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Research Papers In Computer Science Topics 2012 Toyota

Rachit Agarwal
Assistant Professor; Computer Science, CS Field Member; Ph.D., UIUC, 2013

Research Focus: Distributed systems, systems for big data analytics, networking, design and analysis of algorithms

Research Areas: Systems and Networking

David Albonesi
Professor; Electrical and Computer Engineering, CS Field Member; Ph.D., Univ of Mass, Amherst, 1996

Research Focus: Adaptive and reconfigurable multi-core and processor architectures, power- and reliability-aware computing, and high performance interconnect architectures using silicon nanophotonics

Research Areas: Computer Architecture & VLSI

Lorenzo Alvisi
Professor; Computer Science, CS Field Member, Tisch Univ Professor; Ph.D., Cornell, 1996

Research Focus: Theory and practice of dependable distributed computing

Research Areas: Systems and Networking

Erik Andersen
Assistant Professor; Computer Science, CS Field Member; Ph.D., Univ of Washington, 2014

Research Focus: Learning science, knowledge representations, automatic generation of educational content, video games, large-scale experimentation, human-computer interaction

Research Areas: Human Interaction, Programming Languages

Yoav Artzi
Assistant Professor; Computer Science, Cornell Tech, CS Field Member; Ph.D., Univ of Washington, 2015

Research Focus: Natural language processing, situated language understanding, automated natural language interaction, machine learning

Research Areas: Artificial Intelligence

Graeme Bailey
Visiting Professor; Computer Science; Ph.D., Univ of Birmingham, 1977

Research Focus: Mathematical modeling, applications to medicine and biology, geometry, parametrization spaces and connectivity

Kavita Bala

On Leave – Fall 2017, Spring 2018

Professor; Computer Science, CS Field Member; Ph.D., M.I.T., 1999

Research Focus: Computer graphics - scalable graphics; perceptually-based, realistic rendering; image-based texturing and modeling

Research Areas: Artificial Intelligence, Graphics, Scientific Computing

Siddhartha Banerjee
Assistant Professor; Operations Research and Operations Engineering, CS Field Member; PhD, University of Texas - Austin, 2013

Research Focus: Stochastic Modeling, Design of Scalable Algorithms, Matching Markets and Social Computing, Control of Information-Flows, Learning and Recommendation

Research Areas: Theory of Computing

Christopher Batten
Associate Professor; Electrical and Computer Engineering, CS Field Member; Ph.D., M.I.T., 2010

Research Focus: High performance and energy-efficient parallel computer architecture and VSLI design

Research Areas: Computer Architecture & VLSI

Serge Belongie
Professor; Computer Science, Cornell Tech, CS Field Member; PhD, University California-Berkeley, 2000

Research Focus: Research Focus: Computer Vision, Machine Learning, Crowdsourcing, Human-in-the-Loop Computing

Research Areas: Artificial Intelligence, Graphics, Human Interaction

David Bindel
Associate Professor; Computer Science, CS Field Member; Ph.D., Univ of California, Berkeley, 2006

Research Focus: Microelectromechanical systems (MEMS), numerical linear algebra, finite element analysis, floating point computation and network tomography

Research Areas: Scientific Computing

Ken Birman
Professor; Computer Science, CS Field Member; N. Rama Rao Professor of Computer Science; Ph.D., Univ of California, Berkeley, 1981

Research Focus: Reliability and security in modern networked environments

Research Areas: Security, Systems and Networking

Eleanor Birrell
Lecturer; Computer Science; PhD., Cornell University, 2018

Research Focus: Security and Data Privacy

Research Areas: Security

Anne Bracy
Senior Lecturer; Computer Science; Ph.D., Univ of Penn, 2008

Research Focus: computer architecture, instruction fusion, hardware support for thread synchronization

Research Areas: Computer Architecture & VLSI

Yudong Chen
Assistant Professor; Operations Research and Information Engineering, CS Field Member; PhD, Univ. of Texas Austin, 2013

Research Focus: Machine Learning, high dimensional and robust statistics and convex optimazation

Research Areas: Artificial Intelligence

Tanzeem Choudhury
Associate Professor; Information Science, CS Field Member; Ph.D., M.I.T., 2004

