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Carnegie Mellon University Professor Made Foundational Contributions to
Minimally-Supervised Learning


New York, NY, April 8, 2020 – ACM, the Association for Computing Machinery, today named Maria Florina “Nina” Balcan of Carnegie Mellon University the recipient of the 2019 ACM Grace Murray Hopper Award for foundational and breakthrough contributions to minimally-supervised learning. Balcan’s influential and pioneering work in machine learning has solved longstanding open problems, enabled entire lines of research crucial for modern AI systems, and has set the agenda for the field for years to come.


The ACM Grace Murray Hopper Award is given to the outstanding young computer professional of the year, selected on the basis of a single recent major technical or service contribution. This award is accompanied by a prize of $35,000. The candidate must have been 35 years of age or less at the time the qualifying contribution was made. Financial support for this award is provided by Microsoft.


“Nina Balcan wonderfully meets the criteria for the ACM Grace Murray Hopper Award, as many of her groundbreaking contributions occurred long before she turned 35,” said ACM President Cherri M. Pancake. “Although she is still in the early stages of her career, she has already established herself as the world leader in the theory of how AI systems can learn with limited supervision. More broadly, her work has realigned the foundations of machine learning, and consequently ushered in many new applications that have brought about leapfrog advances in this exciting area of artificial intelligence.”


Select Technical Contributions

Semi-supervised Learning

Semi-supervised learning is an approach to machine learning in which algorithms use large amounts of easily available unlabeled data to augment small amounts of labeled data to improve predictive accuracy. When semi-supervised learning was first explored, early research suggested some promising results. However, prior to Balcan’s work, there were no general principles for designing and providing formal guarantees for algorithms that leverage both labeled and unlabeled data. By introducing the first general theoretical framework, Balcan showed how to achieve provable guarantees on the performance of such techniques with concrete implications for many different types of semi-supervised learning methods. Her foundational principles for learning from limited supervision were instrumental in advancing this important tool in machine learning and supporting the subsequent work of many other researchers in this area.


Active Learning/Noise Tolerant Learning

Balcan also made significant contributions in the related area of active learning. In active learning, the algorithm processes large volumes of data and intelligently chooses the datapoints to be labeled. Balcan established performance guarantees for active learning that hold even in challenging cases when “noise” is present in the data. These guarantees hold under arbitrary forms of noise, that is, anything that distorts or corrupts the data. This can include anything from a blurry photo, a unit of data that is improperly labeled, meaningless information, or data that the algorithm cannot interpret. Building on this work, Balcan and her collaborators also developed algorithms that can learn more efficiently under more specialized forms of “label noise.” Examples of label noise might include a researcher not being given all of the health symptoms when annotating data to make predictions about a disease, or the data being encoded incorrectly. Her work in active learning in the presence of noise was regarded as a breakthrough in the field.



Clustering is an unsupervised learning technique in which an algorithm groups datapoints with similar properties. One goal of clustering is to find meaningful structure in data. An early challenge in the field, however, was to establish a theoretical foundation for what constituted a “meaningful structure” in a dataset.  In her early work, Balcan proposed a theoretical foundation for understanding the general kinds of structures that can be detected by clustering, as well as characterizing the functionality of specific clustering algorithms. As she developed her theoretical framework further, she also devised novel clustering algorithms that were derived from these theoretical foundations, and showed applications of these algorithms to computational biology and web search.


Biographical Background

Maria Florina Balcan is an Associate Professor of Computer Science at Carnegie Mellon University. Her research interests include learning theory, machine learning, theory of computing, artificial intelligence, algorithmic economics and algorithmic game theory, and optimization. Balcan received Bachelor’s and Master’s degrees from the University of Bucharest (Romania) in 2000 and 2002, respectively. In 2008, she earned a PhD in Computer Science from Carnegie Mellon University.


