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Minzer’s Dissertation Takes Important Step Toward Proving Unique Games Conjecture


New York, NY, July 16, 2020 – ACM, the Association for Computing Machinery, today announced that Dor Minzer receives the 2019 ACM Doctoral Dissertation Award for his dissertation, “On Monotonicity Testing and the 2-to-2-Games Conjecture.” The key contributions of Minzer’s dissertation are settling the complexity of testing monotonicity of Boolean functions and making a significant advance toward resolving the Unique Games Conjecture, one of the most central problems in approximation algorithms and complexity theory.


Property-testers are extremely efficient randomized algorithms that check whether an object satisfies a certain property, when the data is too large to examine. For example, one may want to check that the distance between any two computers in the internet network does not exceed a given bound. In the first part of his thesis, Minzer settled a famous open problem in the field by introducing an optimal tester that checks whether a given Boolean function (voting scheme) is monotonic.


The holy grail of complexity theory is to classify computational problems to those that are feasible and those that are infeasible. The PCP theorem (for probabilistically checkable proofs) establishes the framework that enables classifying approximation problems as infeasible, showing they are NP-hard.

In 2002, Subhash Khot proposed the Unique Games Conjecture (UGC), asserting that a very strong version of the PCP theorem should still hold. The conjecture has inspired a flurry of research and has had far-reaching implications. If proven true, the conjecture would explain the complexity of a whole family of algorithmic problems. In contrast to other conjectures, UGC had been controversial, splitting the community into believers and skeptics. While progress toward validating the conjecture has stalled, evidence against it had been piling up, involving new algorithmic techniques.


In the second part of his dissertation, Minzer went halfway toward establishing the conjecture, and in the process nullified the strongest known evidence against UGC. Even if UGC is not resolved in the immediate future, Minzer’s dissertation makes significant advances toward solving research problems that have previously appeared out of reach.


Minzer is a postdoctoral researcher at the Institute for Advanced Study (IAS) in Princeton, New Jersey, and will be joining MIT as an Assistant Professor in the fall of 2020. His main research interests are in computational complexity theory, PCP, and analysis of Boolean functions. Minzer received a BA in Mathematics, as well as an MSc and PhD in Computer Science from Tel Aviv University.   


Honorable Mentions

Honorable Mentions for the 2019 ACM Doctoral Dissertation Award go to Jakub Tarnawski, École polytechnique fédérale de Lausanne (EPFL) and JiaJun Wu, Massachusetts Institute of Technology (MIT).

Jakub Tarnawski’s dissertation “New Graph Algorithms via Polyhedral Techniques” made groundbreaking algorithmic progress on two of the most central problems in combinatorial optimization: the matching problem and the traveling salesman problem. Work on deterministic parallel algorithms for the matching problem is motivated by one of the unsolved mysteries in computer science: does randomness help in speeding up algorithms? Tarnawski’s dissertation makes significant progress on this question by almost completely derandomizing a three-decade-old randomized parallel matching algorithm by Ketan Mulmuley, Umesh Vaziriani, and Vijay Vazirani. 

The second major result of Tarnawski’s dissertation relates to the traveling salesman problem: find the shortest tour of n given cities. Already in 1956, George Dantzig et al. used a linear program to solve a special instance of the problem. Since then the strength of their linear program has become one of the main open problems in combinatorial optimization. Tarnawski’s dissertation resolves this question asymptotically and gives the first constant-factor approximation algorithm for the asymmetric traveling salesman problem.

Tarnawski is a researcher at Microsoft Research. He is broadly interested in theoretical computer science and combinatorial optimization, particularly in graph algorithms and approximation algorithms. He received his PhD from EPFL and an MSc in Mathematics and Computer Science from the University of Wrocław, Poland.

JiaJun Wu’s dissertation, “Learning to See the Physical World,” has advanced AI for perceiving the physical world by integrating bottom-up recognition in neural networks with top-down simulation engines, graphical models, and probabilistic programs. Despite phenomenal progress in the past decade, current artificial intelligence methods tackle only specific problems, require large amounts of training data, and easily break when generalizing to new tasks or environments. Human intelligence reveals how far we need to go: from a single image, humans can explain what we see, reconstruct the scene in 3D, predict what’s going to happen, and plan our actions accordingly.

Wu addresses the problem of physical scene understanding—how to build efficient and versatile machines that learn to see, reason about, and interact with the physical world. The key insight is to exploit the causal structure of the world, using simulation engines for computer graphics, physics, and language, and to integrate them with deep learning. His dissertation spans perception, physics and reasoning, with the goal of seeing and reasoning about the physical world as humans do. The work bridges the various disciplines of artificial intelligence, addressing key problems in perception, dynamics modeling, and cognitive reasoning.


Wu is an Assistant Professor of Computer Science at Stanford University. His research interests include physical scene understanding, dynamics models, and multi-modal perception. He received his PhD and SM degree in Electrical Engineering and Computer Science from MIT, and Bachelor’s degrees in Computer Science and Economics from Tsinghua University in Beijing, China.


The 2019 Doctoral Dissertation Award recipients will be formally recognized at the annual ACM Awards Banquet on October 3 in San Francisco. 