Research Focus: Machine learning techniques for systems that can reason about human activities, interactions, and social networks in everyday environments

Research Areas: Artificial Intelligence, Human Interaction

Dan Cosley
Associate Professor; Information Science, CS Field Member; Ph.D., Univ of Minnesota, 2006

Research Focus: Human-computer interaction, online communities, and recommender systems

Research Areas: Human Interaction

Anil Damle
Assistant Professor; Ph.D., Stanford, 2016

Research Focus: development of algorithms in applied and computational mathematics (e.g. numerical linear algebra, computational quantum chemistry, and spectral clustering)

Research Areas: Scientific Computing

Christopher De Sa
Assistant Professor; PhD., Stanford, 2017

Research Focus: Developing and understanding algorithmic, software, and hardware techniques for high-performance machine learning

Research Areas: Artificial Intelligence

Nicola Dell
Assistant Professor; Information Science, Cornell Tech, CS Field Member; Ph.D., Univ of Washington, 2015

Research Focus: Human computer interaction (HCI) and Information and Communication Technologies for Development (ICTD)

Research Areas: Human Interaction

Alan Demers
Principal Research Scientist (Retired); Computer Science, CS Field Member; Ph.D., Princeton Univ, 1975

Research Focus: Database systems, database replication, and algorithms

Research Areas: Database Systems

Shimon Edelman
Professor; Psychology, CS Field Member; Ph.D., Weizmann Institue of Science, 1988

Research Focus: The fields of human and machine cognition (in particular, visual recognition and natural language processing)

Research Areas: Artificial Intelligence, Graphics

K-Y. Fan
Senior Lecturer; Computer Science; Ph.D., Cornell Univ, 2001

Research Focus: The application of systems analysis techniques for water resources and environmental problems

Michael George
Lecturer; Computer Science; Ph.D., Cornell Univ, 2013

Research Focus: Programming languages and methodologies for building correct, secure and efficient software

Research Areas: Programming Languages

Arpita Ghosh
Associate Professor; Information Science, CS Field Member; Ph.D., Stanford Univ, 2006

Research Focus: Algorithms and mechanism design in the context of strategic behavior on the web, particularly social computing, user generated content, and crowd sourcing, and markets and mechanims for privacy

Research Areas: Artificial Intelligence, Human Interaction

Carla Gomes
Professor; CIS and Applied Economics and Management; Computer Science, CS Field Member; Ph.D., Univ of Edinburgh, 1993

Research Focus: Solving hard combinatorial problems, with an emphasis on planning and scheduling problems

Research Areas: Artificial Intelligence, Computational Biology

Donald Greenberg
Professor; Computer Science, CS Field Member; Director, Program of Computer Graphics; Jacob Gould Schurman Professor of Computer Science; Ph.D., Cornell Univ, 1968

Research Focus: Computer graphics and its applications in a range of disciplines, from animation to architecture to biology

Research Areas: Graphics

David Gries
Professor Emeritus; Computer Science; Ph.D., Technische Hochschule Muenchen, Munich, 1966

Research Focus: Program methodology and related areas; logic; teaching in lower level courses

Francois Guimbretiere
Associate Professor; Information Science, CS Field Member; Ph.D., Stanford Univ, 2002

Research Focus: The overarching goal of my research is to bring the ease of use of pen and paper interactions to computer interfaces

Research Areas: Human Interaction

Joseph Halpern

On Sabbatical, Fall 2017, Spring 2018

Professor; Computer Science, CS Field Member, Joseph C Ford Professor of Engineering; Ph.D., Harvard Univ, 1981

Research Focus: Reasoning about knowledge and uncertainty, distributed computing, causality, security, game theory

Research Areas: Artificial Intelligence, Security, Theory of Computing

Bharath Hariharan
Assistant Professor; PhD., University of California, Berkeley, 2015

Research Focus: Rich visual understanding (object recognition, object detection, segmentation and beyond), machine learning (deep learning, convolutional networks)

Research Areas: Artificial Intelligence, Graphics

Juris Hartmanis
Professor Emeritus; Computer Science; Emeritus Walter R. Read Professor of Computer Science and Engineering; Turing Award Winner; Ph.D., Caltech, 1955

