Houssam Nassif, PhD

Additional Public Presentations

In addition to my peer-reviewed publications, here is a list of my other public talks, posters and abstracts.

• Counterfactual Evaluation of Ads Ranking Models through Domain Adaptation
  Radwan MA, Bhattacharjee H, Lanners Q, Zhang J, Karakulak S, Nassif H, and Bayir MA
  ACM Conference on Recommender Systems (RecSys'24) Workshops, Bari, Italy, 2024.
  (Paper, Poster)
  
  
• Estimating the True Effect Size Distribution with SIMEX
  Li Z, Ting D, Gorbachev I, Emamjomeh-Zadeh E, and Nassif H
  Conference on Digital Experimentation @ MIT (CODE@MIT'24), Cambridge, MA, 2024.
  (Paper, Slides)
  
  
• Ad Clustered User Randomized Trials
  Bakhitov E, Zhang C, Sherstobitov I, Daley B, Ting D, Nassif H, and Leonenkov S
  Conference on Digital Experimentation @ MIT (CODE@MIT'24), Cambridge, MA, 2024.
  (Paper, Slides)
  
  
• Counterfactual Evaluation of Ads Ranking Models through Domain Adaptation
  Radwan MA, Bhattacharjee H, Lanners Q, Zhang J, Karakulak S, Nassif H, and Bayir MA
  Conference on Digital Experimentation @ MIT (CODE@MIT'24), Cambridge, MA, 2024.
  (Paper, Slides, Poster)
  
  
• Thinking like an AI scientist
  Nassif H
  Transforming Non-Profit Fundraising with Artificial Intelligence, Clark University, Worcester, MA, 2024.
  (Slides, Video, Course)
  
  
• Factor Learning Portfolio Optimization Informed by Continuous-Time Finance Models
  Geng S, Nassif H, Kuang Z, Reppen AM, and Sircar R
  International Conference on Machine Learning (ICML'23) Workshops, Honolulu, HI, 2023.
  (Paper, Poster)
  
  
• Best of Three Worlds: Adaptive Experimentation for Digital Marketing in Practice
  Fiez T, Nassif H, and Jain L
  Conference on Digital Experimentation @ MIT (CODE@MIT'23), Cambridge, MA, 2023.
  (Paper, Poster, Slides)
  
  
• Factor Learning Portfolio Optimization
  Geng S, Nassif H, Kuang Z, Reppen AM, and Sircar R
  The Institute for Operations Research and the Management Sciences Annual Meeting (INFORMS'22), Indianapolis, IN, 2022.
  (Slides)
  
  
• Instance-Optimal PAC Contextual Bandits
  Li Z, Ratliff LJ, Nassif H, Jamieson K, and Jain L
  The Institute for Operations Research and the Management Sciences Annual Meeting (INFORMS'22), Indianapolis, IN, 2022.
  (Slides)
  
  
• From AI-Curious to AI-Informed: A Practitioner's Guide to Artificial Intelligence
  Nassif H
  Park Innovation Webinar, Samqaniyeh, Lebanon, 2022.
  (Slides part 1, Slides part 2, Video)
  
  
• Innovating in the content space: creation, targeting and measurement
  Nassif H
  Google Research Talk Series, Virtual, 2022.
  (Slides)
  
  
• Solving Inverse Reinforcement Learning, Bootstrapping Bandits, and Adaptive Recommendation
  Nassif H
  Numerical Analysis in Data Science Transition Workshop, The Statistical and Applied Mathematical Sciences Institute (SAMSI), NC Research Triangle, 2021.
  (Slides)
  
  
• Solving Inverse Reinforcement Learning, Bootstrapping Bandits, and Adaptive Recommendation
  Nassif H
  REVEAL 2020 Workshop, RecSys'20, Virtual, 2020.
  (Slides, Abstract)
  
  
• Solving Inverse Reinforcement Learning, and Bandit Adaptive Recommendation
  Nassif H
  The Institute for Operations Research and the Management Sciences Annual Meeting (INFORMS'20), Virtual, 2020.
  (Slides, Video)
  
