Robots today are confined to operate in relatively simple, controlled environments. One reason for this is that current methods for processing visual data tend to break down when faced with occlusions, viewpoint changes, poor lighting, and other challenging but common situations that occur when robots are placed in the real world. I will show that we can train robots to handle these variations by modeling the causes behind visual appearance changes. If robots can learn how the world changes over time, they can be robust to the types of changes that objects often undergo. I demonstrate this idea in the context of autonomous driving, and I will show how we can use this idea to improve performance for every step of the robotic perception pipeline: object segmentation, tracking, and velocity estimation. I will also present some recent work on learning to manipulate objects, using a similar framework of learning environmental changes. By learning how the environment can change over time, we can enable robots to operate in the complex, cluttered environments of our daily lives.

David Held is a post-doctoral researcher at U.C. Berkeley working with Pieter Abbeel on deep reinforcement learning for robotics. He recently completed his Ph.D. in Computer Science at Stanford University with Sebastian Thrun and Silvio Savarese, where he developed methods for perception for autonomous vehicles. David has also worked as an intern on Google’s self-driving car team. Before Stanford, David was a researcher at the Weizmann Institute, where he worked on building a robotic octopus. He received a B.S. and M.S. in Mechanical Engineering at MIT and an M.S. in Computer Science at Stanford, for which he was awarded the Best Master's Thesis Award from the Computer Science Department.

Faculty Host: Martial Hebert

Massive cancer genomics efforts have been undertaken with the hopes of personalizing cancer therapy by using targeted therapies matched to the genetics of the patient’s tumor rather than cytotoxic drugs that kill all proliferating cells.  In recent “basket” clinical trials, targeted therapies are chosen based on somatic alterations affecting specific pathway genes regardless of the cancer type, e.g. patients with activating mutations in PIK3CA are eligible for treatment with PI3K inhibitors whether they have breast cancer or head and neck cancer. Data from such clinical trials shows that the presence of an “actionable mutation” is not sufficient to predict a clinical response to the corresponding targeted therapy, and it is unclear when a targeted therapeutic with efficacy in one cancer will prove useful in another.

To better model the context dependent role of somatic alterations, we first applied a regularized bilinear regression model to link dysregulation of upstream signaling pathways with altered transcriptional response. We used parallel (phospho)proteomic and mRNA sequencing data across the Cancer Genome Atlas (TCGA) tumor data sets for these models. We then developed a systematic regularized regression analysis to interpret the impact of mutations and copy number events in terms of functional outcomes such as (phospho)protein and transcription factor (TF) activities. Our analysis predicted distinct dysregulated transcriptional regulators downstream of similar somatic alterations in different cancers. We validated the context-specific activity of TFs associated to mutant PIK3CA in model systems. These results have implications for the pancancer use of targeted drugs and potentially for the design of combination therapies.

Dr. Hatice Ulku Osmanbeyoglu is a postdoctoral research associate at Memorial Sloan-Kettering Cancer Center. Her research focuses on developing data-driven computational approaches to understand disease mechanisms in order to assist in the development of personalizing anticancer treatments. She obtained her Ph.D. in Biomedical Informatics from University of Pittsburgh, and holds a Master of Science degree in Electrical and Computer Engineering from Carnegie Mellon University and Master of Science in Bioengineering from University of Pittsburgh. She completed her Bachelor of Science in Computer Engineering from Northeastern University (Summa Cum Laude). She has received multiple awards including the NIH Pathway to Independence Award (K99/R00) and Memorial Sloan-Kettering Postdoctoral Research Award.

Faculty Host: Jian Ma
Computational Biology

An artist sculpting a block of marble, a magician pulling a card from thin air, and a surgeon performing a difficult emergency procedure all highlight the brilliant human ability to manipulate the physical world. And yet, even as these high-skill tasks push the limits of human capability, they remind us of the boundary of human dexterity. In this talk, I will present my past, present, and planned research to allow humans of all skill and ability levels, as well as their robotic counterparts, to accomplish previously impossible tasks.

