This summer, four mechanical engineering graduate students had the opportunity to gain hands-on experience working in industry. Through the recently launched Industry Immersion Project Program (I2P), students were paired with a company and tasked with tackling a short-term project. Projects in this inaugural year for the program came from a diverse range of industries, including manufacturing, robotics, and aerospace engineering. A flagship program of the MechE Alliance, the I2P Program matches students with a company and project that best fits within their own academic experience at MIT. Projects are designed to be short term, lasting three to six months. Building upon programs such as the Master of Engineering in Advanced Manufacturing and Design and Leaders for Global Operations, which foster collaborations between students and the manufacturing industry, the I2P Program offers graduate students real-world experiences across industries.
The challenges in the real world get more complex and competing than an online competition. Hackathon might not paint the exact picture, and the success at these competitions should not be mistaken for expertise at the industry level. However, Kaggle, one of the world's finest platforms for data scientists, gives aspirants the best possible introduction into the tricky world of data. Analytics India Magazine has been exclusively covering the stories of top Kagglers, and today we compile a few nuggets of wisdom from those interviews that can guide an aspirant. "A right proportion of hard work, dedication, persistence, never giving up attitude and luck are the most important ingredients that helped me," said Abhishek Thakur when asked about his Kaggle success and what made him the world's first 4x grandmaster. When asked about what it takes to get to the top, Darragh, a Kaggle grandmaster, recollecting Jermey Howard, said that the best practitioners in machine learning all share one particular trait in common; they're very, very tenacious.
KDnuggets recently brought you the Top YouTube Channels for Data Science, employing a qualitative approach to identifying those channels of value on the platform. As the endeavour seemed to be be useful to some of our readers, we have repeated the exercise, this time bringing you the top machine learning channels that YouTube has to offer. For this iteration we changed up our metric for determining the "top" channels. We have maintained our quantitative approach, but tweaked the specifics. The results to this search were gathered on March 21, 2021, and appeared at this URL at the time.
A classical problem in city-scale cyber-physical systems (CPS) is resource allocation under uncertainty. Spatial-temporal allocation of resources is optimized to allocate electric scooters across urban areas, place charging stations for vehicles, and design efficient on-demand transit. Typically, such problems are modeled as Markov (or semi-Markov) decision processes. While online, offline, and decentralized methodologies have been used to tackle such problems, none of the approaches scale well for large-scale decision problems. We create a general approach to hierarchical planning that leverages structure in city-level CPS problems to tackle resource allocation under uncertainty. We use emergency response as a case study and show how a large resource allocation problem can be split into smaller problems. We then create a principled framework for solving the smaller problems and tackling the interaction between them. Finally, we use real-world data from a major metropolitan area in the United States to validate our approach. Our experiments show that the proposed approach outperforms state-of-the-art approaches used in the field of emergency response.