Curriculum vitae.

Academic appointments

Education

Teaching

Course details and earlier teaching

Honors & awards

Research funding

Publications

Search and filter the publication archive or download the bibliography. Preprints, abstracts, and workshop descriptions are labeled explicitly.

2026

Keigo Kusumegi; Daniel E. Acuna; Yukie Sano (2026). Dissecting the gender divide: authorship and acknowledgment in scientific publications. Scientometrics.

Almene De Meran Meguimtsop; Maria Leonor Pacheco; Daniel E. Acuna (2026). SciIntBench: Measuring LLM Compliance with Research Integrity Norms Under Adversarial Framing. arXiv preprint arXiv:2605.29468.Preprint / working paper

Pawin Taechoyotin; Daniel E. Acuna (2026). REM-CTX: Automated Peer Review via Reinforcement Learning with Auxiliary Context. arXiv preprint arXiv:2604.00248.Preprint / working paper

Huaxia Zhou; Lizhen Liang; Daniel E. Acuna (2026). Widespread reference missingness disparities in open scholarly metadata. Quantitative Science Studies, 7, 53–65.

David Popp; Myriam Gregoire-Zawilski; Lizhen Liang; Daniel E. Acuna (2026). Government Funding and the Direction of Academic Energy Research. NBER Working Paper No. 34856.Preprint / working paper

Huimin Xu; Shujing Sun; Meijun Liu; Chenwei Zhang; Yi Bu; Yi Zhang; Daniel E. Acuna; Eric Meyer; Ying Ding (2026). Beyond a number game: Flat team structures improve inclusion and performance in diverse scientific teams. Journal of the Association for Information Science and Technology, 77(9), 1119–1135.

Anna Lou Abatayo; Titipat Achakulvisut; Daniel Acuna; et al. (2026). Assessments of Credibility in the Social and Behavioral Sciences. MetaArXiv.Preprint / working paper

2025

Han Zhuang; Lizhen Liang; Daniel E. Acuna (2025). Estimating the predictability of questionable open-access journals. Science Advances, 11(35), eadt2792.

Daniel E. Acuna; Jian Jian; Tong Zeng; Lizhen Liang; Han Zhuang (2025). Predicting the longevity of resources shared in scientific publications. Humanities and Social Sciences Communications, 12(1), 698.

Pawin Taechoyotin; Daniel E. Acuna (2025). REMOR: Automated Peer Review Generation with LLM Reasoning and Multi-Objective Reinforcement Learning. arXiv preprint arXiv:2505.11718.Preprint / working paper

Chao Zhou; Cheng Qiu; Lizhen Liang; Daniel E. Acuna (2025). Paraphrase Identification with Deep Learning: A Review of Datasets and Methods. IEEE Access, 13, 65797–65822.

2024

Adrien Bibal; Nourah M. Salem; Rémi Cardon; Elizabeth K. White; Daniel E. Acuna; Robin Burke; Lawrence E. Hunter (2024). RecSOI: recommending research directions using statements of ignorance. Journal of Biomedical Semantics, 15(1), 2.

Huimin Xu; Meijun Liu; Yi Bu; Shujing Sun; Yi Zhang; Chenwei Zhang; Daniel E. Acuna; Steven Gray; Eric Meyer; Ying Ding (2024). The impact of heterogeneous shared leadership in scientific teams. Information Processing & Management, 61(1), 103542.

Lizhen Liang; Han Zhuang; James Zou; Daniel E. Acuna (2024). The complementary contributions of academia and industry to AI research. arXiv preprint arXiv:2401.10268.Preprint / working paper

Elizabeth Novoa-Monsalve; David Patterson; Stephanie Ludi; Daniel E Acuna (2024). Science Needs You: Mobilizing for Diversity in Award Recognition. Communications of the ACM, 67(8), 18–21.

Pawin Taechoyotin; Daniel Acuna (2024). MISTI: Metadata-Informed Scientific Text and Image Representation through Contrastive Learning. Proceedings of the Fourth Workshop on Scholarly Document Processing (SDP 2024), 155–164.

Pawin Taechoyotin; Guanchao Wang; Tong Zeng; Bradley Sides; Daniel E. Acuna (2024). MAMORX: Multi-agent Multi-modal Scientific Review Generation with External Knowledge. NeurIPS 2024 Workshop on Foundation Models for Science: Progress, Opportunities, and Challenges.

