Category Archives: Computer Science
Scene at MIT: Ruth Anderson, pioneer of mathematics and computing – MIT News
Ruth Krock Anderson is a mathematician and computing pioneer who has seen a lot in her 102 years. Born in Boston in 1918, she was interested in math from an early age and earned a mathematics degree at Boston Teachers College, now part of the University of Massachusetts. Soon thereafter, Anderson was asked to join the MIT Radiation Laboratory, which made key contributions to the development of microwave radar technology during the second world war. There are quite a few books written about women programmers in World War II to help in the war, and I was one of them, Anderson stated in a 2019 interview.
At MIT, Anderson worked on computer programs that assisted scientists and engineers working on new radar technology. Her colleagues at the Rad Lab included Betty Campbell and Barbara Levine, both of whom would continue on in computer science after the war, as well as Harold Levine, who became a math professor at Stanford University. This photo of Anderson shows her in front of Building 10 on V-J Day in 1945.
Anderson eventually moved to California to work for the U.S. Navy on drone-tracking technology. But she would spend most of her career at the National Bureau of Standards in Washington. There, she worked for Ethel Marden, a computing pioneer who also, according to Andersons daughter Karen, was remembered for lobbying for family-friendly work schedules for employees specifically, my mom, who job-shared after I was born.
Today, Anderson lives in a retirement community in Naples, Florida. She is one of very few people whove lived through both the 1918 influenza pandemic and our current pandemic; on Jan. 19, at age 102, she received her second dose of the Pfizer Covid-19 vaccine.
Have a creative photo of campus life you'd like to share? Submit it to Scene at MIT.
Read the original here:
Scene at MIT: Ruth Anderson, pioneer of mathematics and computing - MIT News
Neural network based on autocatalytic reaction performs image classification – Chemistry World
Researchers in the US have combined an autocatalytic click reaction with automated liquid handling and UV-Vis spectroscopy to create a simple neural network that can classify binary images.
Chemical computing takes inspiration from the complexity and efficiency of living systems and aims to develop chemical counterparts that can perform functions such as information processing and storage. To date, DNA has been the molecule of choice for studies in this area. But simple chemical systems based on small molecules, which can tolerate a wider range of reaction conditions and require fewer components, are starting to open up new avenues in chemical computing.
Autocatalytic reactions share a key feature they are catalysed by their reaction product. As a result, the reaction rate is linked to the amount of product present, which also means that autocatalytic reactions display a non-linear response when it comes to product formation over time. The kinetic models used to describe these reactions have mathematical forms that resemble activation functions used in artificial neural networks. This similarity inspired a team of scientists from Brown University, led by Jacob Rosenstein, to build a winner-take-all neural network using an autocatalytic reaction.
The researchers used an established copper-catalysed cycloaddition reaction between 2-azidoethanol and tripropargylamine. This click reaction produces tris(triazolylmethyl)amine, which increases both the production and activity of Cu(I), resulting in autocatalysis. A key advantage of this work is that the click reaction is really robust and can tolerate different conditions, comments Ekaterina Skorb, an infochemistry researcher from ITMO University in Russia. Its a really simple reaction and [has] clear rules on how we can use it, and what is input and output.
In a winner-take-all neural network, potential classes race between each other and the class that reaches a specific condition first is deemed the winner. In this case, the researchersused the reaction halfway point (transition time, t1/2), which could be programmed by tuning the initial concentration of tris(triazolylmethyl)amine, as the condition for image classification. During training, the network weights were tuned so that the class reaching the t1/2 first represented the image class most similar to image input.
To implement the winner-take-all network chemically, the team encoded binary 1616-pixel image on a well plate, using the initial concentration of tris(triazolylmethyl) to specify pixel color, with one position representing one pixel. Next, the researchers used a robotic fluid handler to remove a certain volume, dependent on weights determined through network training, from each well. The samples were added together into individual pools for each potential image class and mixed with fresh reagents. Finally, the pool that reached the transition time first was classed as the winner, identifying the image class.