  
• Bayesian Meta-Prior Learning Using Empirical Bayes
  Nabi S, Nassif H, Hong J, Mamani H, and Imbens G
  The Institute for Operations Research and the Management Sciences Annual Meeting (INFORMS'20), Virtual, 2020.
  (Slides, Video)
  
  
• Industrial Data Science: Content Recommendation Examples
  Nassif H
  University of Miami Data Analytics Student Association seminar series, Miami, FL, 2020.
  (Slides)
  
  
• An Efficient Bandit Algorithm for Realtime Multivariate Optimization
  Nassif H
  2nd AUB North American Computer Science Alumni Reunion, Palo Alto, CA, 2020.
  (Slides)
  
  
• Multi-Armed Bandits: Introduction and Extensions
  Nassif H
  Topics in Machine Learning lecture, Econ 517 A: Foundations Of Economic Analysis, University of Washington, Seattle, WA, 2020.
  (Slides, Course)
  
  
• ML in practice: Thinking Like a Scientist
  Nassif H
  Amazon MENA Tech (AMTECH) Conference, Amman, Jordan, 2019.
  (Slides)
  
  
• Industrial Data Science: Content Recommendation Examples
  Nassif H
  Capstone Guest Speaker Series, Bellevue College, Bellevue, WA, 2019.
  (Slides)
  
  
• Industrial Data Science: Amazonian Perspective
  Nassif H
  The Institute for Operations Research and the Management Sciences Annual Meeting (INFORMS'19), Seattle, WA, 2019.
  (Slides)
  
  
• Decoupling Learning Rates Using Empirical Bayes
  Nabi S, Nassif H, Hong J, Mamani H, and Imbens G
  The Institute for Operations Research and the Management Sciences Annual Meeting (INFORMS'19), Seattle, WA, 2019.
  (Slides)
  
  
• Machine Learning for Content Recommendation
  Nassif H
  Nike Content Domain Talk Series, Beaverton, OR, 2018.
  (Slides)
  
  
• A practitioner's perspective on Artificial Intelligence
  Nassif H
  UK Lebanon Tech Hub Tech Talk Series, Beirut, Lebanon, 2018.
  (Slides)
  
  
• Adaptive, Personalized Diversity for Visual Discovery
  Nassif H
  1st AUB North American Computer Science Alumni Reunion, Harvard, Cambridge, MA, 2017.
  (Slides)
  
  
• Diversifying Amazon Recommendations
  Nassif H
  Seminar Series special edition, University of Miami Computer Science Department, Miami, FL, 2017.
  (Slides)
  
  
• A platform technology to rapidly identify cancer drug targets and design personalized (single and combinatorial) drug therapies
  Al-Ali H, Nassif H, Gautam P, Wennerberg K, Bixby JL, and Lemmon VP
  Florida Academic Cancer Center Alliance Retreat (FACCA'16), Miami, FL, 2016.
  (Poster)
  
  
• Combining phenotypic and biochemical screening to identify drug targets, exploit polypharmacology, and personalize treatment
  Al-Ali H, Lee DH, Danzi MC, Azzam D, Nassif H, Gautam P, Wennerberg K, Soellner M, Xu XM, Lee JK, Lemmon VP, and Bixby JL
  1st Systems Biology Data Science Symposium (SBDSS'16), Miami, FL, 2016.
  (Poster)
  
  
• Recommendation challenges at Amazon: Airstream use case
  Nassif H
  18th International Conference on Artificial Intelligence and Statistics (AISTATS'15), San Diego, CA, 2015.
  (Slides, Poster)
  
  
• Guest Speaker
  Nassif H
  11th American University of Beirut Computer Science Alumni Chapter Reunion, Beirut, Lebanon, 2014.
  (Address)
  
  
• Score As You Lift (SAYL): A Statistical Relational Learning Approach to Uplift Modeling
  Nassif H, Kuusisto F, Burnside ES, Page D, Shavlik J, and Santos Costa V
  LifeScience Alley Conference (LSA'13), Minneapolis, MN, 2013.
  (Poster)
  
