Step inside MIT’s newest computing hub for a dynamic fusion of knowledge and creativity during the January Independent Activities Period (IAP). In celebration of the opening of Building 45, the brand new home for the MIT Schwarzman College of Computing, we invite you to expand your horizons and join us for a series of bootcamps, workshops, short talks, panels, and roundtable discussions organized by MIT faculty around exciting areas of computing.

January 29

Organizers: Yael Kalai (EECS) and Vinod Vaikuntanathan (EECS)

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

Speakers:

Ron Rivest: Welcome Remarks

Vinod Vaikuntanathan, Encrypted computation: “Can you compute on data while it remains encrypted?” This fascinating question was first asked at MIT in 1978, resulting in the conceptualization of the notion of fully homomorphic encryption. I will talk about 15 years of research building fully homomorphic encryption systems, and the many challenges that lie ahead.

Henry Corrigan-Gibbs, Private web search: This talk will discuss the design of a new privacy-protecting search engine — one that can answer a client’s search query without ever seeing it. While the idea of a “private Google search” has been a dream of cryptographers for many years, recent developments at MIT and elsewhere have finally made it a (near) reality. I will present the state of the art in private search, along with exciting directions for future work.

Yael Kalai, Efficient verification of computation on untrusted platforms: Efficient verification of computation is fundamental to computer science. Recently it has had growing practical significance, especially with the increasing popularity of blockchain technologies and cloud computing. In this talk, I will present schemes for verifying the correctness of a computation and I will discuss both their practical aspects and their impact on areas such as cryptography, quantum complexity, hardness of approximation, and the complexity of finding a Nash equilibrium.

Nickolai Zeldovich, Eliminating bugs in hardware security modules using formal verification: Hardware security modules, such as USB security keys used for two-factor authentication, are a powerful approach for providing strong security even if other parts of the system are vulnerable or compromised. This approach critically depends on the hardware security modules themselves being implemented correctly and securely, but experience shows that developers make a wide range of mistakes, ranging from software bugs, to hardware issues, to subtle data leaks through timing channels. This talk will present recent research results on using formal verification to develop provably-secure implementations of hardware security modules.

Organizer: Adam Chlipala (EECS)

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: We will talk about the development of a dedicated security evaluation framework for early-stage microarchitectural defenses against speculative execution attacks. Amidst the ongoing cat-and-mouse game between hardware attacks and defenses, our framework can assist computer architects in formulating principled responses to the evolving landscape of hardware threats.

Organizers/Speakers: David Clark (CSAIL), Daniel Jackson (EECS/CSAIL), Gerald Jay Sussman (EECS/CSAIL)

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.
 
 

January 30

Organizers: Dimitris Bertsimas (Sloan, ORC) and Antonio Torralba (EECS, CSAIL)

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.

Agenda

  • 9:00 am – Dimitris Bertsimas: Multimodal AI and the Future of Universities
  • 9:30 am – Dylan Hadfield-Menell: Multimodal Learning and Control for Aligned Robot Systems
  • 10:00 am – Antonio Torralba: Visual Perception and Language Models.
  • 10:30 am – BREAK
  • 10:45 am – Marzyeh Ghassemi: The Pulse Of Ethical Machine Learning in Multimodal Health Data
  • 11:15 am – Panel and Q&A

Organizers/Speakers: Phillip Isola (EECS) and Kaiming He (EECS)

This bootcamp 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.

January 31

Speakers:  Mohammad Alizadeh, Manya Ghobadi, Dina Katabi, Tim Kraska, Sam Madden

Machine learning and generative AI promise to redefine computing in the next decade. In this session, we will explore how software and hardware systems for data centers must evolve to meet the demands of these workloads, and similarly how machine learning is redefining the way we build these software systems. In a series of short talks, each of the speakers will look at a different layer of the data center stack, including hardware, networks, operating systems, and databases, and show how new workloads and machine learning capabilities have and will continue to completely transform the way these systems work, resulting in radically different software architectures – with orders of magnitude better performance – than the designs we have used for the past several decades. The session will then conclude with a panel where we look at common trends between these areas of the software stack and make predictions about how systems will continue to evolve.

