Term Paper Topics For Cse Federal Credit

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This is a list of important publications in computer science, organized by field.

Some reasons why a particular publication might be regarded as important:

  • Topic creator – A publication that created a new topic
  • Breakthrough – A publication that changed scientific knowledge significantly
  • Influence – A publication which has significantly influenced the world or has had a massive impact on the teaching of computer science.

Artificial intelligence[edit]

Computing Machinery and Intelligence[edit]

Description: This paper discusses whether machines can think and suggested the Turing test as a method for checking it.

A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence[edit]

Description: This summer research proposal inaugurated and defined the field. It contains the first use of the term artificial intelligence and this succinct description of the philosophical foundation of the field: "every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it." (See philosophy of AI) The proposal invited researchers to the Dartmouth conference, which is widely considered the "birth of AI". (See history of AI.)

Fuzzy sets[edit]

  • Lotfi Zadeh
  • Information and Control, Vol. 8, pp. 338–353. (1965).

Description: The seminal paper published in 1965 provides details on the mathematics of fuzzy set theory.

Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference[edit]

Description: This book introduced Bayesian methods to AI.

Artificial Intelligence: A Modern Approach[edit]

Description: The standard textbook in Artificial Intelligence. The book web site lists over 1100 colleges.

Machine learning[edit]

An Inductive Inference Machine[edit]

  • Ray Solomonoff
  • IRE Convention Record, Section on Information Theory, Part 2, pp. 56–62, 1957
  • (A longer version of this, a privately circulated report, 1956, is online).

Description: The first paper written on machine learning. Emphasized the importance of training sequences, and the use of parts of previous solutions to problems in constructing trial solutions to new problems.

Language identification in the limit[edit]

Description: This paper created Algorithmic learning theory.

On the uniform convergence of relative frequencies of events to their probabilities[edit]

Description: Computational learning theory, VC theory, statistical uniform convergence and the VC dimension.

A theory of the learnable[edit]

Description: The Probably approximately correct learning (PAC learning) framework.

Learning representations by back-propagating errors[edit]

Description: Development of Backpropagation algorithm for artificial neural networks. Note that the algorithm was first described by Paul Werbos in 1974.

Induction of Decision Trees[edit]

Description: Decision Trees are a common learning algorithm and a decision representation tool. Development of decision trees was done by many researchers in many areas, even before this paper. Though this paper is one of the most influential in the field.

Learning Quickly When Irrelevant Attributes Abound: A New Linear-threshold Algorithm[edit]

Description: One of the papers that started the field of on-line learning. In this learning setting, a learner receives a sequence of examples, making predictions after each one, and receiving feedback after each prediction. Research in this area is remarkable because (1) the algorithms and proofs tend to be very simple and beautiful, and (2) the model makes no statistical assumptions about the data. In other words, the data need not be random (as in nearly all other learning models), but can be chosen arbitrarily by "nature" or even an adversary. Specifically, this paper introduced the winnow algorithm.

Learning to predict by the method of Temporal difference[edit]

Description: The Temporal difference method for reinforcement learning.

Learnability and the Vapnik–Chervonenkis dimension[edit]

Description: The complete characterization of PAC learnability using the VC dimension.

Cryptographic limitations on learning boolean formulae and finite automata[edit]

Description: Proving negative results for PAC learning.

The strength of weak learnability[edit]

Description: Proving that weak and strong learnability are equivalent in the noise free PAC framework. The proof was done by introducing the boosting method.

A training algorithm for optimum margin classifiers[edit]

Description: This paper presented support vector machines, a practical and popular machine learning algorithm. Support vector machines often use the kernel trick.

A fast learning algorithm for deep belief nets[edit]

Description: This paper presented a tractable greedy layer-wise learning algorithm for deep belief networks which led to great advancement in the field of deep learning.

Knowledge-based analysis of microarray gene expression data by using support vector machines[edit]

Description: The first application of supervised learning to gene expression data, in particular Support Vector Machines. The method is now standard, and the paper one of the most cited in the area.

Collaborative networks[edit]

  • Camarinha-Matos, L. M.; Afsarmanesh, H. (2005). "Collaborative networks: A new scientific discipline, J". Intelligent Manufacturing. 16 (4–5): 439–452. doi:10.1007/s10845-005-1656-3. 
  • Camarinha-Matos, L. M.; Afsarmanesh, H. (2008). Collaborative Networks: Reference Modeling, Springer: New York.


On the translation of languages from left to right[edit]

Description: LR parser, which does bottom up parsing for deterministic context-free languages. Later derived parsers, such as the LALR parser, have been and continue to be standard practice, such as in Yacc and descendents.[1]

Semantics of Context-Free Languages.[edit]

Description: About grammar attribution, the base for yacc's s-attributed and zyacc's LR-attributed approach.

A program data flow analysis procedure[edit]

Description: From the abstract: "The global data relationships in a program can be exposed and codified by the static analysis methods described in this paper. A procedure is given which determines all the definitions which can possibly reach each node of the control flow graph of the program and all the definitions that are live on each edge of the graph."

A Unified Approach to Global Program Optimization[edit]

Description: Formalized the concept of data-flow analysis as fixpoint computation over lattices, and showed that most static analyses used for program optimization can be uniformly expressed within this framework.

YACC: Yet another compiler-compiler[edit]

Description: Yacc is a tool that made compiler writing much easier.

gprof: A Call Graph Execution Profiler[edit]

Description: The gprofprofiler

Compilers: Principles, Techniques and Tools[edit]

Description: This book became a classic in compiler writing. It is also known as the Dragon book, after the (red) dragon that appears on its cover.

Computer architecture[edit]

Colossus computer[edit]

Description: The Colossus machines were early computing devices used by British codebreakers to break German messages encrypted with the Lorenz Cipher during World War II. Colossus was an early binary electronic digital computer. The design of Colossus was later described in the referenced paper.

First Draft of a Report on the EDVAC[2][edit]

Description: It contains the first published description of the logical design of a computer using the stored-program concept, which has come to be known as the von Neumann architecture.

Architecture of the IBM System/360[edit]

Description: The IBM System/360 (S/360) is a mainframe computer system family announced by IBM on April 7, 1964. It was the first family of computers making a clear distinction between architecture and implementation.

The case for the reduced instruction set computer[edit]

Description: The reduced instruction set computer( RISC) CPU design philosophy. The RISC is a CPU design philosophy that favors a reduced set of simpler instructions.



