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Updated: 14 min 46 sec ago

BIDS Forum: Statistics and Machine Learning Forum, Nov 18

14 min 46 sec ago
Full details about this meeting will be posted here: https://bids.berkeley.edu/events.

The Berkeley Statistics and Machine Learning Forum meets biweekly to discuss current applications across a wide variety of research domains and software methodologies. Hosted by UC Berkeley Physics Professor and BIDS Senior Fellow Uros Seljak, these active sessions bring together domain scientists, statisticians and computer scientists who are either developing state-of-the-art methods or are interested in applying these methods in their research. Practical questions about the meetings can be directed to BIDS Fellow Francois Lanusse. All interested members of the UC Berkeley and LBL communities are welcome and encouraged to attend. To receive email notifications about the meetings and upvote papers for discussion, please register here.

BIDS Forum: Statistics and Machine Learning Forum, Dec 2

14 min 46 sec ago
Full details about this meeting will be posted here: https://bids.berkeley.edu/events.

The Berkeley Statistics and Machine Learning Forum meets biweekly to discuss current applications across a wide variety of research domains and software methodologies. Hosted by UC Berkeley Physics Professor and BIDS Senior Fellow Uros Seljak, these active sessions bring together domain scientists, statisticians and computer scientists who are either developing state-of-the-art methods or are interested in applying these methods in their research. Practical questions about the meetings can be directed to BIDS Fellow Francois Lanusse. All interested members of the UC Berkeley and LBL communities are welcome and encouraged to attend. To receive email notifications about the meetings and upvote papers for discussion, please register here.

BIDS Forum: Statistics and Machine Learning Forum, Dec 16

14 min 46 sec ago
Full details about this meeting will be posted here: https://bids.berkeley.edu/events.

The Berkeley Statistics and Machine Learning Forum meets biweekly to discuss current applications across a wide variety of research domains and software methodologies. Hosted by UC Berkeley Physics Professor and BIDS Senior Fellow Uros Seljak, these active sessions bring together domain scientists, statisticians and computer scientists who are either developing state-of-the-art methods or are interested in applying these methods in their research. Practical questions about the meetings can be directed to BIDS Fellow Francois Lanusse. All interested members of the UC Berkeley and LBL communities are welcome and encouraged to attend. To receive email notifications about the meetings and upvote papers for discussion, please register here.

Trustworthy Autonomy: Behavior Prediction and Validation, Nov 22

14 min 46 sec ago
University of Illinois' Katherine Driggs-Campbell will present Trustworthy Autonomy: Behavior Prediction and Validation at 4 p.m. Nov. 22 at the ITS Transportation Seminar in 290 Hearst Memorial Mining Building.

Regulating TNCs: Should Uber and Lyft set their own rules?, Nov 15

Fri, 2019-11-15 23:33
Sen Li, postdoctoral fellow in the Department of Mechanical Engineering at The University of California, Berkeley, will present Regulating TNCs: Should Uber and Lyft set their own rules? at the ITS Berkeley Transportation Seminar on Nov. 15, 2019 at 4 p.m. in 290 Hearst Memorial Mining Building. Join us for Beverages and cookies at 3:30 p.m.

Abstract: We evaluate the impact of three proposed regulations of transportation network companies (TNCs) like Uber and Lyft: (1) a minimum wage for drivers, (2) a cap on the number of drivers or vehicles, and (3) a congestion surcharge on each TNC trip. The impact is assessed using a queuing theoretic equilibrium model, which incorporates the stochastic dynamics of the app-based ride-hailing matching platform, the ride prices and driver wages established by the platform, and the incentives of passengers and drivers. We show that a floor placed under driver earnings pushes the ride-hailing platform to hire more drivers, at the same time that passengers enjoy faster and cheaper rides, while platform rents are reduced. In contrast to a wage floor, imposing a cap on the number of vehicles hurts drivers, because the platform reaps all the benefits of limiting the supply. We also construct variants of the model to discuss platform subsidy, platform competition, and autonomous vehicles.  

