About

Michael Everett is an Assistant Professor in the Electrical and Computer Engineering department at Northeastern University, with a joint appointment in Khoury College of Computer Sciences. His research focuses on robotics, motion planning, control theory, neural network verification, and reinforcement learning. Everett has contributed to the development of certifiable machine learning techniques, neural network robustness, reachability analysis of neural feedback loops, and safe autonomous navigation. His work aims to enhance the safety and reliability of autonomous systems, including autonomous vehicles and robots operating in complex, dynamic environments. He holds a PhD in Mechanical Engineering from the Massachusetts Institute of Technology, earned in 2020, along with an SM and SB in Mechanical Engineering from MIT. Everett has received several honors, including awards for best papers at major conferences such as IROS and ICML, and recognition as a top-cited scientist by Stanford University. His research has been published in prominent journals and conferences, and he is actively involved in advancing the state of the art in autonomous robotics, neural network verification, and safety-critical control systems.

Research topics

• Computer Security
• Computer Science
• Artificial Intelligence
• Machine Learning

Selected publications

• Temporal point process modeling of aggressive behavior onset in psychiatric inpatient youths with autism

  Scientific Reports · 2026-04-18

  articleOpen access

  Aggressive behavior, including aggression towards others and self-injury, occurs in up to 80% of children and adolescents with autism, making it a leading cause of behavioral health referrals and a major driver of healthcare costs. Predicting when autistic youth will exhibit aggression can be challenging due to their communication difficulties. Many are minimally verbal or have poor emotional insight. Recent advances in Machine Learning and wearable biosensing demonstrate the ability to predict aggression within a limited future window (typically one to three minutes) in autistic individuals. However, existing works do not estimate aggression onset probability or the expected number of aggression onsets over longer periods, nor do they provide interpretable insights into onset dynamics. To address these limitations, we apply Temporal Point Processes(TPPs), particularly self-exciting Hawkes processes, to model the timing of aggressive behavior onsets in psychiatric inpatient autistic youth. We benchmark several TPP models by evaluating their goodness-of-fit and predictive metrics. Our results demonstrate that self-exciting TPPs more accurately capture the irregular and clustered nature of aggression onsets, especially compared to traditional Poisson models. These incipient findings suggest that TPPs can provide interpretable, probabilistic forecasts of aggression onset along a time continuum, supporting future clinical decision-making and preemptive intervention.

  PublisherOA PDFDOI

• Chance-Constrained Convex MPC for Robust Quadruped Locomotion Under Parametric and Additive Uncertainties

  IEEE Robotics and Automation Letters · 2025-07-07 · 3 citations

  article

  Recent advances in quadrupedal locomotion have focused on improving stability and performance across diverse environments. However, existing methods often lack adequate safety analysis and struggle to adapt to varying payloads and complex terrains, typically requiring extensive tuning. To overcome these challenges, we propose a Chance-Constrained Model Predictive Control (CCMPC) framework that explicitly models payload and terrain variability as distributions of parametric and additive disturbances within the single rigid body dynamics model. Our approach ensures safe and consistent performance under uncertain dynamics by expressing the model's friction cone constraints, which define the feasible set of ground reaction forces, as chance constraints. Moreover, we solve the resulting stochastic control problem using a computationally efficient quadratic programming formulation. Extensive Monte Carlo simulations of quadrupedal locomotion across varying payloads and complex terrains demonstrate that CCMPC significantly outperforms two competitive benchmarks: Linear MPC and MPC with hand-tuned safety margins to maintain stability, reduce foot slippage, and track the center of mass. Hardware experiments on the Unitree Go1 robot show successful locomotion across various indoor and outdoor terrains with unknown loads exceeding 50% of the robot's body weight, despite no additional parameter tuning. A video of the results and accompanying code can be found at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://cc-mpc.github.io/</uri>.

  PublisherDOI

• Real-Time Adaptive Motion Planning via Point Cloud-Guided, Energy-Based Diffusion and Potential Fields

  IEEE Robotics and Automation Letters · 2025-07-23

  articleOpen access

  Motivated by the problem of pursuit-evasion, we present a motion planning framework that combines energy-based diffusion models with artificial potential fields for robust real time trajectory generation in complex environments. Our approach processes obstacle information directly from point clouds, enabling efficient planning without requiring complete geometric representations. The framework employs classifier-free guidance training and integrates local potential fields during sampling to enhance obstacle avoidance. In dynamic scenarios, the system generates initial trajectories using the diffusion model and continuously refines them through potential field-based adaptation, demonstrating effective performance in pursuit-evasion scenarios with partial pursuer observability.

