Mathieu Dahan is an Assistant Professor in the H. Milton Stewart School of Industrial and Systems Engineering. His research interests are in combinatorial optimization, game theory, and predictive analytics, with applications to service operations management and disaster logistics. His primary focus is on developing strategies for improving the resilience of large-scale infrastructures — particularly, transportation and natural gas networks — in the face of correlated failures such as security attacks and natural disasters. Current projects include: (i) Strategic design of network inspection systems; and (ii) Analytics-based response operations under uncertainty.

Dr. Dahan received a Ph.D. and M.S. in Computational Science and Engineering from the Massachusetts Institute of Technology, a M.Eng. and B.Eng. from the École Centrale Paris, and a B.S. in Mathematics from Paris-Sud University. He is the recipient of the MIT Robert Thurber Fellowship, the MIT Robert Guenassia Award, the Honorable Mention for the J-WAFS Fellowships, and the Best Poster Award at the Princeton Day of Optimization.

During the summer of 2016, he worked as a research scientist intern at Amazon.com (Seattle) in the Supply Chain Optimization Technologies team. Using Machine-Learning techniques, he worked on predicting the fulfillment cost and developing a prototype to grant a fast and accurate access to future shipping cost estimates.

Dr. Gari Clifford is a tenured Professor of Biomedical Informatics and Biomedical Engineering at Emory University and the Georgia Institute of Technology, and the Chair of the Department of Biomedical Informatics (BMI) at Emory. His research focuses on the application of signal processing and machine learning to medicine to classify, track and predict health and illness. His focus research areas include critical care, digital psychiatry, global health, mHealth, neuroinformatics and perinatal health. After training in Theoretical Physics, he transitioned to AI and Engineering for his doctorate (DPhil) at the University of Oxford in the 1990’s. He subsequently joined MIT as a postdoctoral fellow, then Principal Research Scientist where he managed the creation of the MIMIC II database, the largest open access critical care database in the world. He later returned as an Associate Professor of Biomedical Engineering to Oxford, where he helped found its Sleep & Circadian Neuroscience Institute and served as Director of the Centre for Doctoral Training in Healthcare Innovation at the Oxford Institute of Biomedical Engineering. As Chair, Dr Clifford has established BMI as a leading center for critical care and mHealth informatics, and as a champion for open access data and open source software in medicine, particularly through his leadership of the PhysioNet/CinC Challenges and contributions to the PhysioNet Resource. Despite this, he is a strong supporter of commercial translation, working closely with industry, and serves as CTO of MindChild Medical, a spin out from his research at MIT.

Diego Cifuentes is an Assistant Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. His research centers around the development of mathematical optimization methods, and the application of these methods in engineering areas such as machine learning, statistics, robotics, power systems, and computer vision. He also works in the theoretical analysis of optimization methods, leveraging geometric and combinatorial information to improve efficiency and robustness. Prior to joining ISyE, he served as an applied math instructor in MIT and as a postdoctoral researcher in the Max Planck Institute for Mathematics in the Sciences.

He earned his Ph.D. and M.S. in Electrical Engineering and Computer Science from MIT, and his B.S. in Mathematics and B.S. in Electronics Engineering from Universidad de los Andes.

Xu Chu is an assistant professor in the School of Computer Science at Georgia Tech. He obtained his Ph.D. degree from the University of Waterloo in late 2017, and joined Georgia Tech in Jan 2018. He is a recipient of the JP Morgan Faculty Research Fellow Award, the Microsoft Ph.D. fellowship award, and the David R. Cheriton fellowship award. 

He is broadly interested in data management systems and machine learning. In particular, he focuses on (1) how to leverage advanced machine learning techniques to solve hard and practical data management problems, such as large-scale data integration; and (2) how to build data management systems to tackle the common pain points in practical machine learning, such as the lack of high-quality labeled data.

Dr. Chen is an Assistant Professor in the School of Computational Science and Engineering. Previously he was a Research Scientist at the Oden Institute for Computational Engineering and Sciences at the University of Texas at Austin. Dr. Chen’s research is in the multidisciplinary fields of computational mathematics, data science, scientific machine learning, and parallel computing with various applications in materials, energy, health, and natural hazard. Specifically, his research focuses on developing fast, scalable, and parallel computational methods for integrating data and models under high-dimensional uncertainty to make (1) statistical model learning via Bayesian inference, (2) reliable system prediction with uncertainty quantification, (3) efficient data acquisition through optimal experimental design, and (4) robust control and design by stochastic optimization.

My research focuses on engineering-driven machine learning methodologies for real-world problems in manufacturing and healthcare. The objective is to develop new models that combine learning methods with domain knowledge, in order to improve (i) efficiency, performance, and interpretability of the learning models; and (ii) productivity, scalability, and security of the engineering systems. 

Research areas: Additive Manufacturing, Bio-manufacturing, Healthcare, Machine Learning, Statistics.

Location:
Jialei Chen, Ph.D.
Department of Statistics, University of Georgia 

Vince Calhoun, Ph.D., is the founding director of the tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) where he holds appointments at Georgia State, Georgia Tech and Emory. He is the author of more than 900 full journal articles. His work includes the development of flexible methods to analyze neuroimaging data including blind source separation, deep learning, multimodal fusion and genomics, neuroinformatics tools. Calhoun is a fellow of the Institute of Electrical and Electronic Engineers, The American Association for the Advancement of Science, The American Institute of Biomedical and Medical Engineers, The American College of Neuropsychopharmacology, The Organization for Human Brain Mapping (OHBM) and the International Society of Magnetic Resonance in Medicine. He currently serves on the IEEE BISP Technical Committee and is also a member of IEEE Data Science Initiative Steering Committee as well as the IEEE Brain Technical Committee.

Adonis Bovell leads the algorithm assurance branch in the Assured Software and Information Division of GTRI’s CIPHER Lab. His group’s work focuses on the security benefits and risks associated with the application of advanced algorithms to cybersecurity problems. This entails research on model robustness, trustworthiness and privacy, including data valuation and synthetic data generation, formal verification and adversarial machine learning, and privacy and fairness. Previously, Bovell has worked on automated malware analysis and malicious network traffic detection.

Dr. Borodovsky and his group develop machine learning algorithms for computational analysis of biological sequences: DNA, RNA and proteins. Our primary focus is on prediction of protein-coding genes and regulatory sites in genomic DNA. Probabilistic models play an important role in the algorithm framework, given the probabilistic nature of biological sequence evolution.

Joy Arulraj is an assistant professor in the School of Computer Science at Georgia Institute of Technology. His research interest is in database management systems, specifically large-scale data analytics, main memory systems,  machine learning, and big code analytics. At Georgia Tech, he is a member of the Database group.