Session Co-Chairs: Vana Kalogeraki (Athens University of Economics and Business, Greece),
Nick Koudas (University of Toronto, Canada), and Jianliang Xu (Hong Kong Baptist University, China)
Abstract: Federated Learning (FL) has emerged as a new distributed machine learning paradigm that enables privacy-aware training and inference on mobile devices with help from the cloud. FL has the potential to enable a wide range of new mobile apps that benefit from running machine learning models on mobile sensing data. The privacy-sensitive raw data is used for local training on the devices, and only the model parameters are transferred to the cloud, where a global model is aggregated and shared with all mobile devices.
This keynote talk presents our ongoing work on FL systems and applications. First, we describe FLSys, an end-to-end FL system designed to achieve energy efficiency, tolerance to communication failures, and scalability. In addition, different FL models, accessed concurrently by different apps, are able to work with different FL aggregation methods in the cloud. A common API is provided for third-party app developers to train FL models. FLSys is implemented in Android and AWS cloud. We demonstrate FLSys in the context of human activity recognition (HAR) in the wild, with data collected from the phones of 100+ students. We propose a novel HAR-Wild model, which is based on a skipped Convolution Neural Network model with a data augmentation mechanism to mitigate the non-Independent and Identically Distributed data problem that negatively affects FL model training. We conduct extensive experiments on Android phones and Android emulators, showing that FLSys and HAR-Wild achieve good model utility and practical system performance, in terms of training time and resource consumption on the phones.
Second, we present a system for fine-grained location prediction (FGLP) of mobile users, based on GPS traces collected on the phones. FGLP has two components: an FL framework and a prediction model. The framework runs on the phones of the users and also on a server that coordinates learning from all users in the system. The framework represents the user location data as relative points in an abstract 2D space, which enables learning across different physical spaces. The model merges Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Networks (CNN), where BiLSTM learns the speed and direction of the mobile users, and CNN learns information such as user movement preferences. Our experimental results, using a dataset with over 600,000 users, demonstrate that FGLP outperforms baseline models in terms of prediction accuracy for pedestrians and bicyclists. In addition, benchmark results on several types of Android phones demonstrate FGLP’s feasibility in real-life.
We conclude this talk with lessons learned from building FL systems and applications, and with challenges that still need to be overcome in order to deploy FL models in real-life.
Bio: Cristian Borcea is a Professor of Computer Science and the Associate Dean for Strategic Initiatives in the Ying Wu College of Computing at New Jersey Institute of Technology, USA. He also holds a Visiting Professor appointment at National Institute of Informatics, Tokyo, Japan. Cristian has over 20 years of experience in the fields of mobile computing & sensing; ad hoc & vehicular networks; and cloud & distributed systems. His current research is at the intersection of mobile computing and machine learning. He has published over 100 papers in top international journals and conferences, and his research has been covered in over 20 media articles in the past few years. Cristian has served as Technical Program Chair or General Chair to conferences such as IEEE MDM, IEEE Mobile Cloud, and EAI Mobiquitous. Cristian received his PhD in Computer Science from Rutgers University, USA. More information: http://cs.njit.edu/~borcea/.
Session Co-Chairs: Constantinos Costa (University of Pittsburgh, US),
Konstantinos Pelechrinis (University of Pittsburgh, US), and Meng Chen (Shandong University, China)
Abstract: We increasingly depend on a variety of data-driven algorithmic systems to assist us in many aspects of life. Search engines and recommender systems amongst others are used as sources of information and to help us in making all sort of decisions from selecting restaurants and books, to choosing friends and careers. This has given rise to important concerns regarding the fairness of such systems. In this tutorial, we aim at presenting a toolkit of methods used for ensuring fairness in rankings and recommendations. Our objectives are two-fold: (a) to present related methods of a novel, quickly evolving and impactful domain, and put them into perspective, and (c) to highlight open challenges and research paths for future work.
