Chris Bailey-Kellogg is an associate professor of computer science at Dartmouth College. He earned a BS/MS with Sandy Pentland at MIT and a PhD with Feng Zhao at Ohio State and Xerox PARC, and conducted postdoctoral research with Bruce Donald at Dartmouth. He was an assistant professor at Purdue before being recruited back to Dartmouth. He has received an NSF Career award and an Alfred P. Sloan Foundation fellowship, along with regular grants from the NIH, NSF, and other organizations.

By tightly integrating computation with experiment, Chris seeks to optimize experiments so as to maximize information gain while minimizing experimental complexity. His lab focuses on embedding computation as a core component in elucidating three-dimensional structures of proteins and protein complexes, and in engineering protein variants. For example, he has developed new probabilistic graphical models to represent and reason with information about protein sequence, structure, and function. Using such models, he has developed new optimization algorithms to design interacting proteins for strength and specificity, to select breakpoints for combinatorial recombination and mutations for combinatorial mutagenesis, and to identify deimmunizing mutations that are less likely to disrupt structure and function.

Marcus received his PhD from the University of Toronto where during his training he received two prestigious graduate fellowships, both a National Science and Engineering Research Council (NSERC) of Canada Graduate Scholarship and an Ontario Graduate Scholarship. His research interests lie in the use of methods from computer vision, machine learning, and Bayesian statistics to solve interdisciplinary computational challenges. For example, one of his interests is algorithm development for automatic object detection, recognition, and tracking in a still image or video sequence (e.g., tracking cell or microorganism phenotype). Marcus solved a similar research problem for his PhD where he utilized physics based models of human motion and passive video-sequences to both estimate human pose and understand scene geometry.

In the molecular domain, Marcus developed a method based on Bayesian statistics to estimate protein structures from raw experimental electron cryomicroscopy data. As Cryo-EM is increasingly used to determine the three-dimensional structure of protein complexes, Marcus's method contributes a critical step towards the goal of an automated structure determination pipeline. Finally, Marcus has worked in developing dynamic models of complex systems.

Alex Ihler earned his PhD in Electrical Engineering & Computer Science at MIT and is now faculty at the University of California Irvine's Department of Information and Computer Science. His expertise lies in Artificial Intelligence and Machine Learning, focusing on statistical learning methods and approximate inference techniques. He has published extensively in the areas of data mining and information fusion in sensor networks, computer vision, and computational biology. His research balances developing theoretical and algorithmic advances with applications to the real-world systems of his collaborators. Alex's research focuses on Bayesian models and reasoning under uncertainty, including recent work on "adaptive" or incremental updates to model estimates given new information. He has applied his algorithms to a wide variety of problems, including data mining and structure discovery for gene expression data in biology.

Chris is an associate professor of Computer Science at Carnegie Mellon University. He is also affiliated with CMU's department of Biological Sciences and the University of Pittsburgh's Department of Computational Biology. As an expert in computational biology and chemistry, machine learning, model checking, sepsis modeling, cancer modeling, and molecular dynamics, Chris's research centers around the modeling and simulation of biological systems.

Chris's group works in two areas: Computational Structural Biology and Systems Biology. His work in structural biology focuses on physics-based methods for modeling, simulating, designing, and analyzing biomolecular interactions. Specific applications include protein and drug design. Most of this work involves probabilistic graphical models. His work in systems biology focuses on clinical applications in collaboration with physicians at University of Pittsburgh Medical Center and the Hillman Cancer Center. Specific applications include the development of statistical and mechanistic models of disease processes.

For over 15 years, Ryan's research has focused on the use of advanced computational methods to provide Biologists and Chemists informational leverage in solving their problems. Ryan maintains a faculty appointment at the University of Toronto where he is cross-appointed between the Department of Computer Science and the Centre for Cellular and Biomolecular Research in the Faculty of Medicine. The Gates Foundation recently recognized Ryan's research with a prestigious Grand-Challenges Grant for his ongoing work in Drug Discovery.

