https://dentistry.temple.edu/research/laboratories-centers/smart-biomaterials-research-laboratory
Scope of Research The Smart Biomaterials Research Laboratory is dedicated to the development of advanced biomaterials designed to interact dynamically with biological systems, aiming to enhance oral and systemic health. Our interdisciplinary approach integrates materials science, microbiology, bioengineering, and clinical dentistry to create innovative solutions for disease prevention ...
https://bulletin.temple.edu/courses/ece/
This course introduces basic concepts in Electrical and Computer Engineering, and demonstrates them in the context of real applications. Course topics include basics of DC and AC circuits, transistor, diode and operational amplifier circuits, digital logic gates and power supply operation. Students assemble and test a robot car or mouse as part of the class project.
https://engineering.temple.edu/admissions/graduate-admissions
In our small, tight-knit community you will get the most up-to-date education at a great value to your career. Applications are reviewed on a rolling basis within the deadlines outlined below. It is highly recommended that you apply early to ensure the processing time can be completed and consideration be given for scholarship or university/college funding. Upon receiving all required ...
https://medicine.temple.edu/education/graduate-medical-education/residency-programs-fellowships/anesthesiology/residency-program/meet-our-residents
Categorical Interns (Class of 2028) Sundiata (Sunny) Annacius, DO Dr. Sundiata Annacius is a proud New Jersey native and first-generation Jamaican-American. She completed her undergraduate degree in Biology with a minor in African-American Studies at Montclair State University and earned a Master’s in Biomedical Sciences at Rutgers University in Newark, NJ. She attended the Philadelphia ...
https://sites.temple.edu/moodandcognitionlab/lab-members2/
Iris joined the Mood and Cognition Lab as a graduate student in the fall of 2018. She received her B.A. in Psychology from UCLA and a M.A. in Psychology from Boston University. After graduation, she spent three years working on a NIMH-funded R01 study examining reward and threat related neurocircuits and dimensions of mood and anxiety symptoms as a lab manger at Northwestern University, where ...
https://engineering.temple.edu/research-departments/departments/civil-environmental-engineering-department/civil-environmental-engineering-labs
Dedicated to the investigation of physicochemical processes, environmental chemistry, remediation, and fate & transport, our lab performs experimental research on societally and environmentally relevant issues, and our research addresses both natural and engineered treatment systems. Our approaches include laboratory up to field scale, and the results contribute to society’s understanding of ...
https://cis.temple.edu/~pwang/papers.html
Selected Papers of Pei Wang All the following publications are authored by Pei Wang unless specified otherwise.
https://sites.temple.edu/rtwiseowls/database/
RT Wise Owls Database Searching RT Wise Owls is easy! Use any combination of keywords, drop down filters, and/or the highlighted words within the table to narrow your search.
https://secretary.temple.edu/sites/secretary/files/documents/committee-meetings/board-of-trustees/10102023_PS_Board_MINUTES%20mg.pdf
Election of New Trustee Upon motion duly made and seconded, the Board of Trustees, upon the nomination and recommendation of the Governance and Nominating Committee (9/12/2023), elected Laura Sparks to the Board of Trustees of Temple University – Of The Commonwealth System of Higher Education, for a four-year term effective immediately, in accordance with the bylaws of the University, as set ...
https://cis.temple.edu/~jiewu/research/publications/Publication_files/INFOCOM2024_PSFL%20Parallel-Sequential%20Federated%20Learning%20with%20Convergence%20Guarantees.pdf
Abstract—Federated Learning (FL) is a novel distributed learning paradigm which can coordinate multiple clients to jointly train a machine learning model by using their local data samples. Existing FL works can be roughly divided into two categories according to the modes of model training: Parallel FL (PFL) and Sequential FL (SFL). PFL can speed up each round of model training time through ...