Bio: Evaggelia Pitoura is a Professor at the Univ. of Ioannina, Greece, where she also leads the Distributed Management of Data Laboratory. Her research interests are in data manage- ment systems with a recent emphasis on social networks and responsible data management. Her publications include more than 150 articles in international journals (including TODS, TKDE, PVLDB) and conferences (including SIGMOD, ICDE, WWW) and a highly-cited book on mobile computing. Her research has been funded by the EC and national sources. She has served or serves on the editorial board of ACM TODS, VLDBJ, TKDE, DAPD and as a group leader, senior PC member, or co-chair of many international conferences (including PC chair of EDBT 2016 and ICDE 2012).
Kostas Stefanidis is an Assoc. Professor on Data Science at the Tampere University, Finland. He got his PhD in personal- ized data management from the Univ. of Ioannina, Greece. His research interests lie in the intersection of databases, information retrieval, data mining and the Web, and include personalization and recommender systems, large-scale entity resolution and information integration, and query and data exploration paradigms. His publications include more than 80 papers in peer-reviewed conferences and journals, including SIGMOD, ICDE, and ACM TODS, and a book on entity resolution in the Web of data.
Georgia Koutrika is a Research Director at Athena Research Center in Greece. She has more than 15 years of experience in multiple roles at HP Labs, IBM Almaden, and Stanford. Her work focuses on data exploration, recommendations, and data analytics, and has been incorporated in commercial products, described in 14 granted patents and 26 patent applications in the US and worldwide, and published in more than 90 papers in top-tier conferences and journals. She is Editor-in-chief for VLDB Journal, PC chair for VLDB 2023, associate editor for TKDE, and an ACM Distinguished Speaker. She has served or serves as PC member or co-chair of many conferences.
Session Co-Chairs: Constantinos Costa (University of Pittsburgh, US),
Konstantinos Pelechrinis (University of Pittsburgh, US), and Meng Chen (Shandong University, China)
Session Co-Chairs: Goce Trajcevski (Iowa State University, US) and Maria Papadopouli (University of Crete, Greece).
Panel 1: Life after PhD, Cristian Borcea (Professor and Associate Dean for Strategic Initiatives, New Jersey Institute of Technology, US), Karine Zeitouni (Professor, University of Versailles Saint-Quentin-en-Yvellines, France), John Krumm (Senior Principal Researcher, Microsoft, US), and Anirban Mondal (Associate Professor, Ashoka University, India), Vana Kalogeraki (Athens University of Economics and Business, Greece).
Abstract: It aims to provide a “personalized perspective” on the types of challenges and opportunities that a young researcher may encounter after completing her/his PhD in the field related to mobile data. Moreover, the panelist will discuss issues which are often not taught or experienced throughout the Ph.D. studies, such as pursuing grants, collaboration with other domain-experts, changing of topics.
Bio:
Cristian Borcea
is a Professor of Computer Science and the Associate Dean for Strategic
Initiatives in the Ying Wu College of Computing at New Jersey Institute of
Technology, USA. He also holds a Visiting Professor appointment at National
Institute of Informatics, Tokyo, Japan. Cristian has over 20 years of
experience in the fields of mobile computing & sensing; ad hoc &
vehicular networks; and cloud & distributed systems. His current
research is at the intersection of mobile computing and machine learning.
He has published over 100 papers in top international journals and
conferences, and his research has been covered in over 20 media articles in
the past few years. Cristian has served as Technical Program Chair or
General Chair to conferences such as IEEE MDM, IEEE Mobile Cloud, and EAI
Mobiquitous. Cristian received his PhD in Computer Science from Rutgers
University, USA. More information: http://cs.njit.edu/~borcea/.
John Krumm
earned his PhD in Robotics from the School of Computer Science at Carnegie
Mellon University in Pittsburgh, Pennsylvania, where he worked on computer
vision. His first job was in the robotics division at Sandia Labs in
Albuquerque, New Mexico. Then he moved to Microsoft Research in Redmond,
Washington in 1997, where he has been since. As a senior principal
researcher, his current research focus is location, mostly looking at how
to process, protect, and value personal location data. In 2017, he received
a 10-year impact award for a paper on location privacy from the ACM UbiComp
conference. He holds 83 U.S. patents. He has served as the co-editor in
chief of the Journal of Location Based Services, and he is currently an
associate editor ACM Transactions on Spatial Algorithms and Systems and on
the editorial board of IEEE Pervasive Magazine. Dr. Krumm was a PC chair
for UbiComp 2007, ACM SIGSPATIAL 2013, and ACM SIGSPATIAL 2014. He is
currently in his fourth year on the executive committee of ACM SIGSPATIAL.