About the ACM Doctoral Dissertation Award

Presented annually to the author(s) of the best doctoral dissertation(s) in computer science and engineering. The Doctoral Dissertation Award is accompanied by a prize of $20,000, and the Honorable Mention Award is accompanied by a prize totaling $10,000. Winning dissertations will be published in the ACM Digital Library as part of the ACM Books Series.


About ACM

ACM, the Association for Computing Machinery is the world’s largest educational and scientific computing society, uniting computing educators, researchers and professionals to inspire dialogue, share resources and address the field’s challenges. ACM strengthens the computing profession’s collective voice through strong leadership, promotion of the highest standards, and recognition of technical excellence. ACM supports the professional growth of its members by providing opportunities for life-long learning, career development, and professional networking.



Jim Ormond


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Largest and Oldest Data Mining Conference Goes Virtual for the First Time

New York, NY, August 21, 2020 – The Association for Computing Machinery’s Special Interest Group on Knowledge Discovery and Data Mining (ACM SIGKDD) will hold its flagship annual conference, KDD 2020, virtually, August 23-27. The KDD conference series, started in 1989, is the world’s oldest and largest data mining conference, and is the venue where concepts such as big data, data science, predictive analytics and crowdsourcing were first introduced. Continuing this tradition, KDD 2020 will showcase leading-edge research papers in data science, data mining, knowledge discovery, large-scale data analytics and big data. Despite being a fully virtual event, KDD 2020 will include all the same program offerings as previous years, including exciting keynote addresses, topical panels, invited talks, highly selective research and applied data science papers, informative and hands-on tutorials, and workshops.

“KDD is a ‘must attend’ conference, where the theory and practice in data science, machine learning and artificial intelligence come together in industry-defining innovations,” explained KDD 2020 General Co-chair Rajesh K. Gupta, University of California, San Diego.  “Initially we had hoped that at least a portion of the conference could be ‘in-person,’ but ultimately we decided a fully virtual conference would be the safest option for our community. While organizing a fully virtual conference is unchartered territory, we have made sure all of the program facets from previous years will be part of KDD 2020--from fascinating keynote addresses, to engaging research, workshops and panels. We’ve planned an outstanding program and we are confident we will have record conference registrations.”

“Data science has exploded in the last 30 years and is now reshaping so many different disciplines,” added KDD 2020 General Co-chair Yan Liu, University of Southern California. “An example of this is KDD 2020’s Applied Data Science Invited Speakers track, which we are particularly excited about. This year, we have a roster of 18 leading practitioners in the field, working at companies such as Siemens, Microsoft, Facebook, Google, Amazon and Uber, among many others.”

KDD 2020 will feature four keynote talks, 18 applied data science invited talks, 217 accepted research papers grouped into 43 sessions for oral presentations, workshops and tutorials. A partial listing of highlights follows. The full KDD program is available here.

Keynote Talks
“Explanations that Matter through Meta-Provenance”
Yolanda Gil, University of Southern California
Provenance standards have now been used for many years to generate useful explanations of the data analytic process used to generate a new finding.  These explanations convey the details of analytic steps and the original data used in an analysis.  In this talk, Gil will discuss the need for explanations that provide the context and rationale for how the data analysis process was designed.  She will also illustrate with examples from several domains the kinds of explanations that can be generated from meta-provenance and discuss important areas of future work. 

“AI for Intelligent Financial Services: Examples and Discussion”
Manuela M. Veloso, Carnegie Mellon University
There are many opportunities to pursue AI and ML in the financial domain.  In this talk, I will overview several research directions we are pursuing in engagement with the lines of business, ranging from data and knowledge, learning from experience, reasoning and planning, multi agent systems, and secure and private AI.  Veloso will offer concrete examples of projects, and conclude with the many challenges and opportunities that AI can offer in the financial domain.

“A Look at State-Space Multi-Taper Time-Frequency Analysis”
Dr. Emery Brown, Massachusetts Institute of Technology, Harvard Medical School, Massachusetts General Hospital
Time series arising for studies of physical, biological, economic and sociological systems are an important data class. The growing interest in this type of data has come about because of significant recent advances in sensor, recording and digitization technologies. In this lecture, Brown will discuss his recent work on the development of a state-space multi-taper (SS-MT) framework for the analysis of non-stationary time series.

“Computational Epidemiology at the time of COVID-19”
Alessandro Vespignani, Northeastern University
The data science revolution is finally enabling the development of large-scale data-driven models that provide scenarios, forecasts and risk analysis for infectious disease threats. These models also provide rationales and quantitative analysis to support policy making decisions and intervention plans. Vespignani will review and discuss recent results and challenges in the area and focus on ongoing work aimed at responding to the COVID-19 pandemic.

Best Paper Awards
Best Paper: “On Sampled Metrics for Item Recommendation”
Walid Krichene, Steffen Rendle, Google 
The task of item recommendation requires ranking a large catalogue of items given a context. Item recommendation algorithms are evaluated using ranking metrics that depend on the positions of relevant items. To speed up the computation of metrics, recent work often uses sampled metrics where only a smaller set of random items and the relevant items are ranked. The Google research team investigates sampled metrics in more detail and shows that they are inconsistent with their exact version, in the sense that they do not persist relative statements, e.g., recommender A is better than B, not even in expectation.