Han Zhuang; Daniel E. Acuna (2024). Incorporating costs and benefits to the evaluation of uncertain research results: applications to cancer research funding. Quantitative Science Studies, 5(4), 1047–1069.

Meysam Varasteh; Elizabeth McKinnie; Amanda Aird; Daniel E. Acuna; Robin Burke (2024). Comparative Explanations for Recommendation: Research Directions. Proceedings of the 11th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS 2024), co-located with RecSys 2024.

Alexandria Leto; Shamik Roy; Alexander Hoyle; Daniel Acuna; María Leonor Pacheco (2024). A First Step towards Measuring Interdisciplinary Engagement in Scientific Publications: A Case Study on NLP+ CSS Research. Proceedings of the Sixth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS 2024), 144–158.

2023

Han Zhuang; Tzu-Yang Huang; Daniel E Acuna (2023). A computational analysis of accessibility, readability, and explainability of figures in open access publications. EPJ Data Science, 12(1), 5.

2022

D E Acuna; M Teplitskiy; J. Evans; K. Kording (2022). Author-suggested reviewers rate manuscripts much more favorably: A cross-sectional analysis of the neuroscience section of PLOS ONE. PLOS ONE.

Q. Ke; L. Liang; Y. Ding; S V David; D E Acuna (2022). A dataset of mentorship in bioscience with semantic and demographic estimations. Scientific Data, 9(467).

Meijun Liu; Ajay Jaiswal; Yi Bu; Chao Min; Sijie Yang; Zhibo Liu; Daniel Acuña; Ying Ding (2022). Team formation and team impact: The balance between team freshness and repeat collaboration. Journal of Informetrics, 16(4), 101337.

Daniel E. Acuna; Zijun Yi; Lizhen Liang; Han Zhuang (2022). Predicting the usage of scientific datasets based on article, author, institution, and journal bibliometrics. International Conference on Information, 42–52.

Yi Bu; Meijun Liu; Yujia Zhai; Ying Ding; Feng Xia; Daniel E. Acuña; Yi Zhang (2022). International Workshop on Data-driven Science of Science. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 4856–4857.Workshop announcement

2021

Han Zhuang; Tzu-Yang Huang; Daniel Ernesto Acuna (2021). Graphical integrity issues in open access publications: detection and patterns of proportional ink violations. PloS Computational Biology.

Daniel E. Acuna; Tong Zeng; Han Zhuang; Lizhen Liang (2021). Machine Learning and Artificial Intelligence for Science of Science and Computational Discovery: Principles, Applications, and Future Opportunities. iConference 2021 workshop.Workshop description

Daniel E. Acuna; Kartik Nagre; Priya Matnani (2021). EILEEN: A recommendation system for scientific publications and grants. arXiv:2110.09663.Preprint / working paper

Daniel E. Acuna; Lizhen Liang (2021). Are AI Ethics Conferences Different and More Diverse Compared to Traditional Computer Science Conferences?. Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, 307–315.

2020

Tong Zeng; Daniel E Acuna (2020). Large-scale author name disambiguation using approximate network structures. International Conference on Computational Social Science.

Mainak Jas; Titipat Achakulvisut; Aid Idrizović; et al. (2020). Pyglmnet: Python implementation of elastic-net regularized generalized linear models. Journal of Open Source Software, 5(47), 1959.

Daniel E. Acuna; Ziyue Xiang (2020). Estimating a Null Model of Scientific Image Reuse to Support Research Integrity Investigations. arXiv:2003.00878.Preprint / working paper

Titipat Achakulvisut; Daniel E Acuna; Konrad Kording (2020). Pubmed Parser: A Python Parser for PubMed Open-Access XML Subset and MEDLINE XML Dataset. Journal of Open Source Software, 5(46), 1979.

Ziyue Xiang; Daniel E. Acuna (2020). Scientific Image Tampering Detection Based On Noise Inconsistencies: A Method And Datasets. arXiv:2001.07799.Preprint / working paper

Titipat Achakulvisut; Tulakan Ruangrong; Daniel Ernesto Acuna; Brad Wyble; Dan Goodman; Konrad Kording (2020). neuromatch: Algorithms to match scientists. eLife Labs.Web article

Daniel Ernesto Acuna (2020). Some considerations for studying gender, mentorship, and scientific impact: commentary on AlShebli, Makovi, and Rahwan (2020). OSF Preprints.Archived preprint

Tong Zeng; Daniel E Acuna (2020). Modeling citation worthiness by using attention-based bidirectional long short-term memory networks and interpretable models. Scientometrics, 124(1), 399–428.