Balcan’s honors include a National Science Foundation Career Award in 2009, a Microsoft Faculty Fellowship in 2011, and a Sloan Research Fellowship in 2014, as well as numerous conference paper awards. Balcan has served as the Program Committee Co-chair for all three of the major machine learning conferences: Conference on Neural Information Processing Systems (NeurIPS), International Conference on Machine Learning (ICML), and Conference on Learning Theory (COLT). Balcan’s publications are among the most cited in the machine learning theory field, and she continues to be a prolific author. Her most recent publications include chapters on “Data-Driven Algorithm Design” and “Noise in Classification,” for the book Beyond the Worst-Case Analysis of Algorithms, which will be published later this year.



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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CampaignTester™ was awarded Best Application of Artificial Intelligence to Optimize Creative at the 2020 Campaigns & Elections Reed Awards.

CampaignTester™ is a cutting-edge mobile-based platform that utilizes emotion analytics and machine learning to detect a user’s emotion and engagement level while watching video content. Their proprietary platform aims to deliver key audience insights for organizations to validate, revise and perfect their video content messaging.

Campaigns & Elections Reed Award winners represent the “best-of-the-best” in the political campaign and advocacy industries. The 2020 Reed Awards honored winners across 16 distinct category groups, representing the different specialisms of the political campaign industry, with distinct category groups for International (non-US) work, and Grassroots Advocacy work.

“It was particularly meaningful being recognized among some of the finest marketers and technologists in the world.” Bill Lickson, CampaignTester’s Chief Operating Officer affirmed. “I was thrilled and honored to accept this prestigious award on behalf of our entire talented team.”

Aaron Itzkowitz, Chief Executive Officer and Founder of CampaignTester™ added, “This award is a great start to what looks to be a wonderful year for our client-partners and our company. While our technology was recognized for excellence in political marketing, our technology is for any industry that uses video in marketing”

Source: Express Computer

MENAFN - GetNews) February 24, 2020 - There is also a growing need to groom, guide and fund new ideas and ventures, as well as future founders and executives, especially with new generations entering the ecosystem. Founder and CEO of HunterTech, angel investor and mentor Pradeep Reddy is an industry veteran who offers business mentoring and advisory services.

The startup ecosystem around the world continues to remain strong and abuzz with new ideas. However, to successfully set up and sustain a new business with complete responsibility is no easy task, and that's where Pradeep Reddy is putting his energies into.

'My mission is to promote entrepreneurship and innovation with responsible technology. I like to get engaged in solving bigger problems which will impact our world in a positive direction, says Pradeep Reddy.

Reddy is a global business leader, two times founder with over two decades of industry experience in technology, marketing, sales, business, and investment. He counts among his experience working with global customers from the US, Europe, Canada, Australia, and India in verticals like retail, BFSI, HiTech, manufacturing, healthcare, telecom and more.

As a business advisor and mentor, Reddy's services include one-on-one coaching with the Founders, online mentoring, and programs for small business and corporate growth. Reddy relies upon his rich experience since 2013 of being an investor, mentor, and advisor. He is an investor at SparesHub (a pan-India automobile parts company), angel investor & mentor at EduRev (award-winning e-learning startup based in Gurgaon), and advisor at BeYouPlus (health and aesthetics treatment app).

To date, Reddy has helped grow startups and has created over 1,000 jobs around the world. His dedication is towards only one aim: to grow the companies he is associated with. 'I help early-stage companies to scale up their business and create some positive impact on sustainable economic development, he adds.

Currently, Reddy is promoting HunterTech , a technology consulting firm, that offers digital solutions to small and medium businesses, including web, mobile, IoT, AI, Big Data, Cloud and more. On offer are skills and tech that happen to be in demand.

Reddy's specialties include Certified Hubspot inbound marketing, marketing automation, data-driven marketing, growth hacking, digital marketing, lead generation, business development, partnerships, enterprise application, etc. Mr.Pradeep Reddy works closely with startup founders and upcoming companies from across the globe and in different markets. His exploits have seen him work in the US, Europe, Africa ( Kenya, Nigeria), India, Dubai, Malaysia, Singapore, etc.