Best Student Paper: “TIPRDC: Task-Independent Privacy-Respecting Data Crowdsourcing Framework for Deep Learning with Anonymized Intermediate Representations”
Ang Li
Huanrui Yang, Yiran Chen, Duke University; Yixiao Duan, Jianlei Yang, Beihang University
The research group from Duke University presents TIPRDC, a task-independent privacy-respecting data crowdsourcing framework with anonymized intermediate representation. The goal of this framework is to learn a feature extractor that can hide the privacy information from the intermediate representations; while maximally retaining the original information embedded in the raw data for the data collector to accomplish unknown learning tasks.

Best Paper Runner Up: “Malicious Attacks against Deep Reinforcement Learning Interpretations”
Mengdi Huai, Jianhui Sun, Renqin Cai, Aidong Zhang, University of Virginia; Liuyi Yao, State University of New York at Buffalo
The combination of deep learning and reinforcement learning (RL) and has demonstrated its ability to model dynamics in a plethora of sequential decision-making problems. To improve the transparency, various interpretation methods for DRL have been proposed. However, those DRL interpretation methods make an implicit assumption that they are performed in a reliable and secure environment, which is not true in practical applications. The University of Virginia team investigates the vulnerability of DRL interpretation methods in the malicious environment. Specifically, the first study of the adversarial attacks against DRL interpretations was introduced. An optimization framework was proposed to address the studied adversarial attacks.


2020 ACM SIGKDD Innovation Award
Thorsten Joachims, professor of Computer Science and Information Science at Cornell University, is recognized for his research contributions in machine learning, including influential work studying human biases in information retrieval, support vector machines (SVM) and structured output prediction. Notably, Joachims pioneered methods for eliciting reliable preferences from implicit feedback, methods for unbiased learning-to-rank and ranking methods that provide fairness guarantees. The ACM SIGKDD Innovation Award is the highest honor for technical excellence in the field of knowledge discovery and data mining. It is conferred on an individual or group of collaborators whose outstanding technical innovations have greatly influenced the direction of research and development in the field.

"I am greatly honored by this recognition from the KDD community," said Joachims. "KDD is known for innovation—not only as an academic endeavor, but also with an eye towards real-world impact and social good."

2020 ACM SIGKDD Service Award
Michael Zeller, head of artificial intelligence (AI) strategy and solutions at Temasek, is honored for his contributions to the field through dedication to ACM SIGKDD as the volunteer treasurer and secretary of the executive committee. Zeller has served on the executive board for eight years, playing an instrumental role in planning multiple KDD conferences. With a special emphasis on applied AI, his mission as an executive committee member is to foster strong partnerships between research institutions and industry organizations as a key for the continued success of the KDD community. The ACM SIGKDD Service Award is the highest recognition of service awarded in the field. The award honors an individual or group of collaborators for outstanding contributions to professional KDD societies or society-at-large through applications of knowledge discovery and data mining.

"As a longtime member of ACM SIGKDD, I am always incredibly impressed by the contributions of our volunteers," said Zeller. "Without their dedication and belief in our mission, we would never have been able to create such a vibrant data science community, let alone organize a conference of this magnitude and quality year after year."

2020 ACM SIGKDD Dissertation Award
Rediet Abebe, incoming assistant professor of Computer Science at the University of California at Berkeley, earned this year's ACM SIGKDD Dissertation Award for her Ph.D. thesis, "Designing Algorithms for Social Good." Abebe is the first female computer scientist to be inducted into the Harvard Society of Fellows and co-founded Mechanism Design for Social Good (MDSG), a multi-institutional initiative to improve access to opportunity for historically underserved and disadvantaged communities. Jingbo Shang, assistant professor of Computer Science at University of California at San Diego, earned runner-up for his thesis, "Constructing and Mining Heterogeneous Information Networks from Massive Text." The ACM SIGKDD Dissertation Award recognizes outstanding work done by graduate students in the areas of data science, machine learning and data mining.

2020 ACM SIGKDD Rising Star Award
Danai Koutra, Morris Wellman assistant professor of Computer Science and Engineering at University of Michigan, and Jiliang Tang, assistant professor of Computer Science and Engineering at Michigan State University, both received the first annual ACM SIGKDD Rising Star Award. Koutra's research in large-scale data mining focuses on principled, interpretable and scalable methods for network summarization and multi-network analysis. Tang's notable work includes research into representation learning, especially on graphs and its applications on the internet and social media domains. New this year, the Rising Star Award celebrates individual work done in the first five years after earning a PhD. The award aims to celebrate the early accomplishments of the SIGKDD communities' brightest new minds.

2020 SIGKDD Test of Time Award for Research
The SIGKDD Test of Time award recognizes outstanding KDD papers, at least ten years old, which have had a lasting impact on the data mining research community and continue to be cited as the foundation for new branches of research. This year, the Test of Time Award for Research goes to Victor S. ShengFoster Provost and Panagiotis Ipeirotis for their approach to selective acquisition of multiple labels featured in the 2008 peer-reviewed paper, "Get Another Label? Improving Data Quality and Data Mining Using Multiple, Noisy Labelers."

