Sensitive and fast DNA homology search with profile HMMs in HMMER

Date/Time: Monday, December 5, 2016 from 4:10 p.m. - 5:00 p.m.
Location: Barnard Hall 103
Speaker: Travis Wheeler

Abstract: Sequence database homology searches are an essential part of molecular biology, providing information about the function and evolutionary history of proteins, RNA molecules and DNA sequence elements. I will describe a tool for DNA/DNA sequence comparison that is built on the HMMER framework, which applies probabilistic inference methods based on hidden Markov models to the problem of homology search. This tool, called nhmmer, enables improved detection of remote DNA homologs, and has been used in combination with Dfam and RepeatMasker to improve annotation of transposable elements in the human genome. I will then describe an algorithm, based on the Burrows Wheeler Transform, that speeds one simple but time-consuming part of nhmmer, yielding more than an order of magnitude acceleration over a highly optimized implementation.

Bio: Travis Wheeler is an Assistant Professor at the University on Montana Computer Science Department, where his group develops methods in computational biology, with an emphasis on genomic sequence analysis. For the most part, that involves development of algorithms that increase the speed, power, and accuracy of sequence database homology search using profile hidden Markov models, and application of these methods topics motivated by biology, especially those involving transposable elements and regulatory elements. Travis earned his Bachelors in Evolutionary Biology from the University of Arizona in 1995. He spent several years in industry and academia as a telecom and web software developer, then earned a PhD in Computer Science in 2009, under the guidance of John Kececioglu and Mike Sanderson at the University of Arizona. He worked in Sean Eddy's group (HHMI Janelia Research Campus) as a postdoc and software engineer until 2014, when moved to his current position.

PHENOstruct: Prediction of human phenotype ontology terms using heterogeneous data sources

Date/Time: Monday, November 28, 2016 from 4:10 p.m. - 5:00 p.m.
Location: Barnard Hall 108
Speaker: Indika Kahanda

Abstract: The human phenotype ontology (HPO) was recently developed as a standardized vocabulary for describing the phenotype abnormalities associated with human diseases. At present, only a small fraction of human protein coding genes have HPO annotations. But, researchers believe that a large portion of currently unannotated genes are related to disease phenotypes. Therefore, it is important to predict gene-HPO term associations using accurate computational methods. In this talk I will present PHENOstruct, a novel method based on the structured SVM approach for automatically predicting HPO terms for a given gene. Furthermore, I will highlight a collection of informative data sources suitable for the problem of predicting gene-HPO associations, including large scale literature mining data.

Bio: Dr. Indika Kahanda is an Assistant Teaching Professor in the Gianforte School of Computing at Montana State University. His research interests include Bioinformatics and  Biomedical Natural Language Processing. He works on the application of machine learning, data mining and natural language processing techniques for solving problems related to large-scale biological data. His current work focuses on predicting mental illness categories for biomedical literature, prediction of MicroRNA genes and targets using machine learning, protein function prediction and protein-function relation extraction from biomedical literature. He received his Ph.D. in Computer Science from Colorado State University in 2016 in the area of Bioinformatics, a Master of Science in Computer Engineering from Purdue University in 2010, and a Bachelor of Science in Computer Engineering from University of Peradeniya, Sri Lanka in 2007. 

Some elementary applications of algebraic topology

Date/Time: Monday, November 21, 2016 from 4:10 p.m. - 5:00 p.m.
Location: Barnard Hall 103
Speaker: Ryan Grady

Abstract: In this talk I will prove some fundamental results in (linear) algebra using tools from algebraic topology. Specifically, I will discuss a result of Perron on the spectrum of positive matrices; note that this result is necessary to show completeness of the Google page rank algorithm.

Bio: Ryan Grady is an assistant professor in the Department of Mathematical Sciences. He obtained his PhD from the University of Notre Dame under Stephan Stolz, before being a postdoctoral researcher at Boston University and the Perimeter Institute for Theoretical Physics. His research interests include algebraic topology and mathematical aspects of quantum field theory.