Click reactions will likely be familiar to many researchers. But Skorb says this work is important because it gives new insights into how people can use it for computer science. Rosenstein and colleagues anticipate that autocatalytic reaction networks will play a significant role in new chemical computing systems. With a view to the future, Skorb remarks that it would be really great if we can find a way to combine chemical systems and biological systems, adding that since click chemistry works in water, it could be combined with a more sophisticated chemical computer to make an artificial brain.
Read more from the original source:
Neural network based on autocatalytic reaction performs image classification - Chemistry World
Is Computing With DNA the Wave of the Future? – Walter Bradley Center for Natural and Artificial Intelligence
Why would we want to compute with DNA? Well, first, we are fast approaching the limit of how small we can make computers. So some scientists are turning to the designs in nature for help:
The issue with transistors is that they now exist at the scale of a few nanometers in sizeonly a few silicon atoms thick. They cant practically be made any smaller than they are now.
If they get any smaller, the electrical current flowing through the transistor easily leaks out into other components nearby or deforms the transistor due to heat, rendering it useless. You need a minimum number of atoms to make the transistor work and weve functionally reached that limit.
In 1994, Leonard Adleman used computing with DNA to offer a solution for the infamous Traveling Salesman problem, which computers typically cannot solve, though live amoebas can.
Heres the Traveling Salesman problem:
Given a collection of cities and the cost of travel between each pair of them, the traveling salesman problem, or TSP for short, is to find the cheapest way of visiting all of the cities and returning to your starting point. In the standard version we study, the travel costs are symmetric in the sense that traveling from city X to city Y costs just as much as traveling from Y to X.
The simplicity of the statement of the problem is deceptive the TSP is one of the most intensely studied problems in computational mathematics and yet no effective solution method is known for the general case.
Why is the Traveling Salesmans problem so hard?
With only four cities, the solution is only three possible routes. But as the number of cities increases, the number of possible answers increases exponentially: for six cities, that would be 360 routes; for eight cities, that number swells to 2520. The TSP is classified as an NP-hard problem (non-deterministic polynomial-time hardness), meaning that as the number of cities grows, the time needed for a conventional computer to solve it grows exponentially as well, due to its increased complexity.
In computer science lingo, it becomes an NP-Hard problem: Solving it could quickly require more runtime than the expected lifespan of the universe. Thats because of the way computers process calculations.
So how do amoebas solve such a problem with no brain and little effort? Were not sure but a team from Keio University in Tokyo offers some thoughts,
While the idea of such an amoeba-based computing system might seem dubious at first, what is remarkable is that the time it takes the system to calculate optimal solutions to the TSP grows in a linear fashion, even though the number of possible answers is amplified exponentially, and the amoeba seems to do so by processing information in parallel, rather than serially, though the team still isnt quite sure what exactly makes this system work the way it does.
The mechanism by which how the amoeba maintains the quality of the approximate solution, that is, the short route length, remains a mystery, study lead author Masashi Aono told Phys.org. It seems that spatially and temporally correlated movements of the branched parts of the amoeba located at distant channels are the key. Each of these branches is oscillating its volume with some temporal memory on illuminated experiences. Groups of the branches perform synchronization and desynchronization for sharing information even though they are spatially distant.
Put visually:
In sum, no one knows why amoebas solve the problem much more easily than computers but it might have something to do with the difference between DNA and silicon chips.
What are the general advantages of computing using DNA? It may get past the problem of doing only a single logical operation at a time:
There is no limit to the power that DNA computing can theoretically have since its power increases the more molecules you add to the equation and unlike silicon transistors which can perform a single logical operation at a time, these DNA structures can theoretically perform as many calculations at a time as needed to solve a problem and do it all at once.