2020 SIGKDD Test of Time Award for Applied Science
Jie Tang, Jing Zhang, Limin Yao, Juanzi Li, Li Zhang and Zhong Su received the inaugural Test of Time Award for Applied Science in recognition of their study of mining academic social networks published in the 2008 peer-reviewed paper, "ArnetMiner: Extraction And Mining Of Academic Social Networks." SIGKDD introduced this award to honor influential research in real-world applications of data science.

Research Track Papers
(Partial list: The full program of Research Track Papers can be found here.)

“A Novel Deep Learning Model by Stacking Conditional Restricted Boltzmann Machine and Deep Neural Network”
Tianyu Kang, Ping Chen, Wei Ding, University of Massachusetts Boston; John Quackenbush: Harvard T.H. Chan School of Public Health
A real-world system often exhibits complex dynamics arising from interaction among its subunits. In machine learning and data mining these interactions are usually formulated as dependency and correlation among system variables. The collaborative research teams from Harvard University and University of Massachusetts Boston present a novel deep learning model to tackle functionally interactive features by stacking a Conditional Restricted Boltzmann Machine and a Deep Neural Network (CRBM-DNN). The new model can solve both supervised and unsupervised learning problems. Compared to a regular neural network of the same size, CRBM-DNN has fewer parameters so they require fewer training samples.

“Parameterized Correlation Clustering in Hypergraphs and Bipartite Graphs”
Nate Veldt, Cornell University; David F. Gleich, Purdue University; Anthony Wirth, The University of Melbourne  
Motivated by applications in community detection and dense subgraph discovery, this paper considers new clustering objectives in hypergraphs and bipartite graphs. These objectives are parameterized by one or more resolution parameters in order to enable diverse knowledge discovery in complex data. The experimental results highlight the flexibility of the new framework and the diversity of results that can be obtained in different parameter settings.

“Multi-level Graph Convolutional Networks for Cross-platform Anchor Link Prediction”
Hongxu Chen, Bogdan Gabrys, Katarzyna Musial, University of Technology Sydney; Hongzhi Yin, Tong Chen, The University of Queensland; Xiangguo Sun, Southeast University
Cross-platform account matching plays a significant role in social network analytics, and is beneficial for a wide range of data mining applications. However, existing methods either heavily rely on high-quality user generated content (including user profiles) or suffer from data insufficiency problem if only focusing on network topology, which brings researchers into an insoluble dilemma of model selection. The Australian research team proposes a novel framework that considers multi-level graph convolutions on both local network structure and hypergraph structure in a unified manner. The proposed method overcomes data insufficiency problem of existing work and does not necessarily rely on user demographic information.

“GHashing: Semantic Graph Hashing for Approximate Similarity Search in Graph Databases”
Zongyue Qin, Peking University; Yunsheng Bai, Yizhou Sun, University of California, Los Angeles
Graph similarity search is an important data mining problem. Existing methods are capable of managing databases with thousands or tens of thousands of graphs. However, how to scale the graph similarity search to databases that have hundreds of thousands or even millions of graphs remains a challenging problem. Inspired by the recent success of deep learning-based supervised hashing, called semantic hashing, in image and document retrieval, this paper proposes a novel graph neural network (GNN) based pruning approach, GHashing, for graph similarity search. Exploiting the powerful learning ability of deep neural networks and the efficiency of hashing methods for approximate nearest neighbor, GHashing demonstrates significantly better performance compared to state-of-the-art methods.

Applied Data Science Track Papers
(Partial list: The full program of Applied Data Science Track Papers can be found here.)

“AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types”
Gabriel Blanco Saldana, Saurabh Deshpande, Xin Luna Dong, Xiang He, Andrey Kan, Xian Li, Yan Liang: Jun Ma, Alexandre Michetti Manduca, Jay Ren, Surender Pal Singh, Fan Xiao, Yifan Ethan Xu, Amazon; Chenwei Zhang, Tong Zhao: Amazon; Haw-Shiuan Chang, University of Massachusetts Amherst; Giannis Karamanolakis, Columbia University; Yuning Mao, University of Illinois at Urbana Champaign, Yaqing Wang: State University of New York at Buffalo, Christos Faloutsos, Carnegie Mellon University, Andrew McCallum, University of Massachusetts Amherst, Jiawei Han, University of Illinois at Urbana Champaign
Can one build a knowledge graph (KG) for all products in the world? Knowledge graphs have firmly established themselves as valuable sources of information for search and question answering, and it is natural to wonder if a KG can contain information about products offered at online retail sites. There have been several successful examples of generic KGs, but organizing information about products poses many additional challenges, including sparsity and noise of structured data for products, complexity of the domain with millions of product types and thousands of attributes, heterogeneity across large number of categories, as well as large and constantly growing number of products. In this paper, the authors describe AutoKnow, their automatic (self-driving) system that addresses these challenges.