Tong Zeng; Daniel E Acuna (2020). GotFunding: A grant recommendation system based on scientific articles. Proceedings of the Association for Information Science and Technology, 57(1), e323.

Tong Zeng; Daniel E Acuna (2020). Finding datasets in publications: the Syracuse University approach. Rich Search and Discovery for Research Datasets, 158–165.

Lizhen Liang; Daniel E Acuna (2020). Don’t judge a journal by its cover?: Appearance of a Journal’s website as predictor of blacklisted Open-Access status. Proceedings of the Association for Information Science and Technology, 57(1), e306.

Tong Zeng; Longfeng Wu; Sarah Bratt; Daniel E Acuna (2020). Assigning credit to scientific datasets using article citation networks. Journal of Informetrics, 14(2), 101013.

Lizhen Liang; Daniel E Acuna (2020). Artificial mental phenomena: Psychophysics as a framework to detect perception biases in AI models. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 403–412.

Lizhen Liang; Daniel E Acuna (2020). Are author, affiliation, and citation networks predictive of a journal getting blacklisted?. International Conference on Computational Social Science.

Han Zhuang; Daniel E Acuna (2020). An Automatic Misleading Graph Detection Tool. International Conference on Computational Social Science.

2019

Han Zhuang; Daniel E. Acuna (2019). The effect of novelty on the future impact of scientific grants. arXiv:1911.02712.Preprint / working paper

Taraz G Lee; Daniel E Acuna; Konrad P Kording; Scott T Grafton (2019). Limiting motor skill knowledge via incidental training protects against choking under pressure. Psychonomic bulletin & review, 26(1), 279–290.

Daniel E. Acuna (2019). Helping research misconduct investigations: methods for statistical certainty reporting of inappropriate figure reuse. World Conference on Research Integrity, Hong Kong.Conference presentation

Tong Zeng; Alain Shema; Daniel E Acuna (2019). Dead science: Most resources linked in biomedical articles disappear in eight years. International Conference on Information, 170–176.

Titipat Achakulvisut; Chandra Bhagavatula; Daniel Acuna; Konrad Kording (2019). Claim Extraction in Biomedical Publications using Deep Discourse Model and Transfer Learning. arXiv:1907.00962.Preprint / working paper

2018

Jean F Liénard; Titipat Achakulvisut; Daniel E Acuna; Stephen V David (2018). Intellectual synthesis in mentorship determines success in academic careers. Nature communications, 9(1), 1–13.

Misha Teplitskiy; Daniel Acuna; Aı̈da Elamrani-Raoult; Konrad Körding; James Evans (2018). The sociology of scientific validity: How professional networks shape judgement in peer review. Research Policy, 47(9), 1825–1841.

Daniel E Acuna; Paul S Brookes; Konrad P Kording (2018). Bioscience-scale automated detection of figure element reuse. BioRxiv, 269415.Preprint / working paper

2017

Alain Shema; Daniel E Acuna (2017). Show Me Your App Usage and I Will Tell Who Your Close Friends Are: Predicting User’s Context from Simple Cellphone Activity. Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems, 2929–2935.

2016

Titipat Achakulvisut; Daniel E Acuna; Tulakan Ruangrong; Konrad Kording (2016). Science Concierge: A fast content-based recommendation system for scientific publications. PloS ONE, 11(7), e0158423.

Pavan Ramkumar; Daniel E Acuna; Max Berniker; Scott T Grafton; Robert S Turner; Konrad P Kording (2016). Chunking as the result of an efficiency computation trade-off. Nature communications, 7(1), 1–11.

Christian Ethier; Daniel Acuna; Sara A Solla; Lee E Miller (2016). Adaptive neuron-to-EMG decoder training for FES neuroprostheses. Journal of neural engineering, 13(4), 046009.

2015

Daniel E Acuna; Max Berniker; Hugo L Fernandes; Konrad P Kording (2015). Using psychophysics to ask if the brain samples or maximizes. Journal of vision, 15(3), 7–7.

