Department of Chemistry

DEPARTMENT OF

Chemistry

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Congratulations! LOGIC 2020 EDII Awarded to Nicole Dozois!

Photo: Nicole Dozois & Dr. Amanda Bongers MSc Candidate Nicole Dozois was awarded the Queen’s Chemistry EDII Award which funds her trip to the CWICNetwork LOGIC2020 Retreat! This award was sponsored Queen's University Arts and Science and recognizes her equity, diversity, & inclusion efforts at Chemistry!  The Leaders Overcoming Gender Inequality in Chemistry (LOGIC) retreat will be hosted on May 23rd & 24th, 2020 in Winnipeg, Manitoba. This year the theme is “Beyond the Visible Spectrum”. The two-day program for the LOGIC Retreat will focus on productive discussions for working in the chemical sciences, including aspects of equity, diversity, and inclusion. There are currently five invited speakers, with Dr. Jess Wade (known for tackling gender bias on Wikipedia) as the keynote speaker. In addition, there will be two workshops that will focus on writing EDI statements for grant applications (hosted by Dr. Lisa Willis) and leading with inclusion (hosted by Catalyst). Attendees will have many networking opportunities with time to share their research and present their EDI initiatives. To conclude the event, a panel discussion will occur with representatives from various industries. Learn more about this fantastic retreat hosted by CWIC Network here: https://cwicnetwork.com/logic-retreat/

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Introducing Dr. Farnaz Heidar-Zadeh to the Department of Chemistry

Photo: Farnaz Heidar-Zadeh Dr. Farnaz Heidar-Zadeh’s group in Theoretical and Computational Chemistry will develop new mathematical tools, numerical algorithms, and computer software to qualitatively and quantitatively predict the outcome of chemical phenomena. Her group is the lead developer of the free and open-source ChemTools software package, which encompasses a collection of tools for interpreting the numerical output of quantum chemistry calculations to gain chemical insight. They combine strategies from quantum chemistry and state-of-the-art machine learning methods to develop rapid, accurate, and efficient techniques to computationally predict molecular properties, and ultimately design molecules with desirable properties.

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