To join Pradeep Reddy's growing network, with 3,000+ LinkedIn connections of Founders, Decision Makers, Investors and CXO's globally, please visit:

For more information, please visit:

Media Contact
Company Name: HunterTech Venture Pvt Ltd
Contact Person: Mr. Pradeep Reddy Kamasani
Email: Send Email
Phone: +1-732-790-2937
Address: 3rd floor, SNN Raj Pinnacle, Plot 7f Graphite India Main Road, Phase 2, Doddanakundi Industrial Area 2, EPIP Zone, Whitefield
City: Bengaluru
State: Karnataka - 560048
Country: India

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Premier Conference in Exciting and Fast-Growing Research Area to Feature Fairness, Accountability, and Transparency in Socio-technical Systems

New York, NY, January 21, 2020 – The 2020 ACM Conference on Fairness, Accountability and Transparency (ACM FAT*), to be held in Barcelona, Spain from January 27-30, brings together researchers and practitioners interested in fairness, accountability, and transparency in socio-technical systems. ACM FAT* will host the presentation of research work from a wide variety of disciplines, including computer science, statistics, the social sciences and law.


ACM FAT* grew out of the successful Workshop on Fairness, Accountability, and Transparency in Machine Learning (FAT/ML), as well as workshops on recommender systems (FAT/REC), natural language processing (Ethics in NLP), and data and algorithmic transparency (DAT), among others. Last year, more than 450 academic researchers, policymakers and practitioners attended the conference; this year over 600 registrations are expected and the amount of papers submitted was doubled. 2020 will mark the first year that ACM FAT* is being presented in Europe.


“More and more, algorithmic systems are making and informing decisions which directly affect our lives,” explains ACM FAT* General Co-chair Carlos Castillo, Universitat Pompeu Fabra. “Through a multidisciplinary perspective that encompasses computer science, law and social science, the ACM FAT* Conference not only explores the potential technological solutions regarding potential bias, but also seeks to address pivotal questions about economic incentive structures, perverse implications, distribution of power, and redistribution of welfare, and to ground research on fairness, accountability, and transparency in a legal framework.”


Added General Co-chair Mireille Hildebrandt, Vrije Universiteit Brussel and Radboud University Nijmegen, “This year, we have developed a dedicated LAW track and a dedicated SSH track, next to the CS track, highlighting that cross-disciplinary exchanges are core to this conference. We are also excited to introduce the CRAFT initiative as part of ACM FAT*. CRAFT stands for critiquing and rethinking accountability, fairness and transparency, bringing together a broad spectrum of people working in domains affected by algorithmic decision making, aiming to give voice to those who suffer the consequences, while fostering interaction with computer scientists working on technical solutions.”


In addition to providing a forum for publishing and discussing research results, the FAT* conference also seeks to develop a diverse and inclusive global community around its topics and make the material and community as broadly accessible as feasible. To that end, the conference has provided over 80 scholarships to students and researchers, subsidizes attendance by students and nonprofit representatives, and will be livestreaming the main program content for those who are not able to attend in person. A Doctoral Consortium will support and promote the next generation of scholars working to make algorithmic systems fair, accountable, and transparent.



Keynote Addresses


“Hacking the Human Bias in AI”

Ayanna Howard, Georgia Institute of Technology

Howard maintains that people tend to overtrust sophisticated computing devices, including robotic systems. As these systems become more fully interactive with humans during the performance of day-to-day activities, the role of bias in these human-robot interaction scenarios must be more carefully investigated. She argues that bias is a feature of human life that is intertwined, or used interchangeably, with many different names and labels – stereotypes, prejudice, implicit or subconsciously held beliefs. In the digital age, this bias has often been encoded in and can manifest itself through AI algorithms, which humans then take guidance from, resulting in the phenomenon of “excessive trust.” In this talk, she will discuss this phenomenon of integrated trust and bias through the lens of intelligent systems that interact with people in scenarios that are realizable in the near-term.