Andrea Lancichinetti; M. Irmak Sirer; Jane X. Wang; Daniel Acuna; Konrad Körding; Luís A. Nunes Amaral (2015). High-Reproducibility and High-Accuracy Method for Automated Topic Classification. Physical Review X, 5(1), 011007.

2014

Daniel E Acuna; Nicholas F Wymbs; Chelsea A Reynolds; Nathalie Picard; Robert S Turner; Peter L Strick; Scott T Grafton; Konrad P Kording (2014). Multifaceted aspects of chunking enable robust algorithms. Journal of neurophysiology, 112(8), 1849–1856.

2013

Daniel E Acuna; Orion Penner; Colin G Orton (2013). The future h-index is an excellent way to predict scientistsˈ future impact. Medical Physics, 40(11).

2012

Daniel E Acuna; Stefano Allesina; Konrad P Kording (2012). Predicting scientific success. Nature, 489(7415), 201–202.

Guy Avraham; Ilana Nisky; Hugo L Fernandes; Daniel E Acuna; Konrad P Kording; Gerald E Loeb; Amir Karniel (2012). Toward perceiving robots as humans: Three handshake models face the turing-like handshake test. IEEE Transactions on Haptics, 5(3), 196–207.

2011

Daniel Ernesto Acuna (2011). Rational Bayesian Analysis of Sequential Decision-Making Under Uncertainty In Humans and Machines. University of Minnesota.

2010

Daniel E Acuna; Paul Schrater (2010). Structure learning in human sequential decision-making. PLoS computational biology, 6(12), e1001003.

Daniel Acuna; C. Shawn Green; Paul Schrater (2010). The rational control of aspiration in learning. Computational and Systems Neuroscience 2010.Conference abstract

Daniel E Acuna; Víctor Parada (2010). People efficiently explore the solution space of the computationally intractable traveling salesman problem to find near-optimal tours. PloS ONE, 5(7), e11685.

Daniel E. Acuna; C. Shawn Green; Paul Schrater (2010). Decision-making in unbounded environments using nonparametric Bayesian Reinforcement Learning. NIPS 2010 Workshop on Bounded-rational analyses of human cognition: Bayesian models, approximate inference, and the brain.Workshop presentation

2009

Paul Schrater; Daniel Acuna (2009). Structure learning in sequential decision making. Journal of Vision: Vision Sciences Society Annual Meeting Abstracts, 9(8), 829.Conference abstract

Daniel E Acuna; Paul Schrater (2009). Improving bayesian reinforcement learning using transition abstraction. Proceedings of the ICML/UAI/COLT Workshop on Abstraction in Reinforcement Learning, 1.

2008

Daniel E Acuna; Paul Schrater (2008). Structure learning in human sequential decision-making. Proceedings of the 21st International Conference on Neural Information Processing Systems, 1–8.

Daniel Ernesto Acuna; Paul Schrater (2008). Bayesian modeling of human sequential decision-making on the multi-armed bandit problem. Proceedings of the 30th annual conference of the cognitive science society, 100, 200–300.