I will begin with an overview of my research aimed to better enable humans to teleoperate robots under direct control. The ideal direct-control teleoperation system would enable the operator to complete a given task at least as easily as if he or she were to complete the task directly with his or her own hands. My research improves the usability of teleoperation systems through a human-centered design approach. Specifically, I leverage prior knowledge of the human motor and sensory systems to increase the transparency of and presence provided by teleoperation systems. I will then describe my ongoing research, which investigates the use of shared control and shared autonomy in teleoperation. Finally, I will end with my future plans to expand my research to other areas of collaborative and assistive robotics.

Rebecca Pierce Khurshid is a postdoctoral associate in the Interactive Robotics Group at MIT, where she works to enable human-robot teams to achieve more than either humans or robots can achieve alone. Specifically, she is investigating how humans can best teleoperate robots and how varying levels of robot autonomy affect the team’s performance. She arrived at MIT after completing her PhD and master’s degrees in Mechanical Engineering and Applied Mechanics at the University of Pennsylvania. Her doctoral research leveraged previous scientific knowledge of the human sensory-motor system to design interfaces that allowed a human to teleoperate a humanoid robot. She received an NSF Graduate Research Fellowship to support her work. Prior to Penn, she earned her bachelor’s degree in Mechanical Engineering at Johns Hopkins.

Faculty Host: David Wettergreen

Systematic analysis of RNA regulation using methods that quantitatively measure each step in RNA metabolism is necessary to understand pathophysiological changes in gene regulatory networks. My research integrates biochemical and computational methods to gain mechanistic insights into the RNA metabolic process driven by ribonucleoprotein complexes (RNPs). RNPs regulate every aspect of RNA metabolism with profound implications for human diseases. I will highlight examples of the regulatory insights gained by using genomic technologies to map RBP-RNA interaction sites, as well as to quantify rates of transcription, splicing, degradation, localization and translation in human cell lines. Finally, I will briefly present on-going research utilizing these approaches to investigate the RNA regulatory dynamics of human steroidogenesis.

During the past 10 years, Neelanjan Mukherjee has been fortunate to pursue an interdisciplinary career in experimental biochemistry, molecular biology, and computational biology. I led both the experimental and/or computational aspects of many studies. My research is focused on quantitative RNA systems biology at the interface between experimental and theoretical science. My goal is to understand general principles of human RNA regulatory networks and their importance in cancer and steroid hormone metabolism. I am currently a postdoctoral scientist in Uwe Ohler’s lab at the Berlin Institute for Medical Sytems Biology. I have a Ph.D. in Genetics and Genomics from Jack Keene’s lab at Duke University and a B.Sc. in Biology from UC San Diego.

Faculty Host: Joel McManus
Computational Biology

Whether a robot is assisting a person to move about the home, or packing containers in a warehouse, the fundamental promise of robotics centers on the ability to productively interact with a complex and changing environment in a safe and controlled fashion. However, current robots are largely limited to basic tasks in structured environments--operating slowly and cautiously, afraid of any incidental contact with the outside world. Dynamic interaction, encompassing both legged locomotion and manipulation, poses significant challenges to traditional control and planning techniques. Discontinuities from impact events and dry friction make standard tools poorly suited in scenarios with complex or uncertain contacts between robot and environment. I will present approaches that leverage the interplay between numerical optimization and the mathematical structure of contact dynamics to avoid the combinatorial complexity of mode enumeration. This will include a tractable algorithm for trajectory optimization, without an a priori encoding of the contact sequence, and an approach utilizing sums-of-squares programming to design and provably verify controllers that stabilize systems making and breaking contact.

Michael Posa is a Ph.D. candidate in Electrical Engineering and Computer Science at the Massachusetts Institute of Technology, with an expected graduation in May of 2017. At MIT, he is a member of the Robot Locomotion Group working with Professor Russ Tedrake. He received his B.S. and M.S. in Mechanical Engineering from Stanford University in 2007 and 2008, where he received the Frederick E. Terman Award. Before joining MIT, he worked as an engineer at Vecna Robotics in Cambridge, Massachusetts, designing control systems and simulation tools for the humanoid BEAR robot and other devices. His research emphasizes computational approaches for control and planning of robotic systems with frictional contact. He is a recipient of the Rolf Locher Graduate Fellowship and received the Best Paper award at HSCC in 2013.

Faculty Hosts: Maxim Likhachev (RI), Paul Steif (MechE)

Language provides a precise lens into cognition and neuromotor function, and we are increasingly using machine intelligence to peer through that lens. Approximately 10% of North Americans have a communication disorder, which can originate physically (as in cerebral palsy and Parkinson’s disease), cognitively (as in aphasia and Alzheimer’s disease), or both (as in cardiovascular stroke). In this talk, I describe different technologies we’ve developed in response to these issues. I show how electromagnetic articulography can be used to augment speech recognition for people with cerebral palsy, and how by continuing deeper towards the neurological origins of speech, we can identify speech plans directly from EEG and MEG data. Deeper still, we see how syntactic and semantic differences in spontaneous speech can be indicative of cognitive disorder, and how modern tools, such as conversational robotics, can be used to extend the quality of life for people living with these conditions. I will also survey some of our current work in text informatics, including diagnosis from verbal autopsies, and online information retrieval for caregivers, in the age of post-truth.

Frank Rudzicz is a scientist at the Toronto Rehabilitation Institute (University Health Network), an assistant professor of Computer Science at the University of Toronto, co-founder and President of WinterLight Labs Inc. and President of the international joint ACL/ISCA special interest group on Speech and Language Processing for Assistive Technologies. He is the recent recipient of the Young Investigator award from the Alzheimer's Society, the Early Researcher award from the Government of Ontario, the Excellence in Applied Research award from National Speech-Language & Audiology (Canada), and a best student paper award at Interspeech 2013. His work involves machine-learning, human-computer interaction, speech-language pathology, rehabilitation engineering, signal processing, and linguistics. His work has been profiled in national and international media, including Scientific American and Wired.

Faculty Host: Alan Black

Recent years have seen falling costs of communication and storage technologies and advances in fabrication methods. Sensors, actuators, and processors are being integrated into globally accessible information networks. These trends are promoting a profusion of networked robotic platforms with distinct features and unique capabilities. As we aspire to harness this diverse array of robots to solve increasingly complex problems, heterogeneity and diversity become design features. However, we still lack a fundamental understanding of how to compose and control large-scale systems of heterogeneous robots. Moreover, as we program diverse robots to exploit their technical complementarities, we create interdependencies and critical links.  Such collaborative algorithms give rise to new sources of internal and external threats that lead to unintended failure modes. As a consequence, we need new mechanisms that ensure resilience.

I begin my talk by formalizing diversity in the context of dynamic task allocation for large-scale heterogeneous multi-robot systems. In light of this setting, I show how optimal control policies are impacted by the heterogeneity of the robot team. In the second part of the talk, my focus shifts to the question of how to provide resilience to internal failures through precautionary collaboration mechanisms. By building on foundational concepts of network science and security, I show how we can achieve resilience, allowing robot teams to function in the presence of defective and/or malicious robots. Finally, I consider the importance of providing system-wide protection against external threats, and introduce some new ideas that touch upon privacy.

Amanda Prorok is a Postdoctoral Researcher in the General Robotics, Automation, Sensing and Perception (GRASP) Laboratory at the University of Pennsylvania, where she works with Prof. Vijay Kumar on heterogeneous networked robotic systems. She completed her PhD at EPFL, Switzerland, where she addressed the topic of localization with ultra-wideband sensing for robotic networks. Her dissertation was awarded the Asea Brown Boveri (ABB) award for the best thesis at EPFL in the fields of Computer Sciences, Automatics and Telecommunications.  She was selected as an MIT Rising Star in 2015, and won a Best Paper Award at the 9th International Conference on Bio-inspired Information and Communications Technologies, 2015.

Faculty Host: Katia Sycara

Robotics Institute and Department of Electrical & Computer Engineering

The haptic (touch) sensations felt when interacting with the physical world create a rich and varied impression of objects and their environment. However, humans are spending significantly more time online and are increasingly interacting with people and objects through a digital medium. Unfortunately, digital interactions remain unsatisfying and limited, representing the human as having only visual and auditory inputs.

In this talk I will describe how we can leverage our knowledge of the sense of touch to design haptic systems that allow the human to communicate through the digital world in a natural and intuitive way. I will highlight my contributions in furthering haptic realism in virtual reality through the creation of highly realistic virtual objects that are created by modeling acceleration, force, and speed data recorded during physical interactions. I will then describe advances I have made in novel wearable haptic devices for communicating information to a human using intuitive and natural cues.

Heather Culbertson is a Postdoctoral Research Fellow in the Department of Mechanical Engineering at Stan- ford University where she works in the Collaborative Haptics and Robotics in Medicine (CHARM) Lab. Her re- search focuses on the design and control of haptic de- vices and rendering systems. She received her PhD in the Department of Mechanical Engineering and Applied Mechanics (MEAM) at the University of Pennsylvania in 2015 working in the Haptics Group, part of the General Robotics, Automation, Sensing and Perception (GRASP) Laboratory. She completed a Masters in MEAM at the University of Pennsylvania in May of 2013, and earned a BS degree in mechanical engineering at the University of Nevada, Reno in 2010.

Faculty Hosts: Jessica Hodgins, Paul Steif (Robotics/Mechanical Engineering)

Class room engagement can be improved by techniques rooted in classical rhetorical principles, including active learning, student participation, and listening to students. Additionally, there is a need for techniques to help teaching, and specifically software engineering education, become more inclusive.  I present an overview of a curriculum I designed for an undergraduate software engineering class, building upon these principles. Then, I present a short, sample lecture to illustrate these guiding principles of teaching software engineering. The sample lecture presents Mob Programming, a software development technique where the whole team works on the same code, at the same time, on the same computer.

Faculty Host: Jeffrey Eppinger


A major step in the regulation of gene expression is binding of regulatory proteins called transcription factors (TFs) to specific short DNA sites in the promoters and enhancers of regulated genes. Mutations in TF binding sites can lead to dysregulated gene expression and contribute to disease. Importantly, even small changes in gene expression can lead to disease over time, and even a small increase or decrease in TF-DNA binding affinity can have important phenotypic consequences. Thus, sensitive quantitative approaches are needed to measure and model binding of TFs to the genome, and to understand how changes in TF binding lead to changes in gene expression levels.

As a first step toward a quantitative understanding of transcriptional regulation, we recently developed highly accurate regression models of TF-DNA binding, trained on in vitro data generated in my laboratory. The models use features derived from the DNA sequence and structure of potential binding sites, and they have numerous advantages over current motif models. Importantly, what allows us to train very accurate and quantitative models is the fact that we use a combination of state-of-art machine learning algorithms (e.g. ε-SVR with feature selection based on a modified version of LASSO) and experimental assays carefully designed to alleviate bias and minimize noise (e.g. genomic-context protein binding microarrays).

In two recent studies, our protein-DNA binding data and models led to new insights into the genomic recruitment of human TFs. We found that oncogenic transcription factors from the E2F family, currently believed to bind indirectly to >80% of their genomic target sites, can bind a wide variety of DNA sites in vitro. We used support vector regression models to capture the complex intrinsic binding specificity of E2Fs, and we found that it completely explains their in vivo genomic occupancy. In another study, focused on paralogous TFs with indistinguishable DNA motifs but different in vivo targets and regulatory roles, we found that such TFs interact differently with their genomic targets even in vitro. Using weighted regression models that incorporate information on the variance observed in replicate experiments, we show that differences in intrinsic specificity between paralogous TFs are a major determinant of their differential in vivo binding.

I will also discuss current and future efforts to quantify the influences of various cellular factors (such as protein competitors and cofactors, epigenetic modifications, etc.) on the genomic recruitment and the regulatory activity of human TFs.

Raluca Gordan is an assistant professor in the Center for Genomic & Computational Biology and the Department of Biostatistics and Bioinformatics at Duke University. She graduated from University of Iasi (Romania) in 2005 with a B.S. in Computer Science. She received her Ph.D. in Computer Science in 2009 from Duke University, followed by two years of postdoctoral training in computational and experimental regulatory genomics at the Harvard Medical School. Her research combines computational modeling and high-throughput experiments to study, at a quantitative level, how transcription factors identify and bind to their specific target sites across the genome, given the complexity of the genomic search space and the numerous competitive and cooperative interactions that happen in the cell's nucleus. Her research is currently supported by NIH and NSF. She is the recipient of starter grant awards from the PhRMA and March of Dimes foundations, and a recipient of the SLOAN fellowship in Computational and Evolutionary Molecular Biology.

Faculty Host: Andreas Pfenning


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