Agility and Software Architecture: Why Successful Teams Should Master Both

Date/Time: Monday, November 14, 2016 from 4:10 p.m. - 5:00 p.m.
Location: Barnard Hall 108
Speaker: Ipek Ozkaya

Abstract: In our increasingly software-enabled society, change is the norm rather than the exception. Technologies, business priorities, quality concerns, and team and organizational structures change perpetually. Successful software organizations are those that empower their teams with fundamental skills to adapt to such changes. Within a short duration of five years we have seen the software industry chase after service-oriented architecture, cloud computing, and microservice architecture. The fundamental problem that these approaches purport to solve ironically remains unsolved: enabling agility with minimal business impact. In this presentation driving from the work we do at the Software Engineering Institute, I will discuss how mastering agility and software architecture affords cross-functional teams the greatest likelihood for success. I will discuss why the increasing rate of change in the software business must motivate a consequent change in our approaches to software development. I will in particular focus on practices, research progress and challenges in enabling software engineers to generate and utilize software development data towards this goal. 

Bio: Ipek Ozkaya is a principal researcher and the deputy technical lead for the Architecture Practices (AP) initiative at the Software Engineering Institute at Carnegie Mellon University. She works to develop, apply, and communicate effective methods for software architecture and agile and iterative development to improve software development efficiency. Her most recent work focuses on building the theoretical and empirical foundations of managing technical debt in large-scale, complex software intensive systems. While at the SEI she has had the privilege of working with a wide variety of government and industry organizations helping them improve their software architecture practices. In addition, Ozkaya serves as the chair of the advisory board of the IEEE Software magazine and as an adjunct faculty member for the Master of Software Engineering Program at Carnegie Mellon University. She is the co-author of several articles as well as a frequent invited speaker in software architecture and related topics. She holds doctorate and masters degrees in Computational Design from Carnegie Mellon University.

A Survey on Monitoring Network Flows

Date/Time: Monday, November 7, 2016 from 4:10 p.m. - 5:00 p.m.
Location: Barnard Hall 108
Speaker: Samuel Micka

Abstract: Flows are found in many fields of research that deal with objects moving through a network. Flows can be traffic moving through a roadway, packets moving through the internet, or people walking on trails. Multiple flows moving through a single network makes it increasingly difficult to monitor the paths that the flows take. This survey provides a summary and analysis of three solutions for monitoring different flows in computer networks. Two additional, complimentary, papers are considered and evaluated as well: one focusing on the selection of management/monitoring nodes in dynamic networks, and one focusing on decomposing multi-path flows into single paths. We examine the methods used to solve the problems as well as the implications of the research and future work.

Bio: Samuel Micka is a PhD Student in the Computer Science Department, advised by Dr. Brendan Mumey and  Dr. Brittany Fasy. He is a member of the Networks + Algorithms lab and his research involves finding algorithmic solutions and applying them to the field of computer networks.

Autonomous and connected highway vehicles: what’s passed, and your future

Date/Time: Monday, October 24, 2016 from 4:10 p.m. - 5:00 p.m.
Location: Barnard Hall 108
Speaker: Craig Shankwitz

Abstract: The appearance of carrier-phase RTK GPS in 1994 created a new paradigm for ground transportation:  autonomous vehicles.  Dual-frequency, carrier phase RTK GPS appearing 4 years later made it a reality.  The presentation will highlight RTK-GPS based driver assist and autonomy, its present and future role in an autonomous vehicle world presently dominated by machine vision and lidar, and near- and long-term opportunities for MSU in this extremely disruptive environment where Uber is now worth more than Ford.

Bio: Dr. Craig Shankwitz serves as a Senior Research Engineer for the Connected Vehicle Initiative at the Western Transportation Institute at Montana State University.  He leads the development of a WTI research team that will explore and develop applications of autonomous and connected vehicle technologies to roads and transportation systems in rural areas and small cities.

His research interests include man-machine interaction, vehicle-driver interfaces, sensors, human in the loop control systems, and non-linear vehicle dynamic problems in general. Most recently, Dr. Shankwitz was a principal R&D engineer at MTS Systems in Eden Prairie, MN, where one of his tasks was to design and develop a patented, robotic motorcycle rider which can be used for testing in a wide variety of applications.  Prior to MTS, Dr. Shankwitz served as a Research Associate Professor and the Director of the Intelligent Vehicles Lab at the University of Minnesota. The focus of the IV Lab was the deployment of technology which simultaneously improves mobility and safety for the ground transportation network. Deployments include DGPS- and radar-based Driver Assist Systems for seven Alaska DOT snow-removal machines (to clear runways, roads, and mountain passes in Alaska), ten buses equipped with Driver Assist Systems for narrow bus-only-shoulder operations in the Twin Cities Metropolitan area, and a radar-based rural intersection collision avoidance system which assists drivers safety negotiate rural expressway intersections.