Anirban Mondal
is an Associate Professor of Computer Science at Ashoka University. He has
a Ph.D. in Computer Science from the National University of Singapore, an
MBA from the University of Massachusetts Amherst (UMass) and a B.Tech.
(Hons.) in Computer Science & Engineering from the Indian Institute of
Technology (IIT) Kharagpur, India. His research interests include mobile
and ubiquitous data management, incentive-based mobile crowdsourcing,
spatial databases, database indexing, Big Data, IoT and distributed
systems. He has numerous publications in key conferences/journals and is
actively involved as a General Chair (DASFAA 2021, BDA 2020), PC
Chair/Co-chair, PC member, journal reviewer as well as keynote/tutorial
speaker at reputed international conferences/workshops.
He has previously worked at the University of Tokyo, IIIT Delhi and Xerox
Research (Bangalore, India and Grenoble, France). He has spearheaded
industry research projects in domains such as urban informatics and
finance, leading to four granted patents by the USPTO (US Patent and
Trademark Office) as well as several patent filings. He has also been a
Fellow of the prestigious Japan Society for Promotion of Science (JSPS) as
well as an ACM India Eminent Speaker.
Karine Zeitouni
received her PhD from the University of Paris 6. She is a Professor in
Computer Science at the University of Versailles Saint-Quentin (UVSQ,
Université Paris-Saclay). Her main research interest lies in databases, big
data and data mining, with a focus on spatial and/or temporal data. She
mainly applies her research in the fields of transportation, environment,
universe science and health. She has supervised 16 graduate Ph.D. students,
and co-authored over 120 peer-reviewed journal and conference papers. Her
research is funded by national and European grants within multi-partners
projects. She regularly serves as a PC member in international conferences
in the field of (spatial) databases and data mining, machine learning and
as a reviewer for national and international journals in these domains. She
has (co-)chaired several conferences and workshops, and was General
Co-Chair of IEEE MDM 2020. Webpage: https://pages.david.uvsq.fr/kzeitouni
Vana Kalogeraki
Vana Kalogeraki is a Professor and Chair at the Department of Informatics and a Director of the
Computer Systems and Communications Laboratory at Athens University of Economics and
Business. Previously she has held positions as an Associate and Assistant Professor at the
Department of Computer Science at the University of California, Riverside and as a Research
Scientist at Hewlett-Packard Labs in Palo Alto, CA. She received her PhD from the University of
California, Santa Barbara. Prof. Vana Kalogeraki has been working in the field of distributed and
real-time systems, big data systems, stream processing systems, participatory sensing systems,
peer-to-peer systems, crowdsourcing, mobility, resource management and fault-tolerance for over
20 years and has authored and co-authored over 200 papers in journals and conferences
proceedings, including co-authoring the OMG CORBA Dynamic Scheduling Standard. Prof.
Kalogeraki was invited to give keynote talks at PerFoT2018, MoVid2015, DNCMS 2012, SN2AE
2012, PETRA 2011, DBISP2P 2006 and MLSN 2006 in the areas of IoT, participatory sensing
systems and sensor network middleware and delivered tutorials and seminars on peer-to-peer
computing. She has served as the General co-Chair of MDM 2021, the General co-Chair of SEUS
2009, WPDRTS 2006 and as a Program co-Chair of ACSOS 2021, DASFAA 2021, Middleware 2019,
MDM 2017, DEBS 2016, MDM 2011, ISORC 2009, ISORC 2007, ICPS 2005, WPDRTS 2005 and
DBISP2P 2003, a Tutorial Chair for IEEE ICDE 2020, ACM DEBS 2015, a Workshops Chair for IEEE
SRDS 2015, a Demo Chair for IEEE MDM 2012, a Poster Chair for GEC2021, in addition to other
roles such as Area Chair (IEEE ICDCS 2016, 2012) and as program committee member on over 200
conferences. She was also awarded an ERC Starting Independent Researcher Award, a Marie Curie
Fellowship, three best paper awards at the 11th ACM International Conference on Distributed
Event-Based Systems (DEBS 2017), 24th IEEE International Parallel and Distributed Processing
Symposium (IPDPS 2009) and the 9th IEEE Annual International Symposium on Applications and
the Internet (SAINT 2008), a best technical paper award at ACM PETRA 2018, a Best Student Paper
Award at the 11th IEEE/IPSJ International Symposium on Applications and the Internet (SAINT
2011), an IBM best student paper award runner up at MDM 2014, a UC Regents Fellowship Award,
UC Academic Senate Research Awards and a research award from HP Labs. She has also received an
Award for Excellence in Teaching for the academic year 2018-2019 from the Department of
Computer Science, Athens University of Economics and Business. Her research has been supported
by an ERC Starting Independent Researcher Grant, the European Union, joint EU/Greek "Aristeia"grant, a joint EU/Greek "Thalis" grant, NSF and gifts from SUN and Nokia.