The CRAY-1 Computer System[edit]

Description: The Cray-1 was a supercomputer designed by a team including Seymour Cray for Cray Research. The first Cray-1 system was installed at Los Alamos National Laboratory in 1976, and it went on to become one of the best known and most successful supercomputers in history.

Validity of the Single Processor Approach to Achieving Large Scale Computing Capabilities[edit]

Description: The Amdahl's Law.

A Case for Redundant Arrays of Inexpensive Disks (RAID)[edit]

Description: This paper discusses the concept of RAID disks, outlines the different levels of RAID, and the benefits of each level. It is a good paper for discussing issues of reliability and fault tolerance of computer systems, and the cost of providing such fault-tolerance.

The case for a single-chip multiprocessor[edit]

Description: This paper argues that the approach taken to improving the performance of processors by adding multiple instruction issue and out-of-order execution cannot continue to provide speedups indefinitely. It lays out the case for making single chip processors that contain multiple "cores". With the mainstream introduction of multicore processors by Intel in 2005, and their subsequent domination of the market, this paper was shown to be prescient.

Computer graphics[edit]

The Rendering Equation[edit]

  • J. Kajiya
  • SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques pages 143—150[3]

Elastically deformable models[edit]

  • Demetri Terzopoulos, John Platt, Alan Barr, Kurt Fleischer
  • Computer Graphics, 21(4), 1987, 205–214, Proc. ACM SIGGRAPH'87 Conference, Anaheim, CA, July 1987.
  • Online version(PDF)

Description: The Academy of Motion Picture Arts and Sciences cited this paper as a "milestone in computer graphics".

Computer vision[edit]

The Phase Correlation Image Alignment Method[edit]

  • C.D. Kuglin and D.C. Hines
  • IEEE 1975 Conference on Cybernetics and Society, 1975, New York, pp. 163–165, September

Description: A correlation method based upon the inverse Fourier transform

Determining Optical Flow[edit]

Description: A method for estimating the image motion of world points between 2 frames of a video sequence.

An Iterative Image Registration Technique with an Application to Stereo Vision[edit]

Description: This paper provides efficient technique for image registration

The Laplacian Pyramid as a compact image code[edit]

Description: A technique for image encoding using local operators of many scales.

Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images[edit]

Description: introduced 1) MRFs for image analysis 2) the Gibbs sampling which revolutionized computational Bayesian statistics and thus had paramount impact in many other fields in addition to Computer Vision.

Snakes: Active contour models[edit]

Description: An interactive variational technique for image segmentation and visual tracking.

Condensation – conditional density propagation for visual tracking[edit]

Description: A technique for visual tracking

Object recognition from local scale-invariant features[edit]

Description: A technique (scale-invariant feature transform) for robust feature description

Concurrent, parallel, and distributed computing[edit]

Main article: List of important publications in concurrent, parallel, and distributed computing

Topics covered: concurrent computing, parallel computing, and distributed computing.


A relational model for large shared data banks[edit]

Description: This paper introduced the relational model for databases. This model became the number one model.

Binary B-Trees for Virtual Memory[edit]

  • Rudolf Bayer
  • ACM-SIGFIDET Workshop 1971, San Diego, California, Session 5B, p. 219–235.

Description: This paper introduced the B-Treesdata structure. This model became the number one model.

Relational Completeness of Data Base Sublanguages[edit]

  • E. F. Codd
  • In: R. Rustin (ed.): Database Systems: 65-98, Prentice Hall and IBM Research Report RJ 987, San Jose, California : (1972)
  • Online version (PDF)

Description: Completeness of Data Base Sublanguages

The Entity Relationship Model – Towards a Unified View of Data[edit]

Description: This paper introduced the entity-relationship diagram(ERD) method of database design.

SEQUEL: A structured English query language[edit]

  • Donald D. Chamberlin, Raymond F. Boyce
  • International Conference on Management of Data, Proceedings of the 1974 ACM SIGFIDET (now SIGMOD) workshop on Data description, access and control, Ann Arbor, Michigan, pp. 249–264

Description: This paper introduced the SQL language.

The notions of consistency and predicate locks in a database system[edit]

Description: This paper defined the concepts of transaction, consistency and schedule. It also argued that a transaction needs to lock a logical rather than a physical subset of the database.

Federated database systems for managing distributed, heterogeneous, and autonomous databases[edit]

  • Amit Sheth, J.A. Larson,"
  • ACM Computing Surveys - Special issue on heterogeneous databases Surveys, Volume 22 Issue 3, Pages 183 - 236, Sept. 1990
  • ACM source

Description: Introduced federated database systems concept leading huge impact on data interoperability and integration of hetereogenous data sources.

Mining association rules between sets of items in large databases[edit]

Description: Association rules, a very common method for data mining.

History of computation[


Suggested Undergraduate Research Topics

Links to many research areas in the department may be found at http://www.cs.princeton.edu/research/areas/ while links to projects may be found at http://www.cs.princeton.edu/research/projects/.








Computer Science Faculty:

Prof. Ibrahim Albluwi, 221 Nassau Street
Prof. Andrew Appel, Room 306
Prof. Sanjeev Arora, Room 307 -  On Leave Spring 2017
Prof. David August, Room 221
Prof. Mark Braverman, Room 411 

