Rhythms guide our lives. Almost every biological process reflects a roughly 24-hour periodicity known as a circadian rhythm. Living against these body clocks can have severe consequences for physical and mental well-being, with increased risk for cardiovascular disease, cancer, obesity and mental illness. However, circadian disruptions are becoming increasingly widespread in our modern world. As such, there is an urgent need for novel technological solutions to address these issues. In this talk, I will introduce the notion of “Circadian Computing” – technologies that support our innate biological rhythms. Specifically, I will describe a number of my recent projects in this area. First, I will present novel sensing and data-driven methods that can be used to assess sleep and related circadian disruptions. Next, I will explain how we can model and predict alertness, a key circadian process for cognitive performance. Third, I will describe a smartphone based tool for maintaining circadian stability in patients with bipolar disorder. To conclude, I will discuss a vision for how Circadian Computing can radically transform healthcare, including by augmenting performance, enabling preemptive care for mental health patients, and complementing current precision medicine initiatives.
Saeed Abdullah is a Ph.D. candidate in Information Science at Cornell University, advised by Tanzeem Choudhury. Abdullah works on developing novel data-driven technologies to improve health and well-being. His research is inherently interdisciplinary and he has collaborated with psychologists, psychiatrists, and behavioral scientists. His work has introduced assessment and intervention tools across a number of health related domains including sleep, cognitive performance, bipolar disorder, and schizophrenia. Saeed’s research has been recognized through several accolades, including the $100,000 Heritage Open mHealth Challenge winner, a best paper award, and an Agile Research Project award from the Robert Wood Johnson Foundation.
The Laboratory for Computational Sensing and Robotics will highlight its elite robotics students and showcase cutting-edge research projects in areas that include Medical Robotics, Extreme Environments Robotics, Human-Machine Systems for Manufacturing, BioRobotics and more. JHU Robotics Industry Day will take place from 8 a.m. to 3 p.m. in Hackerman Hall on the Homewood Campus at Johns Hopkins University.
Robotics Industry Day will provide top companies and organizations in the private and public sectors with access to the LCSR’s forward-thinking, solution-driven students. The event will also serve as an informal opportunity to explore university-industry partnerships.
You will experience dynamic presentations and discussions, observe live demonstrations, and participate in speed networking sessions that afford you the opportunity to meet Johns Hopkins most talented robotics students before they graduate.
Please contact Rose Chase if you have any questions.
LEVERING GREAT HALL
8:00 Registration and Continental Breakfast
8:30 Welcome: Larry Nagahara, Associate Dean for Research, JHU
8:35 Introduction to LCSR: Director Russell H. Taylor
8:55 Research and Commercialization Highlights
9:00 Louis Whitcomb, LCSR
9:10 Noah Cowan & Erin Sutton, LCSR
9:20 Marin Kolilarov, LCSR
9:30 Philipp Stolka, Clear Guide Medical
9:40 Mehran Armand, APL and LCSR
9:50 Stephen L. Hoffman, Sanaria, Inc.
10:00 COFFEE BREAK
10:10 Bernhard Fuerst, LCSR
10:20 Bruce Lichorowic, Galen Robotics
10:30 David Narrow, Sonavex, Inc.
10:40 Kelleher Guerin & Benjamin Gibbs, READY Robotics
10:50 Promit Roy, Max and Haley LLC
11:00 John Krakauer, Malone Center for Engineering in Healthcare Update, JHU
11:10 New Faculty Talks
11:10 – Muyinatu Bell
11:30 – Jeremy D. Brown
HACKERMAN HALL B17 LOBBY
HACKERMAN HALL ROBOTORIUM
12:00-1:15 Poster + Demo Sessions
HACKERMAN HALL B17
1:15-3:00 Student and Industry Speed Networking
Spring Break – No Seminar
Robotic platforms now deliver vast amounts of sensor data from large unstructured environments. In attempting to process and interpret this data there are many unique challenges in bridging the gap between prerecorded datasets and the field. This talk will present recent work addressing the application of deep learning techniques to robotic perception. Deep learning has pushed successes in many computer vision tasks through the use of standardized datasets. We focus on solutions to several novel problems that arise when attempting to deploy such techniques on fielded robotic systems. The themes of the talk are twofold: 1) How can we integrate such learning techniques into the traditional probabilistic tools that are well known in robotics? and 2) Are there ways of avoiding the labor-intensive human labeling required for supervised learning? These questions give rise to several lines of research based around dimensionality reduction, adversarial learning, and simulation. We will show this work applied to three domains: self-driving cars, acoustic localization, and optical underwater reconstruction. This talk will show results on field data from the monitoring of Australia’s Coral Reefs, the archeological mapping of a 5,000-year-old submerged city, and the operation of a level-4 self-driving car in urban environments.
Matthew Johnson-Roberson is Assistant Professor of Engineering in the Department of Naval Architecture & Marine Engineering and the Department of Electrical Engineering and Computer Science at the University of Michigan. He received a PhD from the University of Sydney in 2010. There he worked on Autonomous Underwater Vehicles for long-term environment monitoring. Upon joining the University of Michigan faculty in 2013, he created the DROP (Deep Robot Optical Perception) Lab, which researches a wide variety of perception problems in robotics including SLAM, 3D reconstruction, scene understanding, data mining, and visualization. He has held prior postdoctoral appointments with the Centre for Autonomous Systems – CAS at KTH Royal Institute of Technology in Stockholm and the Australian Centre for Field Robotics at the University of Sydney. He is a recipient of the NSF CAREER award (2015).