“Productivity and Power: The Role of Technology in Political Economy”

Yochai Benkler, Harvard Law School

Benkler argues that the revival of the concept “political economy” offers a frame for understanding the relationship between productivity and justice in market societies. It reintegrates power and the social and material context—institutions, ideology, and technology—into our analysis of social relations of production, or how we make and distribute what we need and want to have. Organizations and individuals, alone and in networks, struggle over how much of a society’s production happens in a market sphere, how much happens in nonmarket relations, and how embedded those aspects that do occur in markets are in social relations of mutual obligation and solidarism. These struggles involve efforts to shape institutions, ideology, and technology in ways that trade off productivity and power, both in the short and long term. The outcome of this struggle shapes the highly divergent paths that diverse market societies take, from oligarchic to egalitarian, and their stability as pluralistic democracies.


“Making Accountability Real: Strategic Litigation”

Nani Jansen Reventlow, Digital Freedom Fund

Reventlow asks “How can we make fairness, accountability and transparency a reality?” She points out that litigation is an effective tool for pushing for these principles in the design and deployment of automated decision-making technologies. She also maintains that the courts can be strong guarantors of our rights in a variety of different contexts and have shown already that they are willing to do so in the digital rights setting. At the same time, she argues that, as automated decisions are increasingly impacting every aspect of our lives, we need to engage the courts on these complex issues and enable them to protect our human rights in the digital sphere. We are already seeing cases being taken to challenge facial recognition technology, predictive policing systems, and systems that conduct needs assessments in the provision of public services. However, we still have much work to do in this space. Reventlow will also explore what opportunities do the different frameworks in this area, and especially European regulations such as the GDPR offer, and how can we maximize their potential?

Accepted Papers (Partial List) 
For a complete list of research papers and posters which will be presented at the FAT* Conference, visit 


Auditing Radicalization Pathways on YouTube”

Manoel Horta Ribeiro, Raphael Ottoni, Robert West, Virgílio A. F. Almeida, Wagner Meira, École Polytechnique Fédérale de Lausanne (EPFL)

Non-profits, as well as the media, have hypothesized the existence of a radicalization pipeline on YouTube, claiming that users systematically progress towards more extreme content on the platform. Yet, there is to date no substantial quantitative evidence of this alleged pipeline. To close this gap, the authors conducted a large-scale audit of user radicalization on YouTube. They analyzed 330,925 videos posted on 349 channels, which they broadly classified into four types: Media, the Alt-lite, the Intellectual Dark Web (IDW), and the Alt-right. According to this radicalization hypothesis, channels in the IDW and the Alt-lite serve as gateways to fringe far-right ideology, here represented by Alt-right channels. Processing 72M+ comments, the authors show that the three channel types indeed increasingly share the same user base; that users consistently migrate from milder to more extreme content; and that a large percentage of users who consume Alt-right content now consumed Alt-lite and IDW content in the past. They also probe YouTube's recommendation algorithm, looking at more than 2M video and channel recommendations between May and July 2019. They find that Alt-lite content is easily reachable from IDW channels, while Alt-right videos are reachable only through channel recommendations. Overall, the authors paint a comprehensive picture of user radicalization on YouTube.


Fair Decision Making Using Privacy-Protected Data”

Satya Kuppam, Ryan Mckenna, David Pujol, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau, UMass Amherst

Data collected about individuals is regularly used to make decisions that impact those same individuals. The authors consider settings where sensitive personal data is used to decide who will receive resources or benefits. While it is well known that there is a tradeoff between protecting privacy and the accuracy of decisions, the authors initiate a first-of-its-kind study into the impact of formally private mechanisms (based on differential privacy) on fair and equitable decision-making. They empirically investigate novel tradeoffs on two real-world decisions made using U.S. Census data (allocation of federal funds and assignment of voting rights benefits) as well as a classic apportionment problem. Their results show that if decisions are made using an ϵ-differentially private version of the data, under strict privacy constraints (smaller ϵ), the noise added to achieve privacy may disproportionately impact some groups over others. They propose novel measures of fairness in the context of randomized differentially private algorithms and identify a range of causes of outcome disparities.