DNA also features economical storage methods:
Because there are four building blocks in DNA, rather than the binary 1s and 0s in magnetic hard drives, the genetic storage method is far more dense, explains John Hawkins, another co-author of the new paper. A teaspoon of DNA contains so much data it would require about 10 Walmart Supercenter-sized data centers to store using current technology, he tells Popular Mechanics. Or, as some people like to put it, you could fit the entire internet in a shoe box.
Its also thought to present fewer long-term storage problems:
Beyond that, DNA requires virtually zero maintenance once its stored. After all, fossils preserve DNA sequences after spending millions of years underground. DNA storage doesnt require any energy, eitherjust a cool, dark place to hang out until someone decides to access it. But the greatest advantage, Hawkins says, is that our ability to read and write DNA will never become obsolete.
How DNA computing works:
What are the problems with DNA computing? The principle current difficulty has been slow speed in practice:
Even though it took moments for Adlemans solution to the traveling salesman problem to be encoded into his DNA strands in the test tube, it took days of filtering out bad solutions to find the optimal solution he was looking forafter meticulous preparation for this single computation.
However, the speed is improving as the system is better understood>
DNA-based computing is still very much a project under development. But employing the designs already provided in nature, instead of inventing our own, may help us solve some practical problems more easily.
You may also enjoy: There is a glitch in the description of DNA as software: In contemporary culture, we are asked to believe in an impressive break with observed reality that the code wrote itself.
Continued here:
Computer Science Meets Medicine in Drug Discovery | Womble Bond Dickinson – JDSupra – JD Supra
AI has the potential to revolutionize healthcare worldwide. In drug discovery, AI has already shown success. Sumitomo Dainippon Pharma and the UK-based AI company Exscientia developed DSP-1181 to treat obsessive compulsive disorder. In clinical trials for treatment of solid tumors, the clinical-stage, AI-powered biotech BERGs BPM31510 (ubidecarenone) has already been granted Orphan Drug Designation by the FDA to treat pancreatic cancer and epidermolysis bullosa, a rare skin disorder causing blistering. AI-led drug discovery for COVID-19 is also in the works.
AI can analyze vast amounts of data quickly and predict outcomes using unbiased algorithms less prone to human mistakes. AI can inspire drug discovery by searching and analyzing data on behalf of chemists and recommending subsequent steps. Machine learning (ML), an AI application that allows computer algorithms to improve automatically without explicit programming, can be used to discover molecules that bind to and modify target proteins. ML can optimize the synthesis of molecular compounds, factoring in the availability of chemical components needed. Combining AI with automated systems (e.g., robots), scientists can test more compounds in shorter time, with more accuracy and reproducibility. These computerized systems can collect and search large amounts of records, allowing AI to rapidly identify patterns not readily discernible to humans. Integration of AI in the drug discovery and testing pipeline would increase efficiency and reduce expense.
Since AI and ML require a large volume of data and networking capabilities, computing capacity is critical. In March 2020, IBM, The White House, and the US Department of Energy created the COVID-19 High Performance Computing (HPC) Consortium to provide supercomputing capacity for COVID-19-related research. In its first phase, the HPC Consortium, consisting of industry, government, and academia members worldwide, almost doubled its computing capacity. The amount of data available on COVID-19 also has grown substantially. In its second phase, the HPC Consortium is focusing on projects to help researchers identify potential near term therapies to improve the outcome of COVID-19 patients within a six-month timeframe. Projects include understanding and modeling of patient response to the COVID-19 virus, learning and validation of vaccine response models, evaluation of combination therapies using repurposed molecules, and epidemiological models.
AI and supercomputing capacity provided through the HPC Consortium allows researchers to rapidly search a vast volume of data to identify candidate drugs and compounds in drug discovery. A team at Michigan State University screened data from about 1,600 FDA-approved drugs and found at least two potential candidate antibacterial drugs, proavine and chloroxine, that might be combined and repurposed to treat COVID-19. A team of scientists from PostEra, an ML chemistry startup, processed more than 2,000 compounds from crowdfunded submissions in 48 hours, and quickly created databases with more than 14 billion molecules available worldwide, in their search for a compound to block a key protein of SARS-CoV-2, the virus that causes COVID-19.
