Calendar

Mar
31
Wed
LCSR Seminar: Auke Ijspeert “Investigating animal locomotion using biorobots” @ https://wse.zoom.us/s/94623801186
Mar 31 @ 12:00 pm – 1:00 pm

Link for Live Seminar

Link for Recorded seminars – 2020/2021 school year

 

Abstract:

The ability to efficiently move in complex environments is a fundamental property both for animals and for robots, and the problem of locomotion and movement control is an area in which neuroscience, biomechanics, and robotics can fruitfully interact. In this talk, I will present how biorobots and numerical models can be used to explore the interplay of the four main components underlying animal locomotion, namely central pattern generators (CPGs), reflexes, descending modulation, and the musculoskeletal system. Going from lamprey to human locomotion, I will present a series of models that tend to show that the respective roles of these components have changed during evolution with a dominant role of CPGs in lamprey and salamander locomotion, and a more important role for sensory feedback and descending modulation in human locomotion. I will also present a recent project showing how robotics can provide scientific tools for paleontology. Interesting properties for robot and lower-limb exoskeleton locomotion control will finally be discussed.

 

Biography:

Auke Ijspeert is a professor at EPFL (Lausanne, Switzerland) since 2002, and head of the Biorobotics Laboratory. He has a BSc/MSc in physics from EPFL (1995), a PhD in artificial intelligence from the University of Edinburgh (1999). He is an IEEE Fellow. His research interests are at the intersection between robotics, computational neuroscience, nonlinear dynamical systems and applied machine learning. He is interested in using numerical simulations and robots to gain a better understanding of animal locomotion, and in using inspiration from biology to design novel types of robots and controllers. He is also investigating how to assist persons with limited mobility using exoskeletons and assistive furniture.

 

Apr
7
Wed
LCSR Seminar: Robin Murphy “From the World Trade Center to the COVID-19 Pandemic: Robots and Disasters” @ https://wse.zoom.us/s/94623801186
Apr 7 @ 12:00 pm – 1:00 pm

Link for Live Seminar

Link for Recorded seminars – 2020/2021 school year

 

Abstract:

This talk will describe how ground, aerial, and marine robots have been used in disasters, most recently the coronavirus pandemic. During the pandemic so far, 338 instances of robots in 48 countries protecting healthcare workers from unnecessary exposure, handling the surge in demand for clinical care, preventing infections, restoring economic activity, and maintaining individual quality of life have been reported.  The uses span six sociotechnical work domains and 29 different use cases representing different missions, robot work envelopes, and human-robot interaction dyads.  The dataset also confirms a model of adoption of robotics technology for disasters. Adoption favors robots that maximize the suitability for established use cases while minimizing risk of malfunction, hidden workload costs, or unintended consequences as measured by the NASA Technical Readiness Assessment metrics. Regulations do not present a major barrier but availability, either in terms of inventory or prohibitively high costs, does.  The model suggests that in order to be prepared for future events, roboticists should partner with responders now, investigate how to rapidly manufacture complex, reliable robots on demand, and conduct fundamental research on predicting and mitigating risk in extreme or novel environments.\

 

Biography:

Dr. Robin R. Murphy is the Raytheon Professor of Computer Science and Engineering at Texas A&M University, a TED speaker, and an IEEE and ACM Fellow. She helped create the fields of disaster robotics and human-robot interaction, deploying robots to 29 disasters in five countries including the 9/11 World Trade Center, Fukushima, the Syrian boat refugee crisis, Hurricane Harvey, and the Kilauea volcanic eruption. Murphy’s contributions to robotics have been recognized with the ACM Eugene L. Lawler Award for Humanitarian Contributions, a US Air Force Exemplary Civilian Service Award medal, the AUVSI Foundation’s Al Aube Award, and the Motohiro Kisoi Award for Rescue Engineering Education (Japan). She has written the best-selling textbook Introduction to AI Robotics (2nd edition 2019) and the award-winning Disaster Robotics (2014), plus serving an editor for the science fiction/science fact focus series for the journal Science Robotics. She co-chaired the White House OSTP and NSF workshops on robotics for infectious diseases and recently co-chaired the National Academy of Engineering/Computing Community Consortium workshop on robots for COVID-19.