“What Does It Mean to ‘Solve’ the Problem of Discrimination in Hiring? Social, Technical and Legal Perspectives from the UK on Automated Hiring Systems”
Javier Sánchez-Monedero, Lina Dencik, Cardiff University; Lilian Edwards, University of Newcastle

The ability to get and keep a job is a key aspect of participating in society and sustaining livelihoods. Yet the way decisions are made on who is eligible for jobs, and why, are rapidly changing with the advent and growth in uptake of automated hiring systems (AHSs) powered by data-driven tools. Key concerns about such AHSs include the lack of transparency and potential limitation of access to jobs for specific profiles. In relation to the latter, however, several of these AHSs claim to detect and mitigate discriminatory practices against protected groups and promote diversity and inclusion at work. Yet whilst these tools have a growing user-base around the world, such claims of bias mitigation are rarely scrutinized and evaluated, and when done so, have almost exclusively been from a US socio-legal perspective. In this paper, the authors introduce a perspective outside the US by critically examining how three prominent automated hiring systems (AHSs) in regular use in the UK, HireVue, Pymetrics and Applied, understand and attempt to mitigate bias and discrimination.

“Fairness Is Not Static: Deeper Understanding of Long-Term Fairness via Agents and Environments”
Alexander D'Amour, Yoni Halpern, Hansa Srinivasan, Pallavi Baljekar, James Atwood, D. Sculley, Google

As machine learning becomes increasingly incorporated within high impact decision ecosystems, there is a growing need to understand the long-term behaviors of deployed ML-based decision systems and their potential consequences. Most approaches to understanding or improving the fairness of these systems have focused on static settings without considering long-term dynamics. This is understandable; long term dynamics are hard to assess, particularly because they do not align with the traditional supervised ML research framework that uses fixed data sets. To address this structural difficulty in the field, we advocate for the use of simulation as a key tool in studying the fairness of algorithms. We explore three toy examples of dynamical systems that have been previously studied in the context of fair decision making for bank loans, college admissions, and allocation of attention. By analyzing how learning agents interact with these systems in simulation, we are able to extend previous work, showing that static or single-step analyses do not give a complete picture of the long-term consequences of an ML-based decision system.


“Reducing Sentiment Polarity for Demographic Attributes in Word Embeddings Using Adversarial Learning”  

Christopher Sweeney, Maryam Najafian, Massachusetts Institute of Technology (MIT)

The use of word embedding models in sentiment analysis has gained a lot of traction in the Natural Language Processing (NLP) community. However, many inherently neutral word vectors describing demographic identity have unintended implicit correlations with negative or positive sentiment, resulting in unfair downstream machine learning algorithms. We leverage adversarial learning to decorrelate demographic identity term word vectors with positive or negative sentiment, and re-embed them into the word embeddings. We show that our method effectively minimizes unfair positive/negative sentiment polarity while retaining the semantic accuracy of the word embeddings. Furthermore, we show that our method effectively reduces unfairness in downstream sentiment regression and can be extended to reduce unfairness in toxicity classification tasks.


Integrating FATE/Critical Data Studies into Data Science Curricula: Where Are WGoing and How DWGet There?

Jo Bates, David Cameron, Alessandro Checco, Paul Clough, Frank Hopfgartner, Suvodeep Mazumdar, Laura Sbaffi, Peter Stordy, Antonio de le Vega de León, University of Sheffield

There have been multiple calls for integrating FATE/CDS content into Data Science curricula, but little exploration of how this might work in practice. This paper presents the findings of a collaborative auto-ethnography (CAE) undertaken by a MSc Data Science team based at [anonymised] Information School where FATE/CDS topics have been a core part of the curriculum since 2015/16. In this paper, we adopt the CAE approach to reflect on our experiences of working at the intersection of disciplines, and our progress and future plans for integrating FATE/CDS into the curriculum. We identify a series of challenges for deeper FATE/CDS integration related to our own competencies and the wider socio-material context. We conclude with recommendations for ourselves and the wider FATE/CDS orientated Data Science community.

