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

  • 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.

  • Paramveer S. Dhillon, Jordan Rodu, Michael Collins, Dean P. Foster, Lyle Ungar (2012), Spectral Dependency Parsing with Latent Variables, EMNLP 2012.

Teaching

Current Courses

  • CIS 520 - 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.

    CIS 520001

    CIS 520201

Past Courses

  • AMCS999 - IND STUDY & RESEARCH

    Study under the direction of a faculty member.

  • CIS 099 - 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.

  • CIS 520 - 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.

  • CIS 521 - ARTIFICIAL INTELLIGENCE

    This course investigates algorithms to implement resource-limited knowledge-based agents which sense and act in the world. Topics include, search, machine learning, probabilistic reasoning, natural language processing, knowledge representation and logic. After a brief introduction to the language, programming assignments wil l be in Python.

  • CIS 535 - INTRO TO BIOINFORMATICS

    This course provides overview of bioinformatics and computational biology as applied to biomedical research. A primary objective of the course is to enable students to integrate modern bioinformatics tools into their research activities. Course material is aimed to address biological questions using computational approaches and the analysis of data. A basic primer in programming and operating in a UNIX enviroment will be presented, and students will also be introduced to Python R, and tools for reproducible research. This course emphasizes direct, hands-on experience with applications to current biological research problems. Areas include DNA sequence alignment, genetic variation and analysis, motif discovery, study design for high-throughput sequencing RNA, and gene expression, single gene and whole-genome analysis, machine learning, and topics in systems biology. The relevant principles underlying methods used for analysis in these areas will be introduced and discussed at a level appropriate for biologists without a background in computer science. The course is not intended for computer science students who want to learn about biologically motivated algorithmic problems; BIOL 437/GCB 536 and GCB/CIS/BIOL537 are more appropriate. Prerequisites: An advanced undergraduate course such as BIOL 421 or a graduate course in biology such as Biol 526 (Experimental Principles in Cell and Molecular Biology), BIOL 527 (Advanced Moleclar Genetics), BIOL 540 (Genetic Systems), or equivalent, is a prerequisite.

  • CIS 545 - BIG DATA ANALYTICS

  • CIS 597 - MASTER'S THESIS RESEARCH

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

  • CIS 599 - 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.

  • CIS 620 - ADV TOP IN MACH LEARNING

    This course covers a variety of advanced topics in machine learning, such as the following: statistical learning theory (statistical consistency properties of surrogate loss minimizing algorithms); approximate inference in probabilistic graphical models (variational inference methods and sampling-based inference methods); structured prediction (algorithms and theory for supervised learning problems involving complex/structured labels); and online learning in complex/structured domains. The precise topics covered may vary from year to year based on student interest and developments in the field.

  • CIS 700 - CIS-TOPICS

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

  • CIS 895 - TEACHING PRACTICUM

    Enrollment for students participating in Teaching Practicum.

  • CIS 899 - 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.

  • CIS 990 - MASTERS THESIS

    For master's students who have taken ten course units and need only to complete the writing of a thesis or finish work for incompletes in order to graduate. CIS 990 carries full time status with zero course units and may be taken only once.

  • CIS 995 - 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 995.

  • CIS 999 - THESIS/DISS RESEARCH

    For students pursuing advanced research to fulfill PhD dissertation requirements.

  • COGS001 - 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.

  • COGS301 - INDEPENDENT STUDY

  • COGS398 - 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.

  • EAS 499 - SENIOR CAPSTONE

    The Senior Capstone Project is required for all BAS degree students, in lieu of the senior design course. The Capstone Project provides an opportunity for the student to apply the theoretical ideas and tools learned from other courses. The project is usually applied, rather than theoretical, exercise, and should focus on a real world problem related to the career goals of the student. The one-semester project may be completed in either the fall or sprong term of the senior year, and must be done under the supervision of a sponsoring faculty member. To register for this course, the student must submit a detailed proposal, signed by the supervising professor, and the student's faculty advisor, to the Office of Academic Programs two weeks prior to the start of the term.

  • EAS 898 - CPT RESEARCH PRACTICUM

  • ESE 899 - 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.

  • ESE 999 - THESIS/DISS RES

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

  • GCB 535 - INTRO TO BIOINFORMATICS

    This course provides overview of bioinformatics and computational biology as applied to biomedical research. A primary objective of the course is to enable students to integrate modern bioinformatics tools into their research activities. Course material is aimed to address biological questions using computational approaches and the analysis of data. A basic primer in programming and operating in a UNIX enviroment will be presented, and students will also be introduced to Python R, and tools for reproducible research. This course emphasizes direct, hands-on experience with applications to current biological research problems. Areas include DNA sequence alignment, genetic variation and analysis, motif discovery, study design for high-throughput sequencing RNA, and gene expression, single gene and whole-genome analysis, machine learning, and topics in systems biology. The relevant principles underlying methods used for analysis in these areas will be introduced and discussed at a level appropriate for biologists without a background in computer science. The course is not intended for computer science students who want to learn about biologically motivated algorithmic problems; BIOL 437/GCB 536 and GCB/CIS/BIOL537 are more appropriate. Prerequisites: An advanced undergraduate course such as BIOL 421 or a graduate course in biology such as Biol 526 (Experimental Principles in Cell and Molecular Biology), BIOL 527 (Advanced Moleclar Genetics), BIOL 540 (Genetic Systems), or equivalent, is a prerequisite.

  • GCB 699 - LAB ROTATION

  • GCB 995 - DISSERTATION

  • LING105 - 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.

  • PHIL044 - 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.

  • PSYC207 - 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.

  • PSYC399 - INDIV EMPIRICAL RESEARCH

    Individual 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.

  • STAT995 - DISSERTATION

In the News

Knowledge @ Wharton

Activity

Latest Research

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.
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.

Knowledge @ Wharton - 2020/06/30
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