Organizers: Sertac Karaman (Aero Astro, LIDS) and Daniela Rus (EECS, CSAIL)

This session will feature a series of short talks by MIT faculty on embodied intelligence. We will discuss the emerging frontiers in this area, including sensorimotor learning, reinforcement learning, spatial AI, generative AI in robotics, and bio-inspired robotics.

Sensorimotor learning and reinforcement learning:

Spatial AI:

Bio-inspired robotics:

February 1

Organizers: Jeff Grossman (DMSE) and Vivienne Sze (EECS)

Speakers: Sara Beery (EECS) | Abigail Bodner (EAPS/EECS) | Priya Donti (EECS) | Jinhua Zhao (DUSP)

Climate change is one of the greatest challenges facing humanity. Machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate, ranging from smart grids, disaster management, and improved resilience, to decarbonizing industry and reimaging the built environment, there are many high-impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. On the other hand, the energy consumption and greenhouse gas emissions impact of the information and communications technology (ICT) sector is significant and dynamic. Estimates of “business as usual” future energy and emissions impacts of ICT projects are upwards of 20% of global energy use and carbon dioxide emissions by 2030. These estimates are without consideration of the ubiquitous use of deep neural networks and well before the advent of the large language model. The complexity in architecture, usage patterns, and size of the parameter space associated with generative AI and large language models comes with staggering increased energy use implications of computing both in training and inference.

This panel will describe efforts to leverage these tools while addressing their computational burden, to support mitigation efforts such as decarbonizing electricity systems, enhancing natural ecosystems, and achieving sustainable materials design and manufacture.

Photo credit: Michael George

Organizers: Arvind Satyanarayan (EECS, CSAIL) and Graham M. Jones (Anthropology)

Speakers: Arvind Satyanarayan (EECS, CSAIL) | Graham M. Jones (Anthropology) | Jeanette Andrews (magician, National Arts Club Artist Fellow)

What can the study of magic teach us about data-driven misinformation? In this one-of-a-kind workshop, attendees will explore how magicians have historically used the careful construction of narrative framing devices to shift perceived facts and how these concepts can be applied to data visualization. This session will begin with a presentation on visualizations, misinformation, and data literacy, followed by a discussion on framing devices used by magicians. Magician Jeannette Andrews will perform several pieces from her repertoires. The session will culminate in a hands-on workshop, where participants will explore how retelling of sensationalized information morphs across groups based on data labeling.

Organizer: Daniel Sanchez (EECS, CSAIL)

Join faculty from the MIT Department of Electrical Engineering and Computer Science for short talks on computer architecture and hardware accelerators.

Speakers:

  • Vivienne Sze: Efficient Computing for AI and Robotics
  • Christina Delimitrou: Reducing Datacenter Tax for Microservices: Acceleration and Optimizations
  • Song Han: Accelerating Large Language Model and Generative AI
  • Daniel Sanchez: An Architecture to Accelerate Computation on Encrypted Data

February 2

Organizers: Wojciech Matusik (EECS) and Caitlin Mueller (CEE, Architecture)

This session will showcase the breadth and depth of cutting-edge work in computational design across MIT, and aims to convene a diverse group of faculty and students to share ideas and build new connections. We will begin with a series of short, impactful talks by early- and mid-career faculty that highlight key areas of interest and expertise. Participants will then engage in round table discussions focused on topics of common, cross-disciplinary interest in computational design (e.g. design representation, simulation methods, inverse modeling, interfaces, etc.).

Agenda

Organizers: Dan Huttenlocher (SCC), Asu Ozdaglar (SCC, EECS), David Goldston (MIT Washington Office)

This workshop will consider key questions about how to effectively govern the use of AI. The discussion will build on recently released policy briefs aimed at delivering technically informed recommendations on AI policy, produced by several MIT faculty as part of an initiative led by the MIT Schwarzman College of Computing and MIT Washington Office.

The aim of this discussion-oriented workshop is the continued development of practical, technically informed AI policy recommendations. The first session of the workshop will be dedicated to a discussion of the policy brief on an overarching AI governance framework, as well as issues particular to large-scale generative models. We will use the second session to dive deeper on issues such as auditing, data ownership, and other topics that arise in the discussion.