The CAHSS Creative Achievement and Research Administration Team (CARAT) and Office of Sponsored Programs invites you to attend the UMBC Grants Process Workshop on Wednesday, May 8, 2024, from 12:00 P.M. – 1:30 P.M., in the Public Policy Building, Room 451.

Lunch will be provided at 11:30 A.M.

Space is limited and registration is required. Please respond by Friday, May 3^rd.

Register here.

This workshop is for CAHSS faculty at all career stages, including those who are new to UMBC’s grants process and those who would like a refresher.

Participants will learn about the pre-award grants process, including the requirements for preparing and submitting external funding applications at UMBC. They will also learn about the support available through CARAT. There will be time for Q&A.

 Topics to be covered:

•                  Proposal planning

•                  PI Eligibility and PIRATE training

•                  Developing grant and fellowship proposals

•                  Routing a proposal in Kuali

The URCAD Selfie Contest winner for Best Selfie with a Presentation: The presenters from the Women's Center: Ash Acuña, Tanzila Malik, Carrington Cline, Abby Claytor. They will receive a gift card to the UMBC Bookstore.

Even after the poster session ended, you could find these folx holding up their poster in the UC lobby and doing an impromptu presentation for people who missed it... now that's dedication to the feminist cause of analyzing Barbie!

White, Pink, and Pretty: How Barbie’s Feminism Fails its Viewers

Carrington Cline, Tanzila Malik, Abby Claytor, Nati Acuña, Media and Communications Studies
Lauren Allen, Women's Center; Amelia Meman, Women's Center

The 2023 film Barbie (Gerwig, 2023) is the highest-grossing live-action comedy film of all time; it has been lauded as a feminist film that cunningly subverts gender roles and thoughtfully addresses issues of diversity that are fraught in most Hollywood films. Barbie is an easily digestible media that portrays basic feminist principles for the general public. But if Barbie is a feminist film, is it also intersectional? This study examines the limitations of the Barbie movie’s portrayal of feminism and its failings in regard to intersectionality. We aim to inspire critical thinking around media and intersectionality and shed light on the prevalence of White feminism in popular media. Using content analysis informed by intersectional feminist theory, we analyzed instances in the film that demonstrated (pseudo-)feminist principles and themes in order to determine how intersectional Barbie’s brand of feminism is. We also explored avenues by which Barbie could better depict intersectionality and contemporary feminist thought.

Learning to Synthesize Images with Multimodal and Hierarchical Inputs

Yu Zeng, JHU

April 17, 2024 4:00 – 5:15 PM

ENGR 231, UMBC or Webex

In recent years, image synthesis and manipulation has experienced remarkable advancements driven by deep learning algorithms and web-scale data, yet there persists a notable disconnect between the intricate nature of human ideas and the simplistic input structures employed by the existing models. In this talk, I will present our research towards a more natural way for controllable image synthesis inspired by the coarse-to-fine workflow of human artists and the inherently multimodal aspect of human thought processes. We consider the inputs of semantic and visual modality at varying levels of hierarchy. For the semantic modality, we introduce a general framework for modeling semantic inputs of different levels, which includes image-level text prompts and pixel-level label maps as two extremes and brings a series of mid-level regional descriptions with different precision. For the visual modality, we explore the use of low-level and high-level visual inputs aligning with the natural hierarchy of visual processing. Additionally, as the misuse of generated images becomes a societal threat, I will introduce our findings on the trustworthiness of deep generative models in the second part of this talk and potential future research directions.

Yu Zeng is a Ph.D. candidate at Johns Hopkins University advised by Vishal M. Patel. Her research interest lies in computer vision and deep learning. She has focused on two main areas: (1) deep generative models for image synthesis and editing and (2) label-efficient deep learning. By combining these research areas, she aims to bridge human creativity and machine intelligence through user-friendly and socially responsible models while minimizing the need for intensive human supervision. Yuhas collaborated with researchers at NVIDIA and Adobe through internships. Prior to her Ph.D., she worked as a researcher at Tencent Games. Yu’s research has been recognized by the KAUST Rising Stars in AI, and her Ph.D. study has been supported by a JHU Kewei Yang and Grace Xin Fellowship.

Dance & Bmore seeks outstanding Dancers, Singers, and Drummers. Creative collaborators highly encouraged!

Inquiries welcome! Please send a brief description of experience to danceandbmore@gmail.com.