Abstract: The proliferation in amounts of generated data has propelled the rise of scalable machine learning solutions to efficiently analyze and extract useful insights from such data. Meanwhile, spatial data has become ubiquitous, e.g., GPS data, with increasingly sheer sizes in recent years. The applications of big spatial data span a wide spectrum of interests including tracking infectious disease, climate change simulation, drug addiction, among others. Consequently, major research efforts are exerted to support efficient analysis and intelligence inside these applications by either providing spatial extensions to ex- isting machine learning solutions or building new solutions from scratch. In this 90-minutes seminar, we comprehensively review the state-of-the-art work in the intersection of machine learning and big spatial data. We cover existing research efforts and challenges in three major areas of machine learning, namely, data analysis, deep learning and statistical inference. We also discuss the existing end-to-end systems, and highlight open problems and challenges for future research in this area.
Bio: Ibrahim Sabek (PhD, University of Minnesota) is a Postdoctoral Associate at MIT. His research interests broadly include machine learning for systems, scalable data processing and querying, probabilistic databases, scalable knowledge base construction, and big spatial data management and analysis. Ibrahim has been named an NSF Computing Innovation Fellow (CIFellow) in 2020, and awarded the University of Minnesota Doctoral Dissertation Fellowship in 2019 for his dissertation focus on scalable machine learning for big spatial data and applications. His research work has won the first place of ACM SIGSPATIAL Student Research Competition (SRC) 2019, and has been nominated for the Best Paper Award of ACM SIGSPATIAL 2018. For more information, please visit: http://people.csail.mit.edu/ibrahimsabek/.
Mohamed F. Mokbel (PhD, Purdue University) is a Professor at University of Minnesota. His current research interests focus on building systems for big spatial data and applications. His research work has been recognized by the VLDB 10-years Best Paper Award, four conference Best Paper Awards, and the NSF CAREER Award. Mohamed is the past elected Chair of ACM SIGPATIAL, current Editor-in-Chief for Distributed and Parallel Databases Journal, and on the editorial board of ACM Books, ACM TODS, VLDB Journal, ACM TSAS, and GoeInformatica journals. He has also served as PC Vice Chair of ACM SIGMOD and PC Co-Chair for ACM SIGSPATIAL and IEEE MDM. Mohamed is an IEEE Fellow and an ACM Distinguished Scientist. For more information, please visit: www.cs.umn.edu/∼mokbel.
Abstract: Algorithms play a central role in our lives today, mediating our access to civic engagement, social connections, employment opportunities, news media and more. While the sociotechnical systems deploying these algorithms—search engines, social networking sites, and others—have the potential to dramatically improve human life, they also run the risk of reproducing or intensifying social inequities and tensions. In this talk, I will discuss some of my research addressing whether and how these systems are biased and how those biases impact users, towards the aim of building better sociotechnical systems. I will touch on my prior work examining aesthetic bias in web design and its impacts on users, as well as a recent project auditing gender and race in image search results for common U.S. occupations and measuring people’s responses to such search results. Finally, I will conclude by discussing the implications of such work for researchers, system designers, and policymakers.