“BusTr: Predicting Bus Travel Times from Real-Time Traffic”
Richard Barnes: UC Berkeley; Senaka Buthpitiya, James Cook, Alex Fabrikant, Andrew Tomkins, Fangzhou Xu, Google Research
The authors present BusTr, a machine-learned model for translating road traffic forecasts into predictions of bus delays, used by Google Maps to serve the majority of the world's public transit systems where no official real-time bus tracking is provided. They demonstrate that their neural sequence model improves over DeepTTE, the state-of-the-art baseline, both in performance (-30% MAPE) and training stability. They also demonstrate significant generalization gains over simpler models, evaluated on longitudinal data to cope with a constantly evolving world.

“Identifying Homeless Youth At-Risk of Substance Use Disorder: Data-Driven Insights for Policymakers”
Maryam Tabar, Stephanie Winkler, Dongwon Lee, Amulya Yadav, Pennsylvania State University; Heesoo Park, Sungkyunkwan University; Anamika Barman-Adhikari, University of Denver
Substance Use Disorder (SUD) is a devastating disease that leads to significant mental and behavioral impairments. Unfortunately, there is no definitive data-driven study on analyzing factors associated with SUD among homeless youth.  The authors aim to fill this gap by making the following three contributions: (i) they use a real-world dataset collected from 1,400 homeless youth (across six American states) to build accurate Machine Learning (ML) models for predicting the susceptibility of homeless youth to SUD; (ii) they find a representative set of factors associated with SUD among this population by analyzing feature importance values associated with their ML models; and (iii) they investigate the effect of geographical heterogeneity on the factors associated with SUD.

“Managing Diversity in Airbnb Search”
Mustafa Abdool, Malay Haldar, Prashant Ramanathan, Tyler Sax, Lanbo Zhang, Aamir Manasawala, Shulin Yang, Bradley Turnbull, Qing Zhang, Thomas Legrand: Airbnb
One of the long-standing questions in search systems is the role of diversity in results. From a product perspective, showing diverse results provides the user with more choice and should lead to an improved experience. However, this intuition is at odds with common machine learning approaches to ranking which directly optimize the relevance of each individual item without a holistic view of the result set. In this paper, the authors describe their journey in tackling the problem of diversity for Airbnb search, starting from heuristic based approaches and concluding with a novel deep learning solution that produces an embedding of the entire query context by leveraging Recurrent Neural Networks (RNNs).

Applied Data Science Invited Talks
KDD’s Applied Data Science Invited Talks feature highly influential speakers who have directly contributed to successful data mining applications in finance, healthcare, bioinformatics, public policy, infrastructure, telecommunications, social media and computational advertising. The invited talks and speakers include:

  • “Toward Responsible AI by Planning to Fail,” Saleema Amershi, Microsoft Research
  • “Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learning,” Timnit Gebru, Google
  • Fairness, Accountability, and Transparency in Predictive Models in Criminal Justice,” Kristian Lum (HRDAG)
  • Multimodal Machine Learning for Video and Image Analysis,” Shalini Ghosh, Samsung Research America
  • Unleashing the Power of Subjective Data: Managing Experiences as First-Class Citizens,” Wang-Chiew Tan, Megagon Labs
  • “Geospatial Technologies for Ride-Hailing and Emergency Vehicle Fleets,” Dawn Woodard, Uber
  • “AI: Healthcare’s Prescription for Transformation,” Taha Kass-Hout, Amazon
  • “Using Machine Learning to Detect Cancer Early,” Jan Schellenberger, Grail
  • “Artificial Intelligence for Healthcare,” Dorin Comaniciu, Siemens Healthineers
  • “A perspective from the first U.S. Chief Data Scientist,” DJ Patil, Devoted Health
  • “Innovating with Language AI,” Ashwin Ram, Google
  • “Build the State-of-the-art Machine Technology for the Crypto Economy,” Michael Li, Coinbase
  • “Representation Learning, Inference, and Reasoning,” Fernando Pereira, Google 
  • “Straddling the Boundary Between Contribution and Solution Driven Science,” Daniel Marcu, Amazon
  • “Data Paucity and Low Resource Scenarios: Challenges and Opportunities,” Mona Diab, Facebook AI and The George Washington University
  • “Next-Generation Frameworks,” Anima Anandkumar, Nvidia and Caltech
  • “Preserving Integrity in Online Social Media,” Alon Halevy, Facebook
  • “How AI Can Help Build Resiliency for Small Business in a Global Economic Crisis,” Nhung Ho, Intuit

This year’s conference will feature three panels:

“Fighting a pandemic: convergence of expertise, data science and policy,” a panel of experts from around the globe, will address the challenges and opportunities of using data science to fight a pandemic. Panelists will cite real-world cases where using data science helped the fight against the pandemic and cautionary tales of when it hindered that fight. Panelists include Vittoria Colizza, INSERM & Sorbonne University, France; Lauren Gardner, Johns Hopkins University, US; Marcel Salathé, EPFL, Switzerland; Samuel Scarpino, Northeastern University, US; Joseph T. Wu, University of Hong Kong, Hong Kong, China.

