Adventium Labs

Minneapolis, MN, United States

Adventium Labs

Minneapolis, MN, United States
Time filter
Source Type

News Article | October 28, 2016

Chris Buse, Chief Information Security Officer for the State of Minnesota, has been named this year’s Public Sector Visionary Leader of the Year by the Cyber Security Summit. Mr. Buse will accept the award on behalf of his entire team at MN.IT Services, the State of Minnesota’s central IT organization. He will be presented with the award at 8:30 a.m. on Day 2 of the Summit, Oct. 12, at the JW Marriott Mall of America. To learn more about the Summit and to register to attend, go to Representing one of the diverse paths that can lead to a career in cyber security, Mr. Buse started as a CPA before branching into information technology audit, which eventually led to him to a technical audit group responsible for IT audit in government. After many years working in that capacity, he was named Minnesota’s first CISO in 2007. As an IT group that spans more than 70 different agencies, MN.IT faces the challenge of centralizing processes for organizations that have historically been separate. Prior to appointing a CISO, state agencies had adopted a wide variety of different technologies, making the IT infrastructure less efficient and more difficult to secure. Buse said his role has been to help lead a group that provides a holistic approach to the problem. He credits a strong team for the success MN.IT has achieved in that area. “In government you always struggle with resource issues, but all the men and women on the MN.IT team have done a stellar job putting together services to protect the state of Minnesota,” he says. “Really, the team should get the award. They do all the important work for the state, so I accept this on their behalf.” Mr. Buse is a member of the original group that helped found the Cyber Security Summit. He became involved because he saw the need for a security event geared toward thought leadership and big picture strategic issues impacting information security. Today, with a threat landscape that continues to change and a more diverse array of attacks happening increasingly quickly, he sees a greater need for collaboration than ever before. “You can't be a successful security leader if you live in a vacuum,” he says. “You need to be part of a broader cyber security ecosystem that shares information across boundaries.” As someone who has seen the growing need for security professionals firsthand, Mr. Buse is a strong advocate for workforce development and getting young people involved in cyber security. For the past two years, he has spearheaded a breakfast at the Cyber Security Summit for students interested in information security. “Information security is a wonderful career opportunity with lots of opportunity for growth,” he says. “I would encourage it as a career choice, and there is lots of opportunity in the government sector.” The other award given by the Summit, the 2016 Private Sector Visionary Leader of the Year, went to Brian Isle, senior fellow at the University of Minnesota’s Technological Leadership Institute and co-founder and former CEO of Adventium Labs.

Hing M.M.,University of Ottawa | Michalowski M.,Adventium Labs. | Wilk S.,Poznan University of Technology | Michalowski W.,University of Ottawa | Farion K.,Children Hospital of Eastern Ontario
2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010 | Year: 2010

This paper describes a methodological approach to identifying inconsistencies when reconciling multiple clinical practice guidelines. The need to address these inconsistencies arises when a patient with co-morbidity has to be managed according to different treatment regimens. Starting with a well-known flowchart representation we discuss how to create a formal guideline model that allows for easy manipulations of its components. For this model we present how to identify conflicting actions that are manifested by treatment-treatment and treatment-disease interactions, and how to reconcile these conflicting actions. ©2010 IEEE.

Wilk S.,Poznan University of Technology | Michalowski M.,Adventium Labs | Tan X.,University of Ottawa | Michalowski W.,University of Ottawa
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2014

Clinical practice guidelines (CPGs) were originally designed to help with evidence-based management of a single disease and such single disease focus has impacted research on CPG computerization. This computerization is mostly concerned with supporting different representation formats and identifying potential inconsistencies in the definitions of CPGs. However, one of the biggest challenges facing physicians is the application of multiple CPGs to comorbid patients. While various research initiatives propose ways of mitigating adverse interactions in concurrently applied CPGs, there are no attempts to develop a generalized framework for mitigation that captures generic characteristics of the problem, while handling nuances such as precedence relationships. In this paper we present our research towards developing a mitigation framework that relies on a first-order logic-based representation and related theorem proving and model finding techniques. The application of the proposed framework is illustrated with a simple clinical example. © Springer International Publishing Switzerland 2014.

