Open Research Positions
I no longer have funded positions, but I am looking for an undergraduate and a graduate researcher to build out basic demonstrators into more functional prototypes. For the undergraduate student researcher, requirements include a requisite mathematical background (ideally Calculus, Discrete Math, Stochastic Computing, and Data Analytics) and ability to design and write software in a structured programming language (C++, Java, Python). You must understand data structures and objects. The second position is for a graduate student researcher. Requirements include a background in Machine Learning, enjoyment of tinkering and reduction to practice by implementation of mathematical models. Additionally, you must either have taken CSCI 540- Machine Learning at DSU or have the equivalent knowledge. Both positions concern Machine Learning from sensor data on medical sensors and novel optical instrumentation. Independent study credits can be arranged for this work. I am always seeking Senior Capstone students, independent study, and graduate thesis/project students for projects in robotics applcations in healthcare. If you are an undergraduate or graduate student at DSU and are interested, please contact me by via your DSU email only to arrange an appointment. My research students tend to have an excellent record of success including research publications, numerous awards, graduate school admissions, and industry positions. My outcomes include a 100% placement rate among my research students.
Gary Holness is an associate professor in the Department of Computer and Information Sciences at Delaware State University (DSU).
At DSU, he founded and directs the Laboratory for Intelligent Perceptual Systems (LIPS). He has been a PI or co-PI on research grants totalling over $5.3M dollars.
He played a significant role in designing and launching a new graduate program, has contributed to
the complete re-design of two undergraduate curricula (Computer Science, Information Technology),
and has advised 16 research students and currently (Fall 2016) advises 8 (2 MS, 1 PhD, 3 BS, 2 Capstone) research
Prior to joining Delaware State University, he was a Lead Research Scientist in the Artificial Intelligence and Brain Inspired Computing research groups at Lockheed Martin Advanced Technology Laboratories. Gary received his Ph.D. in computer science from the University of Massachusetts, Amherst (UMass) in 2008, focusing on machine learning, robotics, distributed systems, machine perception, and statistics. His research interests span broadly across a number of areas including Machine Learning, Robotics, Distributed Systems, Healthcare Informatics, and real-word Machine Learning applications.
His PhD thesis contributed a new method for machine learning ensembles. He developed algorithms for training ensembles in a control theoretic framework that exercises direct control over component error distributions by sharing informatin about traning set selection. His algorithm, Discrete Instance Selection and Collective Optimization (DiSCO), guides the construction of ensembles whose component classifiers select complementary error distributions, sometimes making suboptimal individual choices that maximize diversity while minimizing overall ensemble error. Treating ensemble construction as an optimization problem, he developed approaches using local search, global search, and Jaynes' MaxEntropy framework.
At UMass, he was a member of the Machine Learning Lab where he was advised by Professor Paul Utgoff (memorial). His thesis work was supported by the National Science Foundation (award number ATM-0325167) on a collaboration with the UMass Computer Vision Laboratory and Bigelow Laboratories for Ocean Science. While at UMass, he also spent many fruitful years working in the Laboratory for Perceptual Robotics on distributed robot teams and Smart Room research under the advisement of Professor Rod Grupen.
His collaborators (past and present) have included Sokratis Makrogiannis, JinJie Liu, Hacene Boukari, Gour Pati, Tomasz Smolinski, Dragoljub Pokrajack, Allen Hanson, Rod Grupen,Howard Schultz, Dimitri Lisin, Marwan Mattar, Sai Ravela, Matthew Blasckho, Michael Seiracki, Eric Eaton, Dan McFarlane (LM Fellow, Lockheed Martin Advanced Technology Laboratories); In Memoriam: Paul Utgoff, Ed Riseman.
He earned an M.S. in Computer Science at Boston University concentrating in Parallel and Distributed Systems and a B.S. in Electrical Engineering at Tufts University. In his spare time, he also enjoys tinkering with a variety of electronic gadgets.
Computation should be an active part of the world, situated within it, gathering observations and acting upon them. In support of this vision, my research rests at the seams connecting machine learning, robotics, distributed systems, and cyber-physical systems. I am also excited about real world applications of machine learning. Broad topic areas of interest include:
- Ensemble methods: controlling error diversity and understanding ensemble dynamics
- Methods for density estimation: parametric, non-parametric, semi-parametric
- Representation learning: methods that learn features of the environment to best support learning
- Interactive AI: methods providing users with extensive control over reasoning and learning processes
- Autonomic Computing: learning methods that endow complex distriuted systems with autonomy
- Machine Perception: methods for long-term sparse representations enabling systems to discern and reason over the content of their environment from a variety of sensory modes
- Cyber-Physical Systems: reactive environments that marry physical processes with computational processes, sensing, and communication to respond quickly to the non-stationary dynamics of everyday things
- Internet of Things: networked physical devices, vechicles, buildings, etc. embedded with electronics, software, sensors, and actuators.
- Clinical Informatics: sensing, pattern discovery, integration, reasoning, and dissemination of indicators for medical events from clinical data streams.
- Machine Learning in Healthcare: applications in theraputics, chronic illness, wellness, and community
- Intelligent Sensing: pattern discovery from sensor data
- Distributed Systems: frameworks, software, methods
- Systems Integration: rapid prototyping
- Research Training: novel methods for training and mentoring graduate and undergraduate students
Details of my research on these topics can be found on my research and publications pages. This research has also produced a number of software packages, which I make freely available for academic and not-for-profit use.
I am the graduate program director (GPD) for the department. You will find information about our graduate program
on the department web-site by clicking the navigation tab entitled "Programs" on the left hand side of the page. There
you will find information about the curriculum, admissions requirements, and course offerings. Email is my preferred
method of communication.
If you are interested in submitting an application to the graduate program, you are strongly encouraged to
to consult the graduate admissions' frequently asked questions (FAQ).