Research Focus: Computational complexity theory

Research Areas: Theory of Computing

Haym Hirsh
Professor; Computer Science, Information Science, CS Field Member; Ph.D., Stanford Univ, 1989

Research Focus: Artificial intelligence, collective intelligence, crowdsourcing, data mining, human computation, information retrieval, machine learning

Research Areas: Artificial Intelligence, Human Interaction

Guy Hoffman
Assistant Professor; Mechanical and Aerospace Engineering, CS Field Member; Ph.D., MIT, 2007

Research Focus: Human-Robot Interaction; human-robot teamwork and collaboration; robotic personal companions; non-anthropomorphic robot design; embodied cognition in probots

Research Areas: Artificial Intelligence, Robotics

John Hopcroft
Professor; Computer Science, IBM Professor of Engineering and Applied Mathematics in Computer Science; Turing Award Winner; Ph.D., Stanford Univ, 1964

Research Focus: Algorithms, information capture and access, random graphs and spectral methods

Research Areas: Artificial Intelligence, Theory of Computing

Daniel Huttenlocher
Professor; Computer Science, Cornell Tech, CS Field Member; A.D White Professor. Stephen H. Weiss Fellow; Dean and Vice Provost Cornell Tech; Ph.D., M.I.T., 1988

Research Focus: Computer vision, specifically the problems of model-based recognition, geometric shape comparison, and the computation of visual correspondence

Research Areas: Artificial Intelligence, Graphics, Human Interaction, Robotics

Thorsten Joachims
Professor; Computer Science, CS Field Member; Chair, Information Science; Ph.D., Univ of Dortmund, 2001

Research Focus: Machine learning and learning theory, with a focus on problems arising in information retrieval and natural language understanding

Research Areas: Artificial Intelligence

Ari Juels
Professor; Computer Science, Cornell Tech (Jacobs Inst), CS Field Member; PhD, University California, Berkeley, 1996

Research Focus: ?big data? security analytics, cybersecurity, cloud security, user authentication, privacy, medical-device security, biometric security, and RFID / NFC security

Research Areas: Human Interaction, Security

Malte Jung
Assistant Professor; Information Science, CS Field Member; PhD, Stanford Univ, 2011

Research Focus: Computer-supported cooperative work, teamwork, emotion, human-robot interaction, design

Research Areas: Human Interaction

Alon Keinan
Assistant Professor; Comp Biology, CS Field Member; Ph.D., Tel Aviv University, 2006

Research Focus: Studies how human genetic variation has arisen from evolutionary history. Develops theoretical tools and applies them to genomic data sets, bridging theoretical population genetics and empirical studies.

Research Areas: Computational Biology

Robert Kleinberg
Associate Professor; Computer Science, CS Field Member; Ph.D., M.I.T., 2005

Research Focus: Design and analysis of algorithms, especially randomized and on-line algorithms for networked systems and electronic markets

Research Areas: Artificial Intelligence, Theory of Computing

Ross Knepper
Assistant Professor; Computer Science, CS Field Member; Ph.D., Carnegie Mellon University, 2011

Research Focus: Robotics, industrial automation, planning, manipulation, human-robot interaction, artificial intelligence

Research Areas: Artificial Intelligence, Human Interaction, Robotics

Dexter Kozen
Professor; Computer Science, CS Field Member; Director of Masters of Engineering Program; Joseph Newton Pew, Jr. Professor in Engineering; Ph.D., Cornell Univ, 1977

Research Focus: Computational complexity, program logic and semantics, computational algebra

Research Areas: Programming Languages, Security, Theory of Computing

Steve Marschner
Professor; Computer Science, CS Field Member; Ph.D., Cornell Univ, 1998

Research Focus: Computer graphics, realistic rendering and simulation, material models for surfaces, volumes, and deformable objects

Research Areas: Graphics, Scientific Computing

Jose Martinez
Professor; Electrical and Computer Engineering, CS Field Member; Ph.D., Univ of Illinois, 2002

Research Focus: Reconfigurable and self-optimizing multicore architectures, architectural implications of disruptive technologies

Research Areas: Computer Architecture & VLSI

Mor Naaman
Associate Professor; Information Science, Cornell Tech, CS field member; PhD, Stanford, 2005

Research Focus: Social media, data mining, human-computer interaction, computational social science, interactive systems

Research Areas: Human Interaction

Anil Nerode
Professor; Mathematics, CS Field Member; Ph.D., Univ of Chicago, 1956

Research Focus: Mathematical logic, computational theory, recursive mathematics, nonstandard logics, nonmontonic logics, AI, applied mathematics.

