Lyle Ungar

Lyle Ungar
  • Associate Professor of Computer and Information Science, Professor of Chemical and Biomolecular Engineering
  • Professor of Electrical and Systems Engineering, Professor of Operations, Information and Decisions, Professor of Genomics and Computational Biology

Contact Information

  • office Address:

    200 South 33rd Street
    504 Levine Hall
    Philadelphia, PA 19104

Research Interests: information economics, statistical relational learning text mining active learning market-based methods for distributed scheduling and optimization of systems including human and computer agents, and gene and protein expression. clustering and collaborative filtering feature selection genomics, and regulatory network modeling information extraction in biological texts, consumer purchases, machine learning and data mining, on data such as scientific papers, proteomics, shopbots and pricebots auctions and mechanism designs computational biolology and (someday) computer go.

Links: Personal Website

Research

  • Pavel Atanasov, Lyle Ungar, Barbara Mellers, Philip Tetlock (2020), Small steps to accuracy: Incremental belief updaters are better forecasters, Organizational Behavior and Human Decision Processes.

  • Gilmer Valdes, Albert J. Chang, Yannet Interian, Kenton Owens, Shane T. Jensen, Lyle Ungar, Adam Cunha, Timothy D. Solberg, I-Chow Hsu (2018), Salvage HDR Brachytherapy: Multiple Hypothesis Testing Versus Machine Learning Analysis, International Journal of Radiation Oncology - Biology - Physics, 101 (3), pp. 694-703.

  • Barbara Mellers, Eric Stone, Terry Murray, Angela Minster, Nick Rohrbaugh, Michael Bishop, Eva Chen, Joshua Baker, Yuan Hou, Michael Horowitz, Lyle Ungar, Philip Tetlock (2015), Identifying and Cultivating Superforecasters as a Method of Improving Probabilistic Predictions, Perspectives on Psychological Science.

  • Barbara Mellers, Eric Stone, Pavel Atanasov, Nick Rohrbaugh, S. E. Metz, Lyle Ungar, Michael Bishop, Michael Horowitz (2015), The Psychology of Intelligence Analysis: Drivers of Prediction Accuracy in World Politics, Journal of Experimental Psychology: Applied.

  • Jonathan Baron, Barbara Mellers, Philip Tetlock, Eric Stone, Lyle Ungar (2015), Two Reasons to Make Aggregated Probability Forecasts More Extreme, Decision Analysis.

  • Barbara Mellers, Lyle Ungar, Jonathan Baron, Jaime Ramos, Burcu Gurcay, Katrina Fincher, Sydney Scott, Don Moore, Pavel Atanasov, Samuel Swift, Terry Murray, Eric Stone, Philip Tetlock (2014), Psychological Strategies for Winning a Geopolitical Forecasting Tournament, Psychological Science.

  • Barbara Mellers, Lyle Ungar, Jonathan Baron, Jaime Ramos, Burcu Gurcay, Katrina Fincher, Sydney Scott, Don Moore, Pavel Atanasov, Samuel Swift, Terry Murray, Eric Stone, Philip Tetlock (2014), Psychological Strategies for Winning Geopolitical Forecasting Tournaments, Psychological Science.

  • Shay Cohen, Karl Stratos, Michael Collins, Dean P. Foster, Lyle Ungar (2013), Experiments with Spectral Learning of Latent-Variable PCFGs, NAACL-HLT 2013.

  • Shawndra Hill, Raina Merchant, Lyle Ungar (2013), Lessons Learned About Public Health from Online Crowd Surveillance, Big Data.

  • Adam Kapelner, Krishna Kaliannan, A. Schwartz, Lyle Ungar, Dean P. Foster (2012), New Insights from Coarse Word Sense Disambiguation in the Crowd, COLING 2012.

Teaching

All Courses

  • AMCS9999 - Ind Study & Research

    Study under the direction of a faculty member.

  • CIS0099 - Ugrad Resrch/Ind Study

    An opportunity for the student to become closely associated with a professor (1) in a research effort to develop research skills and techniques and/or (2) to develop a program of independent in-depth study in a subject area in which the professor and student have a common interest. The challenge of the task undertaken must be consistent with the student's academic level. To register for this course, the student must submit a detailed proposal, signed by the independent study supervisor, to the SEAS Office of Academic Programs (111 Towne) no later than the end of the "add" period. Prerequisite: A maxium of 2 c.u. of CIS 099 may be applied toward the B.A.S. or B.S.E. degree requirements.