Bio: Dr. Sen Li is a postdoctoral fellow in the Department of Mechanical Engineering at The University of California, Berkeley, working with Prof. Kameshwar Poolla and Prof. Pravin Varaiya. He received his B.S. from Zhejiang University, and Ph.D. from The Ohio State University. Previously, Dr. Li was an intern at Pacific Northwestern National Laboratory, and a visiting student at Harvard University. Dr. Li's research interest lies in the intersection of control, optimization and game theory with applications in large-scale cyber-physical systems. He is particularly interested in renewable energy integration and intelligent transportation systems. He is a finalist of Best Student Paper Award at 2018 European Control Conference.

Wachs Lecture: Integrating Transportation, Land Use, and Environmental Planning for Social Justice and Carbon Reduction: Finding a Way that Works, Nov 14

Thu, 2019-11-14 23:30
Join Professor Emerita Elizabeth Deakin, UC Berkeley for the 12th Annual Martin Wachs lecture Integrating Transportation, Land Use, and Environmental Planning for Social Justice and Carbon Reduction: Finding a Way that Works Thursday, November 14, 5:30 - 7 p.m. in Wurster Hall Auditorium, Room 112.

About the Martin Wachs Distinguished Lecture in Transportation
Now in its twelfth year, the annual Wachs Lecture draws innovative thinkers to the University of California to address today's most pressing issues in transportation. Created by students to honor Professor Martin Wachs upon his retirement from the University, the lecture rotates between Berkeley and UCLA, the campuses at which Marty taught. Marty's commitment and integrity as a scholar, professional, and educator have profoundly affected his students, peers, colleagues and friends. We invite you to act on that inspiration by supporting the lecture. Your tax-deductible gift today will help to endow the lectureship, securing its future as an annual event.

Elizabeth Deakin is Professor of City and Regional Planning at UC Berkeley, where she also is an affiliated faculty member of the Energy and Resources Group and the Master of Urban Design group. She formerly served as Director of the University of California Transportation Research Center (1998-2008) and co-director of the UC Berkeley Global Metropolitan Studies Initiative (2005-2008). Deakin's research focuses on transportation and land use policy and the environmental impacts of transportation. She has published over 200 articles, book chapters, and reports on topics ranging from environmental justice to transportation pricing to development exactions and impact fees. She currently is carrying out a series of studies on urban development and transportation in China, Latin America, and India as well as in California.

Deakin has testified on several occasions for committees of the US Congress and for the California Legislature. She chaired the Congressionally-mandated National Academy of Sciences advisory board that led to the enactment of the federal transportation-environmental research program. She also has served as an appointed member of a number of government posts including city and county transportation commissions and a state advisory board. She is frequently called upon to advise mayors and city council members as well as transit board members.

She is a member of a number of committees and panels of the Transportation Research Board and is editor of the journal Transportation Policy, serving as well on the editorial board of four other journals. She also is an Urban Land Institute Fellow.

Deakin holds degrees in transportation systems analysis and political science from MIT as well as a law degree from Boston College.

BIDS Forum: Statistics and Machine Learning Forum, Nov 4

Mon, 2019-11-04 23:33
Full details about this meeting will be posted here: https://bids.berkeley.edu/events.

The Berkeley Statistics and Machine Learning Forum meets biweekly to discuss current applications across a wide variety of research domains and software methodologies. Hosted by UC Berkeley Physics Professor and BIDS Senior Fellow Uros Seljak, these active sessions bring together domain scientists, statisticians and computer scientists who are either developing state-of-the-art methods or are interested in applying these methods in their research. Practical questions about the meetings can be directed to BIDS Fellow Francois Lanusse. All interested members of the UC Berkeley and LBL communities are welcome and encouraged to attend. To receive email notifications about the meetings and upvote papers for discussion, please register here.