AI-based drug discovery is within the purview of the FDA, which has outlined a multi-step drug development process including discovery and development, preclinical research, clinical research, FDA drug review, and FDA post-market drug safety monitoring. The FDAs Technology Modernization Action Plan will expand and modernize the Agencys technology information systems to ensure that rapid advances in product translate into meaningful results for American consumers and patients.
We need AI to help us because the low hanging fruits are long gone: we need to apply our very best approaches to deliver therapies for new generations. AI will bring about a new era in drug discovery and repurposing for new and complex diseases including COVID-19. With that, AI also brings conundrums for example, who is the inventor of a drug designed and discovered by AI? The US Patent & Trademark Office and the European Patent Office both rejected patent applications naming AI as an inventor, holding that only natural persons can be inventors. Most patent offices and courts around the world provided a similar traditional approach. Then, is the inventor the person who created the algorithm of AI? Or the person who designed the data to feed AI? Can nobody get a patent if an AI system is fully responsible for the invention without human involvement? AI will require us to adopt a new legal and regulatory approach sooner or later.
The rest is here:
Computer Science Meets Medicine in Drug Discovery | Womble Bond Dickinson - JDSupra - JD Supra
Vanderbilt Data Science Institute hosts AI for conservation expert Tanya Berger-Wolf in virtual event on March 19 – Vanderbilt University News
A virtual discussion, Trustworthy AI for Wildlife Conservation: AI and Humans Combating Extinction Together, will take place on March 19 at 2 p.m. CT. Registration is required. The discussionishosted by theVanderbilt Data Science Institute.
Artificial intelligence is increasingly the foundation of decisions big and small, affecting the lives of individuals and the well-being of our planet. Tanya Berger-Wolf, professor of computer science engineering, electrical and computer engineering, and evolution, ecology and organismal biology at Ohio State University, will share how data-driven, AI-enabled decision processes can be deployed in the context of conservation. She will present an example of howsuch processes become trustworthy by opening opportunities for participation, supporting community-building, addressing inherent biases and providing transparent performance measures.
As a computational ecologist, Berger-Wolfs research is at the unique intersection of computer science, wildlife biology and social sciences. She creates computational solutions to address questions such as how environmental factors affect the behavior of social animals, including humans.
Berger-Wolf is the director of the Translational Data Analytics Institute at OSU and director and co-founder of Wild Me, a tech-for-conservation software nonprofit that brings together computer vision, crowdsourcing and conservation. Its key project Wildbook enabled the first-ever full species census of the endangered Grevys zebra through photographs taken by ordinary citizens in Kenya. The resulting numbers are now the official species census used by IUCN Red List. Wildbook also includes whales, sharks, giraffes and many more species.
Berger-Wolf holds a Ph.D. in computer science from the University of Illinois at Urbana-Champaign. She has received numerous awards for her research and mentoring including University of Illinois Scholar, UIC Distinguished Researcher of the Year, National Science Foundation CAREER, Association for Women in Science Chicago Innovator and the UIC Mentor of the Year.
TheVanderbiltData Science Institute acceleratesdata-driven research, promotescollaboration and trainsfuture leaders. The institute brings together experts in data science methodologies with leaders in all academicdisciplinesto sparkdiscoveriesandto study the impact of big data on society. Theinstitute is educating students in computational and statistical data science techniques to become future leaders in industry, government, academia and the nonprofit sector.This is the second discussionin thespring speaker series.
See original here:
Scientists may have solved ancient mystery of ‘first computer’ – The Guardian
From the moment it was discovered more than a century ago, scholars have puzzled over the Antikythera mechanism, a remarkable and baffling astronomical calculator that survives from the ancient world.
