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DTSTAMP:20240510T123108
DTSTART;TZID=America/Detroit:20240425T140000
DTEND;TZID=America/Detroit:20240425T143000
SUMMARY:Careers / Jobs:Intern At Aflac! 2024 Summer Sales Internship
DESCRIPTION:Join our 30-minute virtual internship Info session to learn more about Aflac's 2024 Summer Sales Internship Program! We are seeking motivated\, growth-focused individuals who are interested in learning more about an insurance career.
UID:120654-21845097@events.umich.edu
URL:https://events.umich.edu/event/120654
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:
LOCATION:
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BEGIN:VEVENT
DTSTAMP:20240419T152545
DTSTART;TZID=America/Detroit:20240425T143000
DTEND;TZID=America/Detroit:20240425T153000
SUMMARY:Workshop / Seminar:EEB Thesis Defense: Temporal Variation in Functional Traits of Understory Plant Communities Along a Chronosequence in Northern Michigan
DESCRIPTION:Preview: Functional traits vary among species and individuals\, making them ideal for elucidating the process of community assembly. One area of functional trait ecology that remains understudied is temporal variation in functional traits\, especially in understory forest communities\, and its impact on functional diversity. I leveraged an experimental chronosequence at the University of Michigan Biological Station to investigate how functional traits of understory plants vary at three time scales - within the growing season\, between two years\, and along the chronosequence - and in coordination with environmental changes\, species turnover\, and intraspecific variation. This project calls attention to the temporal variability of plant communities that goes unnoticed in single time-point studies and highlights the use of chronosequences for studying community assembly.
UID:121492-21846607@events.umich.edu
URL:https://events.umich.edu/event/121492
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Biology,AEM Featured,Biosciences,Bsbsigns,Ecology & Biology,Ecology And Evolutionary Biology,eeb,Herbarium,Museum - Herbarium,Museum - Zoology,Museum Of Zoology
LOCATION:Biological Sciences Building - 1010
CONTACT:
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DTSTAMP:20240415T160400
DTSTART;TZID=America/Detroit:20240425T150000
DTEND;TZID=America/Detroit:20240425T160000
SUMMARY:Lecture / Discussion:899 Seminar Series: Ashwin Pananjady
DESCRIPTION:Presenter Bio:\nAshwin Pananjady is an Assistant Professor at Georgia Tech\, with a joint appointment between the Schools of Industrial and Systems Engineering and Electrical and Computer Engineering. He received his PhD in Electrical Engineering and Computer Science from UC Berkeley and his BTech in Electrical Engineering from IIT Madras. His research interests lie in high dimensional statistics\, optimization\, signal processing\, and information theory\, and their applications to machine learning\, reinforcement learning\, and data science. Pananjady has received research awards from Adobe and Amazon\, a Best Paper Prize (runner-up) for Young Researchers in Continuous Optimization from the Mathematical Optimization Society\, the Lawrence D. Brown Award from the Institute of Mathematical Statistics\, the David J. Sakrison Memorial Prize (for the best dissertation in EECS at Berkeley)\, and a Simons-Berkeley Research Fellowship in Probability\, Geometry\, and Computation in High Dimensions. His teaching has been recognized at both UC Berkeley and Georgia Tech.\n\n\nAbstract:\nIterative algorithms are the workhorses of modern signal processing and statistical learning and are widely used to fit complex models to random data. While the choice of an algorithm and its hyperparameters determines both the speed and fidelity of the learning pipeline\, it is common for this choice to be made heuristically\, either by expensive trial-and-error or by comparing upper bounds on convergence rates of various candidate algorithms. Motivated by these issues\, we develop a principled framework that produces sharp\, iterate-by-iterate characterizations of solution quality for complex iterative algorithms on several nonconvex model-fitting problems with random data. Such sharp predictions can provide precise separations between families of algorithms while also revealing nonstandard convergence phenomena. We will showcase the general framework on several canonical models in statistical machine learning.
UID:121494-21846608@events.umich.edu
URL:https://events.umich.edu/event/121494
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:899 Seminar Series,Industrial And Operations Engineering
LOCATION:Industrial and Operations Engineering Building - 2717
CONTACT:
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