Learning for Robust Control and Optimization: Efficiency and Safety of Autonomous Transportation Systems, Nov 1

Fri, 2019-11-01 22:32
University of Connecticut's Fei Miao will present Learning for Robust Control and Optimization: Efficiency and Safety of Autonomous Transportation Systems at 4 p.m. Nov. 1 at the ITS Transportation Seminar in 290 Hearst Memorial Mining Building.

Abstract:
Ubiquitous sensing in smart cities enables large-scale multi-source data collected in real-time, poses several challenges and requires a paradigm-shift to data-driven cyber-physical systems (CPSs) that integrates optimization, control and machine learning. For instance, how to capture the complexity and analyze the dynamic interplay between urban-scale phenomena from data, and take actions to improve service efficiency and safety, is still a challenging problem in transportation systems. In this talk, we first present a data-driven dynamic robust resource allocation framework for autonomous ride-sharing and carpool systems, matching vehicle supply towards both current and predicted future demand. With spatial-temporal uncertainty of demand prediction, we then prove and develop computationally tractable methods that provide probabilistic guarantees for the system’s worst-case and expected performance. A dynamic pricing model is also designed for travel time reliability during peak hours. We show that the performance of the ride-sharing system is improved based on world taxi operational data. Lastly, recent work about an information sharing and decision-making framework considering safety and efficiency of connected autonomous vehicles is introduced.

Biography:
Fei Miao is an Assistant Professor of the Department of Computer Science & Engineering, and she is also affiliated to the Department of Electrical & Computer Engineering, University of Connecticut since 2017. Her research interests lie in the intersection of control, optimization, and machine learning with application in cyber-physical systems efficiency, safety, and security. She has received a couple of awards from NSF, including S&AS, CPS, and S&CC programs. She received a Ph.D. degree, and the “Charles Hallac and Sarah Keil Wolf Award for Best Doctoral Dissertation” in Electrical and Systems Engineering, with a dual Master degree in Statistics at Wharton School from the University of Pennsylvania. She received a B.S. degree majoring in Automation from Shanghai Jiao Tong University. She was a postdoc researcher at the GRASP Lab and the PRECISE Lab of UPenn, from September 2016 to August 2017. She was a Best Paper Award Finalist at the 6th ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS) in 2015.

CITRIS Research Exchange - Yana Feldman, Oct 30

Wed, 2019-10-30 22:35
"A Multimodal-Deep Learning System for Monitoring Nuclear Proliferation Activities Using Open Sources"


About the Talk:

As the amount of available data increases, the human ability to locate, process, and analyze it is strained and eventually overwhelmed. To address this challenge for nonproliferation analysts, we have been designing a large-scale multimodal retrieval system to help analysts triage and search open source science, technology, transaction, and news data for indicators of nuclear proliferation capabilities and activities. Our system relies on a set of deep neural networks (DNNs) trained to evaluate conceptual similarities across data modalities, e.g. text, image, video. These DNNs can be used to search and prioritize data, according to a nuclear fuel cycle (NFC) process template, that are conceptually closest to the seed query items regardless of data modality. We evaluate the system's ability to retrieve NFC related data that have been purposely hidden in collections of unrelated background data. Quantitative and qualitative results for text-to-image, image-to-image, and image-to-video retrievals are demonstrated.

Speaker:

Yana Feldman, Nonproliferation and International Safeguards Analyst, Lawrence Livermore National Laboratory
LinkedIn: https://www.linkedin.com/in/yana-feldman-44baa99/

About CITRIS Research Exchange:

Launched in 2008, CITRIS Research Exchange delivers fresh perspectives on information technology and society from distinguished academic, industry, and civic leaders. CITRIS Research Exchange is free and open to the public. Each one-hour seminar starts at 12 pm and is hosted at the Banatao Auditorium in Sutardja Dai Hall on the UC Berkeley campus unless otherwise noted. Register by the Monday prior to the event to receive lunch.