Bio:Danaë Metaxa (they/them) is a Postdoctoral Scholar with the Stanford University Center on Philanthropy and Civil Society, working on the Program for Democracy and the Internet. Danaë holds a PhD in Computer Science from Stanford University; during their PhD, Danaë was also a fellow with the McCoy Center for Ethics in Society and the winner of an NSF Graduate Research Fellowship. Their research interests focus on sociotechnical systems’ effects on users in high-stakes social contexts like employment and politics. Next year Danaë will be joining the Computer and Information Science department at the University of Pennsylvania as an assistant professor, with a secondary appointment in the Annenberg School for Communication.
Abstract: This talk will briefly explore commonalities across undergraduate research programs designed to recruit students from underrepresented populations into graduate programs and careers in information and computing. It will then introduce the iSchool Inclusion Institute (i3), a national undergraduate research and leadership development program with 11+ years of success, that employs a disruptive model by building its infrastructure around relationships. Together, we can extend the conversation beyond recruitment towards retention and community.
Bio:Kayla Booth (she/her) is the PI/Director of the iSchool Inclusion Institute (i3), as well as a Research Assistant Professor in the Department of Informatics and Networked Systems at the University of Pittsburgh's School of Computing and Information. Her research explores Designing, Implementing, & Evaluating tech-based interventions geared towards marginalized youth populations, specifically in education and mental health contexts.
Session Co-Chairs: Vana Kalogeraki (Athens University of Economics and Business, Greece),
Nick Koudas (University of Toronto, Canada), and Jianliang Xu (Hong Kong Baptist University, China)
Abstract: The whereabouts of regular people from their everyday lives is valuable, both to the people themselves and to organizations that want to learn more about them. And yet the precise value of this data is difficult to pinpoint, both in the minds of the data subjects and the accounting of the data collectors. From the subject’s point of view, is differential privacy the answer? What would motivate a subject to release their data? What if they want to release just a vague idea of their location? From the data collector’s point of view, how can they put a price on location data and decide which data to buy? How do they know when they have enough? This talk will explore these questions, highlighting some of our lab’s research toward clarifying how to protect and value everyday location data.
Bio: John Krumm earned his PhD in Robotics from the School of Computer Science at Carnegie Mellon University in Pittsburgh, Pennsylvania, where he worked on computer vision. His first job was in the robotics division at Sandia Labs in Albuquerque, New Mexico. Then he moved to Microsoft Research in Redmond, Washington in 1997, where he has been since. His current research focus is location, mostly looking at how to process, protect, and value personal location data. In 2017, he received a 10-year impact award for a paper on location privacy from the ACM UbiComp conference. He holds 83 U.S. patents. He has served as the co-editor in chief of the Journal of Location Based Services, and he is currently an associate editor ACM Transactions on Spatial Algorithms and Systems and on the editorial board of IEEE Pervasive Magazine. Dr. Krumm was a PC chair for UbiComp 2007, ACM SIGSPATIAL 2013, and ACM SIGSPATIAL 2014. He is currently in his fourth year on the executive committee of ACM SIGSPATIAL.
(#34)Urban Crowd Density Prediction Based on Multi-relational Graph, Qiming Hao (University of Science and Technology of China), Le Zhang (University of Science and Technology of China), Rui Zha ( University of Science and Technology of China), Ding Zhou (University of Science and Technology of China), Zhe Zhang (University of Science and Technology of China), Tong Xu (University of Science and Technology of China), Enhong Chen (University of Science and Technology of China) (30sec pitch)(10min)(paper)