  • CIS1400 - INTRO COGNITIVE SCIENCE

    How do minds work? This course surveys a wide range of answers to this question from disciplines ranging from philosophy to neuroscience. The course devotes special attention to the use of simple computational and mathematical models. Topics include perception, learning, memory, decision making, emotion and consciousness. The course shows how the different views from the parent disciplines interact and identifies some common themes among the theories that have been proposed. The course pays particular attention to the distinctive role of computation in such theories and provides an introduction to some of the main directions of current research in the field. It is a requirement for the BA in Cognitive Science, the BAS in Computer and Cognitive Science, and the minor in Cognitive Science, and it is recommended for students taking the dual degree in Computer and Cognitive Science.

  • CIS5200 - Machine Learning

    This course covers the foundations of statistical machine learning. The focus is on probabilistic and statistical methods for prediction and clustering in high dimensions. Topics covered include linear and logistic regression, SVMs, PCA and dimensionality reduction, EM and HMMs, and deep learning. Elementary probability, calculus, and linear algebra. Basic programming experience.

  • CIS5220 - Deep Learning/Data Sci

    Deep learning techniques now touch on data systems of all varieties. Sometimes, deep learning is a product; sometimes, deep learning optimizes a pipeline; sometimes, deep learning provides critical insights; sometimes, deep learning sheds light on neuroscience or vice versa. The purpose of this course is to deconstruct the hype by teaching deep learning theories, models, skills, and applications that are useful for applications.

  • CIS5450 - Big Data Analytics

    In the new era of big data, we are increasingly faced with the challenges of processing vast volumes of data. Given the limits of individual machines (compute power, memory, bandwidth), increasingly the solution is to process the data in parallel on many machines. This course focuses on the fundamentals of scaling computation to handle common data analytics tasks. You will learn about basic tasks in collecting, wrangling, and structuring data; programming models for performing certain kinds of computation in a scalable way across many compute nodes; common approaches to converting algorithms to such programming models; standard toolkits for data analysis consisting of a wide variety of primitives; and popular distributed frameworks for analytics tasks such as filtering, graph analysis, clustering, and classification. Recommended: broad familiarity with probability and statistics, as well as programming in Python. Additional background in statistics, data analysis (e.g., in Matlab or R), and machine learning is helpful (example : ESE 542).

  • CIS5970 - Master's Thesis Research

    For students working on an advanced research leading to the completion of a Master's thesis.

  • CIS5990 - Master's Indep Study

    For master's students studying a specific advanced subject area in computer and information science. Involves coursework and class presentations. A CIS 599 course unit will invariably include formally gradable work comparable to that in a CIS 500-level course. Students should discuss with the faculty supervisor the scope of the Independent Study, expectations, work involved, etc.

  • CIS7000 - Cis-Topics

    One time course offerings of special interest. Equivalent to a CIS 5XX level course.

  • CIS8950 - Teaching Practicum

    Enrollment for students participating in Teaching Practicum.

  • CIS8990 - PhD Independent Study

    For doctoral students studying a specific advanced subject area in computer and information science. The Independen t Study may involve coursework, presentations, and formally gradable work comparable to that in a CIS 500 or 600 level course. The Independent Study may also be used by doctoral students to explore research options with faculty, prior to determining a thesis topic. Students should discuss with the faculty supervisor the scope of the Independent Study, expectations, work involved, etc. The Independent Study should not be used for ongoing research towards a thesis, for which the CIS 999 designation should be used.

  • CIS9950 - Dissertation

    For Ph.D. candidates working exclusively on their dissertation research, having completed enrollment for a total of ten semesters (fall and spring). There is no credit or grade for CIS 9950.

  • CIS9990 - Thesis/Diss Research

    For students pursuing advanced research to fulfill PhD dissertation requirements.

  • COGS1001 - INTRO COGNITIVE SCIENCE

    How do minds work? This course surveys a wide range of answers to this question from disciplines ranging from philosophy to neuroscience. The course devotes special attention to the use of simple computational and mathematical models. Topics include perception, learning, memory, decision making, emotion and consciousness. The course shows how the different views from the parent disciplines interact and identifies some common themes among the theories that have been proposed. The course pays particular attention to the distinctive role of computation in such theories and provides an introduction to some of the main directions of current research in the field. It is a requirement for the BA in Cognitive Science, the BAS in Computer and Cognitive Science, and the minor in Cognitive Science, and it is recommended for students taking the dual degree in Computer and Cognitive Science.

  • COGS3998 - Senior Thesis

    This course is a directed study intended for cognitive science majors who have been admitted to the cognitive science honors program. Upon admission into the program, students may register for this course under the direction of their thesis supervisor.