Modeling Human Distraction in the Car Due to Acoustic Annoyances, Oct 25

Fri, 2019-10-25 22:31
UC Berkeley's Ruzena Bajcsy will present Modeling Human Distraction in the Car Due to Acoustic Annoyances at 4 p.m. Oct. 18 at the ITS Transportation Seminar in 290 Hearst Memorial Mining Building.

Abstract: In this presentation we shall review the predictability of safe driving with safety guarantees and investigate the effects of the driver state using a control theoretic driver’s model. For safe driving, others have used visual data, while auditory information has been neglected. Here, we explore the effect of sound and its annoyance on the driver with respect to safety considerations, using psychoacoustic parameters.

Bio: During Ruzena Bajcsy’s 50 years of robotics research, she has pursued research in connecting perception and action, motivated by psychology (J.J. Gibson) and biology. She has investigated this line of research in several applications using different computational models, such as discrete event models and hybrid systems. This work was all done at the University of Pennsylvania’s GRASP laboratory. Upon arrival at UC Berkeley, she focused on modeling people, again using robotic technology, by modeling driver attention during driving, as well as in human-robot interaction. In both of these applications, she has found it necessary to employ planning, perception, and action. This project draws on recent work in animal behavioral studies, especially as they pertain to navigation. Moving forward, the project hopes to validate some algorithms that mimic related animal behavior.

A Geometric Method of Hoverability Analysis for Multirotor UAVs, Oct 22

Tue, 2019-10-22 22:31
Abstract: This talk presents a novel geometric method to investigate what structure of multirotor unmanned aerial vehicles (UAVs) can achieve stable hovering, i.e., hoverability. The hoverability is indispensable for a multirotor UAV to conduct its task safely, and even when a rotor fails, it should be satisfied to prevent an accident. The proposed geometric method reveals the relationship between the position of the center of mass (CoM) and the rotor placement of a multirotor UAV to satisfy the hoverability, which can be applied to a multirotor UAV with any number and position of rotors. This talk also provides its application to investigation of a robust structure against rotor failures. Furthermore, a quantitative measure of the hoverability is newly presented based on the proposed analysis method. It enables us to design a multirotor UAV with an optimal structure in the sense of the hoverability. Finally, experimental validation is performed by using a hexrotor UAV whose CoM position is intentionally shifted.

Biography: Tatsuya Ibuki is an Assistant Professor at the Department of Systems and Control Engineering of Tokyo Institute of Technology, Japan. He received his Ph.D.Eng. degree from Tokyo Tech in 2013. He was a research fellow of the Japan Society for the Promotion of Science from 2012 to 2013, and is currently a visiting scholar at the School of Electrical and Computer Engineering of Georgia Institute of Technology. His research interests include cooperative control of robotic networks, multirotor UAV design and control, and vision-based estimation and control. He received some awards from the Society of Instrument and Control Engineers in Japan on these topics.

CEE Distinguished Lecture: The Pathway towards the Deployment of Self-Driving Vehicle Technology, Oct 21

Mon, 2019-10-21 22:35
Juan Argote (CEE MS '10; PhD '14) will give the fall CEE Distinguished Lecture on Monday, Oct. 21. Argote is the Transportation Research Lead within the Uber Advanced Technologies Group.

He will speak on "The Pathway towards the Deployment of Self-Driving Vehicle Technology."

The lecture will take place at 4-5pm (NEW TIME) in Sutardja Dai's Banatao Auditorium with a reception following in the adjoining atrium.

The lecture is free and open to the public.

Abstract
This lecture will provide an overview of the current state of Self-Driving Vehicle technology, delving into the role that Transportation Engineering and Operations Research will play in the path towards commercialization.

The speaker graduated from Berkeley's Transportation Engineering program in 2014, around the time efforts to develop Self-Driving Vehicle technology became a fixture in mainstream media. In the years since, interest and funding devoted to this technology have continued to grow. Multiple companies are currently testing operations on public roads, but the transition towards large-scale commercial operations will require innovative solutions from multi-disciplinary teams in order to overcome existing barriers towards widespread adoption.