(#55)Towards Predicting Vehicular Data Consumption, Andi Zang (Northwestern University), Xiaofeng Zhu (Microsoft), Yuxiang Guo (Northwestern University), Fan Zhou (School of Information and Software Engineering, University of Electronic Science and Technology of China), Goce Trajcevski (Iowa State University) (30sec pitch)(10min)(paper)
Session Co-Chairs: Lei Chen (Huawei Noah's Ark Lab, Hong Kong, China) and Raghu Ganti (IBM, US)
(#41)OSMRunner : A System for Exploring and Fixing OSM Connectivity, Fares Tabet (University of Washington), Sikha Pentyala (University of Washington, Tacoma), Birva Patel (University of Washington), Abdeltawab Hendawi (University of Rhode Island), Ashley Song (Microsoft Corporation), Peiwei Cao (Microsoft Corporation), Harsh Govind (Microsoft Corporation), Mohamed Ali (University of Washington) (30sec pitch)(10min)(paper)
(#58)A Context, Location and Preference-Aware System for Safe Pedestrian Mobility, Constantinos Costa (University of Pittsburgh), Brian Nixon (University of Pittsburgh), Sayantani Bhattacharjee (University of Pittsburgh), Benjamin Graybill (University of Pittsburgh), Demetrios Zeinalipour-Yazti (University of Cyprus), Walter Schneider (University of Pittsburgh), Panos K. Chrysanthis (University of Pittsburgh) (30sec pitch)(10min)(paper)
Session Co-Chairs: Vana Kalogeraki (Athens University of Economics and Business, Greece),
Nick Koudas (University of Toronto, Canada), and Jianliang Xu (Hong Kong Baptist University, China)
Abstract: Personalised activity and behaviour representations enable intelligent assistant technologies and proactive recommender systems. On an aggregate level, understanding human activity patterns in cities enables a more efficient and sustainable energy, transport, and resource planning. However, most representation learning techniques require a large amount of well-labelled training sets to achieve high performance. This motivates us to explore data-efficient training techniques in learning efficient and compact representations, including with unsupervised learning, weakly-supervised learning, few-shot learning, and self-supervised learning. In this talk, I will present our unsupervised approaches to handle large-scale mutivariate sensor data from heterogeneous sources, prior to modelling them further with the rich contextual signals obtained from the environment. I will also present recent works on self-supervised learning for change point detection and anomaly detection, applicable to various downstream tasks. Finally, examples of projects leveraging transfer learning approaches in smart cities, smart buildings, and health monitoring will be presented.
Bio: Professor Flora Salim is a Professor in the School of Computing Technologies, RMIT University, Melbourne, Australia, the co-Deputy Director of RMIT Centre for Information Discovery and Data Analytics (CIDDA), and an Associate Investigator of ARC Centre of Excellence in Automated Decision Making and Society. Flora leads the IoT Analytics node, or the Context Recognition and Urban Intelligence (CRUISE) group. Her research interests include context and behaviour modelling, time-series and spatio-temporal data mining, machine learning on stream and sensor data, embedded intelligence, and smart cities. She was a Humboldt-Bayer Fellow (from Bayer Foundation GmbH), Humboldt Fellow -experienced researcher (from Alexander von Humboldt Foundation), Victoria Fellow 2018 (from Victorian government). She was the recipient of the the RMIT Vice-Chancellor's Award for Research Excellence–Early Career Researcher 2016; the RMIT Award for Research Impact - Technology 2018; Victorian iAwards (2014), Australian Research Council (ARC) Postdoctoral Research Industry (APDI) Fellow (2012-2015); IBM Smarter Planet Industry Skills Innovation Award (2010). She serves as an Associate Editor of the PACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) and an Area Editor of Pervasive and Mobile Computing journal. Prof. Salim has received several ARC Linkage, a Discovery, and numerous international industry grants, including those funded by Microsoft Research, Northrop Grumman Corporations USA, and Qatar National Research Funds. She was a Visiting Professor at University of Kassel, Germany, and University of Cambridge, UK, in 2019.