Keynotes & invited talks

• Moderator for the panel 'The Other Side: Junk, Fraud, Retractions, and Paper Mills' at the International Conference on Science of Science and Innovation 2024 (Washington, DC)
• November 2, 2023 - invited speaker - Colorado Clinical and Translational Sciences Institute - What research can teach us about research integrity and culture? University of Colorado Anschutz Medical Campus
• December 14, 2022 - invited speaker (virtual) - Forensic Analysis of Scientific Images: Challenges and Opportunities - Detect and Correct Interest Group (Global)
• August 22, 2022 - talk - Large-scale nearest neighbor search for research integrity - Ray Summit, San Francisco, CA
• June 11, 2022 - invited speaker - International Conference on Data Intelligence & Information Service Development, Tianjin Normal University, China (virtual)
• November 18, 2021 - invited speaker and discussant - Future of Privacy Forum (fpf.org) “Promoting Responsible Research Data Access”, Virtual
• June 10, 2019 - Invited talk and panel discussion - Science of bad science, Science of Science conference at the University of Chicago Center in Beijing, Beijing, China
• May 10, 2019 - Keynote speaker - To catch a science cheater: detecting imagery fraud in biomedical research, 8th Annual Ethics in Biomedical Research Lecture, University of Rochester School of Medicine and Dentistry
• March 10, 2019 - Invited talk - The effect of innovation on the future impact of scientific grants, Research Institute of Electrical Communication, Tohoku University, Sendai, Japan
• November 2018 - Invited talk and panel discussion - Bias in Deep Learning Models - Journalist & Artificial Intelligence: Consequences and Opportunities in Emerging Tech - Diversity, Inclusion, & Bias in AI, Newhouse, Syracuse University
• November 2017 - Invited Talk - Data Science of Data Science: Should you improve your Hadoop skills or learn time-series analytics? Computer Science, Syracuse University
• October 2017 - Invited Talk Data Science of Data Science: Should you improve your Hadoop skills or learn time-series analytics? Rochester Institute of Technology
• October 2016 - Invited talk - Improving Scientific Innovation: A Data Science Perspective, Research Computing, Syracuse University
• May 2016 - Invited webinar - Evaluating Merit Review: Content-Based Reviewer-Manuscript Assignment and Bayesian Article Scoring, American Institute of Biological Sciences Scientific, Peer Advisory and Review Services
• April 2016 - Plenary talk - Tools to improve peer review and scholarly research, University of Wisconsin, Madison
• March 2016 - Plenary talk - Data science to understand knowledge discovery and expertise, ChiPy (Chicago Python), Chicago, IL
• “Should journals allow authors to suggest reviewers?” (talk), Quantifying Science, (European) Conference on Complex Systems ’15, Temple, Arizona, Summer 2015
• “Machine learning tools for improving Science” (talk), Metaknowledge Research Network, Summer Retreat, California, Summer 2015
• “Big data science of science” (talk), Metaknowledge Research Network, Spring Retreat, University of Chicago, Winter 2015
• "Automatic detection of figure element reuse in biological science articles", (talk) Science of Team Science Conference, Austin, TX, August 2014
• "Big data machine learning for prediction and classification" (Invited academic speaker, plenary), The Tenth Workshop on the Development of Advanced Algorithms for Security Applications (ADSA10), Boston, MA, April 2014,
• “An investigation of how prior beliefs influence decision-making under uncertainty in a 2AFC task”, (Plenary talk, 3% acceptance rate) Computational and Systems Neuroscience (COSYNE), Salk Lake City, UT, March 2013
• "Rational analysis of human problem solving and sequential decision-making under uncertainty ", (Invited talk) Rehabilitation Institute of Chicago, Northwestern University, Chicago, IL, July 2010
• "Rational analysis of human sequential decision-making under uncertainty and human problem solving," (Invited talk) Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, June 2010

Patent applications

• Daniel E. Acuna, Konrad Kording, "System and method for automated detection of figure element reuse," US Patent App. 16/752,113, 2020 (assignee Syracuse University, Northwestern University, and Rehabilitation Institute of Chicago)
• Konrad Kording, Daniel E. Acuna, Titipat Achakulvisut. “Data Butler.” U.S. Provisional Patent Application No. 62/218,998, filed September 15, 2015 (assignee Rehabilitation Institution of Chicago)
• SYSTEM FOR EXTRACTING AND QUANTIFYING STATISTICAL PROBLEMS IN DOCUMENTS. Inventors: ACUNA, Daniel Ernesto; MONSALVE, Elizabeth Exter. Application Number: PCT/US2024/046747. Publication Date: March 20, 2025.
• SYSTEM AND METHOD FOR EVALUATING THE QUALITY OF VISUAL REPRESENTATIONS. Inventors: ACUNA, Daniel; ZHUANG, Han; BRINGHAM, John C. Application Number: PCT/US2025/017808. Publication Date: September 4, 2025.
• MULTI-AGENT MULTI-MODAL SCIENTIFIC REVIEW GENERATION WITH EXTERNAL KNOWLEDGE. Inventors: WANG, Guanchao; TAECHOYOTIN, Pawin; ZENG, Tong; SIDES, Bradley; ACUNA, Daniel Ernesto. Application Number: US 63/718,281. Filing Date: November 8, 2024.