Prof. Bernard Chazelle, Room 404
Dr. Marshini Chetty, Room 424
Prof. David Dobkin, Room 419 Independent Work Seminar Instructor Spring 2018 
Dr. Robert Dondero, Room 206
Prof. Zeev Dvir, Room 405
Prof. Barbara Engelhardt, Room 322
Prof. Nick Feamster, Sherrerd Hall - Room 310 
Dr. Christiane Fellbaum, Room 412 
Prof. Edward Felten, Sherrerd Hall -  Room 302
Prof. Adam Finkelstein, Room 424 
Dr. Robert S. Fish, Room 206  Independent Work Seminar Instructor Spring 2018 
Prof. Michael Freedman, Room 308 
Prof. Thomas Funkhouser, Room 422 - On sabbitical during the 2017-2018 AY
Dr. Ananda Gunawardena ("Guna"), 221 Nassau Street - Room 103 Independent Work Seminar Instructor Spring 2018 
Prof. Aarti Gupta, Room 220
Prof. Elad Hazan, Room 407
Asst. Prof. Kyle Jamieson, Room 305 
Dr. Alan Kaplan, 221 Nassau Street - Room 105 - Independent Work Seminar Instructor Spring 2018 
Prof. Brian Kernighan, Room 311
Asst. Prof. Zachary Kincaid, Room 315 Independent Work Seminar Instructor Spring 2018 
Prof. Andrea LaPaugh, Room 304
Dr. Dan Leyzberg, Room 208
Prof. Kai Li, Room 321
Dr. Xiaoyan Li, 221 Nassau Street - Room 104
Dr. Jérémie Lumbroso, Room 209
Prof. Margaret Martonosi, Room 410
Dr. Christopher Moretti, Room 208
Dr. Arvind Narayanan, 308 Sherrerd Hall
Dr. Iasonas Petras, Room 209
Prof. Benjamin Raphael, Room 309 
Prof. Jennifer Rexford, Room 222
Prof. Szymon Rusinkiewicz, Room 406
Prof. Robert Sedgewick, Room 319
Prof. Sebastian Seung, Princeton Neuroscience Institute Room 153
Prof. Jaswinder Pal Singh, Room 423  Independent Work Seminar Instructor Spring 2018 
Prof. Mona Singh, Room 420
Prof. Robert Tarjan, Room 324
Prof. Olga Troyanskaya, Room 320
Prof. David Walker, Room 211
Dr. Kevin Wayne, Room 207
Asst. Prof. Matt Weinberg, Room 317
Asst. Prof. Mark Zhandry, Room 314

Opportunities outside the department:

Prof. Sharad Malik, Engineering Quad, Room B224
Prof. Prateek Mittal, Engineering Quadrangle, Room B236
Prof. Ken Norman, PNI 137 Neuroscience Institute
Caroline Savage, Office of Sustainability Phone: (609) 258-7513, cs35@princeton.edu
Janet Vertesi, Sociology Dept, Wallace Hall 122 - On Leave during the 2016-2017 Academic Year
Prof. David Wentzlaff, Engineering Quadrangle, Room 228


Dr. Ibrahim Albluwi, 221 Nassau Street

Prof. Andrew Appel, Room 306 

  • Research Areas: Formal methods, programming languages, compilers, computer security.
  • Independent Research Topics:
    • Automated theorem proving (suggestion: take COS 441 first).
    • Trustworthiness of voting machines and/or internet voting.
    • Computer game-playing programs.

Prof. Sanjeev Arora, Room 307 -  On Leave Spring 2017

  • Computational complexity; Probabilistically Checkable Proofs (PCPs); Efficient algorithms for finding approximate solutions to NP-hard problems (or proving that they don't exist); provable bounds for machine learning.
  • Independent Research Topics: 
    • Show that finding approximate solutions to NP-complete problems is also NP-complete (i.e., come up with NP-completeness reductions a la COS 487). 
    • Experimental Algorithms: Implementing and Evaluating Algorithms using existing software packages. 
    • Studying/designing provable algorithms for machine learning and implementions using packages like scipy and MATLAB, including applications in Natural language processing and deep learning.
    • Any topic in theoretical computer science.

Prof. David August, Room 221

  • Research Areas: Computer Architecture, Compilers, Parallelism, Security, Performance.
  • Independent Research Topics:
    • Quantopian and sites like it let anyone build and backtest investment algorithms.  The best algorithms earn money for their authors.  These sites generally provide the usual sorts of streaming data for algorithms to process (e.g. price, volume, dividents, earnings, M&A sentiment).  I believe there exist many overlooked data sources not commonly used (if at all) that give algorithms an edge over those not using them.  Let's explore this idea.
    • My group has developed a new model for securing extremely complex computing devices with simple components.  This model, called TrustGuard, modivates a number of projects including:  1) formally verifying the simple protective components that form TrustGuard's root of trust, 2) auditing TrustGuard to find vulnerabilities, 3) designing/deploying a capture the flag contest (e.g. steal a secret from a web server) to test TrustGuard against motivated atttackers.
    • Dynamic dependence graphs show much more application parallesism than currently realized, often orders of magnitude more.  Can speculative run-time optimations realize some untapped parallelism by recognising the patterns that emerge in these graphs during execution?  Even a little progress in this area could dramatically speed applications and reduce their energy consumption.
    • Any other interesting topic in computer architecture or compilers.  

Prof. Mark Braverman, Room 411 - 

  • Research Areas: computational complexity, algorithms, applied probability, computability over the real numbers, game theory and mechanism design, information theory, applications of machine learning in healthcare and medicine. 
  • Independent Research Topics: 
    • Topics in computational and communication complexity.
    • Applications of information theory in complexity theory.
    • Algorithms for problems under real-life assumptions.
    • Game theory, network effects, and mechanism design.
    • Computation involving dynamical systems, fractals, and cellular automata. 
    • Game theory applied to problems in healthcare.

Prof. Bernard Chazelle, Room 404

  • Research Areas: Natural Algorithms, Computational Geometry, Sublinear Algorithms. 
  • Independent Research Topics
    • Natural algorithms (flocking, swarming, social networks, etc).
    • Sublinear algorithms
    • Self-improving algorithms
    • Markov data structures

Dr. Marshini Chetty, Room 424

  • Research Areas: Human-Computer Interaction, Ubiquitous Computing, Usable Security, Human-Computer Interaction for Development.
  • Independent Research Topics:
    • Kids and Internet Safety - design apps/browser extensions/tools/user interface cues to help elementary school kids navigate the web safely at home and on the go
    • Software Updates - analyze qualitative data set to see how and why people avoid updates with aim to redesign software updating user interfaces
    • Free Internet Services - analyze qualitative data set from South Africa on Free Basics Users/create new ideas for improved free Internet services for low income users
    • Pollution Attacks - create tools to help users detect and control the "pollution" of Internet content that affects their user profiles and future activities on the Internet such as fake search results, fake news, etc

Prof. David Dobkin, Room 419 

  • Research areas:  processing and machine learning in public data sets, information visualization
    • Visualizing and learning from public data sets
    • Sports analytics
    • Development of interesting mobile phone apps

Dr. Robert Dondero, Room 206

  • Research Areas:  Software engineering; software engineering education.
  • Independent Research Topics:
    • Develop or evaluate tools to facilitate student learning in undergraduate computer science courses at Princeton, and beyond.
    • In particular, can code critiquing tools help students learn about software quality?

