This boot camp will introduce you to the fundamentals of deep learning. What are deep networks and how do they work? We will start by introducing the key data structures and algorithms used by neural nets. Then we will cover popular architectures that build upon these structures, including convolutional networks, residual networks, and transformers. We will look in detail at how these architectures have been applied to the field of computer vision, and we will also give examples of applications in other areas, such as natural language processing and scientific data analysis. The course will also include a hands on tutorial where you will run and code simple networks in Pytorch in your browser.

Part of the Expanding Horizons in Computing IAP series presented by the MIT Schwarzman College of Computing. See the full list of activities at computing.mit.edu/ExpandingHorizons.

Generative AI presents practical and societal challenges that are swiftly growing as the technology scales and is increasingly used. Generative models — some mimicking human capabilities — can be used alongside people to enhance project outcomes in domains from scientific discovery and education to industrial cases (i.e., material discovery, responsible AI, synthetic data, misinformation, and more). Focusing on the governance and regulatory angles, this workshop will convene scientists, technologists, administrators, industry professionals, and counsel to discuss mechanisms to oversee and guide the development of AI models developed in academia and academic-industry collaboration. In particular, three brainstorming sessions will explore mechanisms for developing responsible AI systems: data, the dataset used to define the models’ learning or the synthetic data generated; foundation models, their capabilities and colossal repository of information; and the impact of scaling models, from the growing number of parameters to democratizing miniaturized models and their increasing geographical and social impact.

Registration will open in early January.

Part of the Expanding Horizons in Computing IAP series presented by the MIT Schwarzman College of Computing. See the full list of activities at computing.mit.edu/ExpandingHorizons.

Artificial intelligence leveraging multiple data sources and input modalities (tabular data, computer vision, and natural language) is poised to become a viable method to deliver more accurate results and deployable pipelines across various applications. This session aims to review progress in a variety of applications, including healthcare, meteorology and education, and discuss future directions.

Part of the Expanding Horizons in Computing IAP series presented by the MIT Schwarzman College of Computing. See the full list of activities at computing.mit.edu/ExpandingHorizons.

Are you done with your experiment and wondering how in the world to analyze the mountain of data you generated? Is your advisor hating on your Excel-drawn plots? Join a bootcamp on data management and build a toolbox worthy of your science! We will include relational data management, data preparation, data cleaning, visualizations, parallelism, the basics of machine learning, and a lot of chances to try things out on your own and ask questions. Introductory (very basic, really) python knowledge assumed. Taught by students from the Data Systems Group at CSAIL.

Registration is now closed.

Part of the Expanding Horizons in Computing IAP series presented by the MIT Schwarzman College of Computing. See the full list of activities at computing.mit.edu/ExpandingHorizons.

Join faculty from the MIT Department of Electrical Engineering and Computer Science for short talks on security.

Speakers:

• Ron Rivest: Welcome Remarks
• Henry Corrigan-Gibbs: Private Search
• Nickolai Zeldovich: Verification on Hardware
• Yael Kalai: Verification of Computation
• Vinod Vaikuntanathan: FHE

Part of the Expanding Horizons in Computing IAP series presented by the MIT Schwarzman College of Computing. See the full list of activities at computing.mit.edu/ExpandingHorizons.

The MIT authors of three recent books on design will talk about what design means in their domain, present examples of successful designs, and suggest prospects for the future of design in computing.

Design of Socio-Technical Systems
David Clark, Designing an Internet (MIT Press, 2018)

In this talk I will talk about the design principles of the Internet. I will describe how our understanding of system requirements evolved in the first decades, and how our changing understanding influenced the evolving design.  I will illustrate the space of system requirements and design options by looking at some alternative proposals for how to design an Internet, and the implications of some recent design proposals.

Design of Software Products
Daniel Jackson, The Essence of Software (Princeton University Press, 2021)

I’ll explain how successful innovations in software can usually be traced to just one or two “concepts” that offer new scenarios that, with seemingly small shifts, radically change how an application is used. I’ll give examples from apps such as Zoom, WhatsApp and Photoshop. I’ll also mention how viewing apps through concepts enables use of LLMs for code generation.

