Dr. Boukouvala is originally from Piraeus, which is the port of Athens in Greece. As the daughter of an airforce pilot, she travelled a lot with her family. Her first international move was actually to the USA, where she spent one year in Montgomery, Alabama. She later on lived in Riyadh, Saudi Arabia and Crete, Greece, before returning to Athens to get her B.S Degree in Chemical Engineering from the National Technical University in Athens. In 2008, she moved back to the US to obtain a PhD in Chemical Engineering at Rutgers University in NJ. She then worked as a Postdoctoral Associate in both Princeton University and Texas A&M University. In August 2016, Dr. Boukouvala returned to the South East US, as an Assistant Professor in the School of Chemical & Biomolecular Engineering at Georgia Tech. 

Her research interest in Process Systems Engineering (PSE) started during her PhD years, where she worked under the supervision of Dr. Marianthi Ierapetritou, on modeling and optimization of continuous pharmaceutical manufacturing. Her background on optimization and data-driven modeling was enhanced during her years as a postdoc with the late Christodoulos A. Floudas. Dr. Boukouvala is a proud 4th generation member of the academic family tree of the father of PSE, Roger Sargent.

The Ghosh group engages in cross-disciplinary collaborations and welcomes students from diverse academic backgrounds. Most projects focus on addressing challenges in fundamental physics and astrophysics using computational and AI/ML tools, making the group a natural fit for students with strong skills or interests in these areas. We develop methods to automate theoretical physics calculations using reinforcement learning and LLM agents, enabling rapid testing of new theories. We also work on simulation, experimental design and high-dimensional statistical inference techniques powered by AI to accelerate scientific discovery. Data analysis problems at the scale of the Large Hadron Collider or multi-messenger astronomy often demand rapid decision-making, and we design efficient AI algorithms that can be deployed on fast hardware to meet these challenges.

Alan Ritter is an associate professor in the School of Interactive Computing at Georgia Tech. His research interests include natural language processing, information extraction, and machine learning. He completed his Ph.D. at the University of Washington and was a postdoctoral fellow in the Machine Learning Department at Carnegie Mellon.  His research aims to solve challenging technical problems that can help machines learn to read vast quantities of text with minimal supervision.  His work has been featured in the press including WIRED, TNW and VentureBeat.  Alan is the recipient of an NSF CAREER, an Amazon Research Award, a Sony Faculty Innovation Award, and several paper awards presented at the Annual Meeting of the Association for Computational Linguistics.

Dr. Vijay Ganesh is a professor of computer science at the Georgia Institute of Technology. Prior to joining Georgia Tech in 2023, Vijay was a professor at the University of Waterloo in Canada from 2012 to 2023 and a research scientist at the Massachusetts Institute of Technology from 2007 to 2012. Vijay completed his PhD in computer science from Stanford University in 2007. Vijay's primary area of research is the theory and practice of SAT/SMT solvers, and their application in AI, software engineering, security, mathematics, and physics. In this context he has led the development of many SAT/SMT solvers, most notably, STP, Z3str4, AlphaZ3, MapleSAT, and MathCheck. He has also proved several decidability and complexity results in the context of first-order theories. More recently he has started working on topics at the intersection of learning and reasoning, especially the use of machine learning for efficient solvers, and the use of solvers aimed at making AI more trustworthy, secure, and robust. For his research, Vijay has won over 30 awards, honors, and medals to-date, including an ACM Impact Paper Award at ISSTA 2019, ACM Test of Time Award at CCS 2016, and a Ten-Year Most Influential Paper citation at DATE 2008.

Molei Tao received B.S. in Math & Physics in 2006 from Tsinghua Univ. (Beijing) and Ph.D. in Control & Dynamical Systems with a minor in Physics in 2011 from Caltech (advisor: Houman Owhadi, co-advisor: Jerry Marsden). Afterwards, he worked as a postdoc in Computing & Mathematical Sciences at Caltech from 2011 to 2012, and then as a Courant Instructor at NYU from 2012 to 2014. From 2014 on, he has been an assistant, and then associate professor in School of Math at Georgia Tech. He is a recipient of W.P. Carey Ph.D. Prize in Applied Mathematics (2011), American Control Conference Best Student Paper Finalist (2013), NSF CAREER Award (2019), AISTATS best paper award (2020), IEEE EFTF-IFCS Best Student Paper Finalist (2021), Cullen-Peck Scholar Award (2022), GT-Emory AI.Humanity Award (2023), a Plenary Speaker at Georgia Scientific Computing Symposium (2024), a Keynote Speaker at (2024) International Conference on Scientific Computing and Machine Learning, SONY Faculty Innovation Award (2024), Best Poster Award at 2024 international conference “Recent Advances and Future Directions for Sampling” held at Yale, and Richard Duke Fellowship (2025).