Prof. Zeev Dvir, Room 405

  • Research Areas:  Research Areas: computational complexity, pseudo-randomness, coding theory and discrete mathematics.
  • Independent Research: I have various research problems related to Pseudorandomness, Coding theory, Complexity and Discrete mathematics - all of which require strong mathematical background.

Prof. Barbara Engelhardt, Room 322

  • Research Areas: Machine Learning, Statistics, Statistical Genomics
  • Independent Research Topics:
    • Development of statistical and ML models for large scale data analysis
      • fMRI data analysis
      • genetic, epigenetic, and organismal data
      • medical data: EMRs, time series, longitudinal studies
      • Other: music, movie ratings, recipes, text
    • Statistical models for specific questions:
      • Causal inference and instrumental variable analysis
      • Model checking with posterior predictive checks
      • Inference of undirected network from observational and time-series data

Prof. Nick Feamster, Sherrerd Hall - Room 310  

  • Research Areas: Networking, Security, Anti-Censorship
  • Fall 2017 IW Seminar - Security and Privacy in the Internet of Things (IoT)
  • I am generally interested in topics that improve the performance and accessibility of the Internet and its applications. My interests range from network management and and security (the things that make the network "work", including topics like software defined networking) to Internet censorship and control to performance measurement (especially in far-flung regions of the world, such as developing regions). The type of work I do is general systems-based, empirical, and data-driven; much of the research projects described below involve collection and analysis of large-scale network data (including applications of statistical machine learning), as well as the design and implementation of the systems to support this data collection, analysis, and inference.

Below are some broad areas where I am currently working and have specific project ideas.  I generally find that undergraduate research works best if students select a problem from the topic list below; this allows me to devote more attention to your project, and also to couple you with other students who are working on related problems, thus allowing you to take advantage of a broader knowledge base than just me.

  • Performance measurement of broadband and mobile access networks.  As broadband Internet access becomes more pervasive, understanding the performance that end users receive---and how they use he available capacity---is more important than ever.  Towards this goal, I work on technologies to characterize and improve the performance of fixed and mobile broadband access networks.  My group develops software to perform these measurements, including BISmark (for home routers, Raspberry Pis, etc.) and My Speed Test (for Android), and I am very interested in working with students who want to gain experience developing tools and systems for these environments or with analyzing data collected from these settings.  Below are some possible project ideas:
    • Understanding Mobile Data Performance and Usage. For approximately two years, we have been collecting data from a relatively large deployment of a mobile phone application that we developed called MySpeed Test, which is available in the Google App Store (https://play.google.com/store/apps/details?id=com.num&hl=en). At its peak, the tool had about 5,000 users in more than 30 countries.  Currently, the tool has about 1,000 installed versions running.  The tool collects a variety of data, both about network performance and about network usage.  For example, the tool collects information about the throughput and latency that a mobile device experiences to different network destinations (e.g., Google, Facebook).  In addition to the performance data, the tool also gathers data about user behavior—such as how much data each application on the phone consumes, how often they use each application, and so forth.  Finally, the application collects metadata, such as the devices physical location, remaining battery life, billing cycle, and other environmental characteristics.  We envision a variety of possible data analysis exercises that students could perform using this dataset.  The dataset has such a rich set of features and many analysis questions are possible. One possible starting point would be to ask how user behavior correlates with the network performance, the amount of data they have used in their billing cycle, time of day, the type of device they have, and so forth. 
    • Pinpointing locations of congestion (e.g., Comcast/Netflix). Current events have highlighted the ongoing congestion episodes on the wide-area Internet, with consumers continually complaining of poor performance for various applications and Internet destinations. Yet, despite widespread attention in industry and popular media, we lack accurate methods for pinpointing congestion locations.  We could use distributed measurement frameworks such as BISmark, MySpeedTest, and others (e.g., RIPE Atlas) to try to get more accurate characterizations of the locations of Internet congestion.  See this (flawed) report for a starting point on a problem statement; we can probably do better.
    • Home network performance troubleshooting. We have developed initial algorithms to determine whether a performance bottleneck is caused by a user's home wireless network or by the portion of the path on the wide area Internet.  These algorithms can be deployed on the BISmark home router devices and placed in user homes to study the contribution of users' home wireless networks to various network and application problems.
    • Performance measurement and characterization of emerging technologies. I would like to use our existing measurement platforms (i.e., BISmark, MySpeedTest) to design measurement tools that characterize various ISPs' support for emerging technologies, such as DNSSEC and IPv6?
  • Censorship measurement and circumvention. More than 60 countries around the world implement some form of Internet censorship.  We are developing techniques to better understand these practices and ultimately to assist citizens with circumventing these controls.  Along these lines, two possible project areas involve (1) analyzing measurements of Internet censorship that we have collected (or collecting new ones); (2) better understanding the capabilities and limitations of existing circumvention tools; (3) designing new approaches for circumvention.  A few possibilities are below.
    • Measuring Internet censorship. What is the current state of Internet censorship and how can we fight it? We are interested in who is censoring the Internet, what they are blocking access to, and how they are restricting access. In answering these questions, you could work on anything from developing ways to get around censorship to helping NGOs around the world advocate against censorship. 
    • Analysis of censorship measurements. We have collected extensive measurements of Internet censorship and manipulation using a tool called Encore. It would be interesting to analyze the data on Web filtering that we have been gathering for more than a year, with the goal of identifying both broader patterns across different regions and countries and across time.
    • Extensions to ExitMap. ExitMap is a tool for characterizing properties of Tor exit relays across the Internet.  The tool was written by Philipp Winter, who is now a researcher at Princeton and is an active Tor developer.  Projects involving Exitmap will give you a chance to interact directly with a Tor developer!
      • Add new scanning modules.  For example, add a module that searches for arbitrary keywords, crawls the web, and compares Tor and not-Tor traffic. Or, add a module that looks for exit relays performing traffic monitoring.
      • Add code that makes it possible that exitmap runs autonomously in the background and periodically fetches new server descriptor: <https://github.com/NullHypothesis/exitmap/issues/7>
  • Software-defined networking (SDN). Briefly, SDN makes it possible to control the behavior of the network from a single high-level program (e.g., a Python or Java program). This paradigm opens up many new possibilities, since controlling the network becomes a software development problem, rather than one that is constrained by low-level, vendor-specific, proprietary interfaces.  I'm interested in two areas of SDN:
    • Applications of SDN to Wide-Area Networking. We are developing an SDN controller and working to deploy this in some of the world's largest Internet Exchange Points (IXPs), with the hopes of changing how Internet service providers (e.g., Comcast, Netflix) exchange traffic. The flexibility that SDN provides makes it possible to improve security, enable more flexible interconnection policies, automate traffic management, and so forth. There are many different possibilities for working in this area. Here are some project ideas.
    • Fast, scalable, and accurate inference algorithms that can provide input to network control systems.  Kinetic is one such system that we have built in the past that integrates event stream processing with network control, to solve problems with dynamic control such as authentication and intrusion detection. We are beginning to explore how stream processing engines such as Kafka and Storm can be used to process network event streams, how scalable machine learning can be applied to network inference, and how the output from these algorithms can be used to enable networks that are self-tuning.
  • Internet of Things (IoT) security.  The increasing number and diversity of consumer electronics devices that are connected to the Internet introduces a grave security threat, since many of these devices are what I would call "fundamentally insecure". Given that we are likely to have a large number of insecure devices connected to the network that cannot be patched, we need new capabilities in the network that ensure both the security of these devices and the privacy of their users.  Some possible sub-problems include:
    • Device fingerprinting.  By analyzing the traffic patterns from devices on the network, determine what types of devices are connected to the network?  (For example, can you automatically identify a thermostat, photo frame, etc. based on network traffic patterns?)
    • Anomaly detection.  Develop algorithms that can detect when an IoT device is deviating from normal behavior.  Incorporate these algorithms into a "network firewall" that can automatically detect and mitigate anomalous or attack traffic.