The hand-powered, 2,000-year-old device displayed the motion of the universe, predicting the movement of the five known planets, the phases of the moon and the solar and lunar eclipses. But quite how it achieved such impressive feats has proved fiendishly hard to untangle.
Now researchers at UCL believe they have solved the mystery at least in part and have set about reconstructing the device, gearwheels and all, to test whether their proposal works. If they can build a replica with modern machinery, they aim to do the same with techniques from antiquity.
We believe that our reconstruction fits all the evidence that scientists have gleaned from the extant remains to date, said Adam Wojcik, a materials scientist at UCL. While other scholars have made reconstructions in the past, the fact that two-thirds of the mechanism are missing has made it hard to know for sure how it worked.
The mechanism, often described as the worlds first analogue computer, was found by sponge divers in 1901 amid a haul of treasures salvaged from a merchant ship that met with disaster off the Greek island of Antikythera. The ship is believed to have foundered in a storm in the first century BC as it passed between Crete and the Peloponnese en route to Rome from Asia Minor.
The battered fragments of corroded brass were barely noticed at first, but decades of scholarly work have revealed the object to be a masterpiece of mechanical engineering. Originally encased in a wooden box one foot tall, the mechanism was covered in inscriptions a built-in users manual and contained more than 30 bronze gearwheels connected to dials and pointers. Turn the handle and the heavens, as known to the Greeks, swung into motion.
Michael Wright, a former curator of mechanical engineering at the Science Museum in London, pieced together much of how the mechanism operated and built a working replica, but researchers have never had a complete understanding of how the device functioned. Their efforts have not been helped by the remnants surviving in 82 separate fragments, making the task of rebuilding it equivalent to solving a battered 3D puzzle that has most of its pieces missing.
Writing in the journal Scientific Reports, the UCL team describe how they drew on the work of Wright and others, and used inscriptions on the mechanism and a mathematical method described by the ancient Greek philosopher Parmenides, to work out new gear arrangements that would move the planets and other bodies in the correct way. The solution allows nearly all of the mechanisms gearwheels to fit within a space only 25mm deep.
According to the team, the mechanism may have displayed the movement of the sun, moon and the planets Mercury, Venus, Mars, Jupiter and Saturn on concentric rings. Because the device assumed that the sun and planets revolved around Earth, their paths were far more difficult to reproduce with gearwheels than if the sun was placed at the centre. Another change the scientists propose is a double-ended pointer they call a Dragon Hand that indicates when eclipses are due to happen.
The researchers believe the work brings them closer to a true understanding of how the Antikythera device displayed the heavens, but it is not clear whether the design is correct or could have been built with ancient manufacturing techniques. The concentric rings that make up the display would need to rotate on a set of nested, hollow axles, but without a lathe to shape the metal, it is unclear how the ancient Greeks would have manufactured such components.
The concentric tubes at the core of the planetarium are where my faith in Greek tech falters, and where the model might also falter, said Wojcik. Lathes would be the way today, but we cant assume they had those for metal.
Whether or not the model works, more mysteries remain. It is unclear whether the Antikythera mechanism was a toy, a teaching tool or had some other purpose. And if the ancient Greeks were capable of such mechanical devices, what else did they do with the knowledge?
Although metal is precious, and so would have been recycled, it is odd that nothing remotely similar has been found or dug up, Wojcik said. If they had the tech to make the Antikythera mechanism, why did they not extend this tech to devising other machines, such as clocks?
Read the original:
Scientists may have solved ancient mystery of 'first computer' - The Guardian
Artificial intelligence that more closely mimics the mind – MIT News
For all the progress thats been made in the field of artificial intelligence, the worlds most flexible, efficient information processor remains the human brain. Although we can quickly make decisions based on incomplete and changing information, many of todays artificial intelligence systems only work after being trained on well-labeled data, and when new information is available, a complete retraining is often required to incorporate it.
Now the startup Nara Logics, co-founded by an MIT alumnus, is trying to take artificial intelligence to the next level by more closely mimicking the brain. The companys AI engine uses recent discoveries in neuroscience to replicate brain structure and function at the circuit level.
