Chris Diehl, Ph.D. Email & Phone Number

New Mexico, United States

How do we design responsive geospatial sensemaking tools for envisioning regenerative pathways toward thriving landscapes that support human and planetary health? Holding this level of complexity is impossible without tools and processes that enable rich multi-stakeholder collaboration. My focus is on building technology prototypes to identify where there is opportunity to dramatically change the way we envision possibilities and coordinate action to turn such visions into reality.

Reno, Nevada, Us

Reno, Nevada, Us

Motiva’s mission is bringing people together through trusted marketing conversations. Our team is deeply experienced in artificial intelligence, analytics, and large-scale data mining, which we bring together in beautiful design to create a new class of solutions for marketing and customer engagement. We use cutting-edge machine learning to automatically tune B2B and B2C marketing campaigns to audiences, and finding new, engaged audience populations. By listening carefully and learning from customer behavior, our solutions help marketing teams achieve more with less, and makes the entire marketing operation more effective. Motiva’s custom, proprietary machine learning platform is setting a new standard of performance in AI.

London, Gb

San Francisco, California, Us

The Data Guild works with clients to develop data products addressing critical information needs. By leveraging a network of world-class scientists, engineers and designers, we bring deep experience and varied perspective to each engagement. We focus on helping clients frame high-impact data problems, identify and characterize data opportunities, prototype transformative data products, and scale the resulting solutions to deliver enduring value.

Baltimore, Md, Us

Participated as a team leader in the 2010 Summer Camp for Advanced Language Exploration (SCALE) workshop focused on all-source knowledge base population.

Austin, Texas, Us

Developed new concepts for advanced search and sensemaking within the Jive platform. Deployed an interactive cueing mechanism for uncovering emergent team interaction in Jive communities.

Livermore, Ca, Us

Developed a multi-institution collaborative research program investigating the fundamentals of linguistic style matching in social media and how such indicators relate to relative social status.

Baltimore, Md, Us

One of ten JHU/APL staff members selected by APL and the Whiting School of Engineering to spearhead an initiative aimed at developing stronger collaboration between APL and the engineering departments. Engaged in research on exact incremental support vector machine learning, efficient exact and approximate cross-validation methods and sparse kernel machine learning.

Laurel, Maryland, Us

As a member of the Milton Eisenhower Research Center, my role was to identify opportunities to make transformational impacts on government sponsor problems and develop research programs to meet those challenges. The problems I engaged in with colleagues had machine learning challenges at their core by design, ranging from video surveillance and acoustic signal classification to social network analysis from social media sources. My work with APL colleagues under Office of Naval Research sponsorship resulted in deployed systems fielded in under five years and sustained R&D funding in related areas impacted by our findings.

Washington, D.C., Us

Received a full scholarship to attend Carnegie Mellon. During the summers between my undergraduate years, I served in several positions throughout the Intelligence Community.

Pittsburgh, Pa, Us

My master's research with Jim Hoburg and Dan Stancil focused on modeling electromagnetic wave propagation in buildings for wireless LAN propagation prediction. Dan and I also made the first measurements of the benefits of using a building's HVAC system for efficient indoor propagation. Our initial concept for retrofitting existing HVAC systems for wireless LAN signals resulted in a patent and future research demonstrating the feasibility of data rates in excess of 100 MBits/sec. My Ph.D. research with John Hampshire and Pradeep Khosla focused on statistical machine learning techniques for real-time object classification and novelty detection. Mahesh Saptharishi and I developed the video surveillance algorithms deployed on the CyberATV semi-autonomous all-terrain vehicles developed for DARPA.

Ph.D., Electrical And Computer Engineering

M.S., Electrical And Computer Engineering

B.S., Electrical And Computer Engineering

Education record