Li Jun Li Bio, Age, Parents, Net worth, Movies & TV Shows, Quantico, Wu Assassins, Measurements

Li Jun Li Biography

Li Jun Li is an American actress born 1983 in Shanghai, China and moved to New York City in 1992. She is best known for her portrayal of Iris Chang in the ABC thriller Quantico and Rose Cooper in the Fox television series The Exorcist.

Li Jun Li Mother

Li Jun Li’s mother’s name is Mei Li .

Li Jun Li Age

Jun is 35 years old born 1983.

Li Jun Li Boyfriend | Li Jun Li Married

Jun is dating  Greg Beeman.

Li Jun Li Measurements

No details of measurements found.

Li Jun Li Net Worth

Jun’s Net worth is still under review.

Li Jun Li Photo
Li Jun Li Photo

Li Jun Li Instagram

https://www.instagram.com/p/BvImiYvnkON/?utm_source=ig_web_copy_link

Li Jun Li Quantico

Quantico is an American television thriller series that was broadcast from September 27, 2015 to August 3, 2018 on the American Broadcasting Company (ABC). The series was created by Joshua Safran, produced by ABC Studios, who also served as the showrunner. The executive producers are Mark Gordon, Robert Sertner, Nicholas Pepper and Safran. she played as Iris Chang.

Li Jun Li Chicago Pd

P.D. of Chicago This is an American police procedural TV series created by Dick Wolf and Matt Olmstead as the second installment of the Chicago franchise from Dick Wolf. The series premiered on NBC on January 8, 2014 as a mid-season replacement. The show follows the uniformed patrol officers and the Chicago Police Department’s 21st District Intelligence Unit as they pursue the major street offenses of the city’s perpetrators. She played as Julie Tay.

Li Jun Li Movies And TV Shows

Movies

Year

Title

Role

2017

Extraction

Beck

2015

Front Cover

Miao

2015

Ricki and the Flash

Nail Clerk

2015

Construction

Theresa

2014

Song One

James Forester’s Journalist

2014

Mistress

Claire

2014

The Humbling

Tracy

2013

Hatfields & McCoys

Cara Quo

2013

Chinese Puzzle

Nancy

2012

Americana

Eloise Russell

2012

Freestyle Love Supreme

Danielle

2008

Xu Wu Di Ai

TV Shows

Year

Title

Role

2019

Wu Assassins

Jenny Wah

2018

Gone

Agent Dana Parker

2017

Blindspot

Dr. Karen Sun

2017–2018

The Exorcist

Rose Cooper

2016

Chicago Fire

Julie Tay

2016–2017

Quantico

Iris Chang

2015

One Bad Choice

Lisette Lee

2015

Minority Report

Akeela

2015–2016

Billy and Billie

Denise

2015–2016

Chicago P.D.

Julie Tay

2014

Unforgettable

Natalie

2013

The Following

Meghan Leeds

2013

Hostages

Attractive Woman

2012

Smash

Store Clerk

2011

Body of Proof

Mira Ling

2011

One Life to Live

Gothic Vegas Chapel Assistant

2011

Law & Order: Criminal Intent

Yasmin

2011–2012

Damages

Maggie Huang

2010

Live from Lincoln Center

Liat

2010

Blue Bloods

Nicka

Li Jun Li Height

Jun is 5′ 6″ (168 cm) tall.

Li Jun Li Exorcist

The Exorcist is a supernatural television series of American anthology horror that made its debut on Fox on September 23, 2016. The series stars Ben Daniels, Alfonso Herrera, and is based on the same-name novel by William Peter Blatty. It is part of The Exorcist franchise, a direct sequel to the 1973 film of the same name (which ignores the events of the other films in the series). On May 10, 2016, it was commissioned. she played as Rose Cooper.

Li Jun Li Blindspot

Blindspot is a Martin Gero created American crime television series featuring Sullivan Stapleton and Jaimie Alexander. On September 21, 2015, the series premiered. On October 9, 2015, a back nine order brought a total of 22 episodes to the first season, plus an additional episode bringing the order to 23 episodes. she played as Dr. Karen Sun.

Li Jun Li Twitter

Li Jun Li The Following

The Following is an American television drama series created by Kevin Williamson, and jointly produced by Outerbanks Entertainment and Warner Bros. Television. she played as Meghan Leeds.

Li Jun Li Interview

Li Jun Li News

Ligand selector steers C–N cross-couplings down most sustainable path

Researchers have used machine learning to develop a tool that predicts which ligands for a metal-catalysed coupling reaction will results in a synthetic route with the lowest environmental and financial cost. The idea could be expanded into a system to help pharmaceutical organisations select how to manufacture a drug.

Pharmaceuticals often have complex synthetic routes with several possible paths to the final product. Scientists designing these routes need to pick the optimal one and, historically, such decisions centre on safety, efficiency, cost and product quality.

Given the positive correlation between reaction cost and sustainability, Jun Li and Martin Eastgate at Bristol-Myers Squibb, US, have now designed a machine learning approach that can predict the synthetic route with the lowest environmental impact. Environmental impact is gauged using the cumulative mass intensity ratio – the mass of all the materials used in the synthesis divided by the mass of the final product. Higher values mean more wasted materials and a higher impact.

Li and Eastgate’s tool works on transition metal-catalysed carbon–nitrogen coupling reactions involving phosphine ligands, which frequently feature in pharmaceutical syntheses. Literature reports of coupling reactions with phosphine ligands served as the dataset for the system; the molecular features of ligand electrophiles and nucleophiles provide the input variables, and the phosphine ligands that result in successful reactions are the output. They found that their tool predicts which ligands will provide a successful reaction, and which ones provide the lowest cumulative mass intensity.

Machine intelligence expert Ross King at the University of Manchester, UK, says the research ‘tackles how best to design synthetic paths that not only have high-yields, but are also of low financial and environmental cost is an important subject area, and an area that will only grow in importance. This is yet another successful application of machine learning in chemistry’.

The work ‘helps draw attention to two key challenges in synthesis design that could benefit from computational assistance: ligand selection for catalytic reactions, and evaluation of a route’s greenness after taking that selection into account,’ comments Connor Coley whose research at Massachusetts Institute of Technology, US, uses data and automation to streamline discovery in the chemical sciences says. ‘Hopefully, we will see many more studies like this one that help bring quantitative metrics into route selection beyond cost and number of reaction steps.’

‘We hope this work will help researchers make better decisions during route design,’ comments Eastgate. ‘Keeping sustainability and efficiency in mind, on a holistic level, during these decisions – through predictions and an easy to use app – will help provide greater context to these key decisions and help researchers choose route options with the highest chance of being the most sustainable’.