Amazon Scientists Receive Recent Honors and Awards: Celebrating Outstanding Achievements
Introduction:
Rahul Urgaonkar, a senior applied scientist with Amazon Advertising, has been awarded the prestigious IEEE Communications Society William R. Bennett Prize for his 2020 paper “BOLA: Near-Optimal Bitrate Adaptation for Online Videos”. The paper, authored in collaboration with Kevin Spiteri and Ramesh K. Sitaraman, introduces a new algorithm for adaptive bitrate streaming (ABR) that greatly enhances streaming performance and video quality. Urgaonkar’s work has been highly recognized in academic circles and is regularly cited by researchers in benchmarking their own algorithms. This award not only acknowledges the impact of Urgaonkar’s research but also showcases the exceptional work being done at Amazon Prime Video.
Full Article: Amazon Scientists Receive Recent Honors and Awards: Celebrating Outstanding Achievements
Rahul Urgaonkar Wins IEEE Communications Society William R. Bennett Prize
Rahul Urgaonkar, a senior applied scientist with Amazon Advertising, along with his co-authors Kevin Spiteri and Ramesh K. Sitaraman, has been awarded the prestigious IEEE Communications Society William R. Bennett Prize for their paper titled “BOLA: Near-Optimal Bitrate Adaptation for Online Videos.” The award was presented at the annual IEEE International Conference on Communications (ICC) held in May in Rome. The paper was published in the IEEE/ACM Transactions on Networking and focuses on adaptive bitrate streaming (ABR) techniques used in online video streaming.
What is BOLA?
BOLA, which stands for Buffer Occupancy based Lyapunov Algorithm, is a new algorithm developed for adaptive bitrate streaming (ABR). ABR techniques are used by modern video players to optimize the playback performance of videos streamed online. The BOLA algorithm offers significant improvements in streaming performance, reducing re-buffers and pauses during playback, and enhancing the overall quality of the videos shown. It has become a highly cited paper and is regularly used as a benchmark by other researchers in the field.
Recognition for Impactful Work
Rahul Urgaonkar expressed his excitement and gratitude for winning the award, stating that it is a recognition of the impact of their work on advancing the state-of-the-art in ABR techniques and its practical utility. He also added that winning the award provided an excellent opportunity to showcase the remarkable work being done at Amazon Prime Video to the broader research community.
Yizhou Sun’s Multiple Honors
Yizhou Sun, an Amazon Scholar and associate professor of computer science at UCLA, has recently received several prestigious honors. She was named on the IEEE Intelligent System’s “AI’s 10 to Watch” list in March for her pioneering work in heterogeneous information network (HIN) mining and deep graph learning. Sun’s research has a wide range of applications, including e-commerce, health care, material science, and hardware design.
In addition, Sun received the SIAM International Conference on Data Mining Early Career Data Mining Research Award in 2023, which recognizes outstanding contributions in the field of data analysis within ten years of receiving a PhD. Sun earned her PhD in computer science from the University of Illinois at Urbana-Champaign in 2012.
Furthermore, Sun and her co-authors were honored with the Best Student Paper Award at the ACM Web Conference for their paper titled “A Single Vector Is Not Enough: Taxonomy Expansion via Box Embeddings.” The paper was ranked among the top two papers out of 1,8000 submissions.
Pooyan Amir-Ahmadi Wins 2023 QE Best Paper Prize
Pooyan Amir-Ahmadi, a senior economist on the Supply Chain Optimization Technologies (SCOT) team, along with his co-author Thorsten Drautzburg, received the 2023 Quantitative Economics Best Paper Prize awarded by the Econometric Society. Their paper titled “Identification and Inference with Ranking Restrictions” was published in the journal Quantitative Economics. The prize is given to the best paper published in the corresponding journal during the previous two years.
Alexandros Potamianos Elevated to ISCA Fellow
Alexandros Potamianos, an Amazon Scholar and adjunct associate professor of electrical and computer engineering at USC, has been named a fellow of the International Speech Communication Association (ISCA). Potamianos was honored for his contributions to human-centered speech and multimodal signal analysis and conversational technologies. He will be recognized at Interspeech 2023 in Dublin, Ireland, in August.