Dr. Christiane Fellbaum, Room 412

  • Research Areas: theoretical and computational linguistics, word sense disambiguation, lexical resource construction, English and multilingual WordNet(s), ontology
  • Fall 2017 IW Seminar:
  • Independent Research Topics:
    • Anything having to do with natural language--come and see me with/for ideas suitable to your background and interests. Some topics students have worked on in the past:
    • Developing parsers, part-of-speech taggers, morphological analyzers for underrepresented languages (you don't have to know the language to develop such tools!)
    • Quantitative approaches to theoretical linguistics questions
    • Extensions and interfaces for WordNet (English and WN in other languages),
    • Applications of WordNet(s), including:
    • Foreign language tutoring systems,
    • Spelling correction software,
    • Word-finding/suggestion software for ordinary users and people with memory problems,
    • Machine Translation 
    • Sentiment and Opinion detection
    • Automatic reasoning and inferencing
    • Collaboration with professors in the social sciences and humanities ("Digital Humanities")

Prof. Edward Felten, Sherrerd Hall -  Room 302 

  • Research Areas: Computer security and privacy; Internet software; technology law and policy.

  • Independent Research Topics:

    • Technology for open government.
    • Computer security and privacy.
    • Digital media distribution.
    • Copy protection and peer to peer technologies.
    • Electronic voting.
    • Technology, society and public policy.
    • Any other interesting or offbeat topic.

Prof. Adam Finkelstein, Room 424 

  • Research Areas: computer graphics, animation.
  • Fall 2017 Seminar - Deep Learning
  • Ideas for independent Research Topics:
    • Computer-generated drawings from 3D models.
    • Tools for making visual art using computer graphics.
    • New methods for computer animation.
    • Techniques for medical visualization.
    • Experiments where the data is collected via the internet.
    • Any interesting project in computer graphics.

Dr. Robert S. Fish, Room 206 -

Prof. Michael Freedman, Room 308 

Prof. Thomas Funkhouser, Room 422 - On sabbatical during the 2017-2018 AY. 

  • Research Areas: Computer Graphics, Computer Vision.
  • Independent Research Topics:
    • Develop methods for 3D scanning of interior environments.  
    • Investigate methods for recognizing objects in 3D scans.
    • Help build systems for analyzing 3D models in large repositories.
    • Any other projects related to computer graphics or computer vision.

Dr. Ananda Gunawardena ("Guna"), 221 Nassau Street - Room 103 

  • Research Areas: Learning Sciences, Educational Technology, Pen-based computing
  • Independent Research Topics:
    • Applications of Learning Sciences to Educational Technology platforms
    • Research and develop analytics and pedagogical models for classroom salon (http://classroomsalon.org), a social annotation platform used for collaborative learning. (reference: http://classroomsalon.com)
    • Research and develop usable and scalable pen/stylus based applications with particular emphasis on sketch recognition with domain specific information. (reference: http://www.cs.cmu.edu/~ab/PenComputing/)

Prof. Aarti Gupta, Room 220

  • Research Areas: Formal methods, program analysis, logic decision procedures
  • Independent Research Topics:
    • Finding bugs in open source software using automatic verification tools
    • Software verification (program analysis, model checking, test generation)
    • Decision procedures for logical reasoning (SAT solvers, SMT solvers)

Prof. Elad Hazan, Room 407

  • Research interests: 
  • Machine learning, efficient methods for mathematical optimization, statistical and computational learning theory, regret minimization in games.
  • Independent Research Topics:
    • Implementation and algorithm engineering of learning methods. 
    • Recommendation systems, matrix completion. 
    • Optimization for deep learning. 
    • Learning from missing and sparse data. 
    • Sublinear optimization

Asst. Prof. Kyle Jamieson, 

  • Research areas: Wireless and mobile networking; indoor radar and indoor localization; Internet of Things
  • Fall 2017 IW Seminar - Distributed Radio Sensing Across the Princeton Campus
  • Independent research topics (updated July '15):
    • Investigate the most efficient way to deliver packets to an array of low-cost Wi-Fi APs strung along the ceiling of a tunnel so that they may deliver those data to customers aboard a metro train speeding through the tunnel below.
    • Faster-than-Nyquist is a technique in the wireless physical layer that allows packets to be decoded when their transmissions are speeded up in a certain way.  Can you think of interesting ways of leveraging this at higher layers of the networking stack, for example the way it might interact with Wi-Fi’s link-layer frame aggregation functions?
    • Read papers from my lab and propose an interesting extension or a new “take" on the ideas we’ve explored.
    • See other topics on my independent work ideas page (campus IP and CS dept. login req'd)

Dr. Alan Kaplan, 221 Nassau Street - Room 105 -

  • Research Areas: mobile apps/technology,  mobile app development, internet of things, multi-programming language interoperability
  • Fall 2017 IW Seminar - Random Apps for Kindness
  • Independent Research Topics:
    • Civic Computing/Software for Social Good - These projects should have an impact - locally, nationally or even globally - by helping some community of interest. Projects may involve and/or extend existing (open source) platforms, or t can be entirely new ventures. Projects can involve mobile devices, web platforms, cloud-based backends, open APIs/data, hardware sensors, etc. But the end result should, quite simply, help people and communities in some way.
    • Automated Multi-Language Toolset - Design, develop and evaluate a toolset that helps automate the development of multi-language software (e.g., Android Java SDK and NDK).