Session Chair: Yan Zhao (Aalborg University, Denmark)
(#1)Meta-learning Enhanced Neural ODE for Citywide Next POI Recommendation, Haining TAN (Institute of Computing Technology, Chinese Academy of Sciences), Di Yao (Institute of Computing Technology, Chinese Academy of Sciences), Tao Huang (Institute of Computing Technology, Chinese Academy of Sciences), Baoli Wang (Institute of Computing Technology, Chinese Academy of Sciences), Quanliang Jing (Institute of Computing Technology, Chinese Academy of Sciences), Jingping Bi (Institute of Computing Technology, Chinese Academy of Sciences) (30sec pitch)(10min)(paper)
Session Chair: Cheng Long (Nanyang Technological University, Singapore)
(#66)Crime Monitor: Monitoring Criminals from Trajectory Data, Nicksson Ckayo Arrais De Freitas (Federal University of Ceará), Ticiana L. Coelho da Silva (Federal University of Ceará), Jose Macedo (Federal University of Ceará ), Luís César M. de Vasconcelos (Federal University of Ceará), Francisco C. F. Nunes Junior (Federal University of Ceará) (30sec pitch)(10min)(paper)
(#75)HealthDist: A Context, Location and Preference-Aware System for Safe Navigation, Brian Nixon (University of Pittsburgh), Sayantani Bhattacharjee (University of Pittsburgh), Benjamin Graybill (University of Pittsburgh ), Constantinos Costa (University of Pittsburgh), Sudhir Pathak (University of Pittsburgh), Walter Schneider (University of Pittsburgh), Panos K. Chrysanthis (University of Pittsburgh) (30sec pitch)(10min)(paper)
(#77)UGASP: User and Group Aware Shopping Planner, Christian Baer (Iowa State University), Erich Brandt (Iowa State University), Elizabeth Strzelczyk (Iowa State University), Colin Thurston (Iowa State University), Colin Willenborg (Iowa State University), Tavion Yrjo (Iowa State University), Ashfaq A Khokhar (Iowa State University), Goce Trajcevski (Iowa State University) (30sec pitch)(10min)(paper)
Session Co-Chairs: Vana Kalogeraki (Athens University of Economics and Business, Greece),
Nick Koudas (University of Toronto, Canada), and Jianliang Xu (Hong Kong Baptist University, China)
Abstract: Urban computing connects ubiquitous sensing technologies, advanced data management, and analytics models, and novel visualization methods, to create win-win-win solutions that improve urban environment, life quality, and city operation systems. This talk presents the vision and framework of urban computing, introducing the challenges and the state-of-the-art solutions in each layer of the framework. Based on the vision of urban computing, we have built an intelligent city operation system which has been deployed in over 20 cities as a digital foundation to empower Big Data-driven applications, such as logistic optimizations, traffic/crowd flow predictions, community demand and supply predictions, hazardous chemical management, and public resource allocations.
Bio: Dr. Yu Zheng is the Vice President of JD.COM and head JD Intelligent Cities Research. Before Joining JD.COM, he was a senior research manager at Microsoft Research. He currently serves as the Editor-in-Chief of ACM Transactions on Intelligent Systems and Technology and has served as the program co-chair of ICDE 2014 (Industrial Track), CIKM 2017 (Industrial Track) and IJCAI 2019 (industrial track). He is also a keynote speaker of AAAI 2019, KDD 2019 Plenary Keynote Panel and IJCAI 2019 Industrial Days. His monograph, entitled Urban Computing, has been used as the first text book in this field. In 2013, he was named one of the Top Innovators under 35 by MIT Technology Review (TR35) and featured by Time Magazine for his research on urban computing. In 2016, Zheng was named an ACM Distinguished Scientist and elevated to an IEEE Fellow in 2020 for his contributions to spatio-temporal data mining and urban computing.
Session Co-Chairs: Goce Trajcevski (Iowa State University, US) and Maria Papadopouli (University of Crete, Greece).
Panel 2: Smart Cities and Mobility, Cyrus Shahabi (Professor, University of Southern California, USA), Sanjay Chawla (Professor, Hamad Bin Khalifa University, Qatar), Scott Counts (Principal Researcher, Microsoft, USA), Xin Chen (Senior Manager, Amazon Web Services, USA), and Mina Sartipi (Director of the Center for Urban Informatics and Progress, University of Tennessee at Chattanooga, USA).
Abstract: The use of mobile data in urban settings involves a wealth of research challenges in integrating, querying, behavior and trend modeling and prediction. This panel aims to highlight these challenges as well as exciting avenues for cross-collaboration. It will include a brief introduction and short individual presentations from the panelists, followed by a longer session of general discussion and open Q&A from the audience.
Bio:
Sanjay Chawla
is the Research Director at the Qatar Computing Research Institute (QCRI).
Before joining QCRI in 2014 he was a Professor at the University of
Sydney’s Faculty of Engineering and served as the head of the School of
Information Technologies. His research interests span data mining, machine
learning and spatial data management. He is the co-author on the text,
Spatial Databases: A Tour. His recent projects include planet-scale road
network inference from satellite imagery (asm.qcri.org) and the use of
reinforcement learning for traffic signal optimization. He received his PhD
from the University of Tennessee, Knoxville in 1995.
