent Senior Scientist II, Department of Biosystems Science and Engineering
Time: Wednesday, October 23, from 2:30 to 3:30 pm
Location: Virtual
Zoom link for online audience: https://wayne-edu.zoom.us/j/92845590121?pwd=CpRA5Wa5gzSMn2xiVkR2abD83O5nrH.1
Title: Leveraging Machine Learning to Analyze Stochastic Models of Intracellular Networks
Abstract: Single-cell studies reveal that even genetically identical cells cultured under identical conditions can exhibit significant heterogeneity. This variability often arises from the inherent randomness in the timing of reactions within intracellular networks and pathways. These stochastic effects can profoundly influence the phenotypic traits of cellular populations and give rise to novel biological functions. To effectively capture this intrinsic randomness and assess its impact, the use of stochastic models is essential. In this talk, I will introduce this modeling paradigm, discuss the computational challenges involved in analyzing such models, and explore how modern machine learning methods can help address these challenges.
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CAD Seminar Series: Advancing Knowledge, Innovation, and Collaboration in Computation, AI, and Data Science (CAD)
The CAD Seminar Series is a dedicated platform for advancing knowledge, fostering innovation, and promoting collaboration across the fields of Computation, Artificial Intelligence, and Data Science. This series brings together leading experts, researchers, and professionals to explore the latest developments, tackle emerging challenges, and drive forward-thinking solutions at the convergence of these critical disciplines.
Objectives:
• Advance Knowledge: Share cutting-edge research and insights that push the boundaries of what is known in CAD.
• Foster Innovation: Encourage the development of novel ideas and solutions through interdisciplinary dialogue and creative thinking.
• Promote Collaboration: Unite expertise across disciplines and build bridges between academia, industry, and government to address complex problems and create opportunities for joint ventures.
Schedule: The seminars will be held on a weekly basis (unless otherwise noted) throughout the academic year. For Fall 2024, the seminars are tentatively scheduled for Wednesdays from 2:30 PM to 3:30 PM, and will be available both in-person at 1146 FAB and online. Each seminar will spotlight a particular topic, providing an in-depth exploration and fostering lively discussions.
Target Audience: The CAD Seminar Series is designed for a diverse audience, including faculty, researchers, students, and professionals in Computation, AI, Data Science, and related fields. It serves as a forum for exchanging ideas, networking, and contributing to the growth of these rapidly evolving areas. We highly recommend in-person attendance to enhance engagement and networking opportunities with speakers and fellow participants.
Call for Participation: We welcome contributions from researchers, practitioners, and students. Whether presenting your work, participating in discussions, or attending as a learner, your involvement is crucial to the success of this collaborative initiative.
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