L. Burak Kara is a professor in the Department of Mechanical Engineering, with a courtesy appointment in the Robotics Institute. His research develops new computational analysis, design, and manufacturing technologies with wide-ranging applications in the space of mechanical CAD, topology optimization, additive manufacturing, electronics design, and bio-engineering. To this end, his research combines principles of machine learning, optimization, and geometric modeling to develop new knowledge and computational software for use in next-generation design systems.
Some of his recent projects show how machine learning can aid in many of the conventionally tedious and expensive design steps. Examples include deep learned physics to replace expensive structural simulations, learning from past designs to automatically generate novel products, robust sampling to reduce the cost in combinatorial design optimization scenarios, the use of deep reinforcement learning for electronic chip design, and crowdsourcing to learn semantic maps between human preferred language and 3D computer models.
Kara is the recipient of National Science Foundation Career award and American Society of Mechanical Engineers Design Automation Society Young Investigator Award. At CMU, he teaches courses in AI and Machine learning, Engineering Design, and Linear Algebra and Vector Calculus. He earned his B.S. in Mechanical Engineering from the Middle East Technical University (1998), and his Ph.D. in Mechanical Engineering from Carnegie Mellon University (2005).
Generating 3-D Models Using Simple Interaction Techniques
2004 Ph.D., Mechanical Engineering, Carnegie Mellon University
2000 MS, Mechanical Engineering, Carnegie Mellon University
1998 BS, Mechanical Engineering, Middle East Technical University
Air Force partnership to fuse AI and materials research
CMU and Air Force Research Laboratory establish 5-year, $7.5M Center of Excellence in data-driven materials research.
Polymers, printing, and pathways
A novel approach to 3D printing using a support bath can greatly expand the types of polymers that can be printed, enable chemical reactions of the printed materials to gain novel material properties, and increase the mechanical strength and reduce the print time of mechanical parts through design optimization.
Smarter electronics design through machine learning
Burak Kara is collaborating with Cadence Design Systems, Inc. and NVIDIA on applying advanced machine learning techniques to develop integrated and intelligent design system flows.
Design Automation Conference
Kara speaks on DAC panel, collaborates with Cadence
MechE’s Burak Kara was a panelist at the Design Automation Conference earlier this month, discussing how innovations in machine learning, deep learning, and artificial intelligence impact electronic design automation (EDA). The panel was sponsored by Cadence Design Systems, a company Kara is collaborating with on a new project to automate the design process of electronic circuits and chips.
Lightening the load
Kate Whitefoot and Burak Kara are developing methods allowing manufacturers to redesign multiple parts into one continuous part using 3-D printing.
Using AI to solve real-world problems
In MechE’s Levent Burak Kara’s project-based graduate course, students applied their skills in artificial intelligence and machine learning to solve real-world problems outside the classroom.
Lighter weight, lower costs in 3-D printing
Levent Burak Kara develops ways to best bolster lightweight, 3-D printed materials and reduce production costs.