Academic service

• Member of Program Committee of the 9th Atlanta Conference on Science and Innovation Policy (hosted by the Georgia Institute of Technology)
• Member of Program Committee for the 9th International Conference on Computational Social Science (IC2S2) (hosted by the University of Copenhagen)
• Program Committee Co-Chair for the 2nd International Conference on Science of Science and Innovation (hosted by Northwestern University)
• Organizer of ORI-funded Data2Graph and Text Paraphrasing competitions at Eval.ai (http://competition.cri-conf.org/)
• 2022-present: Member of the ACM Diversity, Equity, and Inclusion (DEI) Council leading the Social Justice in Publications, Reviews, Awards, & Research focus.
• Co-Organizer Data-Driven Science of Science workshop at KDD 2022, August 14 - 18, 2022
• Co-Organizer of them NSF-funded Science of Science Summer School (S4) 2021 and 2022, virtually hosted by Syracuse University, Syracuse, NY (https://s4.scienceofscience.org)
• Organizer of the ORI-funded Computational Research Integrity Conference (CRICONF) 2021 and 2023, Washington, DC (https://cri-conf.org)
• Organizer of iConference 2021 workshop: Machine Learning and Artificial Intelligence for Science of Science and Computational Discovery: Principles, Applications, and Future Opportunities (https://scienceofscience.org/workshops/)
• Editorial Board: Journal of Social Computing (IEEE Xplore), Humanities & Social Sciences Communications (Springer Nature)
• Committee member: 2nd Workshop on Scholarly Document Processing 2021 (https://2021.naacl.org/)
• Member of the Data Science Leadership of the Academic Data Science Alliance (ADSA)
• Associate Chair: Late-Breaking Work CHI 2017
• Reviewer for: Nature Communications, Scientometrics, Journal of Informetrics, Research Policy, IEEE Transactions on Human-Machine Systems, Journal of the Royal Society Interface, Research Evaluation, Operations Research, PLoS Computational Biology, PLoS ONE, NIPS 2009, NIPS 2010, CogSci 2009
• Ad-hoc reviewer: NSF's Science of Science and Innovation Policy, Department of Energy Office of Science’s Office of Advanced Scientific Computing Research

University of Colorado Boulder

• 2025 - Present: Advisory Committee for Student Health, Wellness and Flourishing Initiative
• 2025 - 2026: Graduate Committee (CS) Partial contribution to PhD recruitment
• 2023 - 2024: Chair of NLP Search Committee
• 2022-: Graduate Committee
• 2022-: Participant of the Graduate School DEAI Summit and Subsequent Meetings

Syracuse University

• 2021-2022: Research Technology Committee
• 2020-2021: ADS Program Committee, Doctoral Program Committee
• 2019-2020: ADS Program Committee, Doctoral Program Committee, BLIST adviser
• 2018-2019: Doctoral Program Committee, Black and Latinx Information Science and Technology Society (BLISTS)
• 2017-2018: Doctoral Program Committee, Personnel

Students & committees

Postdoctoral advisor

Qing Ke, Ph.D. (now at the City University of Hong Kong in Department of Data Science)

Visiting scholar

Tong Zeng, School of Information Management, Nanjing University (Fall 2017 - Spring 2021)

Ph.D. advisor

Yifan Tian (since Fall 2025), Christopher Ebuka Ojukwu (since Fall 2024), Pawin Taechoyotin (CU Boulder, since 2024), Almene De Meran Meguimtsop (CU Boulder), Carolina Chávez-Ruelas (CU Boulder, since 2024), Meysam Varasteh (CU Boulder, from 2023 to 2024), Han Zhuang (graduated Spring 2023), Lizhen Liang (graduated Spring 2025) from iSchool, Syracuse University

Ph.D. thesis committees

Sikana Tanupabrungsun (defended 2016), Ivan Shamshurin (defended 2019), Mahboobeh Harandi (EOC only), Yingya Li (defended 2022), Yisi Sang (defended 2022), Alain Shema (defended 2022), Yuheun Kim (first year advisor), Hunter Wapman (CU Boulder)

Research practica

Dipto Das, Research Assistant, iSchool, Fall 2019; Sarah Bratt, Research Practicum, iSchool, Spring 2018; Alex Smith, Graduate Assistant, Teaching and Research Practica, iSchool (Fall 2017-Spring 2018); Mahboobeh Harandi, iSchool, Research Practicum, Spring 2017; Alain Shema, Graduate Assistant, Teaching and Research Practica (Fall 2016 - Spring 2018)

