Merging cell datasets, panorama style
Algorithm stitches multiple datasets into a single “panorama,” which could provide new insights for medical and biological studies.
New approach could accelerate efforts to catalog vast numbers of cells
Data-sampling method makes “sketches” of unwieldy biological datasets while still capturing the full diversity of cell types.
J-Clinic names 18 grant recipients from across Institute
Projects will develop new AI technologies that detect and prevent diseases.
3Q: Assessing MIT’s computing infrastructure needs
In planning for the MIT Schwarzman College of Computing, working group is exploring needs across all parts of the Institute.
Improving security as artificial intelligence moves to smartphones
Researchers unveil a tool for making compressed deep learning models less vulnerable to attack.
Forums address MIT’s plans to reshape its computer science education
Working group discussions focus on how to design the MIT Schwarzman College of Computing to best serve the community and society.
3Q: Setting academic parameters for the MIT Schwarzman College of Computing
Working Group on Curricula and Degrees co-chairs discuss their progress toward establishing credentials and courses for the college.
Can science writing be automated?
A neural network can read scientific papers and render a plain-English summary.
Vivienne Sze wins Edgerton Faculty Award
EECS faculty member is recognized for technical innovation, educational excellence, and efforts to advance women and underrepresented minorities in her field.
3Q: Structuring the MIT Schwarzman College of Computing
Co-chairs of the Organizational Structure working group discuss their goals and progress.
Giving robots a better feel for object manipulation
Model improves a robot’s ability to mold materials into shapes and interact with liquids and solid objects.
3 Questions: Provost Martin Schmidt on building a new college
Working groups of faculty, students, staff are meeting regularly to develop ideas for the MIT Schwarzman College of Computing.
The future of agriculture is computerized
Machine learning can reveal optimal growing conditions to maximize taste and other features.
Machine learning moves popular data elements into a bucket of their own
Counting search queries isn’t easy, but MIT CSAIL’s new LearnedSketch system for “frequency-estimation” aims to help.