 

Apr
21
Wed
LCSR Seminar: Gordon Berman “Measuring behavior across scales” @ https://wse.zoom.us/s/94623801186
Apr 21 @ 12:00 pm – 1:00 pm

Link for Live Seminar

Link for Recorded seminars – 2020/2021 school year

 

Abstract:

When we think of animal behavior, what typically comes to mind are actions – running, eating, swimming, grooming, flying, singing, resting. Behavior, however, is more than the catalogue of motions that an organism can perform. Animals organize their repertoire of actions into sequences and patterns whose underlying dynamics last much longer than any particular behavior. How an organism modulates these dynamics affects its success at accessing food, reproducing, and myriad other tasks essential for survival. Animals regulate these patterns of behavior via many interacting internal states (hunger, reproductive cycle, age, etc.) that we cannot directly measure. Studying these hidden states’ dynamics, accordingly, has proven challenging due to a lack of measurement techniques and theoretical understanding. In this talk, I will outline our efforts to uncover the latent dynamics that underlie long timescale structure in animal behavior. Looking across a variety of organisms, we use a novel methodology to measure animals’ full behavioral repertoires to find the existence of a non-trivial form of long timescale dynamics that cannot be explained using standard mathematical frameworks. I will present how temporal coarse-graining can be used to understand how these dynamics are generated and how the found course-grained states can be related to the internal states governing behavior through a combination of machine learning techniques and dynamical systems modeling.  Inferring these hidden dynamics presents a new opportunity to generate insights into the neural and physiological mechanisms that animals use to select actions.

Biography:

Gordon J. Berman, Ph.D., Assistant Professor of Biology, Emory University Co-Director, Simons-Emory International Consortium on Motor Control Chair of Recruitment for the Emory Neuroscience Graduate Program . Our lab uses theoretical, computational, and data-driven approaches to gain quantitative insight into entire repertoires of animal behaviors, aiming to make connections to the neurobiology, genetics, and evolutionary histories and that underlie them. Get more information here.

 

Apr
28
Wed
LCSR Seminar: Ram Vasudevan “How I Learned to Stop Worrying and Start Loving Lifting to Infinite Dimensions” @ https://wse.zoom.us/s/94623801186
Apr 28 @ 12:00 pm – 1:00 pm

Link for Live Seminar

Link for Recorded seminars – 2020/2021 school year

 

Abstract:

Autonomous systems offer the promise of providing greater safety and access. However, this positive impact will only be achieved if the underlying algorithms that control such systems can be certified to behave robustly. This talk will describe a pair of techniques grounded in infinite dimensional optimization to address this challenge.

The first technique, which is called Reachability-based Trajectory Design, constructs a parameterized representation of the forward reachable set, which it then uses in concert with predictions to enable real-time, certified, collision checking. This approach, which is guaranteed to generate not-at-fault behavior, is demonstrated across a variety of different real-world platforms including ground vehicles, manipulators, and walking robots. The second technique is a modeling method that allows one to represent a nonlinear system as a linear system in the infinite-dimensional space of real-valued functions. By applying this modeling method, one can employ well-understood linear model predictive control techniques to robustly control nonlinear systems. The utility of this approach is verified on a soft robot control task.

 

Biography:

Ram Vasudevan is an assistant professor in Mechanical Engineering and the Robotics Institute at the University of Michigan. He received a BS in Electrical Engineering and Computer Sciences, an MS degree in Electrical Engineering, and a PhD in Electrical Engineering all from the University of California, Berkeley. He is a recipient of the NSF CAREER Award and the ONR Young Investigator Award. His work has received best paper awards at the IEEE Conference on Robotics and Automation, the ASME Dynamics Systems and Controls Conference, and IEEE OCEANS Conference and has been finalist for best paper at Robotics: Science and Systems.

 

May
6
Thu
Computer Integrated Surgery 2 Final Project Presentations
May 6 @ 6:00 pm – 9:00 pm

The Spring 2021 Final Project Presentation Session for Computer Integrated Surgery II will be held Thursday, May 6th from 18:00 to 21:00 Eastern Time via Zoom. This year, we have 18 amazing projects supported by grad students, faculties, surgeons and companies. We are excited to invite you to join our event to see what the students have achieved with the effort of the past semester.