  • COGS3999 - Independent Study

    Departmental permission required

  • EAS8980 - Cpt Research Practicum

  • ESE8990 - PhD Independent Study

    For students who are studying a specific advanced subject area in electrical engineering. Students must submit a proposal outlining and detailing the study area, along with the faculty supervisor's consent, to the graduate group chair for approval. A maximum of 1 c.u. of ESE 899 may be applied toward the MSE degree requirements. A maximum of 2 c.u.'s of ESE 899 may be applied toward the Ph.D. degree requirements.

  • ESE9990 - Thesis/Diss Res

    For students working on an advanced research program leading to the completion of master's thesis or Ph.D. dissertation requirements.

  • GCB9950 - Dissertation

  • LING1005 - INTRO COGNITIVE SCIENCE

    How do minds work? This course surveys a wide range of answers to this question from disciplines ranging from philosophy to neuroscience. The course devotes special attention to the use of simple computational and mathematical models. Topics include perception, learning, memory, decision making, emotion and consciousness. The course shows how the different views from the parent disciplines interact and identifies some common themes among the theories that have been proposed. The course pays particular attention to the distinctive role of computation in such theories and provides an introduction to some of the main directions of current research in the field. It is a requirement for the BA in Cognitive Science, the BAS in Computer and Cognitive Science, and the minor in Cognitive Science, and it is recommended for students taking the dual degree in Computer and Cognitive Science.

  • PHIL1840 - INTRO COGNITIVE SCIENCE

    How do minds work? This course surveys a wide range of answers to this question from disciplines ranging from philosophy to neuroscience. The course devotes special attention to the use of simple computational and mathematical models. Topics include perception, learning, memory, decision making, emotion and consciousness. The course shows how the different views from the parent disciplines interact and identifies some common themes among the theories that have been proposed. The course pays particular attention to the distinctive role of computation in such theories and provides an introduction to some of the main directions of current research in the field. It is a requirement for the BA in Cognitive Science, the BAS in Computer and Cognitive Science, and the minor in Cognitive Science, and it is recommended for students taking the dual degree in Computer and Cognitive Science.

  • PSYC1333 - INTRO COGNITIVE SCIENCE

    How do minds work? This course surveys a wide range of answers to this question from disciplines ranging from philosophy to neuroscience. The course devotes special attention to the use of simple computational and mathematical models. Topics include perception, learning, memory, decision making, emotion and consciousness. The course shows how the different views from the parent disciplines interact and identifies some common themes among the theories that have been proposed. The course pays particular attention to the distinctive role of computation in such theories and provides an introduction to some of the main directions of current research in the field. It is a requirement for the BA in Cognitive Science, the BAS in Computer and Cognitive Science, and the minor in Cognitive Science, and it is recommended for students taking the dual degree in Computer and Cognitive Science.

  • PSYC4998 - Mentored Research

    Mentored research involving data collection. Students do independent empirical work under the supervision of a faculty member, leading to a written paper. Normally taken in the junior or senior year.

  • PSYC4999 - Honors Mentored Research

    The Honors Program has been developed to recognize excellence in psychology among Penn undergraduates and to enhance skills related to psychological research. The 4998 credit signifies an Honors Independent Study, completed as part of the Honors Program. The honors program involves: (a) completing a year-long empirical research project in your senior year under the supervision of a faculty member (for a letter grade). This earns 2 cu's. (b) completing a second term of statistics (for a letter grade) before graduation. (c) participating in the year-long Senior Honors seminar (for a letter grade). This seminar is designed especially for Psychology Honors majors; this receives a total of 1 cu. (d) participating in the Undergraduate Psychology Research Fair in the Spring semester, at which honors students present a poster and give a 15-minute talk about their research. (e) a total of 15 cu's in psychology is required. Students will be selected to be part of the Honors Program in the Spring of their junior year (see application process online)

  • STAT9950 - Dissertation

In the News

Knowledge at Wharton

Activity

Latest Research

Pavel Atanasov, Lyle Ungar, Barbara Mellers, Philip Tetlock (2020), Small steps to accuracy: Incremental belief updaters are better forecasters, Organizational Behavior and Human Decision Processes.
All Research

In the News

Everything You Have Read About Contact Tracing Apps Is Wrong

Contact tracing is critical to reopening society. But are the apps for that any good? Penn's Lyle Ungar answers that question and more in this opinion piece. Read More

Knowledge at Wharton - 6/30/2020
All News