  
• Relational Differential Prediction
  Nassif H, Santos Costa V, Burnside ES, and Page D
  7th Annual University of Wisconsin Carbone Cancer Center Research Retreat (UWCCC'13), Madison, WI, 2013.
  (Poster)
  
  
• Automatic Extraction of BI-RADS Features from Cross-Institution and Cross-Language Free-Text Mammography Reports
  Nassif H, Kitchner T, Cunha F, Moreira IC, and Burnside ES
  Radiological Society of North America Annual Meeting (RSNA'12), Chicago, 2012.
  (Abstract, Slides)
  
  
• Characterizing and Predicting Hexose-Binding Sites
  Nassif H
  Computation and Informatics in Biology and Medicine Seminar (CIBM), Madison, WI, 2011.
  (Slides)
  
  
• Differential Prediction Using Inductive Logic Programming
  Nassif H
  PhD Thesis Proposal, University of Wisconsin - Madison, 2011.
  (Slides)
  
  
• Automatic Extraction of Breast Density Information from Mammography Reports
  Percha B, Nassif H, Lipson J, Burnside E, and Rubin D
  Radiological Society of North America Annual Meeting (RSNA'11), Chicago, 2011.
  (Abstract, Slides)
  
  
• Computational Techniques to Improve the Early Diagnosis of Breast Cancer
  Burnside ES, Woods R, Ayer T, Ayvaci M, Alagoz O, Oliphant L, Liu J, Nassif H, Page CD, Shavlik J, and Gustafson DH
  4th Annual University of Wisconsin Carbone Cancer Center Research Retreat (UWCCC'10), Madison, WI, 2010.
  (Poster)
  
  
• Superior Performance of Bayesian Networks in Predicting the Risk of Invasive Vs. In-Situ Breast Cancer in Older Women
  Ayvaci M, Alagoz O, Nassif H, Sickles E, and Burnside ES
  31st Annual Meeting of the Society for Medical Decision Making (SMDM'09), Los Angeles, 2009.
  (Abstract, Poster)
  
  
• Age Stratified Risk Prediction of Invasive versus In-situ Breast Cancer: A Logistic Regression Model
  Ayvaci M, Alagoz O, Chhatwal J, Lindstrom M, Nassif H, and Burnside ES
  The Institute for Operations Research and the Management Sciences Annual Meeting (INFORMS'09), San Diego, CA, 2009.
  (Slides)
  
  
• Computational Techniques to Improve the Early Diagnosis of Breast Cancer
  Burnside ES, Woods R, Ayer T, Ayvaci M, Alagoz O, Oliphant L, Liu J, Nassif H, Page CD, Shavlik J, and Gustafson DH
  3rd Annual University of Wisconsin Carbone Cancer Center Research Retreat (UWCCC'09), Madison, WI, 2009.
  (Poster)
  
  
• Predicting the Risk of Invasive Versus In Situ Breast Cancer to Aid Clinical Management
  Chhatwal J, Alagoz O, Nassif H, Sickles EA, Jovais C, and Burnside ES
  30st Annual Meeting of the Society for Medical Decision Making (SMDM'08), Philadelphia, PA, 2008.
  (Abstract, Poster)
  
  
• Bayesian Network for Predicting Invasive and In-Situ Breast Cancer Using Mammographic Findings
  Chhatwal J, Alagoz O, Burnside ES, Nassif H, and Sickles EA
  The Institute for Operations Research and the Management Sciences Annual Meeting (INFORMS'08), Washigton, DC, 2008.
  (Slides)
  
  
• Characteristics of Sugar Binding Sites of Enzymatic Proteins: Probing the Spatial and Chemical Features Using SVM
  Khuri S, Nassif H, Al-Ali H, and Keyrouz W
  Annual American University of Beirut Bioinformatics Research and Applications Team Retreat (BRAT'05), Beirut, Lebanon, 2005.
  (Slides)
  
  
• Amino Acid Composition and Folding of the Monosaccharide Binding Sites of Enzymic Proteins
  Khuri S, Nassif H, Al-Ali H, Khalaf K, Khachfe H, and Keyrouz W
  International Conference for Bioinformatics and its Applications (ICBA'04), Fort Lauderdale, FL, 2004.
  (Abstract, Poster)