Connection information

Join Zoom Meeting: https://wse.zoom.us/j/635091574

Meeting ID: 635 091 574

Password: 001987

 

Agenda

– 18:00—18:10 Arrival and greetings

– 18:10—18:30 1 minute teaser presentation

– 18:30—20:30 Interactive session in breakout rooms

– 20:30—20:40 Reconvene and announce finalists

– 20:40—20:55 Presentations by finalists

– 20:55—21:00 Announcement of Best Project winner

May
11
Tue
Deep Learning Final Project Presentations
May 11 @ 9:00 am – 12:00 pm

The Spring 2021 Final Project Presentation Session for Deep Learning will be held Tuesday, May 11th from 9-12pm Eastern Time via Zoom. This year, we have many amazing projects to review and celebrate. We are excited to invite you to join our event to see what the students have achieved with the effort of the past semester.

  • Welcome (Mathias)
  • Deep Learning at Intuitive Surgical (Omid Mohareri)
  • Poster Pitch Presentations, each group has 1.5 min (this will take ~45 min)
  • Breakouts (every group has their own breakout room, this will last around 1h)
  • Awards and Closing

 

Join Zoom Meeting
https://wse.zoom.us/j/98898500603?pwd=dlY2RHlUZXhFUXErK1J6bHcxVUNGdz09

Aug
6
Fri
Closing Ceremonies for Computational Sensing and Medical Robotics (CSMR) REU @ Zoom
Aug 6 @ 9:00 am – 3:00 pm

The closing ceremonies of the Computational Sensing and Medical Robotics (CSMR) REU are set to take place Friday, August 6 from 9am until 3pm at this Zoom link. Seventeen undergraduate students from across the country are eager to share the culmination of their work for the past 10 weeks this summer.

The schedule for the day is listed below, but each presentation is featured in more detail in the program. The event is open to the public and it is not necessary to RSVP.

 

 

2021 REU Final Presentations
Time Presenter Project Title Faculty Mentor Student/Postdoc/Research Engineer Mentors
9:00  

Ben Frey

 

Deep Learning for Lung Ultrasound Imaging of COVID-19 Patients Muyinatu Bell Lingyi Zhao
9:15  

Camryn Graham

 

Optimization of a Photoacoustic Technique to Differentiate Methylene Blue from Hemoglobin Muyinatu Bell Eduardo Gonzalez
9:30  

Ariadna Rivera

 

Autonomous Quadcopter Flying and Swarming Enrique Mallada Yue Shen
9:45  

Katie Sapozhnikov

 

Force Sensing Surgical Drill Russell Taylor Anna Goodridge
10:00  

Savannah Hays

 

Evaluating SLANT Brain Segmentation using CALAMITI Jerry Prince Lianrui Zuo
10:15  

Ammaar Firozi

 

Robustness of Deep Networks to Adversarial Attacks René Vidal Kaleab Kinfu, Carolina Pacheco
10:30 Break
10:45  

Karina Soto Perez

 

Brain Tumor Segmentation in Structural MRIs Archana Venkataraman Naresh Nandakumar
11:00  

Jonathan Mi

 

Design of a Small Legged Robot to Traverse a Field of Multiple Types of Large Obstacles Chen Li Ratan Othayoth, Yaqing Wang, Qihan Xuan
11:15  

Arko Chatterjee

 

Telerobotic System for Satellite Servicing Peter Kazanzides, Louis Whitcomb, Simon Leonard Will Pryor
11:30  

Lauren Peterson

 

Can a Fish Learn to Ride a Bicycle? Noah Cowan Yu Yang
11:45  

Josiah Lozano

 

Robotic System for Mosquito Dissection Russel Taylor, 

Lulian Lordachita

Anna Goodridge
12:00  

Zulekha Karachiwalla

 

Application of dual modality haptic feedback within surgical robotic Jeremy Brown
12:15 Break
1:00  

James Campbell

 

Understanding Overparameterization from Symmetry René Vidal Salma Tarmoun
1:15  

Evan Dramko

 

Establishing FDR Control For Genetic Marker Selection Soledad Villar, Jeremias Sulam N/A
1:30  

Chase Lahr

 

Modeling Dynamic Systems Through a Classroom Testbed Jeremy Brown Mohit Singhala
1:45  

Anire Egbe

 

Object Discrimination Using Vibrotactile Feedback for Upper Limb Prosthetic Users Jeremy Brown
2:00  

Harrison Menkes

 

Measuring Proprioceptive Impairment in Stroke Survivors (Pre-Recorded) Jeremy Brown
2:15  

Deliberations

 

3:00 Winner Announced
Sep
8
Wed
LCSR Seminar: Mark Savage “Telling Your Story: The Resume as a Marketing Tool” @ https://wse.zoom.us/s/94623801186
Sep 8 @ 12:00 pm – 1:00 pm

Link for Live Seminar

Link for Recorded seminars – 2021/2022 school year

 

Mark Savage is the Johns Hopkins Life Design Educator for Engineering Masters Students, advising on all aspects of career development and the internship / job search, with the Handshake Career Management System as a necessary tool.  Look for weekly newsletters to soon be emailed to Homewood WSE Masters Students on Sunday Nights.