As the Transportation Research Lead at Uber Advanced Technologies Group, Juan Argote leads a team of data scientists with backgrounds in Transportation Engineering and Econometrics to inform Uber's self-driving vehicle strategies. Dr. Argote's interests lay at the intersection of technology, simulation, and transportation operations.

Prior to joining Uber, Dr. Argote was a Senior Software Engineer for Aimsun Inc., a firm that develops a widely used transportation simulation software; as well as the Assistant Director for UCCONNECT, a regional University Transportation Center headquartered at UC Berkeley.

BIDS Forum: Statistics and Machine Learning Forum, Oct 21

Mon, 2019-10-21 22:35
Full details about this meeting will be posted here: https://bids.berkeley.edu/events.

The Berkeley Statistics and Machine Learning Forum meets biweekly to discuss current applications across a wide variety of research domains and software methodologies. Hosted by UC Berkeley Physics Professor and BIDS Senior Fellow Uros Seljak, these active sessions bring together domain scientists, statisticians and computer scientists who are either developing state-of-the-art methods or are interested in applying these methods in their research. Practical questions about the meetings can be directed to BIDS Fellow Francois Lanusse. All interested members of the UC Berkeley and LBL communities are welcome and encouraged to attend. To receive email notifications about the meetings and upvote papers for discussion, please register here.

Insights into Why and How Cities are Planning for Autonomous Vehicles, Oct 18

Fri, 2019-10-18 22:31
This webinar is the first in a series being organized by the UC Institute of Transportation Studies (UC ITS) to highlight and discuss the results from research projects funded by the Road Repair and Accountability Act of 2017 (Senate Bill 1). Established in 1947 by the California Legislature, the UC ITS has four branches located at UC Berkeley, UC Davis, UC Irvine, and UCLA.

Autonomous vehicles (AVs) are being widely tested and piloted to carry passengers and freight. When these vehicles will be deployed more broadly and integrated into our current transportation system is debatable. However, a handful of cities across the nation are starting to incorporate AVs into policy conversations and planning activities. These “early adopter” cities are providing insight into the evolving role local policy will have in shaping AV use and deployment in communities. 

This webinar will feature a presentation from UC Berkeley Professor Daniel Chatman featuring new research exploring the motivation of “early adopter” cities engaged in AV testing, regulation, and planning. Professor Chatman will share high-level findings from in-depth interviews with individuals on the frontlines of AV planning and policymaking at the local level supplemented by an extensive review of policy and planning documents. He will also discuss the implications of this research on future AV policy making at varying levels of government.

You can learn more about the research presenting in this webinar by reading the full report “Autonomous Vehicles in the United States: Understanding Why and How Cities and Regions Are Responding” authored by Professor Daniel Chatman and Marcel Moran with UC Berkeley. Download the report at: www.ucits.org/research-project/avs-and-cities.

Strategic Initiatives for Inland Movement of Containerized Imports at San Pedro Bay, Oct 18

Fri, 2019-10-18 22:31
UC Berkeley's Rob Leachman will present Strategic Initiatives for Inland Movement of Containerized Imports at San Pedro Bay at 4 p.m. Oct. 25 at the ITS Transportation Seminar in 290 Hearst Memorial Mining Building.

Abstract: The Ports of Los Angeles and Long Beach (the “SPB Ports”) handle more containers of imported cargo than any other port complex in the United States. In 2015 over three-quarters of the contents of those containers were destined to points far beyond the LA Basin and the Ports’ economic hinterland, yet these shipments resulted in significant intra-regional truck and rail shipments with significant negative environmental impacts. Mitigating those impacts is the focus of this talk.

In the recent past a significant trend has emerged, and is accelerating, that will increase the amount of highway-borne movement of imports within the Basin. Fewer international containers are being shipped “intact” to US inland points by rail while an increasing percentage are drayed to points within the Basin for de-vanning, sorting or inventorying, and finally re-loading the imported goods into domestic containers or trailers. This research explores the forces driving the trend away from the intact shipment of international containers by rail (known as inland point intermodal or “IPI” service), identifies public-private initiatives that should be taken to mitigate its effects, and quantifies the associated air quality and congestion benefits for the LA Basin.