The result is an AI platform that holds a number of advantages over traditional neural network-based systems. While other systems use meticulously tuned, fixed algorithms, users can interact with Nara Logics platform, changing variables and goals to further explore their data. The platform can also begin working without labeled training data, and can incorporate new datasets as they become available. Perhaps most importantly, Nara Logics platform can provide the reasons behind every recommendation it makes a key driver of adoption in sectors like health care.
A lot of our health care customers say theyve had AI systems that give the likelihood of somebody being readmitted to the hospital, for example, but theyve never had those but why? reasons to be able to know what they can do about it, says Nara Logics CEO Jana Eggers, who leads the company with CTO and founder Nathan Wilson PhD 05.
Nara Logics AI is currently being used by health care organizations, consumer companies, manufacturers, and the federal government to do things like lower costs and better engage with customers.
Its for people whose decisions are getting complicated because theres more factors [and data] being added, and for people that are looking at complex decisions differently because there's novel information available, Eggers says.
The platforms architecture is the result of Wilsons decision to embrace the complexities of neuroscience rather than abstract away from them. He developed that approach over more than a decade working in MITs Department of Brain and Cognitive Sciences, which has long held the mission of reverse engineering the human mind.
At Nara Logics, we think neuroscience is on a really good track thats going to lead to really exciting ways to make decisions that we haven't seen before, Wilson says.
Following a passion
Wilson attended Cornell University for his undergraduate and masters degrees, but once he got to MIT in 2000, he stuck around. Over the course of a five-year PhD and a seven-year postdoc, he created mathematical frameworks to simulate brain function.
The community at MIT is really focused on coming up with new models of computation that go beyond what computer science offers, Wilson says. The work is connected with computer science, but also considers what our brain is doing that could teach us how computers work, or how computers could work.
On nights and weekends during the final years of his postdoc, from 2010 to 2012, Wilson was also beginning to translate his algorithms into a commercial system in work that would be the foundation of Nara Logics. In 2014, his work caught the attention of Eggers, who had led a number of successful businesses but had grown jaded about the hype around artificial intelligence.
Eggers became convinced Nara Logics AI engine offered a superior way to help businesses. Even back then the engine, which the company refers to as Nara Logics Synaptic Intelligence, had properties that made it unique in the field.
In the engine, objects in customers data, such as patients and treatments, organize into matrices based on features they share with other objects, in a structure similar to what has been observed in biological systems. Relationships between objects also form through a series of local functions the company calls synaptic learning rules, adapted from cell- and circuit-based neuroscience studies.
What we do is catalog all the metadata and what we call our Connectomes go in and mine the database of unstructured data and build links across all of it that relate these things, Wilson explains. Once you have that background, you can go in and say, I like this, this, and this, and you let the engine crunch the data and give you matches to those parameters. What you didnt have to do is have any notion of what the right answer was for lots of similar people. You skip that whole step.
Each object in Nara Logics Synaptic Intelligence stores its properties and rules locally, allowing the platform to adjust to new data by updating only a small number of associated objects. The bottom-up approach is believed to be used by the brain.
Thats totally different than deep learning or other approaches that just say, Were going to globally optimize everything, and each cell does what the global algorithm tells it, Wilson explains. Neuroscientists are telling us each cell is making decisions on its own accord to an extent.
The design allows users to explore relationships in data by activating certain objects or features and seeing what else gets activated or suppressed.
To give an answer, Nara Logics engine only activates a small number of objects in its dataset. The company says this is similar to the sparse coding believed to be used in higher brain regions, in which only a small number of neurons are activated in any given moment. The sparse coding principal allows the company to retrace its platforms path and give users the reasons behind its decisions.
As the company has matured, Wilson has stayed plugged in to the MIT communitys research, and Nara Logics participated in the STEX25 startup accelerator, run by the MIT Industrial Liaison Program, where Wilson says the company made many contacts that have turned into customers.
Leveraging a mind-like AI
Manufacturers are already using Nara Logics platform to better understand data from internet-of-things devices, consumer companies are using it to better connect with customers, and health care groups are using it to make better treatment decisions.
Were focused on a specific algorithm, which is the mechanics of decision making, Wilson says. We believe its something you can codify, and we believe its something thatll be insanely valuable if you can get that process right.
As Covid-19 disrupted industries and underscored the need for organizations to invest in adaptive software tools, Nara Logics nearly doubled its customer base. The founders are thrilled to be scaling a solution they feel is more collaborative and responsive to humans than other AI systems.
We think the most important difference were contributing to is building an AI where people participate and people are in the loop theyre cognizant and understanding and aware of what its doing, Wilson says. That helps them make smarter decisions every day, and those add up to make a big difference.
Link:
Artificial intelligence that more closely mimics the mind - MIT News
Former Math and Comp Sci Teacher Helps Others Advance at NSA – All Together – Society of Women Engineers
Since joining the National Security Agency (NSA), Christina S. has worked on a list of projects that would excite any security-minded computer scientist.
Vulnerability analysis, cryptanalysis, software reverse engineering, malware analysis shes covered it all, including technical research where the self-described mathematics lover applied static analysis to detect data leaks in Android applications.
But the 19-year NSA veteran didnt stop there, especially not when the Center for Strategic Intelligence Research called her name.
I had the wonderful opportunity to be a 2009 Center for Strategic Intelligence Research Fellow at the National Defense Intelligence College where I researched the application of graph theory to analyze the security of a software system.
Today, Christina is the Deputy Director of the Computer Science Skill Community, a position that allows her to coach, mentor and provide professional development opportunities to other computer scientists.
Employees come to me to help them advance their careers, she explains. I help them improve their knowledge and skills to prepare for their next position.
She also mentors school children to help improve their skills, an activity that has its roots at North Carolina A&T, where Christina pledged Delta Sigma Theta.
My sorority sisters and I embraced the commitment to public service and improving the way of life for the community around us, she says.
Today, her public service includes volunteering as a middle school and high school tutor, judging science fairs, and visiting colleges to give talks, review resumes and serve in hackathon events.
Continue Reading Full Article.
SWE Blog
SWE Blog provides up-to-date information and news about the Society and how our members are making a difference every day. Youll find stories about SWE members, engineering, technology, and other STEM-related topics.
Read the original:
Alumni speak at Women in Data Science Conference – Harvard School of Engineering and Applied Sciences
Rediet Abebe S.M. 16 (applied math) and Jean Liu, S.M. 02 (computer science), gave talks during the Women in Data Science (WiDS) Worldwide Conference on March 8, 2021.
Abebe, a Junior Fellow at the Harvard Society of Fellows and an incoming Assistant Professor of Computer Science at the University of California, Berkeley, gave a Technical Vision Talk titled Roles for Computing in Social Justice.
Liu, President of leading mobile transportation and local services platform Didi Chuxing, gave a technical vision talk titled Be Great, Be You: Building Character and Resilience for Organizations of the Future."
In addition, incoming SEAS faculty member Fernanda Vigas, who is currently a principal scientist at Google, gave a keynote presentation titled Data Visualization as Exploratory Medium: from Scientific Insight to Artistic Impression.
The 24-hour WiDS Worldwide Conference, held on International Womens Day, featured keynote addresses,technical talks, and panel discussions with more than 40 thought leaders around the world, from academia, industry, non-profits, and government.
Read more.
Read more from the original source:
Hackergal and Shaw Communications Partner to Make STEM, Coding, and Computer Science More Accessible to Girls Across Western Canada – Yahoo Finance
New two-year partnership with Shaw to expand National Hackathon program and launch new Hackergal Student Ambassador program in Western Canada
TORONTO, March 08, 2021 (GLOBE NEWSWIRE) -- Hackergal today announced the launch of a new Student Ambassador program in Western Canada and the expansion of its National Hackathon program, both designed to raise awareness and generate enthusiasm for science, technology, engineering and mathematics (STEM) and computer science education among girls in grades six-through-nine across the country.