Prof. Brian Kernighan, Room 311-

  • Research Areas: application-specific languages, document preparation, user interfaces, software tools, programming methodology
  • Fall 2017 IW Seminar - Computer Science Tools and Techniques for Digital Humanities 
  • Independent Research Topics: 
    • Application-oriented languages, scripting languages.
    • Tools; user interfaces; web services.
    • Digital humanities

Asst. Prof. Zachary Kincaid, Room 315 

Research areas: programming languages, program analysis, program verification, automated reasoning 

Independent Research Topics:

  • Develop a practical algorithm for an intractable problem (e.g., by developing practical search heuristics, or by reducing toSAT, or by identifying a tractable sub-problem, ...).
  • Design a domain-specific programming language, or prototype a new feature for an existing language.
  • Any interesting project related to programming languages or logic.

Prof. Andrea LaPaugh, Room 304 

  • Research Areas: Social network analysis; Search and retrieval of information; data mining, particularly clustering; combinatorial algorithms
  • Independent Research Topics:
    • Investigating new characterizations of the graph structure of the Web or other social networks.
    • Experimental evaluations of clustering algorithms, especially new algorithms.
    • Data mining a social network.
    • Other topics with an information discovery or management aspect, including novel applications using databases or information retrieval.

Dr. Dan Leyzberg, Room 208

  • Research Areas: human-robot interaction, human-computer interaction, online tools for computer science education.
  • Independent Research Topics:
    • How do people actually interact with technology? Investigate human-computer or human-robot interaction with experimental methodologies from the social sciences, including psychology and cognitive science. First step: identify the technology and usage/engagement metrics you might be interested in studying. You will write code that collects and analyzes this data.
    • Help me build tools for computer science education at Princeton and beyond. I taught high school computer science for many years and if you took any computer science at the high school level, you know that the tools available to teachers are relatively primitive. Princeton has a few internal tools developed for COS 126 that would be great to make available to high school students or to other colleges students such as the Java Visualizer and Websheets. You are not limited to these existing tools; propose a tool that might be useful to students and we'll try to make it work!

Prof. Kai Li, Room 321

  • Research Areas: Distributed systems; storage systems; content-based search and data analysis of large datasets.
  • Independent Research Topics:
    • Fast communication mechanisms for heterogeneous clusters.
    • Approximate nearest-neighbor search for high dimensional data.
    • Data analysis and prediction of in-patient medical data.
    • Optimized implementation of classification algorithms on manycore processors.

Dr. Xiaoyan Li, 221 Nassau Street - Room 104

  • Research areas: Information retrieval, novelty detection, question answering, AI, machine learning and data analysis.
  • Independent Research Topics:
    • Explore new statistical retrieval models for document retrieval and question answering.
    • Apply AI in various fields.
    • Apply supervised or unsupervised learning in health, education, finance, and social networks, etc.
    • Any interesting project related to AI, machine learning, and data analysis.

Dr. Jérémie Lumbroso, Room 209

  • Research areas: Probabilistic algorithms (data streaming algorithms & random generation), data analysis, data structures, analysis of algorithms,  analytic combinatorics.
  • Possible Independent Research Topics:
    • Develop new algorithms for the distinct sampling problem (useful to in data analysis to get fast representative histograms of a large set of data).
    • Extend existing universal random generation framework (such as Boltzmann Sampling, demo here), or improve their implementation.
    • Design an optimized algorithm for a specific combinatorial class.
    • Analyze an algorithm using precise analytic combinatorics.
    • Text (or data) clustering and processing; linguistic analysis (especially with French, German, Spanish, etc.).
    • ...
  • I am also coordinating the development of new grading and assessment infrastructure at Princeton, that will eventually be open-source and deployed at other universities. These projects focus on automation, using various techniques - such as OCR or OMR -, smart heuristics, and creative UI design, to streamline most tasks associated with a university. The goal is to be more efficient, to collect more data, and to better understand what makes a good course. You would have the opportunity to contribute to something that will be used at Princeton's CS department (of which the intro course has the highest enrollment on campus) and beyond for years to come. Here are some example projects:
    • Design/improve an OMR (Optical Mark Recognition) project that is currently being deployed for the computer assisted grading of exams.
    • Integrate handwritten character recognition to the OMR component.
    • Analyze large quantities of secondary data collected (for example, do students that do the programming assignments in pairs do better in the course or not? how many hours in COS Lab are helpful on average, and when do we hit a point of diminishing returns).
    • Design heuristics and interfaces to spot students in difficulty much earlier in the term, when there is some hope of helping them.
    • Extend the COS Lab Queue so it may be used out of the box in all labs accross campus.
    • Develop a robust testing infrastructure using virtual machines, secure threads, and intelligent feedback to supplant the run-script system currently used.
    • Develop an interface to make grading of assignment done online.
    • Integrate hardware solutions (cardswipe, barcode scanning, etc.) to many of these tools to make them even more frictionless.
    • Some related crowd-sourcing projects...
  • A lot of these projects can include some Big Data component, and involve analyzing data and drawing some observations from it.
  • Finally, I am always up for any ambitious coding project, or survey project in preparation (or not) to an undergrad thesis.

Prof. Margaret Martonosi, Room 410 

  • Research Areas: Evaluating the performance of different hardware synchronization instructions (fences and atomic memory operations) in highly parallel benchmark applications, across different implementations and instruction sets (eg: comparing x86 processors from Intel and AMD, ARM processors from Samsung, Qualcomm, etc).
  • Designing a specialized hardware accelerator for modern workloads (e.g., graph analytics, machine learning algorithms, mobile applications)
  • Investigating security and privacy vulnerabilities in IoT devices, particularly Smart Home architectures.  Particularly interested in solutions for detection and mitigation of race conditions and inconsistent state in IoT devices.
  • Other topics in computer architecture or mobile / IoT systems also possible.