Sep
15
Wed
LCSR Seminar: Tariq Iqbal “Toward Fluent Collaboration in Human-Robot Teams” @ https://wse.zoom.us/s/94623801186
Sep 15 @ 12:00 pm – 1:00 pm

Link for Live Seminar

Link for Recorded seminars – 2021/2022 school year

 

 

Abstract:

Robots currently have the capacity to help people in several fields, including health care, assisted living, and manufacturing, where the robots must share physical space and actively interact with people in teams. The performance of these teams depends upon how fluently all team members can jointly perform their tasks. To be successful within a group, a robot requires the ability to perceive other members’ actions, model interaction dynamics, predict future actions, and adapt their plans accordingly in real-time. In the Collaborative Robotics Lab (CRL), we develop novel perception, prediction, and planning algorithms for robots to fluently coordinate and collaborate with people in complex human environments. In this talk, I will highlight various challenges of deploying robots in real-world settings and present our recent work to tackle several of these challenges.

 

Biography:

Tariq Iqbal is an Assistant Professor of Systems Engineering and Computer Science (by courtesy) at the University of Virginia (UVA). Prior to joining UVA, he was a Postdoctoral Associate in the Computer Science and Artificial Intelligence Lab (CSAIL) at MIT. He received his Ph.D. in CS from the University of California San Diego (UCSD). Iqbal leads the Collaborative Robotics Lab (CRL), which focuses on building robotic systems that work alongside people in complex human environments, such as factories, hospitals, and educational settings. His research group develops artificial intelligence, computer vision, and machine learning algorithms to enable robots to solve problems in these domains.

Sep
22
Wed
LCSR Seminar: Andreas Maier “Known Operator Learning – An Approach to Unite Machine Learning, Signal Processing and Physics” @ https://wse.zoom.us/s/94623801186
Sep 22 @ 12:00 pm – 1:00 pm

Link for Live Seminar

Link for Recorded seminars – 2021/2022 school year

 

Abstract: We describe an approach for incorporating prior knowledge into machine learning algorithms. We aim at applications in physics and signal processing in which we know that certain operations must be embedded into the algorithm. Any operation that allows computation of a gradient or sub-gradient towards its inputs is suited for our framework. We derive a maximal error bound for deep nets that demonstrates that inclusion of prior knowledge results in its reduction. Furthermore, we show experimentally that known operators reduce the number of free parameters. We apply this approach to various tasks ranging from computed tomography image reconstruction over vessel segmentation to the derivation of previously unknown imaging algorithms. As such, the concept is widely applicable for many researchers in physics, imaging and signal processing. We assume that our analysis will support further investigation of known operators in other fields of physics, imaging and signal processing.

Short Bio: Prof. Dr. Andreas Maier was born on 26th of November 1980 in Erlangen. He studied Computer Science, graduated in 2005, and received his PhD in 2009. From 2005 to 2009 he was working at the Pattern Recognition Lab at the Computer Science Department of the University of Erlangen-Nuremberg. His major research subject was medical signal processing in speech data. In this period, he developed the first online speech intelligibility assessment tool – PEAKS – that has been used to analyze over 4.000 patient and control subjects so far.
From 2009 to 2010, he started working on flat-panel C-arm CT as post-doctoral fellow at the Radiological Sciences Laboratory in the Department of Radiology at the Stanford University. From 2011 to 2012 he joined Siemens Healthcare as innovation project manager and was responsible for reconstruction topics in the Angiography and X-ray business unit.
In 2012, he returned the University of Erlangen-Nuremberg as head of the Medical Reconstruction Group at the Pattern Recognition lab. In 2015 he became professor and head of the Pattern Recognition Lab. Since 2016, he is member of the steering committee of the European Time Machine Consortium. In 2018, he was awarded an ERC Synergy Grant “4D nanoscope”.  Current research interests focuses on medical imaging, image and audio processing, digital humanities, and interpretable machine learning and the use of known operators.

 

 

 

Laboratory for Computational Sensing + Robotics