Wilk S.,Poznan University of Technology | Michalowski W.,University of Ottawa | Michalowski M.,Adventium Labs | Farion K.,Childrens Hospital of Eastern Ontario | And 2 more authors.
Journal of Biomedical Informatics | Year: 2013

We propose a new method to mitigate (identify and address) adverse interactions (drug-drug or drug-disease) that occur when a patient with comorbid diseases is managed according to two concurrently applied clinical practice guidelines (CPGs). A lack of methods to facilitate the concurrent application of CPGs severely limits their use in clinical practice and the development of such methods is one of the grand challenges for clinical decision support. The proposed method responds to this challenge.We introduce and formally define logical models of CPGs and other related concepts, and develop the mitigation algorithm that operates on these concepts. In the algorithm we combine domain knowledge encoded as interaction and revision operators using the constraint logic programming (CLP) paradigm. The operators characterize adverse interactions and describe revisions to logical models required to address these interactions, while CLP allows us to efficiently solve the logical models - a solution represents a feasible therapy that may be safely applied to a patient.The mitigation algorithm accepts two CPGs and available (likely incomplete) patient information. It reports whether mitigation has been successful or not, and on success it gives a feasible therapy and points at identified interactions (if any) together with the revisions that address them. Thus, we consider the mitigation algorithm as an alerting tool to support a physician in the concurrent application of CPGs that can be implemented as a component of a clinical decision support system. We illustrate our method in the context of two clinical scenarios involving a patient with duodenal ulcer who experiences an episode of transient ischemic attack. © 2013 Elsevier Inc.

Michalowski M.,Adventium Labs. | Wilk S.,Poznan University of Technology | Michalowski W.,University of Ottawa | Lin D.,McGill University | And 2 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2013

There is a pressing need in clinical practice to mitigate (identify and address) adverse interactions that occur when a comorbid patient is managed according to multiple concurrently applied disease-specific clinical practice guidelines (CPGs). In our previous work we described an automatic algorithm for mitigating pairs of CPGs. The algorithm constructs logical models of processed CPGs and employs constraint logic programming to solve them. However, the original algorithm was unable to handle two important issues frequently occurring in CPGs - iterative actions forming a cycle and numerical measurements. Dealing with these two issues in practice relies on a physician's knowledge and the manual analysis of CPGs. Yet for guidelines to be considered stand-alone and an easy to use clinical decision support tool this process needs to be automated. In this paper we take an additional step towards building such a tool by extending the original mitigation algorithm to handle cycles and numerical measurements present in CPGs. © 2013 Springer-Verlag.

PubMed | Adventium Labs, Ottawa Hospital Research Institute and University of Ottawa
Type: | Journal: Journal of biomedical informatics | Year: 2016

In this work we propose a comprehensive framework based on first-order logic (FOL) for mitigating (identifying and addressing) interactions between multiple clinical practice guidelines (CPGs) applied to a multi-morbid patient while also considering patient preferences related to the prescribed treatment. With this framework we respond to two fundamental challenges associated with clinical decision support: (1) concurrent application of multiple CPGs and (2) incorporation of patient preferences into the decision making process. We significantly expand our earlier research by (1) proposing a revised and improved mitigation-oriented representation of CPGs and secondary medical knowledge for addressing adverse interactions and incorporating patient preferences and (2) introducing a new mitigation algorithm. Specifically, actionable graphs representing CPGs allow for parallel and temporal activities (decisions and actions). Revision operators representing secondary medical knowledge support temporal interactions and complex revisions across multiple actionable graphs. The mitigation algorithm uses the actionable graphs, revision operators and available (and possibly incomplete) patient information represented in FOL. It relies on a depth-first search strategy to find a valid sequence of revisions and uses theorem proving and model finding techniques to identify applicable revision operators and to establish a management scenario for a given patient if one exists. The management scenario defines a safe (interaction-free) and preferred set of activities together with possible patient states. We illustrate the use of our framework with a clinical case study describing two patients who suffer from chronic kidney disease, hypertension, and atrial fibrillation and who are managed according to CPGs for these diseases. While in this paper we are primarily concerned with the methodological aspects of mitigation, we also briefly discuss a high-level proof of concept implementation of the proposed framework in the form of a clinical decision support system (CDSS). The proposed mitigation CDSS insulates clinicians from the complexities of the FOL representations and provides semantically meaningful summaries of mitigation results. Ultimately we plan to implement the mitigation CDSS within our MET (Mobile Emergency Triage) decision support environment.