Dr. Christopher Moretti, Room 208

  • Research areas: Distributed systems, high-throughput computing, computer science/engineering education
  • Independent Research Topics:
    • Expansion, improvement, and evaluation of open-source distributed computing software.
    • Applications of distributed computing for "big science" (e.g. biometrics, data mining, bioinformatics)
    • Software and best practices for computer science education and study, especially Princeton's 126/217/226 sequence or MOOCs development
    • Sports analytics and/or crowd-sourced computing

Dr. Arvind Narayanan, 308 Sherrerd Hall 

Research areas: broadly, privacy and security. Especially online tracking and Bitcoin.

Some topics and questions I'm interested in:

1. What can be inferred about people based on publicly available online data?

If someone ''really'' wanted to find out personal things about you online, how far could they get? Let's investigate this by writing a bot which, given an identity (say, an email address), collects and aggregates information from various sites about that person. Ideally, it should be able to automatically figure out which information is current and which is out of date, reconcile conflicts, make new inferences based on multiple pieces of data, and hook into the "deep web" — pages that are not directly visible on web searches.

2. Reverse engineering the Web to expose poor security and privacy

Web companies track us online and compile extensive databases of our personal information. The personal behavioral profiles created through tracking are used for targeted advertising, price discrimination, targeted political emails, and various other practices that make people uncomfortable. I have an ongoing long-term project to automatically reverse engineer web trackers to determine what information companies have collected about you and what they are using it for. There are many interesting sub-projects to work on.

3. Various Bitcoin-related projects

Can we make a more secure Bitcoin wallet? Just how anonymous are Bitcoin users? Can we create an "early-warning system" that connects to a large number of P2P Bitcoin nodes and looks for signs of possible attacks on the security or stability of the system? What applications can we build on top of block chain technology?

Dr. Iasonas Petras, Room 209

  • Research Areas: Information Based Complexity, Numerical Analysis, Quantum Computation.
  • Prerequisites: Reasonable mathematical maturity. In case of a project related to Quantum Computation a certain familiarity with quantum mechanics is required (related courses: ELE 396/PHY 208).
  • Possible research topics include:

1.   Quantum algorithms and circuits:

  • i. Design or simulation quantum circuits implementing quantum algorithms.
  • ii. Design of quantum algorithms solving continuous problems (such as Eigenvalue problems for Partial Differential Equations).

2.   Information Based Complexity:

  • i. Necessary and sufficient conditions for tractability of Linear and Linear Tensor Product Problems in various settings (for example worst case or average case). 
  • ii. Necessary and sufficient conditions for tractability of Linear and Linear Tensor Product Problems under new tractability and error criteria.

3. Topics in Scientific Computation:

  • i. Randomness, Pseudorandomness, MC and QMC methods and their applications (Finance, etc)

Prof. Benjamin Raphael, Room 309 - 

Research interests: Computational biology and bioinformatics; Cancer genomics; Algorithms and machine learning approaches for analysis of large-scale datasets

Fall 2017 IW Seminar - Computational Genomics
Research projects:

  • Implementation and application of algorithms to infer evolutionary processes in cancer
  •  Identifying correlations between combinations of genomic mutations in human and cancer genomes
  •  Design and implementation of algorithms for genome sequencing from new DNA sequencing technologies
  • Graph clustering and network anomaly detection, particularly using diffusion processes and methods from spectral graph theory

Prof. Jennifer Rexford, Room 222

  • Research areas: networking, software-defined networks, network management
  • Independent Research Topics:
    • Enterprise and data-center networking solutions built on Software Defined Networking (SDN).  For example, middleboxes like firewalls, NATs, intrusion detection systems, and load balancers, adaptive measurement of network traffic, networking in challenged environments (e.g., developing regions, emergency situations, etc.).
    • Research on better programming abstractions for SDN.  Projects could combine computer networking with other areas like programming languages, network optimization, algorithms, and distributed systems.
    • Any interesting project in computer networking.

Prof. Szymon Rusinkiewicz, Room 406

  • Research Areas: computer graphics; acquisition of 3D shape, reflectance, and appearance of real-world objects; novel methods for physical fabrication of objects with particular shape/appearance.
  • Independent Research Topics (updated fall, 2011):
    • Construct an efficient and easy-to-use 3-D scanning system for large collections of fragments of archaeological artifacts.
    • Investigate algorithms for computing and visualizing differences between ancient coins struck from similar, but slightly different, dies.
    • Develop a system combining body-mounted cameras and/or Kinect with tactile or auditory feedback to help blind people avoid obstacles.
    • Use computer-controlled milling machines to fabricate bas-reliefs, using substrates of heterogeneous materials.
    • Adapt a MakerBot or other hobbyist-grade manufacturing device to use multiple materials.
    • Implement (and perform the appropriate theoretical sampling/aliasing analysis for) a rendering system that explicitly accounts for the red/green/blue sub-pixels of LCD displays.
    • Other projects in computer graphics and vision, or technologies for documenting and studying cultural heritage objects.

Prof. Robert Sedgewick, Room 319

  • Research Areas: Scientific analysis of algorithms, Analytic combinatorics
  • Independent Research Topics:
    • Professor Sedgewick is willing to advise any student who comes up with an idea for independent work from his books, papers, courses, or in his current areas of active research.  Send mail or stop by to discuss possible topics if you are interested.

Prof. Sebastian Seung, Princeton Neuroscience Institute - Room 153

  • Research Areas: computational neuroscience, connectomics, "deep learning" neural networks, social computing, crowdsourcing, citizen science
  • Independent Research Topics:
    • Gamification of neuroscience, e.g. EyeWire 
    • Hierarchical segmentation and object detection in biological images 
    • Crowdsourcing of computer science problems
    • Neural network theories of visual computation in the retina

Prof. Jaswinder Pal Singh, Room 423

  • Research Areas: Boundary of technology and business/applications; building and scaling technology companies with special focus at that boundary; parallel computing systems and applications: parallel and distributed applications and their implications for software and architectural design; system software and programming environments for multiprocessors.
  • Independent Research Topics:
    • Develop a startup company idea, and build a plan/prototype for it.
    • Explore tradeoffs at the boundary of technology/product and business/applications in a chosen area.
    • Study and develop methods to infer insights from data in different application areas, from science to search to finance to others. 
    • Design and implement a parallel application. Possible areas include graphics, compression, biology, among many others. Analyze performance bottlenecks using existing tools, and compare programming models/languages.
    • Design and implement a scalable distributed algorithm.

