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DTSTAMP:20240515T123103
DTSTART;TZID=America/Detroit:20240430T120000
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SUMMARY:Careers / Jobs: Exact Sciences Virtual Meet and Greet - LinkedIn Tips from Recruiters
DESCRIPTION:About the Event:\nIn today's competitive job market\, having acompelling LinkedIn profile is essential for showcasing your skills\, experiences\, and ambitions to recruiters and hiring managers. At this event\, you'll have the opportunity to hear from experienced recruiters who specialize in talent acquisition for one of the leading biotechnology companies in the field. Whether you're a LinkedIn novice or looking to optimize your existing profile\, this session is designed to help you unlock the fullpotential of your LinkedIn presence.\n\nWhat You'll Learn:\n-Strategies for creating a standout LinkedIn profile that highlights your strengths andachievements in the biotech industry\n-Tips for crafting engaging and relevant content to attract recruiters and industry professionals\n-Techniques for expanding your professional network and connecting with key influencers in the biotech field\n-Insights into the importance of personal branding and how to effectively communicate your unique value proposition\n-Advice on leveraging LinkedIn features such as job search\, groups\, and messaging to advance your career goals\n\nWhy Attend:\n-Gain valuable insights and advice from recruiters who understand what biotech companies look for in candidates on LinkedIn\n-Network with fellow students interested in pursuing careers in biotechnology and related fields\n-Receive actionable tips and resources to help you build a strong LinkedIn presence and advance your career aspirations\n-Explore potential internship\, co-op\, and job opportunities at Exact Sciences \n\nWho Should Attend:\nThis event is perfect for college students studying any majors who are eager to enhance their LinkedIn presence and unlock new career opportunities in the biotech sector. Whether you're a freshman just starting to build your network or a senior preparing to enter the workforce\, everyone is welcome!\n\nDon't miss out on this chance to gain valuable insights and take your LinkedIn profileto the next level. Be sure to RSVP early to secure your spot!\n\n\nAt Exact Sciences\, we are cancer fighters. We are united by our mission to change lives by providing earlier\, smarter answers. Through advances in cancer detection and treatment guidance\, we will help eradicate the disease and the suffering it causes. We are driven to find ambitious\, dynamic individuals who thrive in a team-based environment and can help us take anotherstep toward winning the war on cancer through early detection.
UID:120646-21845089@events.umich.edu
URL:https://events.umich.edu/event/120646
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:
LOCATION:
CONTACT:
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DTSTAMP:20240321T082137
DTSTART;TZID=America/Detroit:20240430T120000
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SUMMARY:Workshop / Seminar:The Phase of Fat: Mechanism and Physiology of Lipid Metabolism-Department of Biological Chemistry Lands Lecture
DESCRIPTION:Dr. Tobias Walther will deliver the William E.M. Lands Lectureship on Tuesday April 30th\, 2024 in 5330 MS I.
UID:120521-21844859@events.umich.edu
URL:https://events.umich.edu/event/120521
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Basic Science,biolgical chemistry,biological,biological chemistry,biological science,biology,Biosciences
LOCATION:Medical Science Unit I - 5330 MS I
CONTACT:
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DTSTAMP:20240415T104031
DTSTART;TZID=America/Detroit:20240430T123000
DTEND;TZID=America/Detroit:20240430T143000
SUMMARY:Lecture / Discussion:Regression Methods To Uncover Heterogeneous Effects With Applications To Analyzing Education Disparity
DESCRIPTION:Abstract: In today's era of large-scale data\, academic institutions\, businesses\, and government agencies are increasingly faced with heterogeneous datasets. Consequently\, there is a growing need to develop effective methods for extracting meaningful insights from this type of data. Quantile\, expectile\, and expected shortfall regression methods offer useful tools to detect heteroscedasticity in data. Beyond traditional metrics like mean and variance\, quantile\, expectile\, and superquantile (also known as the expected shortfall or conditional value-at-risk) analyses can capture nuanced effects of predictors on the extremes of the response distribution. The importance of these regression methods is evident in the numerous publications on their applications and theories in both statistical and scientific literature. Our specific contributions to this field of regression methods\, which consider the tail of a distribution\, are outlined below.\nFirst\, we introduce a novel approach\, termed robust expectile regression (retire)\, which tackles heteroscedasticity in high-dimensional data through iteratively reweighted l1-penalization. Theoretical analysis establishes its statistical properties in both low and high-dimensional regimes\, demonstrating oracle properties and efficient convergence rates. Empirical evaluations showcase its superior performance compared to existing methods\, offering a promising tool for robust and efficient estimation.\nSecond\, we propose a unified algorithm for penalized convolution smoothed quantile regression\, overcoming computational challenges inherent in fitting penalized quantile regression models in high-dimensional settings. This algorithm\, implemented in an R-language package conquer\, exhibits superior statistical accuracy and computational efficiency\, demonstrated through extensive numerical studies and exemplified by a fused lasso additive quantile regression model applied to real-world happiness data.\nThird\, we investigate the impact of distance learning on academic outcomes in STEM courses\, particularly focusing on underserved and lower-performing students. Utilizing a large dataset spanning several years\, the study employs expected shortfall regression to analyze disparities in academic outcomes between different student groups during distance and in-person learning. Findings underscore the challenges of online education\, highlighting the effectiveness of targeted instructional interventions in narrowing academic disparities\, thereby emphasizing the importance of equitable strategies in higher education.\nThe first two chapters contribute novel methods for high-dimensional expectile and quantile regression\, which represent alternatives to traditional least squares regression by allowing for the evaluation of the entire distribution of the response variable rather than solely focusing on its conditional mean. These methods are particularly valuable in settings where data heterogeneity and non-normality are prevalent\, offering robust and flexible approaches to modeling complex relationships. The third chapter extends the application of these advanced regression techniques by utilizing expected shortfall regression\, a form of quantile regression\, to investigate the impact of distance learning on academic outcomes in STEM courses. By applying sophisticated statistical methods to address real-world challenges in educational research\, this chapter demonstrates the practical relevance and versatility of high-dimensional regression approaches.
UID:121462-21846574@events.umich.edu
URL:https://events.umich.edu/event/121462
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Dissertation
LOCATION:West Hall - 438
CONTACT:
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