Master’s students

Tzu-Yang (Peter) Huang, Faculty Engagement Scholar, iSchool, since Fall 2020; Hanlin Zhang, Computer Science, Fall 2019 - Spring 2021; Jian Jian, iSchool, Spring 2020 - Spring 2021; Rashika Singh, Faculty Engagement Scholar, iSchool, Fall 2019 - Spring 2021; Sourabh Ghosh, Faculty Engagement Scholar, iSchool, Fall 2019 - Fall 2020; Megha Ramesh Jakhotia, Faculty Engagement Scholar, previously with prof. Yang Wang, Fall 2019 - Spring 2020; Ziyue (Alan) Xiang, Computer Science, Fall 2018 - Spring 2020, now a Ph.D. student at Purdue University; Ananth Raj GV, iSchool, Spring 2019 - Spring 2020, a data scientist with Bank of America; Omkar Buchade, Computer Science, since Fall 2018 (in Summer 2019 internship with CBS Interactive); Mengyu (Mike) Liu, Computer Science, Fall 2018 - Summer 2019; Priya Matnani, iSchool (IM with CAS in Data Science), Faculty Engagement Scholar, Fall 2017 - Spring 2019 (Summer 2018 internship at Airbnb, San Francisco), data scientist at Airbnb; Xinxuan Wei, iSchool, Spring 2018 - Spring 2019, data scientist in Shanghai, China; Woojin Park, iSchool (Applied Data Science program), Faculty Engagement Scholar, Fall 2018 - Spring 2019, currently at CMU; Zexin Yao, Computer Science, Spring 2018 - Spring 2019 (Summer 2018 internship at NetEase Games, Guangzhou City, China) - will be a software engineer for Google starting June 2019; Puzhen (Price) Qian, Computer Science, Fall 2018 - Spring 2019; Shloak Gupta, iSchool, Fall 2018; Kartik Nagre, iSchool, Fall 2016 - Spring 2018, Data Scientist at NewtonX; Ziyi Qiu, Computer Engineering, Summer 2017 - Spring 2018, looking for positions in Data Science; Shengjun Zhang, MS (2017), iSchool, Bytedance Inc.; Zhida Zhao, MS (2017), iSchool, looking for positions in Data Science; Manas Sikri, MS (2017), iSchool, Goldman Sachs; Shrutik Katchhi, MS (2017), iSchool, Ernst & Young

Media

• Our work on Estimating the predictability of questionable open-access journals has received widespread attention in:
• Science: AI tool labels more than 1000 journals as ‘questionable’ for possibly shady practices
• Nature: AI flags 1000s of ‘predatory’ journals: what now?
• The Scientist: AI Helps Flag Potentially Problematic Journals for the First Time
• ScienceDaily: New AI tool identifies 1,000+ questionable scientific journals
• The Register: AI spies questionable science journals
• Digital Journal: AI clamps down on ‘fake science’ journals
• CU Boulder Today: New AI tool identifies 1,000+ questionable scientific journals
• Interview for The New Yorker (2021) “How a Sharp-Eyed Scientist Became Biology’s Image Detective” https://www.newyorker.com/science/elements/how-a-sharp-eyed-scientist-became-biologys-image-detective
• Interview for Nature News (2020) “Pioneering duplication detector trawls thousands of coronavirus preprints” https://www.nature.com/articles/d41586-020-02161-3
• Mention in Nature Machine Learning Editorial (2020) “A match for virtual conferences” https://www.nature.com/articles/s42256-020-0182-5
• Mention in Nature News (2020) “Publishers launch joint effort to tackle altered images in research papers” https://www.nature.com/articles/d41586-020-01410-9
• Nature Feature interview about Elisabeth Bik (2020) “Meet this super-spotter of duplicated images in science papers” https://www.nature.com/articles/d41586-020-01363-z
• Interview for Nature News (2018) "Researchers have finally created a tool to spot duplicated images across thousands of papers" https://www.nature.com/articles/d41586-018-02421-3
• Interviews: Nature Podcast, The Chronicle of Higher Education, NPR Science Friday, The Scientists, The Daily Orange, Nature Editorial, Wired, Phys.org, BioTechniques,

Software tools

ReviewerZero AI

Tools for scientific peer review.

EILEEN

Recommendation system for scientific articles and grants.

Dr. Figures

Figure element reuse detection to support research integrity.