Bio: Rob Leachman is a Professor of Industrial Engineering and Operations Research at the University of California at Berkeley. Dr. Leachman is the author of more than 90 technical publications concerning operations management and productivity improvement. He has supervised more than 30 PhD dissertations in the area. He also is President and CEO of Leachman and Associates LLC, a firm providing consulting and software for supply chain and operations management to corporations and governments world-wide. He received the AB degree in Mathematics and Physics, the MS degree in Operations Research and the PhD degree in Operations Research, all from U. C. Berkeley, and has been a member of the U C Berkeley faculty since 1979. Dr. Leachman is a one-time winner and a two-time finalist in the Franz Edelman Award Competition sponsored by the Institute for Operations Research and the Management Sciences (INFORMS), recognizing his work to design and implement automated production planning systems and his work for automated floor scheduling and cycle time reduction in the semiconductor industry. The Edelman Award is the highest accolade from INFORMS, given annually recognizing outstanding practice of the management sciences.

Insights into Why and How Cities are Planning for Autonomous Vehicles, Oct 18

Mon, 2019-10-14 12:34
Autonomous vehicles (AVs) are being widely tested and piloted to carry passengers and freight. When these vehicles will be deployed more broadly and integrated into our current transportation system is debatable. However, a handful of cities across the nation are starting to incorporate AVs into policy conversations and planning activities. These “early adopter” cities are providing insight into the evolving role local policy will have in shaping AV use and deployment in communities.

This webinar (register: https://registration.techtransfer.berkeley.edu/CourseStatus.awp?&course=192ITS021018) will feature a presentation from UC Berkeley Professor Daniel Chatman featuring new research exploring the motivation of “early adopter” cities engaged in AV testing, regulation, and planning. Professor Chatman will share high-level findings from in-depth interviews with individuals on the frontlines of AV planning and policymaking at the local level supplemented by an extensive review of policy and planning documents. He will also discuss the implications of this research on future AV policy making at varying levels of government.

You can learn more about the research presenting in this webinar by reading the full report “Autonomous Vehicles in the United States: Understanding Why and How Cities and Regions Are Responding” authored by Professor Daniel Chatman and Marcel Moran with UC Berkeley. Download the report at: www.ucits.org/research-project/avs-and-cities.

There is no cost to attend this webinar.

This webinar is the first in a series being organized by the UC Institute of Transportation Studies (UC ITS) to highlight and discuss the results from research projects funded by the Road Repair and Accountability Act of 2017 (Senate Bill 1). Established in 1947 by the California Legislature, the UC ITS has four branches located at UC Berkeley, UC Davis, UC Irvine, and UCLA.

Human Mobility and Urban Resilience in America's Cities, Oct 11

Thu, 2019-10-10 16:34
Northeastern University's Ryan Qi Wang will present Human Mobility and Urban Resilience in America's Cities at 4 p.m. Oct. 11 at the ITS Transportation Seminar in 290 Hearst Memorial Mining Building.

Abstract: Mobility, one of the basic human behaviors, is significantly influenced by environmental shocks and social stresses. In this talk, Wang will present the collaborative work on two interrelated areas: human movement perturbation under the influence of natural disasters, and mobility equality in big cities. In the first area, we explore the uniformity and heterogeneity of human movements under the impact of major natural disasters such as Hurricane Harvey. The exploration will pave a path to real-time track and response to evacuations and recoveries. In the second area, we develop a test of neighborhood isolation that leverages fine-grained dynamic data from Twitter on the everyday movement of residents in America’s 50 largest cities. We find surprisingly high consistency across neighborhoods of different race and income characteristics in the metropolitan region; however, we uncover residents of primarily black and Hispanic neighborhoods are far less exposed to either nonpoor or white middle-class neighborhoods than residents of primarily white neighborhoods. The studies demonstrate that human mobility is a key force for, as well as an important consequence of, urban resilience.