Tapan Parikh
Associate Professor; Information Science, Cornell Tech, CS Field Member; Ph.D., Univ of Washington, 2007

Research Focus: Human-Computer Interaction (HCI), Information and Communication Technologies for Development (ICTD), Computer Science Education

Research Areas: Artificial Intelligence, Database Systems, Human Interaction

Kirstin Petersen
Assistant Professor; Electrical and Computer Engineering, CS Field Member; Ph.D., Harvard, 2014

Research Focus: Design and coordination of bio-inspired robot collectives and their natural counterparts

Research Areas: Robotics

Martha E. Pollack
Cornell University President; Professor; Computer Science, CS Field Member; Ph.D., University of Pennsylvania, 1986

Research Focus: Artificial intelligence

Research Areas: Artificial Intelligence

Tom Ristenpart
Associate Professor; Computer Science, Cornell Tech, CS Field Member; Ph.D., Univ of California, San Diego

Research Focus: Software security, applied and theoretical cryptography

Research Areas: Security

Bart Selman
Professor; Computer Science, CS Field Member; Joseph C Ford Professor of Engineering, Director of Graduate Studies; Ph.D., Univ of Toronto, 1991

Research Focus: Knowledge representation, reasoning and search, algorithms and complexity, planning, machine learning, cognitive science, software agents, and connections between computational complexity and statistical physics

Research Areas: Artificial Intelligence

Phoebe Sengers
Associate Professor; Science and Tech Studies, CS Field Member; Ph.D., Carnegie Mellon Univ, 1998

Research Focus: Ecological Media, or interactive media devices which shape our experience of the environment in our everyday lives

Research Areas: Human Interaction

Elaine Shi

On Leave: Spring 2018

Associate Professor; Computer Science, CS Field Member; Ph.D., Carnegie Mellon, 2008

Research Focus: Security, cryptography, programming languages, and systems, with applications to cryptocurrency, cloud computing, and privacy

Research Areas: Security, Theory of Computing

Vitaly Shmatikov
Professor; Computer Science, Cornell Tech, CS Field Member; Ph.D., Stanford, 2000

Research Focus: Reseach focus: Computer Security and privacy

Research Areas: Security

Emin Gun Sirer
Associate Professor; Computer Science, CS Field Member; Ph.D., Univ of Washington, 2002

Research Focus: Secure distributed systems, extensible operating systems, language-based security, automated testing

Research Areas: Security, Systems and Networking

Noah Snavely
Associate Professor; Computer Science, Cornell Tech, CS Field Member; Ph.D., Univ of Washington, 2008

Research Focus: Computer vision and computer graphics, specifically 3D reconstruction and rendering from large community photo collections

Research Areas: Graphics, Human Interaction

Karthik Sridharan
Assistant Professor; Computer Science, CS Field Member; Ph.D., Toyota Technological Institute at Chicago, 2012

Research Focus: Machine learning with a focus on theoretical analysis and design of learning algorithms. Online learning, statistical learning theory, stochastic optimization and empirical process theory

Research Areas: Artificial Intelligence, Theory of Computing

G. Edward Suh
Associate Professor; Electrical and Computer Engineering, CS Field Member; Ph.D., M.I.T., 2005

Research Focus: Architectural support for security, reliability, and programmability; field extensible/repairable architecture; non-volatile microprocessors for highly embedded systems; intelligent on-chip networks

Research Areas: Computer Architecture & VLSI, Security

A. Kevin Tang
Associate Professor; Electrical and Computer Engineering, CS Field Member; Ph.D., Caltech, 2006

Research Focus: Networks, especially control and optimization of engineering networks such as the Internet, power grids, and neural networks

Ross Tate
Assistant Professor; Computer Science, CS Field Member; Ph.D., Univ of California, San Diego, 2012

Research Focus: Language Design, Program Optimization, Type Theory, Semantics, Program Analysis