Alexandre Belloni Receives Bank of America Faculty Award
Alexandre Belloni, an Amazon Scholar and the Westgate Distinguished Professor of Decision Sciences at Duke University’s Fuqua School of Business, received the 2022 Bank of America Faculty Award. This award is Fuqua’s highest faculty honor and recognizes outstanding teaching performance, research performance, leadership, and service to the school and community. Belloni’s research focuses on mechanism design and machine learning, specifically within Amazon’s Supply Chain Optimization Technologies (SCOT) organization for third-party sellers.
Conclusion
These outstanding researchers and scientists have been recognized for their contributions to various fields, from video streaming optimization to data analysis, graph learning, speech communication, and decision sciences. Their work demonstrates the innovation, excellence, and impact that Amazon’s scholars and employees bring to their respective fields. Congratulations to Rahul Urgaonkar, Yizhou Sun, Pooyan Amir-Ahmadi, Alexandros Potamianos, and Alexandre Belloni for their remarkable achievements and well-deserved accolades.
Summary: Amazon Scientists Receive Recent Honors and Awards: Celebrating Outstanding Achievements
Rahul Urgaonkar, a senior applied scientist with Amazon Advertising, has been awarded the IEEE Communications Society William R. Bennett Prize for his paper on “BOLA: Near-Optimal Bitrate Adaptation for Online Videos”. The paper introduces a new algorithm for adaptive bitrate streaming (ABR) that enhances streaming performance and customer experience. Yizhou Sun, an Amazon Scholar and associate professor, has also received multiple honors for her work in computer science, including being named on the IEEE Intelligent System’s “AI’s 10 to Watch” list and receiving the SIAM International Conference on Data Mining Early Career Data Mining Research Award. In addition, Pooyan Amir-Ahmadi, a senior economist on the Supply Chain Optimization Technologies team, and his co-author Thorsten Drautzburg have won the 2023 Quantitative Economics Best Paper Prize for their paper on “Identification and Inference with Ranking Restrictions”. Finally, Alexandros Potamianos, an Amazon Scholar and adjunct associate professor, has been elevated to ISCA fellow for his contributions to speech and multimodal signal analysis. Alexandre Belloni, an Amazon Scholar and professor at Duke University, has received the 2022 Bank of America Faculty Award for his outstanding contributions to teaching and research.
Frequently Asked Questions:
Q1: What is Machine Learning?
A1: Machine Learning is a sub-field of artificial intelligence that involves the development of algorithms and statistical models that enable computers to learn and improve from data without explicit programming. It focuses on the development of computer systems that can automatically analyze and interpret complex patterns and relationships in data, and subsequently make predictions or take actions based on that analysis.
Q2: How does Machine Learning work?
A2: Machine Learning algorithms work by training the computer system on a large set of data, known as a training set, allowing it to learn and identify patterns or correlations between data points. This training process involves adjusting the algorithm’s parameters to minimize errors and optimize performance. Once trained, the algorithm can make predictions or decisions when presented with new data.
Q3: What are the different types of Machine Learning?
A3: Machine Learning can be broadly categorized into three main types: supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, the algorithm learns from labeled examples to make predictions or classify new data. Unsupervised learning involves finding patterns or structures in unlabeled data, without predefined outputs. Reinforcement learning focuses on training algorithms to make decisions based on trial-and-error interactions with an environment, receiving feedback in the form of rewards or penalties.
Q4: What are some real-world applications of Machine Learning?
A4: Machine Learning has numerous applications across various industries. Some common examples include:
– Fraud detection in banking and finance
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– Predictive maintenance in manufacturing and industrial settings.
Q5: What are the challenges in implementing Machine Learning?
A5: While Machine Learning offers great potential, there are several challenges to consider when implementing it. Some of these challenges include:
– Data quality and availability: High-quality, relevant, and diverse data is crucial for effective Machine Learning, but obtaining and preparing such data can be time-consuming and difficult.
– Interpretability: Some Machine Learning models, such as deep neural networks, can be difficult to interpret, hindering the transparency and trustworthiness of the generated results.
– Scalability: Machine Learning systems need to be scalable to handle large volumes of data and increasing complexity as more variables and interactions are included.
– Ethical considerations: Ensuring fairness, transparency, and accountability in Machine Learning systems is important to prevent biased or discriminatory outcomes.
– Skill requirements: Implementing and maintaining Machine Learning systems requires a skilled team with knowledge of data science, statistics, programming, and domain expertise.
Note: This content has been generated by OpenAI’s language model, GPT-3. It is important to verify the accuracy and update the information as needed.