Shankwitz received his Ph.D. in Electrical Engineering from the University of Minnesota in 1992 in the area of control theory, a Master of Science in Mechanical Engineering from the University of Illinois, Champaign-Urbana in 1985, and a Bachelor of Science in Mechanical Engineering from Iowa State University in 1983. He holds seven patents, with two pending.

Student Summer Internship Presentations

Date/Time: Monday, September 26, 2016 from 4:10 p.m. - 5:00 p.m.

Location: Barnard Hall 108

Speaker: Sean Yaw
Abstract: Blackmore Sensors and Analytics is a 3D imaging company developing advanced laser ranging (lidar) systems and analysis algorithms to support a broad space of applications.  Lidar systems produce a precise cloud of 3D geospatial points representing the scanned scene. Realizing the full potential of this data requires novel data analytics to track objects, identify targets, and index into sophisticated data management schemes. This talk will introduce the base technology generating the data, as well as outline the analytical challenges being faced and some strategies developed to address them.

Speaker: Guangchi Liu
Abstract: As the development of online business, many enterprises now own tons of commercial data generated from costumers and clients.  These companies look forward ideas/insights from these data to help improve their product/service and make decision/strategies in the future. In sight of this huge market, many tech and consulting companies are providing data analysis services for those enterprises. In this summer, I go to intern in Stratifyd.Inc, a data analysis startup company located in NC. My job is conducting sentiment analysis and Chinese word segmentation on the comments and reviews of particular products/services and analysis costumers' opinions on these products/services.  By using NLP and data mining techniques including regression models, probabilistic models and neural network models, I have obtained promising as well as interesting results from the data.  

Speaker: Clint Cooper
Abstract: Stanford Research Institute International (SRI) is an independent research and development organization interested in the creation of innovative technology and business solutions for government agencies and commercial businesses. For more than 70 years, they have been developing the latest technology in many fields including Health, Technology, Education and Business . In the field of technology, they have developed many notable technologies: LCD, Optical Video Disks (Basic CD-ROM), CMOS, the computer mouse, Top Level Domain names, LED, Fax Machines, 911-Call System, Ultrasound, VPNs, and SIRI, just to name a few. SRI also hosted one of the four original nodes of ARPANET and advised DARPA (then ARPA) for development of the network.  For the duration of the summer, I had the opportunity to work on a project at SRI. This project is a document processor for analyzing research documents, with the goal of answering interesting questions regarding them. This is realized through a Scala written AI that utilizes SRI's own language processing libraries.

Student Summer Internship Presentations

Date/Time: Monday, September 19, 2016 from 4:10 p.m. - 5:00 p.m.

Location: Barnard Hall 108

Speaker: Mohammadbagher Parsa Gharamaleki
Abstract: We have developed a software package for online isolations and stimulation triggering of neuronal cells in the brain, which operates in conjunction with a Hardware Processing Platform (HPP). The HPP is a system on a chip solution enabling real-time signal processing on neuronal signals. Employing the HPP programmable device for template matching both accelerates spike sorting and provides the low-latency triggering of stimulation required to detect connectivity between brain areas.

Speaker: Sean Yew
Abstract: Blackmore Sensors and Analytics is a 3D imaging company developing advanced laser ranging (lidar) systems and analysis algorithms to support a broad space of applications.  Lidar systems produce a precise cloud of 3D geospatial points representing the scanned scene.  Realizing the full potential of this data requires novel data analytics to track objects, identify targets, and index into sophisticated data management schemes. This talk will introduce the base technology generating the data, as well as outline the analytical challenges being faced and some strategies developed to address them.

Speaker: Utkarsh Goel
Abstract: The growing popularity of interactive Web applications attract large number of mobile users. Content providers, such as Facebook, Netflix, and others, desire to improve user experience and generate more revenue. However, the opaqueness of mobile networks today introduces several challenges to meet the goals of faster mobile Web. In this talk, I plan to discuss one of these challenges from the perspective of the largest content delivery network, Akamai. My talk will provide an overview of large scale measurements that we perform to gather detailed information about Internet performance. I believe my contributions in this area will motivate new research directions to provide far better understanding of the Internet ecosystem, than what we have today.

