Speaker Bios

Dr. Vijay Ganesh is a professor of computer science at the Georgia Institute of Technology. He serves as the Associate Director of the IDEaS Institute and is affiliated with Tech AI. 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.

Dr. C. David Sherrill is a Regents’ Professor with joint appointments in the School of Chemistry and Biochemistry and School of Computational Science and Engineering.  He obtained his B.S. in Chemistry from MIT  in 1992, and his Ph.D. in Chemistry from the University of Georgia in 1996.  Dr. Sherrill serves as Associate Director of the Institute for Data Engineering and Science at Georgia Tech.   He has published over 175 peer-reviewed articles on the development and application of new theoretical methods and new algorithms in computational quantum chemistry.  He is a Fellow of the American Association for the Advancement of Science (AAAS), the American Chemical Society, and the American Physical Society, and he has been Associate Editor of the Journal of Chemical Physics since 2009.  Dr. Sherrill has received a Camille and Henry Dreyfus New Faculty Award, the International Journal of Quantum Chemistry Young Investigator Award, an NSF CAREER Award, and Georgia Tech’s W. Howard Ector Outstanding Teacher Award. In 2017, he was named the Outreach Volunteer of the Year by the Georgia Section of the American Chemical Society for his many years of working with K-12 teachers during National Chemistry Week. 

Jake is a graduate student at UC Irvine, studying applications of machine learning in theoretical particle physics. He is particularly interested in developing tools that can assist human theorists in exploring vast spaces of possible physics theories, possibly elucidating regions beyond a physicist’s intuition. Before Irvine, Jake worked as a software developer in the Accelerator Directorate at SLAC National Lab. 

Alex Gagliano is an NSF IAIFI Fellow at MIT and the Harvard–Smithsonian Center for Astrophysics. He develops scalable machine learning for real-time inference on astrophysical transients and studies the final years of massive stars. He earned his Ph.D. at the University of Illinois and has held appointments at the Flatiron Institute’s Center for Computational Astrophysics and Los Alamos National Laboratory.

Austin is a PhD candidate in Chemistry at Georgia Tech in the Sherrill group, studying non-covalent interactions using quantum chemistry methods. As a National Science Foundation Graduate Student Fellow, he has worked on benchmarking and lowering the cost of symmetry-adapted perturbation theory (SAPT) methods to be used in downstream machine-learning applications on larger molecular systems. During this time, he has furthered his passion for high-throughput quantum chemistry through developing MPI-distributed software for job management and incorporating QCFractal/QCArchive for long-term data standardization, computation, and storage. As a MolSSI Software Fellow, he is working on implementing an accurate functional group-based SAPT in Psi4, an open-source quantum chemistry package, to more effectively train atomic-pairwise neural networks. In collaboration with the ARTESAN group at Georgia Tech, he has been working on developing an AI4QC framework to build a computational chemistry assistant to assistant both experts and non-experts alike to correctly select an appropriate level of theory and run quantum chemistry calculations to answer research questions through computation.

Piyush Jha is a Ph.D. student in Computer Science at Georgia Tech advised by Prof. Vijay Ganesh. His research focuses on AI for Science and neurosymbolic techniques, developing computational methods that combine machine learning, symbolic reasoning, and reinforcement learning to discover new physics theories and design new materials with targeted properties. Piyush brings industry and research experience from Amazon Science, where he designed LLM-based autonomous model-building agents, and from Quadrical AI, a leading solar AI startup where he developed predictive analytics and digital-twin solutions for renewable energy systems. He has also contributed to multiple academic labs, building high-performance tools at the intersection of AI and scientific discovery. A two-time SMT-COMP winner, he has received the University of Waterloo Graduate Scholarship and the Pasupalak Scholarship in Robotics and AI, and his work has been published in top venues including ACM Transactions, IJCAI, AAAI, and ECAI.

Prithwish Jana is a Ph.D. student in Computer Science at Georgia Tech, advised by Prof. Vijay Ganesh and Prof. Sriram Vishwanath. He earned his M.Tech. (2022) from IIT Kharagpur and B.E. (2020) from Jadavpur University, graduating top of his class as the Institute Silver Medalist in his Master’s and the University Gold Medalist in his Bachelor’s. In addition to his academic research, he brings industry experience from Amazon Science (AWS, Seattle), where he worked as an Applied Scientist Intern with the Next Gen DevX (NGDE) team, designing LLM-based coding agents for Infrastructure-as-Code.
Prithwish’s research lies at the intersection of neuro-symbolic AI, formal methods, AI for code, and AI for mathematics. He develops neuro-symbolic techniques for fine-tuning large language models (LLMs), integrating symbolic reasoning tools and formal methods to enhance their reasoning capabilities in software engineering (e.g., code translation and code generation) and in mathematical reasoning (e.g., automated proof synthesis in Lean and proof auto-formalization).