Prof. Mona Singh, Room 420

  • Research Areas: computational molecular biology, as well as its interface with machine learning and algorithms.
  • Independent Research Topics:
    • Whole and cross-genome methods for predicting protein function and protein-protein interactions.
    • Analysis and prediction of biological networks.
    • Computational methods for inferring specific aspects of protein structure from protein sequence data.
    • Any other interesting project in computational molecular biology.

Prof. Robert Tarjan, Room 324 - On sabbatical during the 2017-2018 AY

  • Research Areas: Data structures; graph algorithms; combinatorial optimization; computational complexity; computational geometry; parallel algorithms.
  • Independent Research Topics:
    • Implement one or more data structures or combinatorial algorithms to provide insight into their empirical behavior.
    • Design and/or analyze various data structures and combinatorial algorithms.

Prof. Olga Troyanskaya, Room 320

  • Research Areas: Bioinformatics; analysis of large-scale biological data sets (genomics, gene expression, proteomics, biological networks); algorithms for integration of data from multiple data sources; visualization of biological data; machine learning methods in bioinformatics.
  • Independent Research Topics:
    • Implement and evaluate one or more gene expression analysis algorithm.
    • Develop algorithms for assessment of performance of genomic analysis methods.
    • Develop, implement, and evaluate visualization tools for heterogeneous biological data.

Prof. David Walker, Room 211 

  • Research Areas: Programming languages, type systems, compilers, domain-specific languages, software-defined networking and security
  • Independent Research Topics:  Any other interesting project that involves humanitarian hacking, functional programming, domain-specific programming languages, type systems, compilers, software-defined networking, fault tolerance, language-based security, theorem proving, logic or logical frameworks.

Dr. Kevin Wayne, Room 207

  • Research Areas: design, analysis, and implementation of algorithms; data structures; combinatorial optimization; graphs and networks.
  • Independent Research Topics:
    • Design and implement computer visualizations of algorithms or data structures.
    • Develop pedagogical tools or programming assignments for the computer science curriculum at Princeton and beyond.
    • Develop assessment infrastructure and assessments for MOOCs.

Asst. Prof. Matt Weinberg,  Room 317

  • Research Areas: algorithms, algorithmic game theory, mechanism design, game theoretical problems in {Bitcoin, networking, healthcare}.
  • Independent Research Topics:
    • Theoretical questions related to COS 445 topics such as matching theory, voting theory, auction design, etc. 
    • Theoretical questions related to incentives in applications like Bitcoin, the Internet, health care, etc. In a little bit more detail: protocols for these systems are often designed assuming that users will follow them. But often, users will actually be strictly happier to deviate from the intended protocol. How should we reason about user behavior in these protocols? How should we design protocols in these settings?

Asst. Prof. Mark Zhandry, Room 314

  • Research Areas: Cryptography, Privacy, Quantum Computation
  • Independent Research Topics: Various topics in theoretical cryptography, including but not limited to those at the intersections of cryptography and differential privacy or quantum computation/information.


Opportunities outside the department

We encourage students to look in to doing interdisciplinary computer science research and to work with professors in departments other than computer science.  However, every CS independent work project must have a strong computer science element (even if it has other scientific or artistic elements as well.)  To do a project with an advisor outside of computer science you must have permission of the department.  This can be accomplished by having a second co-advisor within the computer science department or by contacting the independent work supervisor about the project and having he or she sign the independent work proposal form.

Here is a list of professors outside the computer science department who are eager to work with computer science undergraduates.

Prof. Sharad Malik, Engineering Quad, Room B224

  • Research Areas: 
    • Design of reliable hardware systems
    • Verifying complex software and hardware systems

Prof. Prateek Mittal, Engineering Quadrangle, Room B236

  • Research Areas: 
    • Internet security and privacy 
    • Social Networks
    • Privacy technologies, anonymous communication
    • Network Science
  • Ideas for Independent Research Topics:
    • Internet security and privacy: The insecurity of Internet protocols and services threatens the safety of our critical network infrastructure and billions of end users. How can we defend end users as well as our critical network infrastructure from attacks?
    • Trustworthy social systems: Online social networks (OSNs) such as Facebook, Google+, and Twitter have revolutionized the way our society communicates. How can we leverage social connections between users to design the next generation of communication systems?
    • Privacy Technologies: Privacy on the Internet is eroding rapidly, with businesses and governments mining sensitive user information. How can we protect the privacy of our online communications? The Tor project (https://www.torproject.org/) is a potential application of interest.

Prof. Ken Norman,  Psychology Dept, PNI 137

Potential research topics

  • Methods for decoding cognitive state information from neuroimaging data (fMRI and EEG) 
  • Neural network simulations of learning and memory

Caroline Savage, Office of Sustainability, Phone: (609) 258-7513, cs35 (@princeton.edu)

The Campus as Lab program supports students using the Princeton campus as a living laboratory to solve sustainability challenges. The Office of Sustainability has created a list of campus as lab research questions, filterable by discipline and topic, on its website.

An example from Computer Science could include using TigerEnergy, a platform which provides real-time data on campus energy generation and consumption, to study one of the many energy systems or buildings on campus. Three CS students used TigerEnergy to create a live energy heatmap of campus.

Other potential projects include:

  • Design an app that incorporates campus-wide data to compare individual energy use with average use and encourage individuals to track their energy consumption
  • Refine and expand TigerEnergy and/or the Heatmap to include all buildings in the campus carbon footprint as well as buildings in the greater community
  • Design a student feedback tool on the level of comfort regarding temperature, humidity…etc. in campus buildings




Janet Vertesi, Sociology Dept, Wallace Hall 122 - On leave during the 2016-2017 Academic Year

  • Research areas: Sociology of technology; Human-computer interaction; Ubiquitous computing.
  • Possible projects: At the intersection of computer science and social science, my students have built mixed reality games, produced artistic and interactive installations, and studied mixed human-robot teams, among other projects.

Prof. David Wentzlaff, Engineering Quadrangle, Room 228

Computing, Operating Systems, Sustainable Computing.

  • Independent Research Topics:
  • Instrument Princeton's Green (HPCRC) data center
  • Investigate power utilization on an processor core implemented in an FPGA
  • Dismantle and document all of the components in modern electronics. Invent new ways to build computers that can be recycled easier.
  • Other topics in parallel computer architecture or operating systems
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