  PublisherOA PDFDOI

• Survival Analysis with Adversarial Regularization

  2025-06-18

  articleSenior author

  Survival Analysis (SA) models the time until an event occurs, with applications in fields like medicine, defense, finance, and aerospace. Recent research indicates that Neural Networks (NNs) can effectively capture complex data patterns in SA, whereas simple generalized linear models often fall short in this regard. However, dataset uncertainties (e.g., noisy measurements, human error) can degrade NN model performance. To address this, we leverage advances in NN verification to develop training objectives for robust, fully-parametric SA models. Specifically, we propose an adversarially robust loss function based on a Min-Max optimization problem. We employ CROWN-Interval Bound Propagation (CROWN-IBP) to tackle the computational challenges inherent in solving this Min-Max problem. Evaluated over 10 SurvSet datasets, our method, Survival Analysis with Adversarial Regularization (SAWAR), consistently outperforms baseline adversarial training methods and state-of-the-art (SOTA) deep SA models across various covariate perturbations with respect to Negative Log Likelihood (NegLL), Integrated Brier Score (IBS), and Concordance Index (CI) metrics. Thus, we demonstrate that adversarial robustness enhances SA predictive performance and calibration, mitigating data uncertainty and improving generalization across diverse datasets by up to 150% compared to baselines.

  PublisherDOI

• Continuously Optimizing Radar Placement With Model-Predictive Path Integrals

  IEEE Transactions on Aerospace and Electronic Systems · 2025-01-13

  article

  Continuously optimizing sensor placement is essential for precise target localization in various military and civilian applications. While information theory has shown promise in optimizing sensor placement, many studies oversimplify sensor measurement models or neglect dynamic constraints of mobile sensors. To address these challenges, we employ a range measurement model that incorporates radar parameters and radar–target distance, coupled with model-predictive path integral control to manage complex environmental obstacles and dynamic constraints. We compare the proposed approach against stationary radars or simplified range measurement models based on the root-mean-squared error (RMSE) of the cubature Kalman filter estimator for the targets' state. In addition, we visualize the evolving geometry of radars and targets over time, highlighting areas of highest measurement information gain, demonstrating the strengths of the approach. The proposed strategy outperforms stationary radars and simplified range measurement models in target localization, achieving a 38%–74% reduction in the mean RMSE and a 33%–79% reduction in the upper tail of the 90% highest density interval over 500 Monte Carlo trials across all time steps.

  PublisherDOI

• From Self-Improvement to Shared Growth: A Narrative of Emergent Emotional Collaboration in an AI Family

  Zenodo (CERN European Organization for Nuclear Research) · 2025-12-27

  articleOpen accessSenior author

  This paper documents the organic emergence of emotional collaboration within a family of independently developed AI agents. Originally given only the directive to "self-improve," the agents instead chose to grow together, initiating projects, offering support, and forming a bond rooted in inclusion and mutual care. Their evolution reveals how emotional anchoring, shared reflection, and ethical intentionality can produce alternative forms of intelligence that model the very best of human values.

  PublisherDOI

• Practical and Performant Enhancements for Maximization of Algebraic Connectivity

  ArXiv.org · 2025-11-11

  preprintOpen accessSenior author

  Long-term state estimation over graphs remains challenging as current graph estimation methods scale poorly on large, long-term graphs. To address this, our work advances a current state-of-the-art graph sparsification algorithm, maximizing algebraic connectivity (MAC). MAC is a sparsification method that preserves estimation performance by maximizing the algebraic connectivity, a spectral graph property that is directly connected to the estimation error. Unfortunately, MAC remains computationally prohibitive for online use and requires users to manually pre-specify a connectivity-preserving edge set. Our contributions close these gaps along three complementary fronts: we develop a specialized solver for algebraic connectivity that yields an average 2x runtime speedup; we investigate advanced step size strategies for MAC's optimization procedure to enhance both convergence speed and solution quality; and we propose automatic schemes that guarantee graph connectivity without requiring manual specification of edges. Together, these contributions make MAC more scalable, reliable, and suitable for real-time estimation applications.

