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Biography

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Dr. Sebastian Palacios received his Ph.D. from MIT's College of Computing in the Department of Electrical Engineering and Computer Science, where his doctoral thesis focused on developing machine learning methods and algorithms for engineering next-generation gene therapeutics. He has been featured in Colombian news outlets as one of the brightest persons of Colombian descent. 

During his graduate studies, he received the National Science Foundation (NSF) Graduate Research Fellowship, the Harvard-MIT Martha Gray Prize for Excellence in Research, MIT's Shillman Fellowship, MIT's University Center for Exemplary Mentoring Sloan Fellowship, an NIH/MIT predoctoral training fellowship, and was awarded for excellence in leadership at the MIT Awards Convocation. He received M.S. and B.S. degrees from MIT and Georgia Tech in electrical engineering, computer engineering, and computer science. During his undergraduate studies, he received a nearly perfect GPA and received the award for most outstanding undergraduate researcher in the College of Engineering.

He is conducting postdoctoral research in Jim Collin's Group at MIT developing advanced artificial intelligence systems for biological design. He is also conducting postdoctoral research in Domitilla Del Vecchio's Group at MIT using computational and experimental methods to study and engineer epigenetic cell memory.

Research Interests

My research interests focus on three broad categories: 

1. The development of machine learning systems for biological design with an emphasis on designing next-generation therapeutics. 

2. The development of synthetic genetic systems that comprise both memory and computation. In particular, I am interested in elucidating and engineering epigenetic cell memory as a framework for engineering novel useful functions in living cells. 

3. The development of systems and tools for reliably engineering human cells, combining genome engineering, synthetic biology, machine learning, computer engineering and mathematical modeling for robust design and engineering.  

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