Discover trends and innovations in AI technology for software development
The MIT AI in Software Engineering Forum is an exclusive event for MIT Industrial Liaison Program (ILP) members and select guests. This forum offers a unique opportunity to explore the transformative role of AI and machine learning in software development. It will bring together software engineering leaders from top global organizations for a day of cutting-edge insights, innovation, and networking.
The forum will feature presentations by renowned MIT faculty, covering key areas such as requirements analysis, code generation, security, testing, lifecycle management, and more. Attendees will also have the opportunity to engage with MIT-connected startups from the MIT Startup Exchange program, showcasing breakthrough solutions designed to enhance software engineering quality and productivity.
Held the day after MIT’s flagship spring AI conference, this forum provides an exclusive platform to discover pioneering technologies, connect with industry peers, and gain actionable insights to address today’s software engineering challenges.
IMPORTANT: If your company is a member of the MIT Industrial Liaison Program (ILP), contact your program director for complementary access.
NOTE: Forum attendees will be automatically registered for the MIT Startup Exchange Live Demo Day, taking place in the afternoon.
Before MIT, Jim was the assistant dean of research business development at the UMass Amherst College of Information and Computer Sciences. Jim founded, built, and sold multiple technology companies in fintech and online media. He has bootstrapped startups and closed venture capital, angel, and private equity funding rounds. Jim also served as the Chief Operating Officer of a public company and a subsidiary of Pitney Bowes. He began his career at AT&T as a software developer, hardware engineer, and national account manager. Jim has authored patents and wrote one of the first books on Java programming. Out of all the roles he's held, Jim's favorite job title by far is dedicated dad of four. He earned a BS from Manhattan College and an MBA with concentrations in finance and international business from New York University.
Distinguished Professor of Computing, MIT Schwarzman College of Computing Professor, MIT Department of Electrical Engineering and Computer Science (EECS) Associate Director and COO, MIT Computer Science & Artificial Intelligence Laboratory (CSAIL)
Professor Armando Solar-Lezama, who is an Associate Director and the COO of MIT CSAIL and leads the Computer-Aided Programming Group, aims to reduce the skill and effort required to develop software that is secure, reliable, and efficient. One of his research group’s central contributions to this goal is the development of new approaches to software synthesis that can combine information from different sources to produce the code that the programmer wants. The group’s research ranges from the design of new analysis techniques and automated reasoning mechanisms to the development of new programming models that automate challenging aspects of programming.
Prof. Armando Solar-Lezama, a leading expert in program synthesis and software systems, will delve into the key transformative effects of AI on software engineering, from automating code generation to optimizing complex systems. Drawing on cutting-edge research and real-world applications, this talk will highlight the current capabilities, challenges, and future potential of AI-driven tools and techniques in revolutionizing how software is designed, developed, and maintained. Attendees will gain insights into how these innovations are reshaping the industry and what lies ahead.
Professor of Computer Science, MIT Associate Director, MIT Computer Science & Artificial Intelligence Laboratory (CSAIL)
Daniel Jackson is a professor of computer science at MIT and associate director of CSAIL. For his research in software, he won the ACM SIGSOFT Impact Award, and the ACM SIGSOFT Outstanding Research Award and was made an ACM Fellow.
He is the lead designer of the Alloy modeling language and author of Software Abstractions. He chaired a National Academies study on software dependability and has collaborated on software projects with NASA on air-traffic control, with Massachusetts General Hospital on proton therapy, and with Toyota on autonomous cars.
His most recent book, Essence of Software, offers a fresh approach to software design and shows how thinking about software in terms of concepts and their relationships can lead to more usable and effective software.
According to GitHub, programmers using Copilot, its AI-based code assistant, are now generating half their code with it. But look more carefully, and it turns out that almost all the code being generated is small fragments, often single functions. How might we use AI to generate entire apps? Prof. Daniel Jackson, a renowned authority on software modeling and design, will explain how success with AI in software development will depend on having radically modular structure in our apps. He’ll describe a new kind of modularity (called concept design) and show how it can lead to better development processes and better software, whether built by humans or bots.
Arthur J. Conner (1888) Professor, MIT Department of Electrical Engineering and Computer Science (EECS)
Adam's background is in programming languages and formal methods. He is interested in developing simpler and more effective abstractions for building correct, secure, and performant systems -- usually taking advantage of machine-checked mathematical proofs somehow. His work applies ideas like object-capability systems, proof-carrying code, transactions, type systems, and whole-program optimizing compilers for high-level languages; with applications in computer architecture, cryptography, databases, and operating systems, including novel designs that span traditional layers.
Integrated Development Environments (IDEs) are evolving with AI, becoming smarter and more intuitive to empower developers. In this talk, Prof. Adam Chlipala, an expert in formal methods and programming languages, will explore how AI is enhancing IDE capabilities—from intelligent code assistance and error detection to advanced refactoring and debugging tools. Highlighting the intersection of AI and programming environments, Prof. Chlipala will discuss breakthroughs, practical applications, and the road ahead for AI-driven software development workflows.
Group Leader of AI Technology and Systems, MIT Lincoln Laboratory
Dr. Dennis Ross is the leader of the Artificial Intelligence Technology and Systems Group at MIT Lincoln Laboratory. His research experience is in machine learning/artificial intelligence, extremal graph theory, and cyber analytics.
Ross joined Lincoln Laboratory in 2016 and has led programs developing cyber-AI and cyber operations capabilities for U.S. Cyber Command, U.S. Transportation Command, Federal Aviation Administration, U.S. Army, and the Intelligence Community. He also works closely with major academic AI conferences as well as military fellow programs from each service branch.
Prior to joining Lincoln Laboratory, Ross developed analytics and technology to drive research and food security policy as part the Feed the Future Initiative at USAID’s Global Center for Food Systems Innovation. Ross holds a PhD degree in computer science and engineering and an MS degree in mathematics, both from Michigan State University. He also completed his BA degree in mathematics from Albion College.
This session will discuss using machine learning and AI to detect vulnerabilities, strengthen software security, and respond to emerging threats in real-time.
Principal Research Scientist, MIT Laboratory for Information and Decision Systems
Kalyan is a Principal Research Scientist in the Laboratory for Information and Decision Systems (LIDS, MIT). He leads a group called Data-to-AI. The group is interested in Big data science, Machine learning, and developing AI applications to address societal needs. His primary research interests are in building statistical models that enable the extraction of information from large amounts of data.
Our world runs on software applications. More and more of these software applications are data-driven; that is, the logic of the application depends on the data that comes in, which determines which pathway is taken through the application during run time. In order to test these applications, developers need data. Currently, their options are to wait to get access to production data, to create fake data using Faker or test data management tools, or to manually generate data.
We set out to test an alternative: We wanted to see whether AI-generated synthetic data could help improve the supply of test data. This new paradigm involves learning a generative AI model from a very small subsample of the production data. Once the model is trained, the developer can port it to different environments, sample as much data as they want, and even sample data that fits specific conditions.
Training a generative AI model to create realistic data for enterprise-grade applications required a number of foundational developments, as we needed to improve generative AI’s ability to create realistic data and to handle the complexities that come with enterprise data. This included incorporating the ability to model relational databases, to capture and model data patterns pertaining to business logic, to model data based on context not available in data schemas, and to address a sprawl of data types. In this talk, I will cover how we are revolutionizing generative AI models so that they can produce data for enterprise-grade applications and datasets, and go over some recent success stories.
I will also cover another important aspect of generative AI that is often overlooked. Adopting generative AI algorithms at an enterprise level will require human involvement—they are not as automatic as we think, and require new innovations and deliberate, value-driven planning in order to succeed in this environment. I will also cover how MIT plays a unique role in an enterprise's AI adoption journey. In a field crowded with analysts, media, influencers and numerous other avenues that, while educational, are far from where rubber hits the road, this talk is an opportunity to get real about this technology and what it can do.
Dr. Lisa Amini is the Director of IBM Research Cambridge, which is home to the MIT-IBM Watson AI Lab, and of IBM's AI Horizons Network. She also leads IBM's AI Automation and Scaling Research efforts globally and is an IBM Distinguished Engineer.
Lisa was previously Director of Knowledge & Reasoning Research in the Cognitive Computing group at IBM’s TJ Watson Research Center in New York. She was also the founding Director of IBM Research Ireland, and the first woman Lab Director for an IBM Research Global (i.e., non-US) Lab (2010-2013). In this role she developed the strategy and led researchers in advancing science and technology for intelligent urban and environmental systems (Smarter Cities), with a focus on creating analytics, optimizations, and systems for sustainable energy, constrained resources (e.g., urban water management), transportation, and the linked open data systems that assimilate and share data and models for these domains.
Previously, Lisa was Senior Manager of the Exploratory Stream Processing Research Group at the IBM TJ Watson Research Center. She was the founding Chief Architect for IBM's InfoSphere Streams product. The Streams product is the result of a Research technology, System S, for which Lisa was also architectural lead from inception. Streams is a software platform for continuous, high throughput, and low latency mining of intelligence from massive amounts of sensor and other machine generated data. She also led her team in formative Smarter Planet/Cities pilots analyzing real-time data for cyber security, manufacturing, telecom, market data analysis, radio astronomy, environmental (water) monitoring, and transportation.
Lisa has served on program committees, hosted panels, and presented keynotes and publications in numerous IEEE, ACM and other conferences and workshops. She has worked at IBM the areas of AI and Cognitive Computing, Smarter Cities, Stream Processing, Distributed and high performance systems, Content Distribution, Multimedia, and Networking for over 25 years. She earned her PhD degree in Computer Science from Columbia University.
Leaders from the industry will share their experiences leveraging AI to transform software engineering practices. This panel will explore how AI is being applied to tackle real-world challenges, from automating development workflows to enhancing system reliability and scalability. Featuring insights from IBM, Humana, and other top innovators, the discussion will provide a deep dive into the successes, lessons learned, and emerging trends shaping the future of AI-powered software engineering. Attendees will gain valuable perspectives on the practical applications and strategic considerations of integrating AI into their development processes.