The “Women’s Panel” brings together a diverse group of women scientists and leaders to learn more about their journeys, what it takes to have a successful career in the field of Data Science and Artificial Intelligence, and their crucial work on some of the pressing problems of today. Prakruthi Prabhakar from LinkedIn will moderate the panel, which includes Calandra Moore, Data Scientist, Department of Defense; Elaine O. Nsoesie, Assistant Professor, Boston University School of Public Health; Karin Kimbrough, Chief Economist, LinkedIn; Sihem Amer-Yahia, Research Director, CNRS; Subarna Sinha Engineering Leader, Machine Learning, 23andMe; and Vanessa Murdock, Applied Science Manager, Amazon.

The panel “The Near Future of Automated Data Science” will explore the demand for creative, technically-skilled data scientists in the United States. Although the demand for data scientists is booming, our ability to train students to fill those jobs is falling behind. There is also a growing concern that technical skill alone is insufficient for long-term data science career success, partially due to the fact that many data science tasks are being automated. The panel will discuss the skill sets data scientists should focus on, such as business understanding, explanation, and storytelling. Panelists include Danielle Gewurz, Deloitte Consulting; Shubha Nabar, Faras AI; Monica Rogati, Data Science and AI Advisor; Horst Samulowitz, IBM Watson Research Center.



The ACM Special Interest Group for Knowledge Discovery from Data (SIGKDD), is a professional society comprising of world-renowned data scientists from industry and academia. KDD is the premier international conference that brings together researchers and practitioners from both academia and industry to deep-dive into novel ideas, latest research results and share in-the-trenches experiences and innovations. More details can be found at


About ACM

ACM, the Association for Computing Machinery, is the world’s largest educational and scientific computing society, uniting computing educators, researchers, and professionals to inspire dialogue, share resources and address the field’s challenges. ACM strengthens the computing profession’s collective voice through strong leadership, promotion of the highest standards, and recognition of technical excellence. ACM supports the professional growth of its members by providing opportunities for life-long learning, career development, and professional networking.



Jim Ormond  

This email address is being protected from spambots. You need JavaScript enabled to view it.




New Officers Emphasize Role of Computing to Address Societal Challenges

New York, NY, May 28, 2020
 – ACM, the Association for Computing Machinery, has announced the election of new officers who will lead the organization for a two-year term beginning July 1. Heading the new team will be incoming President Gabriele Kotsis. Kotsis is Professor and Head of the Department of Telecooperation at Johannes Kepler University in Linz, Austria. Joining Kotsis as Vice President will be Joan Feigenbaum, Grace Murray Hopper Professor of Computer Science at Yale University; and, as Secretary/Treasurer, Elisa Bertino, Samuel Conte Professor of Computer Science at Purdue University.  

In addition, Members-at-Large elected to four-year terms are Nancy M. Amato, Abel Bliss Professor and Department Head of Computer Science, University of Illinois at Urbana-Champaign; Tom Crick, Professor of Digital Education & Policy, Swansea University, Swansea, UK; Susan Dumais, Technical Fellow and Director, Microsoft Research Labs, New England; Mehran Sahami, Professor (Teaching) and Associate Chair for Education, Stanford University; and Alejandro Saucedo, Engineering Director (Machine Learning), Seldon Technologies and Chief Scientist, The Institute for Ethical AI & Machine Learning, London, UK.


President-elect Kotsis said her key priorities include using ACM’s global reach and the expertise of its membership to address pressing problems in society. “No other discipline or technology will have more impact on shaping our future than computer science and technology,” said Kotsis. “Global problems must be addressed in a global way, independently from a particular individual, national or commercial interest. Computing can play a role in fighting the CO2 dilemma, fertilizing medical research and healthcare, and protecting our democracy.” Kotsis also emphasized that ACM must continue to be a leading voice for fostering ethics in the field. “Our community must lead, not only from a scientific and technical perspective in being able to provide correct solutions, but also from an ethical and societal point of view,” she added.


Kotsis is a founding member of the ACM Europe Council, serving from 2008 to 2016. She has organized ACM conferences and workshops, and in 2016 received an award in appreciation of her accomplishments regarding the ACM womENcourage conference series. In 2014, she became an ACM Distinguished Member for her contributions to workload characterization for parallel and distributed systems, and for founding ACM Europe. Since 2016, she has been an elected Member-at-Large of the ACM Council.

Joan Feigenbaum, incoming ACM Vice President, echoed Kotsis’s vision for ACM to play an active role in utilizing computing to foster a better future. “ACM members can address myriad threats now facing society,” said Feigenbaum. “These threats combine sophisticated computation in critical ways with politics (as in ‘election hacking’), economics (as in technology-induced unemployment), journalism (as in ‘fake news’), law (as in mass surveillance in the name of national security), international relations (as in ‘cyberwar’), finance (as in bitcoin speculation), and many other fields. In tackling them, computer scientists will work collaboratively with people in social sciences, law, and many disciplines besides the STEM fields with which we have collaborated for decades.”


