2025 Mandala @ MITCo-sponsored by MIT Prajnopaya, Buddhist Student Club, Simmons Hall

Time:Location: Rome, GA / Berry College

Writing Together Online offers the structured writing time to help you stay focused and productive during the busy fall months. Join our daily 90-minute writing sessions and become part of a community of scholars who connect online, set realistic goals, and write together in the spirit of accountability and camaraderie. We offer writing sessions every workday, Monday through Friday. The program is open to all MIT students, postdocs, faculty, staff, and affiliates who are working on papers, proposals, thesis/dissertation chapters, application materials, and other writing projects.Please register for any number of sessions:Monday, Wednesday, and Friday 9:00–10:30am (EST) Tuesday and Thursday, 8:00–9:30am and 9:30-11:00am (EST)For more information and to register, go to this link or check the WCC website. Please spread the word and join with colleagues and friends. MIT Students and postdocs who attend at least 5 sessions per challenge will be entered into a gift-card raffle.

🥳 You’re invited – please join us on Friday, October 17 for our MIA 10-Year Anniversary Celebration!📅 Friday, October 17📍 Broad Institute – 300 Binney St. (2110 - Charles)✍️ Register here➕ Add to calendar📝 Agenda:9:30 - 10:00 am: Welcome, breakfast10:00 - 11:00 am: Panel discussion11:00 - 11:30 am: Lightning talks, networking👩‍🔬 Panelists:💬 Alex Bloemendal🏫 Broad Institute (MIA co-founder and co-chair)💬 Aleksandrina Goeva🏫 University of Toronto💬 Gevorg Grigoryan🏫 Generate:Biomedicines💬 Aviv Regev🏫 Genentech ResearchOver the past decade, MIA (Models, Inference & Algorithms) has grown into an invaluable resource, bridging the worlds of biology, medicine, mathematics, statistics, machine learning, and computer science. Through pedagogically-driven primers and seminars, our members have gained new perspectives from leading global experts and rising talent, learning about not just their methods, but their intuition and philosophy, and leaving talks inspired with ideas on new avenues for their own research.We are celebrating MIA on October 17 and reflecting on its past, present, and future.The event will be at 300 Binney St. (2110 – Charles) and broadcast over Zoom. Sign up at bit.ly/MIACast to receive Zoom links to join virtually.🧑‍🏫 Read the panelist bios and see our updated schedule: broad.io/MIA

Speaker: Navid Azizan (MIT)Title: Hard-Constrained Neural NetworksAbstract: Incorporating prior knowledge and domain-specific input-output requirements, such as safety or stability, as hard constraints into neural networks is a key enabler for their deployment in highstakes applications. However, existing methods often rely on soft penalties, which are insufficient, especially on out-ofdistribution samples. In this talk, I will introduce hardconstrained neural networks (HardNet), a general framework for enforcing hard, input-dependent constraints by appending a differentiable enforcement layer to any neural network. This approach enables end-to-end training and, crucially, is proven to preserve the network’s universal approximation capabilities, ensuring model expressivity is not sacrificed. We demonstrate the versatility and effectiveness of HardNet across various applications: learning with piecewise constraints, learning optimization solvers with guaranteed feasibility, and optimizing control policies in safety-critical systems. This framework can be used even for problems where the constraints themselves are not fully known and must be learned from data in a parametric form, for which I will present two key applications: data-driven control with inherent Lyapunov stability and learning chaotic dynamical systems with guaranteed boundedness. Together, these results demonstrate a unified methodology for embedding formal constraints into deep learning, paving the way for more reliable AI.Biography: Navid Azizan is the Alfred H. (1929) and Jean M. Hayes Assistant Professor at MIT, where he holds dual appointments in Mechanical Engineering (Control, Instrumentation & Robotics) and IDSS and is a Principal Investigator in LIDS. His research interests broadly lie in machine learning, systems and control, mathematical optimization, and network science. His research lab focuses on various aspects of reliable intelligent systems, with an emphasis on principled learning and optimization algorithms with applications to autonomy and sociotechnical systems. His work has been recognized by several awards, including Research Awards from Google, Amazon, MathWorks, and IBM, and Best Paper awards and nominations at conferences including ACM Greenmetrics and the Learning for Dynamics & Control (L4DC). He was named in the list of Outstanding Academic Leaders in Data by CDO Magazine for two consecutive years in 2024 and 2023, received the 2020 Information Theory and Applications (ITA) “Sun” (Gold) Graduation Award, and was named an Amazon Fellow in AI in 2017 and a PIMCO Fellow in Data Science in 2018.