SIGBITS October 2023 | Worcester Polytechnic Institute

Fall 2023 SIGBITS

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New Faculty Joining the Department: We have a number of new faculty joining our department this year. Professors Sherry Sun, Jun Dai, and Harmony Zhan are faculty members joining Computer Science to support both the teaching and research missions. Prof. Roee Shraga is joining the Computer Science and the Data Science program in a similar role. Profs. Taylor Andrews and Sami Sun are joining the Computer Science department to support the teaching mission. In January 2024, Prof. Sakire Arslan Ay will also be joining the department to support the teaching mission.

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Promotions: Prof. Tian Guo and Prof. Robert Walls were awarded tenure and promoted to the rank of Associate Professor. Prof. Craig Shue was promoted to Full Professor. All promotions were effective July 1, 2023.

Grants and External Awards:

Prof. Emmanuel Agu is the PI of the NIH grant 1R01EB031910-01A1 Smartphone-based wound infection screener by combining thermal images and photographs using deep learning methods, PI Agu, co-PIs: Lindsay, Tulu and Strong. Award amount: $2,458,174. Project dates: Sept 1, 2022, to May 31, 2026.
Prof. Emmanuel Agu is the PI of the methods core of the 1P50MH129701-01A1 Center for Accelerating Practices to End Suicide through Technology Translation (CAPES) P50, NIH/NIMH, Boudreaux and Kiefe MPIs, MPI Methods Core, (Over 20 researchers from WPI and UMMS). Grant amount: $16,000,000. Grant dates: April 2023 to March 2028.
Prof. Emmanuel Agu is one of the PIs on the grant “IMPACT: Integrative Mindfulness-Based Predictive Approach for Chronic Low Back Pain Treatment”, UG3, NIH HEAL Initiative, King, Agu and Morone MPIs, Nephew, Ruiz, Wu, Rodriguez (co-I’s), Grant amount: $8,842,270. Grant dates: Sept 19, 2023, to October 2028.
Prof. Rose Bohrer is PI on NSF CRII grant “Homotopical Logic Programs”, amount $164,646.00, 7/23–6/25.
Prof. Mark Claypool received additional funding for “Enhancing Slow-start for Improved TCP & QUIC Performance over Satellite Networks”, ViaSat, $145,252, June 2021–July 2024.
Prof. Fabricio Murai is senior personnel in a 2-year grant from FAPESP (São Paulo Research Foundation, Brazil). The grant, titled “Quantifying Uncertainty in Adversarial Federated Learning”, is from Aug 2023 to July 2025. This work is in collaboration with Victoria University of Wellington, UMass Amherst, UFMG, and UNICAMP.
Prof. Rodica Neamtu is the Principal Investigator of a 6-year $2.5 million NSF S-STEM grant “Creating a Path to Achieving Success and Sense of Belonging in Computer Science.” The grant will award scholarships to 28 talented undergraduate students, with a demonstrated financial need, who are pursuing degrees in Computer Science.
Prof. Craig Shue serves as a PI and Prof. Robert Walls serves as a co-PI on the “2023 DoD Cyber Scholarship Program: Worcester Polytechnic Institute” award from the Department of Defense. Award amount: $69,878 (from August 2023 to November 2024).
Prof. Erin Solovey and Prof. Gillian Smith were nominated to serve on a National Academies Panel on Assessment of Humans in Complex Systems.
Professor Elke Rundensteiner received the IEEE InfoVis Test- of - Time Award for pioneering work accomplished in 2003 that still influences the Visual Analytics community 20 years later, Prof. Rundensteiner was awarded the prestigious test of time award in Visual Analytics in Nov. 2023. Link to article: Elke Rundensteiner Receives the Prestigious IEEE Test-of-Time Award for Groundbreaking Visual Data Analytics Work | Worcester Polytechnic Institute (wpi.edu)
Prof. Elke Rundensteiner is the PI together with Prof. K. Katherine Dixon-Gordon (UMASS Amherst) of one of the signature projects "LEMURS: Leveraging Early Mental Health Uncovering Risk for Suicide” in the 1P50MH129701-01A1 Center for Accelerating Practices to End Suicide through Technology Translation (CAPES) P50, NIH/NIMH Center Grant, Boudreaux and Kiefe MPIs, with 20 researchers from WPI and UMMS participating. Grant amount: $16,000,000. Grant dates: April 2023 to March 2028.

Publications:

Prof. Emmanuel Agu’s Publications:

Journal Papers:

Monica Ahluwalia, Jacques Kpodonu, Emmanuel Agu. Risk Stratification in Hypertrophic Cardiomyopathy Leveraging Artificial Intelligence to Provide Guidance in the Future, JACC Advances (accepted, to appear).
Apiwat Ditthapron, Adam C. Lammert, and Emmanuel O. Agu. Multi-task Deep Learning Methods for Improving Human Context Recognition from Low Sampling Rate Sensor Data, IEEE Sensors Journal (accepted, to appear).
Apiwat Dittrapron, Adam Lammert, Emmanuel Agu. ADL-GAN: Data Augmentation to Improve In-the-wild ADL Recognition using GANs, IEEE Access (accepted, to appear).
Atifa Sarwar and Emmanuel Agu. CovidRhythm: A Deep Learning Model for Passive Prediction of Covid-19 using Biobehavioral Rhythms Derived from Wearable Physiological Data, 2023. IEEE Open Journal of Engineering in Medicine and Biology (OJEMB).
Ziyang Liu, Emmanuel Agu, Peder Pedersen, Clifford Lindsay, Bengisu Tulu and Diane Strong. Chronic Wound Image Augmentation and Assessment using Semi-Supervised Progressive Multi-Granularity EfficientNet, IEEE Open Journal of Engineering in Medicine and Biology (OJEMB), 2023.
Ruojun Li, Emmanuel Agu, Atifa Sarwar, