AutMn: Trustworthy Autonomy
Department of Computer Science,
The University of Alabama

Director & PI: Dr. Bineet Ghosh

[Update] Dr. Ghosh (sole PI) receives NSF funding ($364,155).

[Update] Dr. Ghosh serves as a joint Repeatability Evaluation Chair at HSCC/ICCPS 2026!

[Hiring!] We are hiring PhD, Masters and Undergraduate students to join our lab. Feel free to send us an email with your CV if you are interested.


It's AutMn at UA!

About Us

AutMn: Trustworthy Autonomy is a research lab at The University of Alabama ("It's AutMn at UA!"). Led by Dr. Bineet Ghosh, Assistant Professor in the Department of Computer Science, this lab focuses on designing and verifying autonomous systems. Our main objective is to ensure safety and correctness of autonomous systems in presence of uncertainties, with complex system dynamics, with heterogeneous and distributed computing resources. We strive to incorporate intelligence, bridge the gap between models and their implementations, and tackle other related complexities. To learn more about our research, please visit our research page (or Dr. Ghosh's research page).
 /bineet/

[Hiring!] We are currently hiring fully funded PhD students to join our lab and contribute to these exciting projects on Trustworthy Autonomy. If you are interested, feel free to send an email to Dr. Ghosh with your CV. Additionally, we also have openings for undergraduate and masters students, so don't hesitate to reach out if you are interested.

Lately, we are trying initiate a pilot project aimed at creating teaser videos (whenever we find time) that showcase our lab's research, with the goal of reaching a wider audience. The primary objective of this is to expand our reach and connect with a wider audience, including high-school students, to encourage them in STEM subjects. This initiative would also be helpful to establish connections with students, practitioners, and scientists from diverse research areas, encouraging interdisciplinary collaboration and the exchange of innovative ideas. These teaser videos draw inspiration from the increasing trend of short-form video content, incorporating interesting effects and music, making them accessible to a broader.

LinkedIn /bineetkumarghosh/ Instagram /autmn_lab/ Twitter /BineetGhosh3/ Facebook /autmn.lab/

Research Statement

Trustworthy Autonomy

Formal Methods

Safe Robotics

Cyber-Physical Systems

Control Theory

Real-Time and Embedded Systems

Broad interest lies in Trustworthy Autonomy — Design and Verification , which lies at the intersection of Formal Methods, Real-Time and Embedded Systems, Control Theory with applications in Robotics, Automotive, Industrial and Home Automation Systems. Check out the research page for details.

Trustworthy Autonomy

Autonomous Systems offer hope towards moving away from mechanized, unsafe, manual, often inefficient practices. The last decade has seen several small, but important, steps towards making this dream into reality. These advancements have helped us to achieve limited autonomy in several places, such as, driving, factory floors, surgeries, wearables, and home assistants, etc. Nevertheless, autonomous systems are required to operate in a wide range of environments with uncertainties (viz., sensor errors, timing errors, dynamic nature of the environment, etc.). Such environmental uncertainties, even when present in small amounts, can have drastic impact on the safety of the system—thus hampering the goal of achieving higher degree of autonomy, especially in safety critical domains.

To this end, our research is on developing formal techniques that can verify and design autonomous systems for safety, even under the presence of such uncertainties, allowing for their trustworthy deployment in the real world. This area of research can be referred to as Trustworthy Autonomy, which lies at the intersection of Formal Methods, Real-Time and Embedded Systems, Control Theory with applications in Robotics, Automotive, Industrial and Home Automation Systems. Check out the research page for details, and our list of publications here.