Across engineering disciplines, we break down the creative process of research and invention into four distinct parts – design, simulation, fabrication, and control – and examine how machine intelligence can transform them.

A Hardware Evaluation Framework for Large Language Model Inference

Hengrui Zhang, August Ning, Rohan Prabhakar, David Wentzlaff

Hardware
A 5’ UTR Language Model for Decoding Untranslated Regions of mRNA and Function Predictions

Yanyi Chu, Dan Yu, Yupeng Li, Kaixuan Huang, Yue Shen, Le Cong, Jason Zhang, Mengdi Wang

Bioengineering
ZeroSwap: Data-driven Optimal Market Making in DeFi

Viraj Nadkarni, Jiachen Hu, Ranvir Rana, Chi Jin, Sanjeev Kulkarni, Pramod Viswanath

Finance
Vendi Sampling For Molecular Simulations: Diversity As A Force For Faster Convergence And Better Exploration

Amey Pasarkar ,Gianluca Bencomo ,Simon Olsson ,Adji Bousso Dieng

Molecules and Materials
Multimodal Prediction of Tearing Instabilities in a Tokamak

Jaemin Seo; Rory Conlin; Andrew Rothstein; SangKyeun Kim; Joseph Abbate; Azarakhsh Jalalvand, Egemen Koleman

Renewable Energy
Machine Learning in Environmental Research: Common Pitfalls and Best Practices

Jun-Jie Zhu, Meiqi Yang, Zhiyong Jason Ren

Environment
Data-Driven Design of Polymer-Based Biomaterials: High-throughput Simulation, Experimentation, and Machine Learning

Roshan A. Patel, Michael A. Webb

Bioengineering
Active Uncertainty Reduction for Human-Robot Interaction: An Implicit Dual Control Approach

Jaime Fernández Fisac 

Control
Robotics
Amortized Inference for Heterogeneous Reconstruction in Cryo-EM

Axel Levy, Gordon Wetzstein, Julien Martel, Frederic Poitevin, Ellen D. Zhong

Bioengineering
Closed-Loop Control of Direct Ink Writing via Reinforcement Learning

Michal Piovarci (IST Austria), Michael Foshey (MIT CSAIL), Jie Xu (MIT CSAIL), Timothy Erps (MIT CSAIL), Vahid Babaei (MPI Informatics), Piotr Didyk (USI Lugano), Szymon Rusinkiewicz (Princeton University), Wojciech Matusik (MIT CSAIL), Bernd Bickel (IST Austria)

Additive Manufacturing
Data-driven Discovery of Chemotactic Migration of Bacteria via Machine Learning

Yorgos M. Psarellis, Seungjoon Lee, Tapomoy Bhattacharjee, Sujit S. Datta, Juan M. Bello-Rivas, Ioannis G. Kevrekidis

Dynamics
Machine-learning-optimized Cas12a barcoding enables the recovery of single-cell lineages and transcriptional profiles

Nicholas W. Hughes, Yuanhao Qu, Jiaqi Zhang, ..., Monte M. Winslow, Mengdi Wang, Le Cong

Bioengineering
Machine learning for topology optimization: Physics-based learning through an independent training strategy

Fernando V. Senhora, Heng Chi, Yuyu Zhang, Lucia Mirabella, Tsz Ling Elaine Tang, Glaucio H. Paulino

Mechanics
Deep Learning based Modeling and Inverse Design for Arbitrary Planar Antenna Structures at RF and Millimeter-Wave

Emir Ali Karahan, Aggraj Gupta, Uday K. Khankhoje, Kaushik Sengupta

Electromagnetics
Deep Learning-Enabled Inverse Design of 30-94 GHz Psat,3dBSiGe PA Supporting Concurrent Multiband Operation at Multi-Gb/s

Zheng Liu, Emir Ali Karahan, Kaushik Sengupta

Electromagnetics
Optogenetic control of the Bicoid morphogen reveals fast and slow modes of gap gene regulation

Anand P. Singh, Ping Wu,Sergey Ryabichko, ..., Eric Wieschaus,Thomas Gregor, Jared E. Toettcher

Bioengineering
Machine Learning for Mechanical Ventilation Control

Daniel Suo, Naman Agarwal, Wenhan Xia, Xinyi Chen, Udaya Ghai, Alexander Yu, Paula Gradu, Karan Singh, Cyril Zhang, Edgar Minasyan, Julienne LaChance, Tom Zajdel, Manuel Schottdorf, Daniel Cohen, Elad Hazan

Control
Machine learning generative models for automatic design of multi-material 3D printed composite solids

Tianju Xue, Thomas J. Wallin, Yigit Menguc, Sigrid Adriaenssens, Maurizio Chiaramonte

Mechanics