Xin Chen
is a senior manager and head of automotive vertical at Amazon Machine
Learning Solutions Lab. He leads his team to help AWS customers identify
and build machine learning solutions to address their organization’s
highest return-on-investment machine learning opportunities. Prior to
Amazon, Xin was a director of engineering at HERE Technologies whose team
completed pioneering work to achieve the automation of next generation map
creation. Xin is an adjunct faculty at Northwestern U. and Illinois
Institute of Technology. Xin obtained his Ph.D. in Computer Science and
Engineering from the University of Notre Dame.
Scott Counts
is a Senior Principal Researcher at Microsoft Research, working in the area
of computational social science. His work focuses on applying insights from
analyses oflarge scale naturalistic data to problems of
interest to society. As research manager of the Urban Innovation group at
Microsoft Research Redmond, his research is centered around productivity
and economic development, environmental impact, and social equity in urban
areas.
Mina Sartipi
is the Founding Director of the Center for Urban Informatics and Progress
(CUIP) at the University of Tennessee at Chattanooga (UTC), where she is
also a Guerry Professor in the Computer Science and Engineering Department.
Her research, funded by NSF, NIH, DOE, State of the Tennessee, Lyndhurst
Foundation, and industry organizations, focuses on data-driven approaches
to tackle real-world challenges in smart city applications focused on
mobility, energy, and health. At CUIP, she coordinates cross-disciplinary
research and strategic visions for urbanism and smart cities advancement
with a focus on people and quality of life. She received her BS in
Electrical Engineering from Sharif University of Technology, Tehran, Iran,
in 2001 and her MS and PhD degrees in Electrical and Computer Engineering
from Georgia Tech in 2003 and 2006, respectively.
Cyrus Shahabi
is a Professor of Computer Science, Electrical & Computer Engineering
and Spatial Sciences; Helen N. and Emmett H. Jones Professor of
Engineering; the chair of the Computer Science Department; and the director
of the Integrated Media Systems Center (IMSC) at USC’s Viterbi School of
Engineering.He was co-founder of two USC spin-offs,
Geosemble Technologies and Tallygo, which both were acquired, in July 2012
and March 2019, respectively. He received his B.S. in Computer Engineering
from Sharif University of Technology and his M.S. and Ph.D. Degrees in
Computer Science from the University of Southern California. He authored
two books and more than three hundred research papers in databases, GIS and
multimedia with more than 12 US Patents. He is currently on the editorial
board of the ACM Transactions on Spatial Algorithms and Systems (TSAS) and
ACM Computers in Entertainment. He was the chair of ACM SIGSPATIAL for the
2017-2020 term and also chaired the founding nomination committee of ACM
SIGSPATIAL for its first term (2011-2014 term). Dr. Shahabi is a fellow of
IEEE, and a recipient of the ACM Distinguished Scientist award, U.S.
Presidential Early Career Awards for Scientists and Engineers (PECASE), and
the NSF CAREER award.
Session Chair: Nick Koudas (University of Toronto, Canada) (awards)
(#3)Best MDM 2021 Paper, "A Differentially Private Task Planning Framework for Spatial Crowdsourcing" by Qian Tao (Beihang University); Yongxin Tong (Beihang University); Shuyuan Li (Beihang University); Yuxiang Zeng (Hong Kong University of Science and Technology); Zimu Zhou (Singapore Management University); Ke Xu (Beihang University)
Award Committee: Takahiro Hara (Chair), Panos K. Chrysanthis, Evaggelia Pitoura, and Kai Zheng.
(30sec pitch)(10min)(paper)
(#Award)Test-of-Time Award, "Infrastructure for data processing in large-scale interconnected sensor networks",
by Karl Aberer (EPFL, Switzerland), Manfred Hauswirth (TU Berlin & Fraunhofer FOKUS, Germany), and Ali Salehi (Teradata, Australia), Published at IEEE MDM 2007
Award Committee: Christian S. Jensen (chair), Vana Kalogeraki, and Jianliang Xu (paper)
(paper)![](https://summerschool.acm.org/2019/wp-content/uploads/2019/05/evi-small-4.jpg")
(awards)