K-12 Outreach Through Practical Software Research and Development in the Software Factory Environment

Date/Time: Monday, September 12, 2016 from 4:10 p.m. - 5:00 p.m.

Location: Barnard Hall 108

Presenter: Jessica Jorgenson, Michael O’Hara, Amber Nabity, Anna Jinneman, Riley Roberts, Xuying Wang, Ryan Darnell

Abstract: Teaching software development in environments that mimic industry practices is essential for teaching applicable real-word development skills. In addition, these kinds of delivery based projects engage students in meaningful design work that encourages clear, sustainable code. The Software Factory has provided such an environment to students at MSU since 2014.

This project aimed to explore the effectiveness of such a setting for high school students with limited programming experience. Five students from Bozeman High School were selected to work in a team with two undergraduates with the goal of improving upon a Sorting Guide android application built during last summer’s project. In order to accomplish this goal, the students were also taught the tools  and languages necessary to build an application. These students were exposed to Java, XML, Git, various sorting algorithms, and software development practices inside an industry setting. We will present a demonstration of the students’ work as well as discuss the benefits and challenges with this teaching method within the Software Factory.

Welcome Seminar

Date/Time: Monday, August 29, 2016 from 4:10 p.m. - 5:00 p.m.

Location: Barnard Hall 108

Presenter: John Paxton, Gianforte School of Computing, Montana State University

Abstract: This seminar will provide new and continuing graduate students with (1) useful information, (2) an opportunity to meet other students, staff and faculty, and (3) an opportunity to ask questions.

Departmental Awards Seminar

Date/Time: April 25, 2016 from 4:10 p.m. - 5:00 p.m.

Location: EPS 108

Presenter: John Paxton, Dept. Computer Science, MSU

Abstract: At the end of every academic year, we celebrate the accomplishments of our graduate students and faculty. Join us for this year's celebration where several departmental awards will be given. Refreshments will be served.

A New Discrete Particle Swarm Optimization Algorithm

Date/Time: April 18, 2016 from 4:10 p.m. - 5:00 p.m.

Location: EPS 108

Presenter: Shane Strasser, Dept. Computer Science, MSU

Abstract: Particle Swarm Optimization (PSO) has been shown to perform very well on a wide range of optimization problems. One of the drawbacks to PSO is that the base algorithm assumes continuous variables. In this paper, we present a version of PSO that is able to optimize over discrete variables. This new PSO algorithm, which we call Integer and Categorical PSO (ICPSO), incorporates ideas from Estimation of Distribution Algorithms (EDAs) in that particles represent probability distributions rather than solution values, and the PSO update modifies the probability distributions. In this paper, we describe our new algorithm and compare its performance against other discrete PSO algorithms.  In our experiments, we demonstrate that our algorithm outperforms comparable methods on both discrete benchmark functions and NK landscapes, a mathematical framework that generates tunable fitness landscapes for evaluating EAs.

Video recording available:

Using Machine Learning to Detect Distant Evolutionary Relationships between Protein Families

Date/Time: April 11, 2016 from 4:10 p.m. - 5:00 p.m.

Location: EPS 108

Presenter: Mensur Dlakic, Dept. Microbiology & Immunology, MSU

Abstract: Given the relative ease of genomic sequencing, improving the functional annotations of known proteins has become more important than the production of additional protein sequences. Since experimental determination of all protein functions is impractical, in most cases functional assignments are still done using only computational methods. We develop machine learning methods and software tools to find distant relationships between protein families at the level of sequence even in the absence of statistically significant scores. In addition, interactive web servers will provide general public with an easy access to these methods. The combination of human expertise and machine learning techniques will allow us in the long term to systematically catalog many of unclassified proteins and infer their biological functions.

Video recording available:

Discovery and Analysis of Communities in Evolving Political Contribution Networks

Date/Time: April 4, 2016 from 4:10 p.m. - 5:00 p.m.

Location: EPS 108

Presenter: Scott Wahl, Dept. Computer Science, MSU

Abstract: An important aspect of social networks is the discovery and partitioning of the complex graphs into dense sub-networks referred to as communities. The goal of such partitioning is to find groups who have similar attributes or behaviors. In the realm of politics, it is possible to group individuals with similar political behavior by analyzing campaign finance records. In this paper we show the effectiveness of hierarchical fuzzy spectral clustering over political contribution networks. The results show that clustering the data into two communities generally shows strong correlation between fuzzy membership values and existing estimates of political ideology. Further breakdowns of the data highlight different patterns of behavior. Analyzing these networks in time segments shows how individual behaviors and ideologies may change over time.