Boddy M.,Adventium Labs
ICAPS 2012 - 22nd International Conference on Automated Planning and Scheduling; COPLAS 2012 - Proceedings of the Workshop on Constraint Satisfaction Techniques for Planning and Scheduling Problems | Year: 2012

We are concerned with the problem of optimizing network resource allocations to mission tasks. The model includes unreliable network assets, multiple mission tasks and phases, and the possibility of over-provisioning one or more tasks as a means of increasing the likelihood of task success. In this paper, we describe an implemented approach to optimizing network resources so as to optimize the expected utility of the mission. This differs significantly from previous work on cloud and network management, where the objective was to optimize some operational measure of the network itself, rather than the effect of network failures on a specific task. The work described here is preliminary: we describe the problem and the approach, define an architecture, and present the current state of the implementation. Copyright © 2012, Association for the Advancement of Artificial Intelligence ( All rights reserved.

Michalowski M.,Adventium Labs | Boddy M.,Adventium Labs | Neilsen M.,Adventium Labs
Proceedings of the National Conference on Artificial Intelligence | Year: 2011

Computer Go presents a challenging problem for machine learning agents. With the number of possible board states estimated to be larger than the number of hydrogen atoms in the universe, learning effective policies or board evaluation functions is extremely difficult. In this paper we describe Cortigo, a system that efficiently and autonomously learns useful generalizations for large state-space classification problems such as Go. Cortigo uses a hierarchical generative model loosely related to the human visual cortex to recognize Go board positions well enough to suggest promising next moves. We begin by briefly describing and providing motivation for research in the computer Go domain. We describe Cortigo's ability to learn predictive models based on large subsets of the Go board and demonstrate how using Cortigo's learned models as additive knowledge in a state-of-the-art computer Go player (Fuego) significantly improves its playing strength. Copyright © 2011, Association for the Advancement of Artificial Intelligence. All rights reserved.

Gohde J.,Adventium Labs | Boddy M.,Adventium Labs | Shackleton H.,Adventium Labs | Johnston S.,Adventium Labs
Proceedings of the National Conference on Artificial Intelligence | Year: 2015

In previous work, we described G2I2, a system that adjusts the cost function used by an off-road route planning system in order to more closely mimic the route choices made by humans. In this paper, we report on an extension to G2I2, called GUIDE, which adds significant new capabilities. GUIDE has the ability to induce a cost function starting with a set of historical tracks used as training input, with no requirement that these tracks be even close to cost-optimal. Given a cost function, either induced as above or provided from elsewhere, GUIDE can then compare planned routes with the actual tracks executed to adjust that cost function as either the environment or human preferences change over time. The features used by GUIDE in both the initial induction of the cost function and subsequent tuning include time-varying meta-data such as the temperature and precipitation at the time a given track was executed. We present results showing that, even when presented with tracks that are very far from cost-optimal, GUIDE can learn a set of preferences that closely mimics terrain choices made by humans. © Copyright 2015, Association for the Advancement of Artificial Intelligence (

A device to prevent, detect and respond to one or more security threats between one or more controlled hosts and one or more services accessible from the controlled host. The device determines the authenticity of a user of a controlled host and activates user specific configurations under which the device monitors and controls all communications between the user, the controlled host and the services. As such, the device ensures the flow of only legitimate and authorized communications. Suspicious communications, such as those with malicious intent, malformed packets, among others, are stopped, reported for analysis and action. Additionally, upon detecting suspicious communication, the device modifies the activated user specific configurations under which the device monitors and controls the communications between the user, the controlled host and the services.

Loading Adventium Labs collaborators
Loading Adventium Labs collaborators