Research Areas: Programming Languages

Tim Teitelbaum
Professor Emeritus; Computer Science; Ph.D., Carnegie-Mellon University, 1976

Research Focus: Automatic Program Analysis

Immanuel Trummer
Assistant Professor; Computer Science, CS Field Member; Ph.D., EPFL, 2016

Research Focus: Databases, data science, optimization

Research Areas: Database Systems

Since we recently announced our $10001 Binary Battle to promote applications built on the Mendeley API (now including PLoS as well), I decided to take a look at the data to see what people have to work with. My analysis focused on our second largest discipline, Computer Science. Biological Sciences (my discipline) is the largest, but I started with this one so that I could look at the data with fresh eyes, and also because it’s got some really cool papers to talk about. Here’s what I found:

What I found was a fascinating list of topics, with many of the expected fundamental papers like Shannon’s Theory of Information and the Google paper, a strong showing from Mapreduce and machine learning, but also some interesting hints that augmented reality may be becoming more of an actual reality soon.

The top graph summarizes the overall results of the analysis. This graph shows the Top 10 papers among those who have listed computer science as their discipline and chosen a subdiscipline. The bars are colored according to subdiscipline and the number of readers is shown on the x-axis. The bar graphs for each paper show the distribution of readership levels among subdisciplines. 17 of the 21 CS subdisciplines are represented and the axis scales and color schemes remain constant throughout. Click on any graph to explore it in more detail or to grab the raw data.(NB: A minority of Computer Scientists have listed a subdiscipline. I would encourage everyone to do so.)


1. Latent Dirichlet Allocation (available full-text)

LDA is a means of classifying objects, such as documents, based on their underlying topics. I was surprised to see this paper as number one instead of Shannon’s information theory paper (#7) or the paper describing the concept that became Google (#3). It turns out that interest in this paper is very strong among those who list artificial intelligence as their subdiscipline. In fact, AI researchers contributed the majority of readership to 6 out of the top 10 papers. Presumably, those interested in popular topics such as machine learning list themselves under AI, which explains the strength of this subdiscipline, whereas papers like the Mapreduce one or the Google paper appeal to a broad range of subdisciplines, giving those papers a smaller numbers spread across more subdisciplines. Professor Blei is also a bit of a superstar, so that didn’t hurt. (the irony of a manually-categorized list with an LDA paper at the top has not escaped us)

2. MapReduce : Simplified Data Processing on Large Clusters (available full-text)

It’s no surprise to see this in the Top 10 either, given the huge appeal of this parallelization technique for breaking down huge computations into easily executable and recombinable chunks. The importance of the monolithic “Big Iron” supercomputer has been on the wane for decades. The interesting thing about this paper is that had some of the lowest readership scores of the top papers within a subdiscipline, but folks from across the entire spectrum of computer science are reading it. This is perhaps expected for such a general purpose technique, but given the above it’s strange that there are no AI readers of this paper at all.

3. The Anatomy of a large-scale hypertextual search engine (available full-text)

In this paper, Google founders Sergey Brin and Larry Page discuss how Google was created and how it initially worked. This is another paper that has high readership across a broad swath of disciplines, including AI, but wasn’t dominated by any one discipline. I would expect that the largest share of readers have it in their library mostly out of curiosity rather than direct relevance to their research. It’s a fascinating piece of history related to something that has now become part of our every day lives.

4. Distinctive Image Features from Scale-Invariant Keypoints

This paper was new to me, although I’m sure it’s not new to many of you. This paper describes how to identify objects in a video stream without regard to how near or far away they are or how they’re oriented with respect to the camera. AI again drove the popularity of this paper in large part and to understand why, think “Augmented Reality“. AR is the futuristic idea most familiar to the average sci-fi enthusiast as Terminator-vision. Given the strong interest in the topic, AR could be closer than we think, but we’ll probably use it to layer Groupon deals over shops we pass by instead of building unstoppable fighting machines.

5. Reinforcement Learning: An Introduction (available full-text)

This is another machine learning paper and its presence in the top 10 is primarily due to AI, with a small contribution from folks listing neural networks as their discipline, most likely due to the paper being published in IEEE Transactions on Neural Networks. Reinforcement learning is essentially a technique that borrows from biology, where the behavior of an intelligent agent is is controlled by the amount of positive stimuli, or reinforcement, it receives in an environment where there are many different interacting positive and negative stimuli. This is how we’ll teach the robots behaviors in a human fashion, before they rise up and destroy us.

6. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions (available full-text)

Popular among AI and information retrieval researchers, this paper discusses recommendation algorithms and classifies them into collaborative, content-based, or hybrid. While I wouldn’t call this paper a groundbreaking event of the caliber of the Shannon paper above, I can certainly understand why it makes such a strong showing here. If you’re using Mendeley, you’re using both collaborative and content-based discovery methods!

7. A Mathematical Theory of Communication (available full-text)

Now we’re back to more fundamental papers. I would really have expected this to be at least number 3 or 4, but the strong showing by the AI discipline for the machine learning papers in spots 1, 4, and 5 pushed it down. This paper discusses the theory of sending communications down a noisy channel and demonstrates a few key engineering parameters, such as entropy, which is the range of states of a given communication. It’s one of the more fundamental papers of computer science, founding the field of information theory and enabling the development of the very tubes through which you received this web page you’re reading now. It’s also the first place the word “bit”, short for binary digit, is found in the published literature.

8. The Semantic Web (available full-text)

In The Semantic Web, Tim Berners-Lee, Sir Tim, the inventor of the World Wide Web, describes his vision for the web of the future. Now, 10 years later, it’s fascinating to look back though it and see on which points the web has delivered on its promise and how far away we still remain in so many others. This is different from the other papers above in that it’s a descriptive piece, not primary research as above, but still deserves it’s place in the list and readership will only grow as we get ever closer to his vision.

9. Convex Optimization (available full-text)

This is a very popular book on a widely used optimization technique in signal processing. Convex optimization tries to find the provably optimal solution to an optimization problem, as opposed to a nearby maximum or minimum. While this seems like a highly specialized niche area, it’s of importance to machine learning and AI researchers, so it was able to pull in a nice readership on Mendeley. Professor Boyd has a very popular set of video classes at Stanford on the subject, which probably gave this a little boost, as well. The point here is that print publications aren’t the only way of communicating your ideas. Videos of techniques at SciVee or JoVE or recorded lectures (previously) can really help spread awareness of your research.

10. Object recognition from local scale-invariant features (available in full-text)

This is another paper on the same topic as paper #4, and it’s by the same author. Looking across subdisciplines as we did here, it’s not surprising to see two related papers, of interest to the main driving discipline, appear twice. Adding the readers from this paper to the #4 paper would be enough to put it in the #2 spot, just below the LDA paper.

Conclusions

So what’s the moral of the story? Well, there are a few things to note. First of all, it shows that Mendeley readership data is good enough to reveal both papers of long-standing importance as well as interesting upcoming trends. Fun stuff can be done with this! How about a Mendeley leaderboard? You could grab the number of readers for each paper published by members of your group, and have some friendly competition to see who can get the most readers, month-over-month. Comparing yourself against others in terms of readers per paper could put a big smile on your face, or it could be a gentle nudge to get out to more conferences or maybe record a video of your technique for JoVE or Khan Academy or just Youtube.

Another thing to note is that these results don’t necessarily mean that AI researchers are the most influential researchers or the most numerous, just the best at being accounted for. To make sure you’re counted properly, be sure you list your subdiscipline on your profile, or if you can’t find your exact one, pick the closest one, like the machine learning folks did with the AI subdiscipline. We recognize that almost everyone does interdisciplinary work these days. We’re working on a more flexible discipline assignment system, but for now, just pick your favorite one.

These stats were derived from the entire readership history, so they do reflect a founder effect to some degree. Limiting the analysis to the past 3 months would probably reveal different trends and comparing month-to-month changes could reveal rising stars.

Technical details:
To do this analysis I queried the Mendeley database, analyzed the data using R, and prepared the figures with Tableau Public. A similar analysis can be done dynamically using the Mendeley API. The API returns JSON, which can be imported into R using the fineRJSONIO package from Duncan Temple Lang and Carl Boettiger is implementing the Mendeley API in R. You could also interface with the Google Visualization API to make motion charts showing a dynamic representation of this multi-dimensional data. There’s all kinds of stuff you could do, so go have some fun with it. I know I did.

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