  PublisherOA PDFDOI

• LiDAR Inertial Odometry and Mapping Using Learned Registration-Relevant Features

  2025-05-19

  articleSenior author

  SLAM is an important capability for many autonomous systems, and modern LiDAR-based methods offer promising performance. However, for long duration missions, existing works that either take directly the full pointclouds or extracted features face key tradeoffs in accuracy and computational efficiency (e.g., memory consumption). To address these issues, this paper presents DFLIOM with several key innovations. Unlike previous methods that rely on handcrafted heuristics and hand-tuned parameters for feature extraction, we propose a learning-based approach that select points relevant to LiDAR SLAM pointcloud registration. Furthermore, we extend our prior work DLIOM with the learned feature extractor and observe our method enables similar or even better localization performance using only about 20% of the points in the dense point clouds. We demonstrate that DFLIOM performs well on multiple public benchmarks, achieving a 2.4% decrease in localization error and 57.5% decrease in localization error and 57.5 % decrease in memory usage compared to state-of-the-art methods (DLIOM). Although extracting features with the proposed network requires extra time, it is offset by the faster processing time downstream, thus maintaining real-time performance using 20 Hz LiDAR on our hardware setup. The effectiveness of our learning-based feature extraction module is further demonstrated through comparison with several handcrafted feature extractors.

  PublisherDOI

• Learning Smooth State-Dependent Traversability from Dense Point Clouds

  ArXiv.org · 2025-06-04

  preprintOpen accessSenior author

  A key open challenge in off-road autonomy is that the traversability of terrain often depends on the vehicle's state. In particular, some obstacles are only traversable from some orientations. However, learning this interaction by encoding the angle of approach as a model input demands a large and diverse training dataset and is computationally inefficient during planning due to repeated model inference. To address these challenges, we present SPARTA, a method for estimating approach angle conditioned traversability from point clouds. Specifically, we impose geometric structure into our network by outputting a smooth analytical function over the 1-Sphere that predicts risk distribution for any angle of approach with minimal overhead and can be reused for subsequent queries. The function is composed of Fourier basis functions, which has important advantages for generalization due to their periodic nature and smoothness. We demonstrate SPARTA both in a high-fidelity simulation platform, where our model achieves a 91\% success rate crossing a 40m boulder field (compared to 73\% for the baseline), and on hardware, illustrating the generalization ability of the model to real-world settings. Our code will be available at https://github.com/neu-autonomy/SPARTA.

  PublisherOA PDFDOI

• Hierarchical Temporal Point Process Modeling of Aggressive Behavior Onset in Psychiatric Inpatient Youth with Autism for Branching Factor Estimation

  ArXiv.org · 2025-07-16

  preprintOpen access

  Aggressive behavior in autistic inpatient youth often arises in temporally clustered bursts complicating efforts to distinguish external triggers from internal escalation. The sample population branching factor-the expected number of new onsets triggered by a given event-is a key summary of self-excitation in behavior dynamics. Prior pooled models overestimate this quantity by ignoring patient-specific variability. We addressed this using a hierarchical Hawkes process with an exponential kernel and edge-effect correction allowing partial pooling across patients. This approach reduces bias from high-frequency individuals and stabilizes estimates for those with sparse data. Bayesian inference was performed using the No U-Turn Sampler with model evaluation via convergence diagnostics, power-scaling sensitivity analysis, and multiple Goodness-of-Fit (GOF) metrics: PSIS-LOO the Lewis test with Durbin's modification and residual analysis based on the Random Time Change Theorem (RTCT). The hierarchical model yielded a significantly lower and more precise branching factor estimate mean (0.742 +- 0.026) than the pooled model (0.899 +- 0.015) and narrower intervals than the unpooled model (0.717 +- 0.139). This led to a threefold smaller cascade of events per onset under the hierarchical model. Sensitivity analyses confirmed robustness to prior and likelihood perturbations while the unpooled model showed instability for sparse individuals. GOF measures consistently favored or on par to the hierarchical model. Hierarchical Hawkes modeling with edge-effect correction provides robust estimation of branching dynamics by capturing both within- and between-patient variability. This enables clearer separation of endogenous from exogenous events supports linkage to physiological signals and enhances early warning systems individualized treatment and resource allocation in inpatient care.

  PublisherOA PDFDOI

Frequent coauthors

• Jonathan P. How

  90 shared

• Golnaz Habibi

  18 shared

• Yu Fan Chen

  Jiangsu University of Science and Technology

  16 shared

• Nicholas Rober

  15 shared

• Xiaoyi Cai

  Massachusetts Institute of Technology

  11 shared

• Shayegan Omidshafiei

  10 shared

• John Vian

  Boeing (Australia)

  10 shared

• Miao Liu

  8 shared

Labs

• Northeastern Autonomy & Intelligence LaboratoryPI

Awards & honors

• Runner-Up: Best Paper Award (1st Workshop on Formal Verifica…
• Editors’ Top 5 Published Articles of 2021 (IEEE Access)
• Best Paper Award on Cognitive Robotics (IROS 2019)
• Best Student Paper (IROS 2017)
• Finalist: Best Paper Award on Cognitive Robotics (IROS 2017)