Agent-Based Modeling in Electrical Energy Markets Using Dynamic Bayesian Networks and Relevance Vector Machines

Date/Time: March 28, 2016 from 4:10 p.m. - 5:00 p.m.

Location: EPS 108

Presenter: Kaveh Dehghanpour, Dept. Electrical & Computer Engineering, MSU

Abstract: Electrical power Generation Companies (GenCos) compete with each other on wholesale electrical energy markets over the supply of electrical power to the consumers. Energy markets are based on auction mechanisms in which each player tries to maximize its immediate pay-off value by optimizing its bidding strategy. However, the complexity of the problem is due to the fact that the players need to make decisions under uncertain conditions: firstly, the players do not have access to their competitors' private cost data, which means that each player is a source of uncertainty to its competitors. Secondly, the actual value of electrical load is unknown, a priori, and needs to be predicted. In this project, a hierarchical agent-based framework is presented to model the decision-making problem of GenCos in an electrical energy market. Each GenCo is modeled as an agent with its private computational and decision making capabilities. The concept of Dynamic Bayesian Network (DBN) is employed to represent the "belief space" of GenCo agents. Each agent trains/updates its private belief system using relevance vector machine (i.e., sparse Bayesian learning). The trained DBN is then used to predict the best response to competitors for the incoming rounds of auction. It is shown that as the GenCo agents track their best response to their competitors the market approaches its Nash equilibrium over time.

Video recording available:

Hearing the Earth's Music Through Computing: Sonification of GPS Data

Date/Time: March 21, 2016 from 4:10 p.m. - 5:00 p.m.

Location: EPS 108

Presenter: Linda Antas, School of Music (Music Technology), MSU

Abstract: Sonification is the mapping of data onto musical/sonic parameters. It is used to assist in data interpretation where visual representations of data are problematic, insufficient, or unavailable. For the computer musician, sonification techniques provide diverse opportunities for research and creativity. The data used, mapping strategies, and how—if at all—to manipulate the data for best musical outcomes are all significant factors. Examples in this presentation will be drawn from a work in progress that sonifies GPS data collected in the Montana wilderness. The data is mapped using an algorithmic composition program that can output to a variety of formats, allowing it to be accessed as standard musical notation, or to be sent directly to a computer music programming language for creating synthesized sounds.

Video recording available:

Monothetic Clustering and Extensions to Clustering Functional Data

Date/Time: March 7, 2016 from 4:10 p.m. - 5:00 p.m.

Location: EPS 108

Presenter: Mark Greenwood, Dept. Mathematical Sciences, MSU

Abstract: Cluster analysis seeks to find groups of observations that are similar within and different among the created groups. Monothetic clustering algorithms create groups of observations that share common traits, in contrast to the more common polythetic clustering algorithms that create groups that are similar “on average”. The basic ideas and advantages of monothetic clustering are reviewed, including its connections to multivariate regression trees. Progress on the development of an R implementation will also be provided. High dimensional responses that can be considered as having been measured continuously over an argument, such as time or wavelength, are often called functional data. By dividing the functional domain into subregions and recursively partitioning the overall curves based on information from the subregions, a new algorithm, called Partitioning using Local Subregions (PULS), is developed. PULS seems to be competitive with other common functional clustering techniques and shares some characteristics with the monothetic clustering algorithm even though it is no longer monothetic. This is joint work with Tan Tran (Montana State University) and David Hitchcock (University of South Carolina).

The Science of Stories: The Narrative Policy Framework

Date/Time: February 29, 2016 from 4:10 p.m. - 5:00 p.m.

Location: EPS 108

Presenter: Elizabeth Shanahan (with Michael D. Jones), Dept. Political Science, MSU

Abstract: Narratives are the lifeblood of politics. However, not until the development of the Narrative Policy Framework (NPF) (co-developed by Drs. Shanahan-MSU, McBeth-ISU and Jones-OSU) have narratives been operationalized into a class of variables to test the impact of narratives on decision making. This presentation will provide an overview of the NPF, with two examples of research at the mico- and meso-levels of analysis. The former is an experimental design testing the effects of narrative strategies on individual opinion regarding the introduction of bison in the northern Montana prairie. The latter is preliminary network analyses of narrative constructions of 4 groups with different cultural cognitions (Kahan et al. 2011) regarding campaign finance reform.

