Microservices have been transforming the computing landscape with web-scale infrastructures like Facebook, Google, Amazon, and telecom infrastructures like AT&T and Ericsson adopting them. The microservices paradigm has proven to promote better scalability, fault tolerance, and deployability. However, it also significantly increases the space of configuration options and performance problems, rendering traditional approaches to management ineffective.
Deep Learning (DL) is a rapidly growing field that has found a set of wide-ranging applications across various industries, such as transportation, banking and finance, healthcare, and more. As the use of DL becomes more widespread, DL frameworks, such as TensorFlow and PyTorch, have, in turn, become increasingly popular, and are being used to build models that are applied even in security-critical settings. Thus, with their increasing popularity, the importance of keeping these frameworks secure has become crucial.
Last summer, I interned at Brown University’s Data Science Initiative (DSI) with Professor Ritambhara Singh. The work I was doing, in computational genetics, was fascinating. And the office space was modern and light-filled. But most importantly, the lab was filled with welcoming grad students and amazing professors who worked right next to me.
In 2022, generative AI went mainstream. Mere months ago, it still seemed exclusively the province of the ML research community and of Twitter, where meme accounts like @weirddalle shared the results of feeding text-to-image generation models off-the-wall prompts like "a bottle of ranch dressing testifying in court."
Cut to the present, where generative AI startups like Stability AI and Jasper are raising $100 million rounds of funding, big players like Microsoft have announced upcoming integrations of text-to-image generation, and perhaps all of us have used OpenAI's DALL-E 2 to create create something delightful like this "chicken nugget dressed as a …
The 30th European Symposium on Algorithms (ESA) last month awarded the ESA 2021 Test-of-Time Award to Brown CS Professor Sorin Istrail and his collaborators for their ESA 2001 paper called “SNPs Problems, Complexity, and Algorithms.” The award recognizes “excellent papers in algorithm research that were published … 19-21 years ago and which are still influential and stimulating for the field today,” according to the ESA.
With recent advancements in artificial intelligence and breakthroughs in semiconductor industries, are we really that far away from reaching our wildest dreams — living in harmony with robots we created and being liberated from the daily mundane chores? This is the answer we are trying to find in CSCI 2952-O A Practical Introduction to Advanced 3D Robot Perception.
Congratulations! We are very proud of your achievements!
The theme of this letter is inspired by an artistic work created by one of our Computer Science graduates of the Class of 2020.
Recent studies show that, in developing regions (e.g. Africa and Southeast Asia), fast internet connectivity lifted approximately 2.5 million people out of extreme poverty [1] and techniques to improve web performance (e.g. Google’s AMP) improved profit by 20% [2]. Given these studies, it is unsurprising that significant work and effort have gone into improving connectivity in these regions. These efforts range from improving the mobile networks (from 2G to 4G and now 5G) and improving networking infrastructure to reducing the cost of mobile data rates and increasing the amount of local language content. Although these efforts offer tremendous benefits, a …