“Roles for Computing in Social Change”

Rediet Abebe, Solon Barocas, Jon Kleinberg, Karen Levy, Manish Raghavan, David G. Robinson, Cornell University

A recent normative turn in computer science has brought concerns about fairness, bias, and accountability to the core of the field. Yet recent scholarship has warned that much of this technical work treats problematic features of the status quo as fixed, and fails to address deeper patterns of injustice and inequality. While acknowledging these critiques, we posit that computational research has valuable roles to play in addressing social problems --- roles whose value can be recognized even from a perspective that aspires toward fundamental social change. In this paper, we articulate four such roles, through an analysis that considers the opportunities as well as the significant risks inherent in such work.


CRAFT Sessions
For full descriptions, visit the ACM FAT* Craft Sessions program page

  • When Not to Design, Build, or Deploy
  • Fairness, Accountability, Transparency in AI at Scale: Lessons from National Programs
  • Creating Community-Based Tech Policy: Case Studies, Lessons Learned, and What Technologists and Communities Can Do Together
  • Lost in Translation: An Interactive Workshop Mapping Interdisciplinary Translations for Epistemic Justice
  • From Theory to Practice: Where do Algorithmic Accountability and Explainability Frameworks Take Us in the Real World
  • Burn, Dream and Reboot! Speculating Backwards for the Missing Archive on Non-Coercive Computing
  • Algorithmically Encoded Identities: Reframing Human Classification
  • Ethics on the Ground: From Principles to Practice
  • Deconstructing FAT: Using Memories to Collectively Explore Implicit Assumptions, Values and Context in Practices of Debiasing and Discrimination-Awareness
  • Bridging the Gap from AI Ethics Research to Practice
  • Manifesting the Sociotechnical: Experimenting with Methods for Social Context and Social Justice
  • Centering Disability Perspectives in Algorithmic Fairness, Accountability & Transparency
  • Infrastructures: Mathematical Choices and Truth in Data
  • Hardwiring Discriminatory Police Practices: the Implications of Data-Driven Technological Policing on Minority (Ethnic and Religious) People and Communities
  • CtrlZ.AI Zine Fair: Critical Perspectives (Off-site)

For full descriptions, visit the ACM FAT* Tutorials page

  • Probing ML Models for Fairness with the What-If Tool and SHAP
  • AI Explainability
  • Experimentation with fairness-aware recommendation using librec-auto
  • Leap of FATE: Human Rights as a Complementary Framework for AI Policy and Practice
  • Can an algorithmic system be a 'friend' to a police officer's discretion?
  • Two computer scientists and a cultural scientist get hit by a driver-less car: Situating knowledge in the cross-disciplinary study of F-A-T in machine learning
  • Positionality-Aware Machine Learning
  • Policy 101: An Introduction to Participating in the Policymaking Process
  • From the Total Survey Error Framework to an Error Framework for Digital Traces of Humans
  • The Meaning and Measurement of Bias: Lessons from NLP
  • Assessing the intersection of Organizational Structure and FAT* efforts within industry
  • Explainable AI in Industry: Practical Challenges and Lessons Learned
  • Gender: What the GDPR does not tell us (But maybe you can?)
  • What does “fairness” mean in (data protection) law?

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.



Computing Professionals Honored for Foundational Work in Diverse Areas

New York, NY, December 11, 2019 – ACM, the Association for Computing Machinery, has named 58 members ACM Fellows for wide-ranging and fundamental contributions in areas including artificial intelligence, cloud computing, combating cybercrime, quantum computing and wireless networking. The accomplishments of the 2019 ACM Fellows underpin the technologies that define the digital age and greatly impact our professional and personal lives. ACM Fellows comprise an elite group that represents less than 1% of the Association’s global membership.