Exploring Ethics in Data and Technology Research

Date/Time: February 22, 2016 from 4:10 p.m. - 5:00 p.m.

Location: EPS 108

Presenter: Sara Mannheimer, Scott Young, Jason Clark, Justin Shanks, University Libraries, MSU

Abstract: Technology is advancing at a pace so rapid that ethical inquiry is often left unaddressed. The MSU Library is currently conducting several research projects with user data, including Twitter sentiment analysis, personalized search development, and semantic web development. Over the course of these projects, ethical gray areas have emerged, prompting questions regarding ethical practice in “big data” research. Although huge amounts of data are freely available to us, much of it comes from human creators. Simply because we can use this data, does not necessarily mean that we should use it. Acknowledging this straightforward, but often overlooked dictum gives rise to various multifaceted ethical questions. This seminar will not only introduce ongoing data-oriented research occurring at MSU Library, but will also consider the broader ethical components embedded within research and product development. Who is affected by our research? Do users understand when and how their data is being used? How can we anticipate user expectations and values? What is the balance between personalized services and user privacy? Join us for a discussion of data-driven research and its ethical implications.

Secure Knowledge Management

Date/Time: February 8, 2016 from 4:10 p.m. - 5:00 p.m.

Location: EPS 108

Presenter: Dalal Alharthi

Abstract: Knowledge has become one of the most important driving forces for organizational success. Organizations are becoming more knowledge intensive. Therefore, taking care of knowledge is important for every organization nowadays. Knowledge must be managed. Knowledge management (KM) seeks to increase organizational capability to use knowledge as a source of competitive advantage. The challenge for organizations is to develop effective strategies for managing the knowledge.

Security of Information is a major concern for organizations nowadays. Secure knowledge management is one of the emerging areas in knowledge management and information system. It refers to the management of knowledge while adhering to principles of security and privacy. As KM has become a more central part of organizational activities, securing organizational knowledge has become one of the most important issues in the KM field. Knowledge needs to be protected so that it is properly secured from threats. Knowledge security addresses the protection of knowledge in organizations.

In this presentation, I will deal with knowledge management and knowledge security. I will try to explore different approaches used to secure knowledge management. Then, I will seek to identify a number of challenging security issues in Knowledge Management associated with protecting knowledge. Finally, I will illustrate the application of knowledge management and security by providing some examples from the national government of Saudi Arabia.

Establishing a Prospective, Long-term Follow-up, Pilot Study of Mental Health Biosignatures

Date/Time: January 25, 2016 from 4:10 p.m. - 5:00 p.m.

Location: EPS 108

Presenter: Matt Byerly, MD, Center for Mental Health Research and Recovery

Abstract: Mental illness affects 25% of the US population each year, 6% having serious mental illness.  It also strikes the young, with 50% developing illness by age 14 and 75% by age 24, making these illnesses the most disabling disorders before age 50, and most costly of all health conditions world-wide, with estimated annual US costs of more than $300 billion.  Montana has especially significant mental health challenges including the highest suicide rate in the country, at nearly twice the national rate; large populations at high risk of mental illness including Native Americans and military veterans, and; rural settings with limited mental health treatment resources.

The new MSU Center for Mental Health Research and Recover (CMHRR) is in the process of developing a prospective, long-term follow-up, pilot study of mental health biosignatures.  A biosignature, commonly used in multiple areas of medicine, is a unique combination of measureable, biologic features of a person and their illness that aids in making a diagnosis. To date, biosignatures are not used in the routine diagnosis and treatment of mental disorders.  This research will determine if we can match biologically-based diagnoses with response to specific treatments.  The result would be diagnostic “biosignatures” for individual patients that could identify their best initial treatment choice, speeding up recovery for each person with mental illness.  The work would also further identify brain signatures of illness development and progression that could aid in early and accurate diagnosis and, in turn, guide the focus of intensive preventive interventions for those at especially high risk.

Dr. Byerly, Director of the CMHRR, will discuss the background of biosignature work in mental illnesses, review the proposed study, and discuss potential relevance of the work to the computer sciences.


Seminars from 2015.