Ryan has contributed papers in the areas of Protein Redesign, Drug Discovery, Clinical Medicine, Structural Biology (Electron Cryo-Microscopy, X-Ray Crystallography, and NMR Spectroscopy), Mass Spectrometry, Search and Optimization, Human Computer Interfaces, Machine Learning, and Machine Vision. His algorithm for identifying macromolecular poses consistent with observed non-crystallographic symmetry enabled the solution the structure of dihydrofolate reductase-thymidylate synthase from Cryptosporidium hominis (a parasite listed as a Category B bioterrorism threat) – now a pharmacologic target. Ryan developed ensemble-based algorithms for modeling molecular flexibility and applied these approaches to both Structure-Based Drug Discovery and Protein Redesign resulting in one of the earliest experimentally verified novel protein redesigns and a lead compound for treating a subtype of pediatric leukemia. Ryan’s code forms the core of the open-source protein redesign software OSPREY.

Ram completed his B.S., M.S., and Ph.D. degrees at the University of Texas at Austin in Computer Science. Ram's dissertation research focused on developing approximation algorithms for basic problems in resource placement and clustering. After graduating from UT-Austin, he became interested in computational biology and took a postdoctoral position at Dartmouth College from 2002-2005. Since then, Ram has held a faculty position in the Department of Electrical and Computer Engineering at University of Massachusetts Amherst. In 2007, Ram received the prestigious NSF CAREER award to develop algorithms for problems in structural biology.

Ram's research began in the area of discrete optimization and approximation algorithms but has since grown to include the development algorithms with provable guarantees on running time and solution quality for challenging problems in structural biology. He has published papers in highly selective conferences and journals in the areas of Approximation Algorithms, Discrete Optimization, Randomized Algorithms, Networking, Machine Learning, Structural Biology (Structure Determination and Protein-Protein Interactions), and Mass Spectrometry. Ram also regularly reviews contributions to conferences, journals and funding agencies in these areas.

Brian completed a Ph.D. in Biochemistry at Dartmouth and his B.A. at Skidmore with a double major in Biology and Chemistry. His graduate research focused on understanding and modifying the substrate specificity of the phenylalanine-adenylating domain of gramicidin synthetase. Since graduate school, Brian has focused on teaching while directing novel research on the genetic associations of ADD/ADHD and the phylogeny of the invasive Asian Longhorned Beetle.

Brian’s breadth of expertise spans molecular biology, biochemistry, microscopy, and structural biology. For example, Brian was directly involved in every step of an enzyme redesign project from subcloning and mutagenesis through steady-state and pre-steady-state analysis of the enzyme kinetics. His experience in optimizing protein crystallization conditions, collecting X-Ray diffraction data, and processing these datasets facilitated the solving of multiple high-resolution protein structures.

Bruce Tidor is a Professor of Biological Engineering and Computer Science at the Massachusetts Institute of Technology. He graduated summa cum laude with an A.B. in Chemistry and Physics from Harvard College, earned a M.Sc. in Biochemistry at Oxford University, and received his Ph.D. in Biophysics from Harvard. Bruce serves as Co-Chairman and founding co-Director of Computational and Systems Biology Initiative at Massachusetts Institute of Technology. With over 100 peer-reviewed publications, Bruce's research is focused on the analysis of complex biological systems at both the molecular level and the systems level.

His molecular work concentrates on the structure and properties of proteins, nucleic acids, and their complexes. His group aims to dissect the molecular interactions responsible for the specific structure of folded proteins and the binding geometry of molecular complexes. His work at the systems level involves the construction and analysis of correlated patterns of gene expression and their relation to biochemical regulatory networks and signal transduction pathways in cells. The methods of theoretical and computational biophysics and approaches from artificial intelligence, applied mathematics, and chemical engineering play key roles in Bruce's work.

Before obtaining his PhD in Computer Science, Tony studied Physics at Berkeley and Cornell. Tony's work focuses on solving interdisciplinary research problems by using algorithms with provable guarantees. For his thesis, Tony combined experimental Nuclear Magnetic Resonance data (Residual Dipolar Coupling (RDCs) and Nuclear Overhauser Efffect (NOEs)) with ideas related to configuration space, inverse kinematics, and computational geometry to obtain provably complete ensembles of symmetric protein structures.

Tony's interests include, well, everything in math, physics, chemistry, and computation. In particular he's interested in compressed sensing, protein flexibility, entropy, denatured protein structures, NMR assignment, mass spectroscopy, and X-ray crystallography. Tony also dabbles in computer graphics and animation.