A member of ACM since graduate school, Feigenbaum has served in many roles, including SIGACT Executive Committee member from 2005 to 2009 and SIGEcom Vice Chair from 2005 to 2011. During her tenure with SIGEcom, she played a leading role in establishing ACM Transactions on Economics and Computation (TEAC). Most recently, she led the creation of the ACM Symposium on Computer Science and Law and served as General Chair for the inaugural symposium in 2019.


Newly-elected ACM Secretary/Treasurer Elisa Bertino, an ACM member for 38 years, would like ACM to be a leading voice in areas including AI and data ethics, data transparency, and sustainability. “I will also focus on important matters, such as broadening diversity in our field, supporting younger researchers and open access to data and publications,” said Bertino. “Last, but not least, I would like to make sure that ACM stays technically relevant by organizing workshops and conferences on new emerging technologies and applications.”


Bertino served as Editor-in-Chief of IEEE Transactions on Dependable and Secure Computing and    coordinating Co-Editor-in-Chief of Very Large Database Systems (VLDB). She chaired ACM’s Special Interest Group on Security, Audit and Control (SIGSAC) from 2009 to 2013.  In 2011, she co-founded ACM’s Conference on Data and Application Security and Privacy (CODASPY), now considered the main forum for high-quality research on data privacy and security. She is the recipient of the 2019-2020 ACM Athena Lecturer Award.



About ACM

ACM, the Association for Computing Machinery, is the world’s largest educational and scientific computing society, uniting computing educators, researchers and professionals to inspire dialogue, share resources and address the field’s challenges. ACM strengthens the computing profession’s collective voice through strong leadership, promotion of the highest standards, and recognition of technical excellence. ACM supports the professional growth of its members by providing opportunities for life-long learning, career development, and professional networking.



Jim Ormond                              


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Pune, May 25, 2020 (GLOBE NEWSWIRE) -- The global AI market is set to gain momentum from the rising utilization of cloud-based services and applications worldwide. Also, the increasing adoption of connected devices would impact the market positively in the coming years. This information is published by Fortune Business Insights™ in a recent report, titled, “Artificial Intelligence (AI) Market Size, Share and Industry Analysis By Component (Hardware, Software, Services), By Technology (Computer Vision, Machine Learning, Natural Language Processing, Others), By Industry Vertical (BFSI, Healthcare, Manufacturing, Retail, IT & Telecom, Government, Others) and Regional Forecast, 2019-2026.” The report further states that the global AI market size stood at USD 20.67 billion in 2018 and is projected to reach USD 202.57 billion by 2026, thereby exhibiting a CAGR of 33.1% during the forecast period.

Highlights of This Report:

  • Profiles of the prominent enterprises operating in the market.
  • Thorough analysis of the crucial strategies adopted by these enterprises to aid the client in understanding the competitive scenario of the market.
  • Elaborate research on the AI-based product positioning.
  • In-depth information regarding the emerging and current market dynamics, trends, growth drivers, and obstacles.

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An Overview of the Impact of COVID-19 on this Market:

The emergence of COVID-19 has brought the world to a standstill. We understand that this health crisis has brought an unprecedented impact on businesses across industries. However, this too shall pass. Rising support from governments and several companies can help in the fight against this highly contagious disease. There are some industries that are struggling and some are thriving. Overall, almost every sector is anticipated to be impacted by the pandemic.

We are taking continuous efforts to help your business sustain and grow during COVID-19 pandemics. Based on our experience and expertise, we will offer you an impact analysis of coronavirus outbreak across industries to help you prepare for the future.

Click here to get the short-term and long-term impact of COVID-19 on this Market.

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Drivers & Restraints-

Rising Demand for Industrial Robots to Propel Growth 

The rising demand for customized robots is a vital driver of the AI market growth. Numerous reputed organizations in the developed nations are presently engaging in the development and supply of industrial robots equipped with the AI technology. Japan and South Korea, for instance, supplied approximately 38,600 and 41,400 units of industrial robots in 2016, respectively. Also, in the same year, China provided almost 87,000 units across the globe. Apart from that, AI technology is mainly required in the retail sector for enhancing customer service. Coupled with this, the increasing usage of machine learning (M2P and M2M) would contribute to the market growth. However, the rising concerns regarding the unreliability of AI algorithms and data privacy may hamper the market growth.


Natural Language Processing Segment to Dominate Owing to Its Usage in Various Applications

In terms of technology, the market is segregated into natural language processing, machine learning, computer vision, and others. Amongst these, the computer vision segment held 22.5% AI market share in 2018. This system helps in identifying and detecting patterns. It also synthesizes, analyses, and acquires realistic interactive interfaces. Then, it utilizes the ID tags to showcase pictures of associated items. The natural language processing segment currently accounts of the maximum share as it is adopted for a wide range of applications, such as Informational Retrieval (IR), speech processingsemantic disambiguationtext parsing, and machine translation.

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Regional Analysis-

Rising Adoption of AI by Biopharma Companies to Favor Growth in Asia Pacific

In 2018, North America procured USD 9.72 billion revenue and is set to remain in the leading position throughout the forecast period. This growth is attributable to the ongoing technological advancements in the fields of natural language processing, machine learning, and analytical tools. Besides, the rising awareness programs regarding the benefits of AI tools and systems would propel growth in this region. Asia Pacific, on the other hand, is expected to grow considerably backed by the major contribution of China. The government of this country is planning to merge with Baidu to support the implementation of AI and develop a deep learning laboratory consisting of military, manufacturing, smart agriculture, and intelligent logistics. Apart from that, AI is being extensively adopted by a large number of biopharma companies in this region. Developed nations, such as Japan are investing hefty amounts of money in creating AI algorithms to analyze large volumes of data. 