Deep Networks are very successful for many visual task but their performance still fall far short of human visual abilities. Humans can learn from a few examples, with very weak supervision, can adapt to unknown factors like occlusion, can generalize from objects we know to objects which we do not. This talk will describe some state of the art work on deep networks but also discuss some of their limitations.
Alan Yuille received his B.A. in mathematics from the University of Cambridge in 1976, and completed his Ph.D. in theoretical physics at Cambridge in 1980. He then held a postdoctoral position with the Physics Department, University of Texas at Austin, and the Institute for Theoretical Physics, Santa Barbara. He then became a research scientists at the Artificial Intelligence Laboratory at MIT (1982-1986) and followed this with a faculty position in the Division of Applied Sciences at Harvard (1986-1995), rising to the position of associate professor. From 1995-2002 he worked as a senior scientist at the Smith-Kettlewell Eye Research Institute in San Francisco. From 2002-2016 he was a full professor in the Department of Statistics at UCLA with joint appointments in Psychology, Computer Science, and Psychiatry. In 2016 he became a Bloomberg Distinguished Professor in Cognitive Science and Computer Science at Johns Hopkins University. He has won a Marr prize, a Helmholtz prize, and is a Fellow of IEEE.
The sophistication of Unmanned Aerial Vehicles (UAV), otherwise known as drones, is increasing while their cost is decreasing and is quickly approaching consumer prices. This technology, like most others, adds tremendous value to humanity but also challenges. This dichotomy has motivated our research from the early 90’s to develop more capable platforms and more recently to explore technologies that can mitigate the risks associated with drone proliferation. We will present examples of our work on both sides of this spectrum. One particular area with tremendous potential impact is on the sensor and processing (payload) side. We have been the thought leaders on computational sensors and are on a path to reaching size, weight, and power constraints commensurate or exceeding biological equivalents. This revolution in integrated sensing and computing is likely to enable a new class of autonomous and very capable systems. In particular, we are exploring the interface between biological and engineered systems. Biological creatures are highly efficient, autonomous, and mobile with minimal sensory requirements. Their endurance and mobility remain far unmatched especially as the size decreases and that is the subject of intense research. We believe that solutions that build on the best of both worlds may produce better performance than either on its own and our focus is on the optimal integration of engineered payloads with natural hosts. Another complementary area of our research is small robotics with our recent focus for endoscopic medical procedures. In particular, we are developing a self-propelled aiding endoscope based on biomimetic peristaltic locomotion, and potential solutions may reside in what is becomingly known as soft robotics.
Dr. Rizk is currently an Associate Research Professor for JHU ECE, a lecturer for JHU ME, a Science and Technology (S&T) and Innovation consultant for JHU APL, local industries, and government leadership, and an entrepreneur. Prior to Nov 2016, he was a Principal Staff, Systems/Lead Engineer, S&T Advisor, Innovation Lead, member of the S&T committee, and member of the Innovation Steering Group for the Air and Missile Defense Sector at APL. He has had 15 intellectual property filings since 2014 and received 9 internal and external achievement awards. He has been recognized as a top innovator, thought leader, and successful Principal Investigator, and has demonstrated an effective model for R&D that yielded multiple innovative and far-reaching concepts and technologies. He was a pioneer in UAV technology and led a small team that developed and demonstrated the first four-rotor (quad copter) UAV system in the early 90’s. More recently, he has been the forerunner in developing a new multi-mode / multi-mission sensor architecture that is low C-SWaP and likely to revolutionize the associated missions/applications space and platforms. In addition, he is currently developing a new vision for future unmanned systems. Dr. Rizk has been teaching the Mechatronics courses at JHU since Spring of 2015 and is developing a new design course to be offered in Fall 2017 for which he was awarded a teaching innovation grant. During his APL tenure, he also provided systems engineering and S&T support to senior DOD leadership and large acquisition programs. In addition to providing effective technical, innovative, and mentoring leadership and management, Dr. Rizk has demonstrated a collaborative spirit, successfully working with various FFRDC’s, government labs, academia, and industry of various sizes. He also made key contributions during his time at Rockwell Aerospace, McDonald Douglas, and Boeing. He is a senior member of IEEE, AIAA, and a member of AUVSI.
Image-guided therapy is a clinical procedure under 2-D or 3-D image guidance such as MRI and CT images to accurately deliver surgical devices to diseased or cancerous tissue. This emerging field is interdisciplinary, combining the technology of robotics, computer science, engineering and medicine. Image-guided therapy allows faster, safer and more accurate minimally invasive surgery and diagnosis. In this talk, Dr. Tse will present the technological challenges in the field, followed by his research in MRI-guided therapy for brachytherapy, ablation and stem cell treatment in the prostate, the heart and the spine. These procedures consist of the latest imaging and robotic technology in minimally invasive therapy.
Dr. Zion Tse is an Assistant Professor in the College of Engineering and the Principal Investigator of the Medical Robotics Lab at the University of Georgia. Formerly, he was a visiting scientist in the Center for Interventional Oncology at National Institutes of Health, and a research fellow in the Radiology Department at Harvard Medical School, Brigham and Women’s Hospital. He received his PhD in Medical Robotics from Imperial College London, UK. His academic and professional experience has related to mechatronics, medical devices and surgical robotics. Dr. Tse has designed and prototyped a broad range of novel clinical devices, most of which have been tested in animal and human trials.