With support from Shaw Communications Inc., the Western Canadian Student Ambassador program will build on Hackergals existing network by providing girls with ongoing training workshops, industry connections, internships and scholarship opportunities. Through these activities, Hackergal will pave the way for a more diverse and equitable future in STEM-related industries and organizations.
The Student Ambassador Program is a community for like-minded girls to be inspired, supported, and connected to industry experts and opportunities as they pursue technology-related studies, said Lucy Ho, Co-Founder & Executive Director, Hackergal. These Ambassadors are Canadas change-makers, storytellers, and future leaders, and we are excited to partner with Shaw to launch this program in Western Canada to support the growing number of girls who wish to further their STEM and computer science education.
Hackergal was founded with the mission to help bridge the gender gap in technology and computer science fields by introducing female students to code through their Hackathon programs inspiring and empowering girls to later pursue careers in STEM. Since 2015, over 20,000 girls have been exposed to computer science education through Hackergals programs.
As a regional partner, Shaw will also support Hackergals national all-girls Hackathon. Hackergal will expand programming to support girls in Western Canada with the goal to provide coding education to more than 3,000 girls over the next two years.
Story continues
By providing interesting and engaging material through their Hackathons and Student Ambassador program, Hackergal is inspiring young girls across Canada to pursue careers in science and technology helping to make the gender gap in these fields a thing of the past, said Chethan Lakshman, Vice President, External Affairs, Shaw Communications. Through our partnership, we are excited to help Hackergal expand their programming to educate and inspire young women to feel empowered, to dream big, and challenge the status quo.
About HackergalHackergal was founded in 2015 with the mission to introduce girls across Canada to computer science through its hackathon program. The charitys goal is to create a national movement of girls coding, ultimately closing the gender gap in technology and computer science by sparking their interest and confidence at an early age. Hackergal has four programmatic pillars to support their mission:
Hackergal Hackathon: The Hackergal Hackathon invites middle school girls across Canada to work in teams to code a project focused on creating social impact change.
Hackergal Hub: Hackergal Hub is a free online learning platform that equips girls with the tools to lead their own coding journeys!
Scholarships and Ambassador Program: Hackergal is committed to providing ongoing support to girls who have participated in our programs and are keen to pursue their interest in computer science and technology related fields. Hackergal will provide internships, scholarships and networking opportunities to pave the way for a more equitable future in technology.
Growing Hackergal - Diversity and Inclusion: Hackergal is focused on expanding our reach and impact on girls and facilitators across Canada to support BIPOC communities, building inclusive programs that reflect the diversity of the communities we serve.
Hackergal has exposed computer science education to over 20,000 girls across Canada since late 2015 through their national Hackathon Program. For more information, visit us at http://www.hackergal.org.
About Shaw Communications Shaw Communications Inc. is a leading Canadian connectivity company. The Wireline division consists of Consumer and Business services. Consumer serves residential customers with broadband Internet, Shaw Go WiFi, video and digital phone. Business provides business customers with Internet, data, WiFi, digital phone and video services. The Wireless division provides wireless voice and LTE data services through an expanding and improving mobile wireless network infrastructure.
Shaw is traded on the Toronto and New York stock exchanges and is included in the S&P/TSX 60 Index (Symbol: TSX - SJR.B, SJR.PR.A, SJR.PR.B, NYSE SJR, and TSXV SJR.A). For more information, please visit http://www.shaw.ca
For media inquiries, please contact:
HackergalSarah Coombs(416) 729-8550scprcanada@gmail.com
Shaw Communications Inc.Chethan Lakshman, VP, External Affairs(403) 930-8448chethan.lakshman@sjrb.ca
See the rest here: