Spring 2022 ILP's in-person conferences will be open to fully vaccinated individuals only, excepting those individuals who have a medical condition or religious exemption. Conference attendees will use an application developed at MIT -- Tim Tickets -- to grant campus access or scan into an event.
Masks are strongly encouraged.
Computing – from hardware and software to algorithms and AI – is the intellectual backbone supporting advancement across science and technology fields, molding society in profound ways. With skyrocketing demand for computer science education, as well as the pressing need for concrete, sustainable, and responsible solutions across technological domains, the MIT Schwarzman College of Computing invests in new artificial intelligence, data science, and computer science research. The 2022 MIT Efficient AI and Computing Technologies Conference will showcase the most recent developments and tangible impacts of computing technologies in AI hardware and software for improved efficiency and explainability, as well as their applications in diverse technological areas. Presented by the MIT Schwarzman College of Computing and the MIT Industrial Liaison Program, you will hear insights from MIT faculty, MIT Startup Exchange entrepreneurs, and industry executives from presentations, lightning talks, exhibitions, and a panel discussion.
***REGISTRATION IS CLOSED: ONSITE REGISTRATION WILL BE AVAILABLE***
* Startup Exchange Member: Complimentary Send email for code.
* MIT Alum: 70% discount Send email for a discount code.
* Sloan Exec Ed & Professional Education Member: 70% discount Send email for a discount code.
Visiting MIT: https://www.mit.edu/visitmit/
Where to Stay: https://institute-events.mit.edu/visit/where-to-stay
Registration Questions: firstname.lastname@example.org
Limited number of rooms are available at Boston Marriott Cambridge for $309 USD group rate per night. (Last Day to Book: Tuesday, April 12) Book your room now.
John Roberts has been Executive Director of MIT Corporate Relations (Interim) since February 2022. He obtained his Ph.D. in organic chemistry at MIT and returned to the university after a 20-year career in the pharmaceutical industry, joining the MIT Industrial Liaison Program (ILP) in 2013. Prior to his return, John worked at small, medium, and large companies, holding positions that allowed him to exploit his passions in synthetic chemistry, project leadership, and alliance management while growing his responsibilities for managing others, ultimately as a department head. As a program director at MIT, John built a portfolio of ILP member companies, mostly in the pharmaceutical industry and headquartered in Japan, connecting them to engagement opportunities in the MIT community. Soon after returning to MIT, John began to lead a group of program directors with a combined portfolio of 60-80 global companies. In his current role, John oversees MIT Corporate Relations which houses ILP and MIT Startup Exchange.
Irina Sigalovsky works in the Office of Corporate Relations at MIT where she builds mutually beneficial partnerships between corporations and MIT. Dr. Sigalovsky comes to MIT with 10 years of international experience in innovation strategy, technology forecasting and external innovation. Prior to MIT, Irina worked at GEN3 Partners, Inc. as a senior principal collaborating with Fortune 1000 companies to focus their innovation investments, execute strategic innovation agendas, and develop business globally. Throughout her career, Irina has taught at Tufts University, MIT Sloan, X-Prize Lab@MIT, MIT HST, Boston and Harvard Universities.
Dean, MIT Stephen A. Schwarzman College of Computing
Daniel Huttenlocher is the inaugural dean of the MIT Stephen A. Schwarzman College of Computing. He began his academic career at Cornell University in 1988, where he was a member of the computer science faculty. In 1998, he chaired the task force that led to the creation of Cornell’s interdisciplinary Faculty of Computing and Information Science, later serving as its dean starting in 2009. In 2012, he became the founding dean of the new Cornell Tech campus in New York City.
Huttenlocher has extensive industry experience, having served as a scientist and lab director at Xerox’s Palo Alto Research Center for 12 years before leaving to help establish a financial technology startup, Intelligent Markets, in 2000.
Huttenlocher’s research and scholarship in computer science is broad and interdisciplinary, spanning algorithms, social media, and computer vision. He has earned the Longuet-Higgins Award for Fundamental Advances in Computer Vision (2010), and various fellowships and awards from the National Science Foundation, the Association for Computing Machinery, IEEE, and Phi Beta Kappa.
He is a member of the boards of directors of Amazon and Corning, and of the John D. and Catherine T. MacArthur Foundation, where he has served as chair since 2018.
Huttenlocher earned a bachelor’s degree from the University of Michigan in 1980, double-majoring in computer and communication sciences and experimental psychology. An MIT alumnus, he earned an SM in electrical engineering and computer science in 1984 and a PhD in computer science in 1988.
The MIT Stephen A. Schwarzman College of Computing was founded to lead the transformation of education and research in this time of rapid advances in computing and its increasing influence on so many aspects of daily life. Launched in 2019, the College represents the first major structural change at MIT in three-quarters of a century. The mission of the College is three-fold: to support the rapid growth and evolution of computer science, artificial intelligence and related computing fields; facilitate collaborations in computing education and research across departments and disciplines; and focus on social and ethical responsibilities of computing. Together, these are critically important in meeting the opportunities and challenges posed by today’s and tomorrow’s computing technologies.
Antonio Torralba is the Thomas and Gerd Perkins Professor of Electrical Engineering and Computer Science at MIT. He also heads the faculty of artificial intelligence and decision-making in the MIT Schwarzman College of Computing. Previously, he led the MIT Quest for Intelligence as its inaugural director and the MIT–IBM Watson AI Lab as its MIT director. He researches computer vision, machine learning and human visual perception, with an interest in building systems that can perceive the world as humans do. He has received an NSF Career Award, the International Association for Pattern Recognition's JK Aggarwal Prize, a Frank Quick Faculty Research Innovation Fellowship and a Louis D. Smullin (’39) Award for Teaching Excellence. Torralba earned a BS from Telecom BCN, Spain, and a PhD from the Institut National Polytechnique de Grenoble, France.
The importance of data in modern computer vision is hard to overstate. The ImageNet dataset, with its millions of labelled images, is widely thought to have spurred the era of deep learning, and since then the scale of vision datasets has been increasing at a rapid pace. These datasets come with costs: curation is expensive, and they inherit human biases. To counter these costs, interest has surged in learning with unlabeled images as it avoids the curation efforts, or using simulated environments, but content creation is also labor intensive. In our work we go a step further and ask if we can do away with real image datasets entirely, instead learning from noise processes. Noise processes produce images that are reminiscent of abstract art, where images contain textures and shapes, but there are no recognizable objects. Our findings show that good performance on real images can be achieved even with training images that are far from realistic.
Michael Carbin is an assistant professor in MIT’s Department of Electrical Engineering and Computer Science and a principal investigator at the Computer Science and Artificial Intelligence Laboratory, where he leads the Programming Systems Group. His group investigates the semantics, design, and implementation of systems that operate in the presence of uncertainty in their environment (perception), implementation (neural networks or approximate transformations), or execution (unreliable hardware). Carbin has received a Sloan Research Fellowship, a Facebook Research Award, a Google Faculty Research Award and an NSF Career Award. He earned a BS in computer science from Stanford University, and an MS and PhD in electrical engineering and computer science from MIT.
The cost of training modern deep learning systems, such as GPT-3, has put the impressive capabilities of these systems beyond the reach of many individuals and institutions. However, a key property of these systems is that they are approximate in that there is a natural trade-off between the quality of the results these systems produce and their performance and energy consumption. Exploiting this fact, researchers have developed a variety of new mechanisms that automatically change the exact behavior of a system to enable the system to execute more efficiently and cost effectively through techniques like quantization, distillation, and pruning. In this talk, I will present how such approximation mechanisms serve as a central opportunity for efficient ML, with still critical work to be done in understanding how to compose, analyze, and characterize the behavior of the resulting approximate systems.
Sertac Karaman is an Associate Professor of Aeronautics and Astronautics at the Massachusetts Institute of Technology (since Fall 2012). He is the Director of the Laboratory for Information and Decision Systems (LIDS) - interdepartmental research center committed to advancing research and education in the analytical information and decision sciences. He has obtained B.S. degrees in mechanical engineering and in computer engineering from the Istanbul Technical University, Turkey, in 2007; an S.M. degree in mechanical engineering from MIT in 2009; and a Ph.D. degree in electrical engineering and computer science also from MIT in 2012. His research interests lie in the broad areas of robotics and control theory. In particular, he studies the applications of probability theory, stochastic processes, stochastic geometry, formal methods, and optimization for the design and analysis of high-performance cyber-physical systems. The application areas of his research include driverless cars, unmanned aerial vehicles, distributed aerial surveillance systems, air traffic control, certification and verification of control systems software, and many others. He delivered the the Robotics: Science and Systems Early Career Spotlight Talk in 2017. He is the recipient of an Amazon Faculty Award in 2020, IEEE Robotics and Automation Society Early Career Award in 2017, an Office of Naval Research Young Investigator Award in 2017, Army Research Office Young Investigator Award in 2015, National Science Foundation Faculty Career Development (CAREER) Award in 2014, AIAA Wright Brothers Graduate Award in 2012, and an NVIDIA Fellowship in 2011. He serves as the technical area chair for the Transactions on Aerospace Electronic Systems for the robotics area, a co-chair of the IEEE Robotics and Automation Society Technical Committee of Algorithms for the Planning and Control of Robot Motion. He serves on the Robotics: Science and Systems (RSS) Foundation Board and acts as the Secretary of the RSS Foundation. He is also co-founder of Optimus Ride, a Boston-based MIT-spinoff startup company that is developing self-driving vehicle technologies to enable accessible, equitable, safe and sustainable mobility for all.
A broad range of next-generation mobile robotics applications will be enabled by low-energy sensing and computing, in particular, by miniaturization. Specifically, we envision (i) insect-size drones for consumer applications, (ii) pill-size medical robots that can intelligently navigate the human digestive system, (iii) intelligent satellites on chip that can navigate far corners of the Solar system, (iv) low-energy glider drones and underwater vehicles that can operate continuously for months to monitor our planet towards a sustainable future. These applications and many more will be enabled by low-energy sensing, computation and actuation, and in some cases their miniaturization.
Dina Katabi is the Thuan and Nicole Pham Professor of Electrical Engineering and Computer Science, and the director of MIT’s Center for Wireless Networks and Mobile Computing (Wireless@MIT). Katabi is also a MacArthur Fellow and a Member of the National Academy of Engineering. She received her PhD and MS from MIT and her BS from Damascus University. Katabi has received the ACM Grace Murray Hopper Award, the Faculty Research Innovation Fellowship, the Sloan Fellowship, the NBX Career Development chair, and the NSF CAREER award. Katabi's doctoral dissertation won an ACM Honorable Mention award and a Sprowls award for academic excellence. Further, her work was recognized by the IEEE William R. Bennett prize, three ACM SIGCOMM Best Paper awards, an NSDI Best Paper award, the SIGCOMM Test-of-Time award, and a TR10 award for her work on the sparse Fourier transform. Several start-ups have been spun out of Katabi's lab, such as PiCharging and Emerald.
Ambient intelligence is the ability of observing people and their actions even when they are separated from the observer by walls and occlusions, capturing their physiological signals without putting sensors on their bodies, understanding their emotions even when they do not show on their faces, and detecting specific neuro-behavioral diseases before they are clinically diagnosed. We argue that all this information is encoded in radio waves that bounce off people’s bodies, and hence can be captured in a passive and touchless manner. This talk will describe new technologies and machine learning models that analyze radio signals using specialized neural networks to decode and interpret this information, including the systems foundations that allow us to take such technologies from a lab setting to the real-world. The proposed research fundamentally changes what is feasible today and expands our understanding of how to sense and interact with people and objects without physical contact.
Ariadna Rodenstein is a Program Manager at MIT Startup Exchange. She joined MIT Corporate Relations as an Events Leader in September 2019 and is responsible for designing and executing startup events, including content development, coaching and hosting, and logistics. Ms. Rodenstein works closely with the Industrial Liaison Program (ILP) in promoting collaboration and partnerships between MIT-connected startups and industry, as well as with other areas around the MIT innovation ecosystem and beyond.
Prior to working for MIT Corporate Relations, she worked for over a decade at Credit Suisse Group in New York and London, in a few different roles in event management and as Director of Client Strategy. Ms. Rodenstein has combined her experience in the private sector with work at non-profits as a Consultant and Development Director at New York Immigration Coalition, Immigrant Defense Project, and Americas Society/Council of the Americas. She also served as an Officer on the Board of Directors of the Riverside Clay Tennis Association in New York for several years. Additionally, she earned her B.A. in Political Science and Communications from New York University, with coursework at the Instituto Tecnológico y de Estudios Superiores de Monterrey in Mexico City, and her M.A. in Sociology from the City University of New York.
Jeff received his PhD his EECS from UC Berkeley as an NDSEQ fellow, was a Batelle Post-Doctoral Scholar at MIT, and is an Entrepreneurial Research Fellow at Active. Prior to Sync, he was a technical staff member at MIT Lincoln Laboratory.
Dr. Michael Fleder’s MIT research forms the basis for Covariance.ai - a machine-learning startup that enables companies to track competitors and markets with previously-impossible clarity and precision. Michael’s work has featured in MIT News (2021, 2019) and leading modeling conferences. Michael has extensive background in robotics (MIT, NASA/JPL), quantitative trading, and technology advising for C-Suite at retail banks. Michael earned his bachelor’s, master’s and doctorate degrees from MIT.
Henry Valk graduated from Wake Forest University in 2017 with a degree in Cognitive Psychology. Upon graduating, he worked in Dr. David Poeppel's cognitive neuroscience lab at NYU, where he conducted research on neural synchrony in collaborative learning environments. Since that time, he has worked as a Data Scientist at Pison Technology.
Daisy Zhuo, PhD, is a Co-Founding Partner at Interpretable AI. During her PhD in Operations at MIT, she has developed a range of cutting-edge machine learning techniques such as Optimal Imputation and Robust Classifications, with publications in top machine learning and operations research journals. These algorithms have since become the core software modules of Interpretable AI. With expertise in mixed integer optimization and machine learning, she continues to research and develop new machine learning algorithms at Interpretable AI as well as applying them to solve real world industry problems. She has led the development of successful solutions in a wide range of industries including health care, insurance, finance, real estate, and manufacturing.
Elaheh Ahmadi is a co-founder and CEO of Themis AI. She received her BSc and MEng in Electrical Engineering and Computer Science from MIT. Ahmadi and her peers at MIT CSAIL spun-off Themis AI with the vision to bring fair AI into the industry. Themis AIis a leader in providing high-performance and risk-robust AI solutions — identifying and tackling bias, uncertainty, and other real-world generalization challenges.
Sebastian is the CEO of Ubicept which provides new imaging solutions in challenging environments such as seeing motion in the dark at unprecedented quality. He has spent his career exploring new kinds of imaging with a focus on signal processing and information extraction. He holds BS/MS/PhD degrees in Electrical Engineering from the Karlsruhe Institute of Technology (KIT), Germany.
Dr. Andy Wang is the founder and CEO of Prescient, a Boston-based technology startup that provides data automation and data management software for the edge. Prior to Prescient, he was the founder and CTO of GTI IoT Technology, which developed low-power wireless sensing solutions. Dr. Wang received his Ph.D. degree in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology.
Julie Choi is VP and Chief Growth Officer at MosaicML responsible for marketing, community, and customer relationships. Prior to MosaicML, she was VP / GM of AI Products and Research Marketing at Intel Corporation, responsible for Intel's product marketing, ecosystem partnerships, and sales enablement at global scale. During Julie's tenure, Intel reported over $4.5B revenue for AI technologies and became a credible provider of compute for AI workloads. Prior to Intel, Julie led product marketing at Hewlett Packard Enterprise, Mozilla and Yahoo, focused on developer and enterprise audiences. Julie holds a bachelor’s degree from MIT and a master’s degree from Stanford, both in Management Science.
Di Wu, PhD, is the co-founder and CEO of OmniML, focusing on enabling Edge AI for all ML tasks and all hardware. OmniML is an enterprise artificial intelligence (AI) company that aims to effortlessly empower edge AI everywhere. The company enables and amplifies powerful machine learning capabilities to edge devices by bringing greater speed, accuracy, and efficiency in AI through deep learning models that bridge the gap between those devices and AI applications.
Previously, Di was a software engineer in PyTorch at Scale team in AI Infra at Facebook, focusing on large-scale AI systems using hardware acceleration.
Before Facebook (Meta), Di worked on hardware acceleration for computational genomics at a high-tech startup company that was later acquired by Xilinx. He received his PhD in CS from UCLA focusing on system design for domain-specific hardware acceleration, including deep learning, medical imaging, cognitive computing, etc.
Mark Tibbetts is Product and Data Science Lead based out of Arundo’s Houston office. He gained his PhD in High Energy Physics at Imperial College in 2010 before joining Berkeley National Laboratory as a postdoctoral researcher working on the Large Hadron Collider at CERN, Switzerland. In 2016 he joined Arundo’s Oslo office as a Data Scientist and then moved to Houston in 2019 to take on the role of Lead Data Scientist. He is currently the Product Manager for Arundo’s Marathon application which delivers actionable machine learning insights to heavy asset operations and maintenance teams.
Sync Computing | Covariance | Pison | Interpretable AI | Themis AI | UbiCept
Prescient | MosaicML | OmniML | Arundo
Startups at Lunch Exhibit Only
Jesus A. del Alamo is the Donner Professor and Professor of Electrical Engineering at Massachusetts Institute of Technology. He obtained a Telecommunications Engineer degree from the Polytechnic University of Madrid and MS and PhD degrees in Electrical Engineering from Stanford University. From 1985 to 1988 he was with Nippon Telegraph and Telephone LSI Laboratories in Japan and since 1988 he has been with the Department of Electrical Engineering and Computer Science of Massachusetts Institute of Technology. From 2013 until 2019, he served as Director of the Microsystems Technology Laboratories at MIT. His current research interests are focused on nanoelectronics based on compound semiconductors and ultra-wide bandgap semiconductors.
Prof. del Alamo was an NSF Presidential Young Investigator. He is a member of the Royal Spanish Academy of Engineering and Fellow of the Institute of Electrical and Electronics Engineers, the American Physical Society and the Materials Research Society. He is the recipient of the Intel Outstanding Researcher Award in Emerging Research Devices, the Semiconductor Research Corporation Technical Excellence Award, the IEEE Electron Devices Society Education Award, the University Researcher Award by Semiconductor Industry Association and Semiconductor Research Corporation, the IPRM Award and the IEEE Cledo Brunetti Award. He currently serves as Editor-in-Chief of IEEE Electron Device Letters. He is the author of “Integrated Microelectronic Devices: Physics and Modeling” (Pearson 2017, 880 pages), a rigorous and up to date description of transistors and other contemporary microelectronic devices.
Advances in hardware performance thanks to Moore’s Law and chip architectural innovations constitute one of the pillars on top of which the recent roaring progress in artificial intelligence applications has been built. Future gains in AI capabilities hinge on continuous hardware advances. With core CMOS technology evolution on a path of diminishing returns, new architectural innovations, new system and circuit paradigms and new devices and materials that operate on new physical principles are going to be required. Innovation up and down the abstraction stack is mandatory. This creates wonderful opportunities for engineers and scientists of all disciplines and operating at all levels to propel the next wave of the electronics revolution.
Song Han is an assistant professor in MIT's Department of Electrical Engineering and Computer Science. His research focuses on efficient deep learning computing. He has proposed “deep compression” as a way to reduce neural network size by an order of magnitude, and the hardware implementation “efficient inference engine” that first exploited model compression and weight sparsity in deep learning accelerators. He has received best paper awards at the International Conference on Learning Representations and Field-Programmable Gate Arrays symposium. He is also a recipient of an NSF Career Award and MIT Tech Review's 35 Innovators Under 35 award. Many of his pruning, compression, and acceleration techniques have been integrated into commercial artificial intelligence chips. He earned a PhD in electrical engineering from Stanford University.
Modern deep learning requires a massive amount of computational resources, carbon footprint, and engineering efforts. On mobile devices, the hardware resource and power budget are very limited, and on-device machine learning is challenging; retraining the model on-device is even more difficult. We make machine learning efficient and fit tiny devices (TinyML). Our research is highlighted by full-stack optimizations, including the neural network topology, inference library, and the hardware architecture, which allows a larger design space to unearth the underlying principles.
Joel Emer is a Professor of the Practice in the Computer Science and Electrical Engineering department at MIT. He also spends part time as a Senior Distinguished Research Scientist at Nvidia in Westford, MA, where he is responsible for exploration of future architectures as well as modeling and analysis methodologies. Prior to joining NVIDIA, he worked at Intel where he was an Intel Fellow and Director of Microarchitecture Research. Previously he worked at Compaq and Digital Equipment Corporation.
He has held various research and advanced development positions investigating processor micro-architecture and developing performance modeling and evaluation techniques. He has made architectural contributions to a number of VAX, Alpha and X86 processors and is recognized as one of the developers of the widely employed quantitative approach to processor performance evaluation. He has also been recognized for his contributions in the advancement of simultaneous multi-threading technology, analysis of the architectural impact of soft errors, memory dependence prediction, pipeline and cache organization, performance modeling methodologies and spatial architectures. His current research interests include memory hierarchy design, processor reliability, spatial architectures and performance modeling.
He received a bachelor's degree with highest honors in electrical engineering in 1974, and his master's degree in 1975 -- both from Purdue University. He earned a doctorate in electrical engineering from the University of Illinois in 1979. Emer holds over 25 patents and has published more than 40 papers. He is a Fellow of both the ACM and the IEEE and a member of the NAE, and was the 2009 recipient of the Eckert-Mauchly award for lifetime contributions in computer architecture.
Dr. Rajesh Pankaj is a senior vice president of engineering and head of the Corporate Research and Development (CR&D) division at Qualcomm Technologies, Inc. His team conducts research and development in artificial intelligent systems for mobile, edge and cloud computing. His team is also responsible for the commercial deployment of AI software through all of Qualcomm’s product lines. Under his leadership his division has broken new grounds on designing energy efficient artificial intelligence (AI) systems and hardware. These systems are being deployed in numerous applications in mobile devices and beyond.
Rajesh joined Qualcomm in 1997 as an engineer working on a new high-speed mobile internet access technology that was commercialized throughout the world as 1xEV-DO. He was promoted to lead the systems engineering group at Qualcomm’s CR&D in 2003. In this role, he oversaw the growth of Qualcomm’s R&D group into a global organization with a diverse portfolio of projects covering mobile communications and mobile computing. In 2011 he became the head of Corporate Research and Development.
Prior to joining Qualcomm, Rajesh spent 5 years at the University of Toronto as an assistant professor in the department of electrical and computer engineering. He received his Master of Science in electrical engineering and PhD in electrical engineering and computer science from the Massachusetts Institute of Technology, and a Bachelor of Technology in electrical engineering from the Indian Institute of Technology, Kanpur.
Varada is a signal processing system engineer spending over 25 years in designing and developing many new to world consumer electronic devices. Recently, he has been with Amazon’s Devices Software and Services Organization playing a key role in launching early generations of Amazon Devices including Kindle and Echo. He is currently working on solving compute challenges arising from Edge ML applications in Amazon Devices.
Wojciech Matusik is a Professor of Electrical Engineering and Computer Science at the Computer Science and Artificial Intelligence Laboratory at MIT, where he leads the Computational Fabrication Group and is a member of the Computer Graphics Group. Before coming to MIT, he worked at Mitsubishi Electric Research Laboratories, Adobe Systems, and Disney Research Zurich. He studied computer graphics at MIT and received his PhD in 2003. He also received a BS in EECS from the University of California at Berkeley in 1997 and MS in EECS from MIT in 2001. His research interests are in computer graphics, computational design and fabrication, computer vision, robotics, and hci. In 2004, he was named one of the world’s top 100 young innovators by MIT’s Technology Review Magazine. In 2009, he received the Significant New Researcher Award from ACM Siggraph. In 2012, Matusik received the DARPA Young Faculty Award and he was named a Sloan Research Fellow. In 2014, he received Ruth and Joel Spira Award for Excellence in Teaching.
We are developing scalable, high-resolution, conformal tactile sensor arrays that can be automatically manufactured with inexpensive materials. Obtaining synchronized data from different sensing modalities enables an efficient learning system of humans and the environment that does not require extensive labeled data. The developed prototypes allow us to record synchronized vision and tactile data from various human activities and human-object interactions. This can significantly facilitate the development of self-supervised learning systems and build models that can sense the environment like a human.
Julie Shah is an Associate Professor in the Department of Aeronautics and Astronautics at MIT and leads the Interactive Robotics Group of the Computer Science and Artificial Intelligence Laboratory. Shah received her SB (2004) and SM (2006) from the Department of Aeronautics and Astronautics at MIT, and her PhD (2010) in Autonomous Systems from MIT. Before joining the faculty, she worked at Boeing Research and Technology on robotics applications for aerospace manufacturing. She has developed innovative methods for enabling fluid human-robot teamwork in time-critical, safety-critical domains, ranging from manufacturing to surgery to space exploration. Her group draws on expertise in artificial intelligence, human factors, and systems engineering to develop interactive robots that emulate the qualities of effective human team members to improve the efficiency of human-robot teamwork. In 2014, Shah was recognized with an NSF CAREER award for her work on “Human-aware Autonomy for Team-oriented Environments," and by the MIT Technology Review TR35 list as one of the world’s top innovators under the age of 35. Her work on industrial human-robot collaboration was also recognized by the Technology Review as one of the 10 Breakthrough Technologies of 2013, and she has received international recognition in the form of best paper awards and nominations from the International Conference on Automated Planning and Scheduling, the American Institute of Aeronautics and Astronautics, the IEEE/ACM International Conference on Human-Robot Interaction, the International Symposium on Robotics, and the Human Factors and Ergonomics Society.
The Ethical Computing Protocol is an original project from the Social and Ethical Responsibility of Computing cross-cutting unit in the MIT College of Computing. The on-line platform is designed to help people identify, address, and communicate about the ethical dimensions of their AI and computing projects, including societal and environmental impacts of their work. The platform allows to develop a richer picture of the outcome of a project, how it affects people and how to conceptualize, design, build, and deploy a project aligned with positive societal benefits. The project allows to create an Ethical Impact Report, a document that reflects the work put into the protocol process and the decisions made as a result.
Tomás Palacios is a Professor in the Department of Electrical Engineering and Computer Science at MIT. He received his Ph.D. from the University of California - Santa Barbara in 2006 and his undergraduate degree in Telecommunication Engineering from the Universidad Politécnica de Madrid (Spain). His current research focuses on demonstrating new electronic devices and applications for novel semiconductor materials such as graphene and gallium nitride. His work has been recognized with multiple awards, including the Presidential Early Career Award for Scientists and Engineers, the 2012 and 2019 IEEE George Smith Award, and the NSF, ONR, and DARPA Young Faculty Awards, among many others. Prof. Palacios is the founder and director of the MIT MTL Center for Graphene Devices and 2D Systems, as well as the Chief Advisor and co-founder of Finwave Semiconductor, Inc. He is a Fellow of IEEE.