School of Computing Seminar Schedule

Weihang Wang
Assistant Professor
University of Southern California

Understanding WebAssembly via Program Transformation

Weihang Wang is an Assistant Professor of Computer Science at the University of Southern California. She is broadly interested in software engineering, software security, and computer systems. Her work aims to build testing and analysis techniques for improving the reliability, security, and efficiency of complex software systems. She was awarded a University at Buffalo Exceptional Scholar - Young Investigator Award in 2022, an NSF CAREER Award in 2021, a Facebook Testing and Verification Research Award in 2019, a Mozilla Research Award in 2019, and a Maurice H. Halstead Memorial Research Award in 2018. Before joining USC, she was an Assistant Professor at University at Buffalo from 2018 to 2022.

Shaun Kane
Google Research
University of Colorado Boulder

Redefining Accessibility in the Age of AI

Shaun Kane is a Research Scientist in Responsible AI and Human-Centered Technology at Google Research, and Associate Professor in the Department of Computer Science at the University of Colorado Boulder. His research focuses on understanding emerging accessibility problems and empowering people and organizations to solve these problems. He is the recipient of an Alfred P. Sloan Fellowship, a National Science Foundation CAREER Award, and the ACM SIGACCESS Lasting Impact Award. He received his Ph.D. from The Information School at the University of Washington.

Xiaolong Ma
Assistant Professor of Electrical and Computer Engineering
Clemson University

Towards Efficient Deep Neural Network Execution with Model Compression and Platform-specific Optimization

Dr. Xiaolong Ma is an assistant professor in the Department of Electrical and Computer Engineering at Clemson University. He was the winner of the Contributed Article of CACM in 2021, for his contribution on the research of model compression for real-time inference on mobile devices. His highly efficient dynamic sparse training framework won the Best Paper Award in ICLR workshop of Hardware Aware Efficient Training (HAET), and also received the Spotlight Paper Award in NeurIPS in 2021. His work on efficient machine learning with stochastic number generator was nominated for the Best Paper Award in ISQED 2017. He has published in the top conference ranging from NeurIPS, ICML, CVPR, ICLR, ECCV, AAAI, IJCAI, ASPLOS, ISCA, MICRO, DAC, ICS, PACT, and top journal such as TPAMI, TNNLS and CACM.

Kristen Shinohara
School of Information at the Rochester Institute of Technology

Including Disability and Accessibility in Computing Education

Kristen Shinohara is an Assistant Professor in the School of Information at the Rochester Institute of Technology where she co-directs the Center for Accessibility and Inclusion Research (CAIR) Lab. Kristen’s research is at the intersection of human-computer interaction, accessibility, and design, with a focus on accessible design, research, and computing education. She developed the Design for Social Accessibility (DSA) perspective and method cards, which supports how designers engage with disabled user needs and preferences, particularly for social situations. Her NSF funded research projects focus on how to empower disabled graduate students and designers, and on the prevalence of accessibility practice in the tech industry and how to improve teaching accessibility in computing education. Her work has received Best Paper and Honorable Mention awards from the CHI Conference in Human Factors in Computing and has appeared as the cover story in the Communications of the ACM. She is the recipient of a 2022 Google Scholar Award to improve user centered design methods for deaf and hard of hearing designers, and she is a faculty member of RIT’s AWARE-AI NSF Research Traineeship Program.

Dean Sullivan
Electrical and Computer Engineering
University of New Hampshire

Practical Electromagnetic Interference Attacks on Touchscreen-based Devices

Dean Sullivan is assistant professor for electrical and computer engineering at the University of New Hampshire. He received a BS degree in electrical engineering and MS degree in computer engineering from the University of Central Florida and his PhD from the University of Florida in electrical and computer engineering. His research focus includes systems and architectural security, side-channels, and program analysis. He received best paper awards at DAC and IEEE Security & Privacy and is the recipient of the Graduate Student Preeminence award and David Howell Jr. and Dan Howell Endowment ECE fellowship.

Martez Mott
Senior Researcher in the Ability group at Microsoft Research

Designing Accessible Virtual Reality Experiences for People with Limited Mobility

Dr. Martez Mott is a Senior Researcher in the Ability group at Microsoft Research. He conducts research in the fields of human-computer interaction (HCI), accessibility, mixed reality, and human-centered AI. His research focuses on designing, implementing, and evaluating novel intelligent interactive technologies that are guided by scientific understandings of people’s experiences with computers and information. A core tenet of this work is the consultation and inclusion of potential users and beneficiaries of technologies in all aspects of the research process. He is best known for his research on improving accessibility for people with limited mobility by identifying and dismantling accessibility barriers found in a range of computing technologies, including touch screens, gaze-based text entry, and virtual reality hardware. Dr. Mott is passionate about improving diversity in Computer and Information Science. He co-chaired the 2020 and 2021 CHI Mentoring Workshops at the ACM CHI conference, the premier international conference for HCI research, and co-founded the Black Researchers @ Microsoft Research employee resource group. Dr. Mott received a B.S. and M.S. in Computer Science from Bowling Green State University and a M.S. and Ph.D. in Information Science from the Information School at the University of Washington.

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