Design of Programs

Gerald Jay Sussman, Software Design for Flexibility (MIT Press, 2021)

It is hard to build systems that have acceptable behavior over a larger class of situations than was anticipated by their designers.  The best systems are evolvable: they can be adapted to new situations with only minor modification.  How can we design systems that are flexible in this way?

We have often programmed ourselves into corners and had to expend great effort refactoring code to escape from those corners.  We have now accumulated enough experience to feel that we can identify, isolate, and demonstrate strategies and techniques that we have found to be effective for building large systems that can be adapted for purposes that were not anticipated in the original design. I will illustrate such strategies with examples.

Part of the Expanding Horizons in Computing IAP series presented by the MIT Schwarzman College of Computing. See the full list of activities at computing.mit.edu/ExpandingHorizons.

This session will feature short talks by faculty from the MIT Department of Electrical Engineering and Computer Science on trustworthy systems.

Adam Chlipala, Correct-by-Construction Cryptographic Software: Important cryptographic algorithms have many different variations for different parameters and target hardware platforms, and conventionally, expert engineers need to reimplement an algorithm for each such combination, to get good performance.  The Fiat Cryptography project provides a generator that automates that specialization work that was previously highly manual.  As a bonus, the Fiat Cryptography code generator has a machine-checked mathematical proof of correctness.  It has been adopted to produce parts of a number of popular open-source libraries.

Srini Devadas, Security With Minimal Trust: We describe an approach to build computing systems that provide integrity of computation and data privacy for users while minimizing software and hardware that needs to be trusted.

Frans Kaashoek, Verifying Distributed Systems With Concurrent Separation Logic: Distributed systems are at the heart of cloud computing and bugs in them can lead to outages of Web sites. Unfortunately distributed systems are hard to get right because they must handle concurrency, crash recovery, replication, and reconfiguration, which interact in subtle ways.  A promising approach to verifying such systems (and thereby systematically eliminating bugs) is based on concurrent separation logic, which allows components to be verified independently yet handle tricky interaction between components.

Mengjia Yan, Principled Hardware Defenses Against Side-channel Attacks

Part of the Expanding Horizons in Computing IAP series presented by the MIT Schwarzman College of Computing. See the full list of activities at computing.mit.edu/ExpandingHorizons.

Are you done with your experiment and wondering how in the world to analyze the mountain of data you generated? Is your advisor hating on your Excel-drawn plots? Join a bootcamp on data management and build a toolbox worthy of your science! We will include relational data management, data preparation, data cleaning, visualizations, parallelism, the basics of machine learning, and a lot of chances to try things out on your own and ask questions. Introductory (very basic, really) python knowledge assumed. Taught by students from the Data Systems Group at CSAIL.

Registration is now closed.

Part of the Expanding Horizons in Computing IAP series presented by the MIT Schwarzman College of Computing. See the full list of activities at computing.mit.edu/ExpandingHorizons.

Generative AI presents practical and societal challenges that are swiftly growing as the technology scales and is increasingly used. Generative models — some mimicking human capabilities — can be used alongside people to enhance project outcomes in domains from scientific discovery and education to industrial cases (i.e., material discovery, responsible AI, synthetic data, misinformation, and more). Focusing on the governance and regulatory angles, this workshop will convene scientists, technologists, administrators, industry professionals, and counsel to discuss mechanisms to oversee and guide the development of AI models developed in academia and academic-industry collaboration. In particular, three brainstorming sessions will explore mechanisms for developing responsible AI systems: data, the dataset used to define the models’ learning or the synthetic data generated; foundation models, their capabilities and colossal repository of information; and the impact of scaling models, from the growing number of parameters to democratizing miniaturized models and their increasing geographical and social impact.

Registration will open in early January.

Part of the Expanding Horizons in Computing IAP series presented by the MIT Schwarzman College of Computing. See the full list of activities at computing.mit.edu/ExpandingHorizons.

Join faculty from the MIT Department of Electrical Engineering and Computer Science for short talks on security.

Speakers:

• Ron Rivest: Welcome Remarks
• Henry Corrigan-Gibbs: Private Search
• Nickolai Zeldovich: Verification on Hardware
• Yael Kalai: Verification of Computation
• Vinod Vaikuntanathan: FHE

Part of the Expanding Horizons in Computing IAP series presented by the MIT Schwarzman College of Computing. See the full list of activities at computing.mit.edu/ExpandingHorizons.