“Computing technology has had a tremendous impact in shaping how we live and work today,” said ACM President Cherri M. Pancake in announcing the 2019 ACM Fellows. “All of the technologies that directly or indirectly influence us are the result of countless hours of collaborative and/or individual work, as well as creative inspiration and, at times, informed risk-taking. Each year, we look forward to welcoming some of the most outstanding individuals as Fellows. The ACM Fellows program is a cornerstone of our overall recognition effort. In highlighting the accomplishments of the ACM Fellows, we hope to give credit where it is due, while also educating the public about the extraordinary array of areas in which computing professionals work.”  

Underscoring ACM’s global reach, the 2019 Fellows hail from universities, companies and research centers in Australia, Canada, China, Egypt, France, Germany, Israel, Italy, Switzerland, and the United States.

The contributions of the 2019 Fellows run the gamut of the many sub-disciplines of the computing field―including artificial intelligence, cloud computing, computer graphics, computational biology, data science, security and privacy, software engineering, quantum computing, and web science, to name a few.

ACM will formally recognize its 2019 Fellows at the annual Awards Banquet, to be held in San Francisco on June 20, 2020. Additional information about the 2019 ACM Fellows, as well as previously named ACM Fellows, is available through the ACM Fellows site.

2019 ACM Fellows


Scott J. Aaronson

University of Texas

For contributions to quantum computing and computational complexity


Tarek F. Abdelzaher

University of Illinois at Urbana-Champaign

For interdisciplinary contributions that bridge cyber-physical systems, social sensing, real-time computing, and control


Saman Amarasinghe

Massachusetts Institute of Technology

For contributions to high performance computing on modern hardware platforms, domain-specific languages, and compilation techniques


Kavita Bala

Cornell University

For contributions to rendering and scene understanding


Magdalena Balazinska

University of Washington

For contributions to scalable distributed data systems


Paul Beame

University of Washington

For contributions in computational and proof complexity and their applications, and for outstanding service


Emery D. Berger

University of Massachusetts Amherst

For contributions in memory management and programming language implementation


Ronald F. Boisvert

National Institute of Standards and Technology

For contributions to mathematical software and service to the profession


Christian Cachin

University of Bern

For contributions to secure distributed computing and cryptographic protocols

Brad Calder


For contributions to cloud storage, processor simulation, replay, and feedback-directed optimization of systems and applications


Diego Calvanese

Free University of Bozen-Bolzano

For contributions to description logics and their applications in data management and software engineering


Srdjan Capkun

Swiss Federal Polytechnic, Zurich

For contributions to systems and wireless network security


Claire Cardie

Cornell University

For contributions to natural language processing, including coreference resolution, information and opinion extraction


Timothy M. Chan

University of Illinois at Urbana-Champaign

For contributions to computational geometry, algorithms, and data structures


Kanianthra Mani Chandy

California Institute of Technology

For contributions to queueing networks, performance analysis, distributed and parallel programming, and distributed simulation


Xilin Chen

Institute of Computing Technology, Chinese Academy of Sciences

For contributions to face and sign language recognition and multimedia systems


Elizabeth F. Churchill


For contributions to human-computer interaction and service to ACM


Philip R. Cohen

Monash University

For contributions to the theory and practice of multi-agent systems, human-computer dialogue, and multimodal interaction


Vincent Connitzer

Duke University

For contributions to game theory, social choice theory, and mechanism design


Noshir Contractor

Northwestern University

For contributions to advances in computational social science, network science and web science


Matthew B. Dwyer

University of Virginia

For contributions to the specification and analysis of software


Elena Ferrari

University of Insubria

For contributions to security and privacy of data and social network systems


Michael J. Freedman

Princeton University

For contributions to robust distributed systems for the modern cloud


Deborah Frincke

US National Security Agency

For contributions in education, the practice of research, and the leadership of cybersecurity


Lise Getoor

University of California, Santa Cruz

For contributions to machine learning, reasoning under uncertainty, and responsible data science


Maria L. Gini

University of Minnesota

For contributions to robotics and multi-agent systems and a lifelong commitment to diversity in computing