Presenter: Ryan Qi Wang is an Assistant Professor in the Department of Civil and Environmental Engineering, Northeastern University and the Associate Director of Research on Social Media at the Boston Area Research Initiative (BARI). He is interested in the interplay between data science and computational social science. His research focuses on two interrelated areas: human movement perturbation under the influence of natural and manmade disasters, and mobility equality in big cities. Before joining in Northeastern, Ryan was a postdoc fellow at the Department of Sociology, Harvard University. He received my Ph.D. degree from the Department of Civil and Environmental Engineering at Virginia Tech. During his time at Virginia Tech, he was also the first Ph.D. Fellow at BioBuild, an interdisciplinary program, and a Via Teaching Fellow. He obtained his M.S. in Construction Management from Michigan State University and B.S. in Chemical Engineering from Tianjin University (China).

Explore Urban Air Mobility (UAM) from every angle: Vehicles, Technology, Society, Oct 7-8

Tue, 2019-10-08 22:33
Sustainable Aviation Symposium 2019 (SAS) asks critical questions about equity in the future of air transit, through a holistic examination of technology, platforms, urbanism and other emerging topics. SAS will explore the ideas that will shape UAM and the global transition to accessible, safe, electric aviation for all. Registration required.

Ada Lovelace Day Celebration of Women in Robotics, Oct 8

Tue, 2019-10-08 22:33
WITI@UC, a joint program of CITRIS and the Banatao Institute and the UC Berkeley College of Engineering, in collaboration with the CITRIS People and Robotics initiatives are excited to present the “Ada Lovelace Day Celebration of Women in Robotics” on Tuesday, October 8 at the UC Berkeley campus. Take a deep dive in robotic applications for good and discover how to get into robotics with networking, mentoring opportunities, and demos from impressive student groups and supportive community organizations including Women in Robotics/SVR. The key note will be by robotics industry leader and UC Berkeley professor Anca Dragan; the panels will feature leading founders of robotics startups; and the top 25 Women in Robotics list will be released.

CITRIS Research Exchange - Rama Akkiraju, Oct 9

Tue, 2019-10-08 16:33
About the Talk:

There is renewed interest among companies these days to implement and deploy AI models in their business processes either to increase automation, or to improve human productivity. AI models are making their way as chat bots in customer support scenarios, as doctors' assistants in hospitals, as legal research assistants in legal domain, as marketing manager assistants in marketing, and as face detection applications in security domain, just to name a few use cases. Making AI work for enterprises requires a whole new and different set of concerns to be addressed than those for traditional software applications or for consumer-facing AI models such as targeted advertising and product recommendations. These new concerns include robustness (R), accuracy and adaptability (A), continuous learning (C), explainability (E), fairness (F), accountability (A), consistency (C) and transparency (T). In addition, building high quality and scalable AI models requires specific kind of discipline, methodology and tools. Data Scientists and practitioners need prescriptive guidance, tools, methods, and best practices on how to procure data, and build, improve and manage their AI models while addressing the concerns mentioned above. In this talk, I will present our best practices for making AI work for enterprises based on our first-hand experience of building scalable AI models for enterprises.

Speaker:

Rama Akkiraju is an IBM Fellow, Master Inventor and IBM Academy Member, and a Director, at IBM’s Watson Division where she leads the AI operations team with a mission to scale AI for Enterprises.
LinkedIn: https://www.linkedin.com/in/ramaakkiraju/

About CITRIS Research Exchange:

Launched in 2008, CITRIS Research Exchange delivers fresh perspectives on information technology and society from distinguished academic, industry, and civic leaders. CITRIS Research Exchange is free and open to the public. Each one-hour seminar starts at 12 pm and is hosted at the Banatao Auditorium in Sutardja Dai Hall on the UC Berkeley campus unless otherwise noted. Register by the Monday prior to the event to receive lunch.

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