Alexandria Smith is currently an Assistant Professor of Music at the Georgia Institute of Technology. She received her Ph.D. from the University of California, San Diego, and holds an M.M. and B.M. from Mannes the New School for Music.

Alexandria specializes in recording/mixing/mastering music that mixes different genres and experimental music. Her work has been referred to by Downbeat as “splendidly engineered.” Alexandria’s recent project as tracking/mix/mastering engineer and co-producer of Grammy-nominated bassist Mark Dresser’s Tines of Change was favorably reviewed by Downbeat, the Wire Magazine, San Diego Union Tribune, Percorsi Musical, All About Jazz, jazz-fun.de-Magazin für Jazz Musik, and more and was rated as one of the ‘Best Solo Albums of the Year’ by bestofjazz.org and best of 2023 by Downbeat. She has worked on recordings by Basher, Filera (Carmina Escobar, Natalia Pérez Turner, and Wilfrido Terrazas), Alvin Lucier, Rand Steiger, Treesearch, TJ Borden, Judith Hamann, and more. Her audio engineering work can be heard on labels such as Pyroclastic Records, Infrequent Seams, Black Truffle, New Focus Recordings, 577 Records, 1980 Records, and Blank Forms.

Saurabh Sinha received his Ph.D. in Computer Science from the University of Washington, Seattle, in 2002, and after post-doctoral work at the Rockefeller University with Eric Siggia, he joined the faculty of the University of Illinois, Urbana-Champaign, in 2005, where he held the positions of Founder Professor in Computer Science and Director of Computational Genomics in the Carl R. Woese Institute for Genomic Biology until 2022. He joined Georgia Institute of Technology in 2022, as Wallace H. Coulter Distinguished Chair in Biomedical Engineering, with joint appointments in Biomedical Engineering and Industrial & Systems Engineering. Sinha’s research is in the area of bioinformatics, with a focus on regulatory genomics and systems biology. Sinha is an NSF CAREER award recipient and has been funded by NIH, NSF and USDA. He co-directed an NIH BD2K Center of Excellence and was a thrust lead in the NSF AI Institute at UIUC. He led the educational program of the Mayo Clinic-University of Illinois Alliance, and co-led data science education for the Carle Illinois College of Medicine. Sinha has served as Program co-Chair of the annual RECOMB Regulatory and Systems Genomics conference and served on the Board of Directors for the International Society for Computational Biology (2018-2021). He was a recipient of the University Scholar award of the University of Illinois, and selected as a Fellow of the AIMBE in 2018.

Mohit Singh is a Coca-Cola Foundation Professor in the H. Milton Stewart School of Industrial and Systems Engineering and the Director of the Algorithms and Randomness Center (ARC). Prior to this, he served as a researcher in the Theory Group at Microsoft Research in Redmond, Washington.

Singh’s research interests include discrete optimization, approximation algorithms, and convex optimization. His research is focused on optimization problems arising in cloud computing, logistics, network design, and machine learning.

Singh received the Tucker Prize in 2009 given by the Mathematical Optimization Society for an outstanding doctoral thesis on “Iterative Methods in Combinatorial Optimization.” He also received the best paper award for his work on the traveling salesman problem at the Annual Symposium on Foundations of Computer Science (FOCS) in 2011.

Previously, Singh was an assistant professor at McGill University from 2010-2011 and a postdoctoral researcher at Microsoft Research, New England from 2008-2009.

He obtained his Ph.D. in 2008 from the Tepper School of Business at Carnegie Mellon University.

Sholl’s research focuses on materials whose macroscopic dynamic and thermodynamic properties are strongly influenced by their atomic-scale structure. Much of this research involves applying computational techniques such as molecular dynamics, Monte Carlo simulations and quantum chemistry methods to materials of interest. Although the group's work is centered on computational methods, it involves extensive collaboration with experimental groups and industrial partners.

My current research involves the modeling of certain types of human activity that exhibit regular spatio- and/or temporal patterns. As a case study, we have generally focused on various types of criminal behavior, since there are clear patterns in this activity and we have access to relatively large amounts of data. A large portion of this work aims to model the formation and dynamics of crime "hotspots" - spatio-temporal regions of increased criminal activity. Working with data provided by the Los Angeles and Long Beach police departments, we have developed methods of measuring the repeat and near-repeat criminal events that are the hallmarks of hotspot formation. We have also constructed a family of discrete models that allow for such patterns to develop from natural criminal behavior, and have derived continuum approximations of these discrete models. Some output from one of many simulations (right) illustrates this finding, with "hot" areas in red and "cold" areas in purple. 

In addition to the work on crime hotspots, this overarching project has also included: more accurate predictions of when and where crimes will occur, based on self-exciting point process models borrowed from seismology; the study of gang territoriality, modeled via diffusive Lotka-Volterra equations; gang retaliatory violence, and how the police may be able to solve such crimes using constrained optimization; the evolution of gang rivalry networks in the presence of retaliation and third-party effects; game theoretic models for the levels of both crime and cooperation with the authorities in society; and new methods for finding the "anchor points" of criminals given the locations of crimes they committed, based on models inspired by animal foraging.

Alexander Shapiro is the A. Russell Chandler III Chair and Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. 

Dr. Shapiro’s research interests are focused on stochastic programming, risk analysis, simulation based optimization, and multivariate statistical analysis.   In 2013 he was awarded Khachiyan Prize of INFORMS for lifetime achievements in optimization, and in 2018 he was a recipient of the Dantzig Prize awarded by the Mathematical Optimization Society and Society for Industrial and Applied Mathematics. In 2020 he was elected to the National Academy of Engineering. In 2021 he was a recipient of John von Neumann Theory Prize awarded by the Institute for Operations Research and the Management Sciences (INFORMS). 

Dr. Shapiro served  on editorial board of a number of professional journals. He was an area editor (optimization) of the Operations Research Journal and the editor-in-chief of the Mathematical Programming, Series A, Journal. 

Nicoleta Serban is the Peterson Professor of Pediatric Research in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech.

Dr. Serban's most recent research focuses on model-based data mining for functional data, spatio-temporal data with applications to industrial economics with a focus on service distribution and nonparametric statistical methods motivated by recent applications from proteomics and genomics. 

She received her B.S. in Mathematics and an M.S. in Theoretical Statistics and Stochastic Processes from the University of Bucharest. She went on to earn her Ph.D. in Statistics at Carnegie Mellon University.

Dr. Serban's research interests on Health Analytics span various dimensions including large-scale data representation with a focus on processing patient-level health information into data features dictated by various considerations, such as data-generation process and data sparsity; machine learning and statistical modeling to acquire knowledge from a compilation of health-related datasets with a focus on geographic and temporal variations; and integration of statistical estIMaTes into informed decision making in healthcare delivery and into managing the complexity of the healthcare system.