Subbarao Kambhampati

Arizona State University

For contributions to automated planning and human-aware AI systems and leadership within the field


Tamara G. Kolda

Sandia National Laboratories

For innovations in algorithms for tensor decompositions, contributions to data science, and community leadership


Xiang-Yang Li

University of Science and Technology of China

For contributions to the design, analysis and optimization of IoT and mobile systems


Songwu Lu

University of California, Los Angeles

For helping create a more resilient and performant cellular network


Wendy Elizabeth Mackay

University of Paris-Sud

For contributions to human-computer interaction, mixed reality and participatory design, and leadership in ACM SIGCHI


Diana Marculescu

University of Texas at Austin

For contributions to the design and optimization of energy-aware computing systems


Sheila McIlraith

University of Toronto

For contributions to knowledge representation and its applications to automated planning and semantic web services


Rada Mihalcea

University of Michigan

For contributions to natural language processing, with innovations in data-driven and graph-based language processing


Robin R. Murphy

Texas A&M University

For contributions in founding and advancing the field of computing for disasters and robotics

Marc Najork


For contributions to web search and web science


Jason Nieh

Columbia University

For contributions to operating systems, virtualization, and computer science education


Hanspeter Pfister

Harvard University

For contributions to volume rendering, visualization, computer graphics, and computer vision applications


Timothy M. Pinkston

University of Southern California
For contributions to interconnection network routing algorithms and architectures, and leadership in expanding computing research


Mihai Pop

University of Maryland, College Park

For contributions to computational biology, algorithms, and software for DNA sequence analysis and sequence assembly


Andreas Reuter

Heidelberg University/Heidelberg Laureate Forum Foundation
For contributions to database concurrency control and for service to the community


Jeffrey S. Rosenschein

Hebrew University

For contributions to multi-agent systems, in particular, the use of game theory in multi-agent systems


Srinivasan Seshan

Carnegie Mellon University

For contributions to computer networking, mobile computing and wireless communications


Prashant J. Shenoy

University of Massachusetts Amherst

For contributions to the modeling and design of distributed systems


Peter W. Shor

Massachusetts Institute of Technology

For contributions to quantum computing, information theory, and randomized algorithms


Mona Singh

Princeton University

For contributions to computational biology, spearheading algorithmic and machine learning approaches for characterizing proteins and their interactions


Ramesh K. Sitaraman

University of Massachusetts Amherst

For contributions to content delivery networks, distributed systems, and scalable Internet services


Dawn Song

University of California, Berkeley

For contributions to security and privacy


Salvatore J. Stolfo

Columbia University

For contributions to machine-learning-based cybersecurity and parallel hardware for database inference systems


Dacheng Tao

The University of Sydney

For contributions to representation learning and its applications


Moshe Tennenholtz


For contributions to AI and algorithmic game theory


Giovanni Vigna

University of California, Santa Barbara
For contributions to improving the security of the Internet and combating cybercrime


Nisheeth K. Vishnoi

Yale University

For contributions to theoretical computer science and its connections with mathematics, sciences, and social sciences


Darrell Whitley

Colorado State University

For technical and professional leadership in the field of genetic and evolutionary computation


Yuan Xie

University of California, Santa Barbara

For contributions to the design techniques and tools for the implementation and evaluation of computer architectures


Moustafa Amin Youssef

Alexandria University
For contributions to location tracking algorithms

Carlo A. Zaniolo

University of California, Los Angeles

For contributions to the theory and practice of data and knowledge-base systems


Lidong Zhou

Microsoft Research Asia

For contributions to trustworthy distributed computing and to systems research and education in China


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.


About the ACM Fellows Program

The ACM Fellows Program initiated in 1993, celebrates the exceptional contributions of the leading members in the computing field. These individuals have helped to enlighten researchers, developers, practitioners and end users of information technology throughout the world. The new ACM Fellows join a distinguished list of colleagues to whom ACM and its members look for guidance and leadership in computing and information technology.


Jim Ormond


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