Competitive Landscape-

Key Players Focus on Launching New Products to Strengthen Position

The market is fragmented with various companies operating across the world. They are mainly focusing on investing huge sums to develop new products. Numerous start-ups are adopting the strategy of mergers and acquisitions. Some of the others are considering the impact of the outbreak of Covid-19 pandemic and are making novel solutions to help people in performing various tasks. Below are a couple of the recent industry developments:

  • May 2020: Voicezen, a start-up based in Delhi, was successfully acquired by Bharti Airtel in an all-cash deal. The former specializes on conversational AI technologies. This deal would aid Airtel in gaining access to the former’s technologies. The company is planning to deploy those technologies in multiple languages across its large consumer touch points.
  • May 2020: TCS iON unveiled its latest AI-based product called Remote Assessments. The company developed the product by keeping in mind the effects of Covid-19 pandemic on the education system. It has impacted the examination schedules of universities, schools, and colleges worldwide. This new product would help in conducting exams efficiently and securely from the students’ choice of location.

Fortune Business Insights™ lists out the names of all the AI service providers present in the global market. They are as follows:

  • Verint Systems Inc. (Next IT Corp)
  • Baidu
  • Alphabet (Google Inc.)
  • Apple Inc.
  • Qlik Technologies Inc.
  • IBM Corporation
  • MicroStrategy, Inc.
  • Microsoft Corporation
  • IPsoft

Quick Buy – 
AI Market Research Report

Detailed Table of Content

  • Introduction
    • Definition, By Segment
    • Research Approach
    • Sources
  • Executive Summary
  • Market Dynamics
    • Drivers, Restraints and Opportunities
    • Emerging Trends
  • Key Insights
    • Macro and Micro Economic Indicators
    • Consolidated SWOT Analysis of Key Players
    • Porter’s Five Forces Analysis
  • Global Artificial Intelligence (AI) Market Analysis, Insights and Forecast, 2015-2026
    • Key Findings / Summary
    • Market Size Estimates and Forecasts
      • By Offering (Value)
        • Hardware
        • Software
        • Services
      • By Technology (Value)
        • Computer Vision
        • Machine Learning
        • Natural Language Processing
        • Others
      • By End Use Industry (Value)
        • Healthcare
        • Retail
        • Advertising & Media
        • BFSI
        • Automotive & Transportation
        • Government
        • Manufacturing
        • Others
      • By Geography (Value)
        • North America
        • Europe
        • Asia Pacific
        • Middle East and Africa
        • Latin America

TOC Continued...!!!

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Have a Look at Related Research Insights:

Home Automation Market Size, Share and Industry Analysis by Product Type (Luxury, Mainstream, Managed, DIY Do It Yourself Home Automation System), Application (Safety and Security, Lighting, Entertainment, Heating, Ventilation and Air conditioning), Networking Technology (Wired & Wireless) and Regional Forecast 2018-2025

Identity And Access Management Market Size, Share and Industry Analysis By Component (Provisioning, Directory Services, Single Sign-On, Others), By Deployment Model (Cloud, On-Premises), By Enterprise Size (Large Enterprises, Small and Medium Enterprises), By Industry Vertical (BFSI, IT and Telecom, Retail and Consumer Packed Goods, Others) And Regional Forecast 2019-2026

Speech and Voice Recognition Market Size, Share & Industry Analysis, By Component (Solution, Services), By Technology (Voice Recognition, Speech Recognition), By Deployment (On-Premises, Cloud), By End-User (Healthcare, IT and Telecommunications, Automotive, BFSI, Government, Legal, Retail, Travel and Hospitality and Others) and Regional Forecast, 2019 - 2026

Internet of Things (IoT) Market Size, Share and Industry Analysis By Platform (Device Management, Application Management, Network Management), By Software & Services (Software Solution, Services), By End-Use Industry (BFSI, Retail, Governments, Healthcare, Others) And Regional Forecast, 2019 - 2026

About Us:

Fortune Business Insights™ offers expert corporate analysis and accurate data, helping organizations of all sizes make timely decisions. We tailor innovative solutions for our clients, assisting them address challenges distinct to their businesses. Our goal is to empower our clients with holistic market intelligence, giving a granular overview of the market they are operating in. 

Our reports contain a unique mix of tangible insights and qualitative analysis to help companies achieve sustainable growth. Our team of experienced analysts and consultants use industry-leading research tools and techniques to compile comprehensive market studies, interspersed with relevant data. 

At Fortune Business Insights™, we aim at highlighting the most lucrative growth opportunities for our clients. We therefore offer recommendations, making it easier for them to navigate through technological and market-related changes. Our consulting services are designed to help organizations identify hidden opportunities and understand prevailing competitive challenges.

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