This session will feature short talks by faculty from the MIT Department of Electrical Engineering and Computer Science on trustworthy systems.

Adam Chlipala, Correct-by-Construction Cryptographic Software: Important cryptographic algorithms have many different variations for different parameters and target hardware platforms, and conventionally, expert engineers need to reimplement an algorithm for each such combination, to get good performance.  The Fiat Cryptography project provides a generator that automates that specialization work that was previously highly manual.  As a bonus, the Fiat Cryptography code generator has a machine-checked mathematical proof of correctness.  It has been adopted to produce parts of a number of popular open-source libraries.

Srini Devadas, Security With Minimal Trust: We describe an approach to build computing systems that provide integrity of computation and data privacy for users while minimizing software and hardware that needs to be trusted.

Frans Kaashoek, Verifying Distributed Systems With Concurrent Separation Logic: Distributed systems are at the heart of cloud computing and bugs in them can lead to outages of Web sites. Unfortunately distributed systems are hard to get right because they must handle concurrency, crash recovery, replication, and reconfiguration, which interact in subtle ways.  A promising approach to verifying such systems (and thereby systematically eliminating bugs) is based on concurrent separation logic, which allows components to be verified independently yet handle tricky interaction between components.

Mengjia Yan, Principled Hardware Defenses Against Side-channel Attacks

Part of the Expanding Horizons in Computing IAP series presented by the MIT Schwarzman College of Computing. See the full list of activities at computing.mit.edu/ExpandingHorizons.

The MIT authors of three recent books on design will talk about what design means in their domain, present examples of successful designs, and suggest prospects for the future of design in computing.

Design of Socio-Technical Systems
David Clark, Designing an Internet (MIT Press, 2018)

In this talk I will talk about the design principles of the Internet. I will describe how our understanding of system requirements evolved in the first decades, and how our changing understanding influenced the evolving design.  I will illustrate the space of system requirements and design options by looking at some alternative proposals for how to design an Internet, and the implications of some recent design proposals.

Design of Software Products
Daniel Jackson, The Essence of Software (Princeton University Press, 2021)

I’ll explain how successful innovations in software can usually be traced to just one or two “concepts” that offer new scenarios that, with seemingly small shifts, radically change how an application is used. I’ll give examples from apps such as Zoom, WhatsApp and Photoshop. I’ll also mention how viewing apps through concepts enables use of LLMs for code generation.

Design of Programs

Gerald Jay Sussman, Software Design for Flexibility (MIT Press, 2021)

It is hard to build systems that have acceptable behavior over a larger class of situations than was anticipated by their designers.  The best systems are evolvable: they can be adapted to new situations with only minor modification.  How can we design systems that are flexible in this way?

We have often programmed ourselves into corners and had to expend great effort refactoring code to escape from those corners.  We have now accumulated enough experience to feel that we can identify, isolate, and demonstrate strategies and techniques that we have found to be effective for building large systems that can be adapted for purposes that were not anticipated in the original design. I will illustrate such strategies with examples.

Part of the Expanding Horizons in Computing IAP series presented by the MIT Schwarzman College of Computing. See the full list of activities at computing.mit.edu/ExpandingHorizons.

This boot camp will introduce you to the fundamentals of deep learning. What are deep networks and how do they work? We will start by introducing the key data structures and algorithms used by neural nets. Then we will cover popular architectures that build upon these structures, including convolutional networks, residual networks, and transformers. We will look in detail at how these architectures have been applied to the field of computer vision, and we will also give examples of applications in other areas, such as natural language processing and scientific data analysis. The course will also include a hands on tutorial where you will run and code simple networks in Pytorch in your browser.

Part of the Expanding Horizons in Computing IAP series presented by the MIT Schwarzman College of Computing. See the full list of activities at computing.mit.edu/ExpandingHorizons.

Artificial intelligence leveraging multiple data sources and input modalities (tabular data, computer vision, and natural language) is poised to become a viable method to deliver more accurate results and deployable pipelines across various applications. This session aims to review progress in a variety of applications, including healthcare, meteorology and education, and discuss future directions.

Part of the Expanding Horizons in Computing IAP series presented by the MIT Schwarzman College of Computing. See the full list of activities at computing.mit.edu/ExpandingHorizons.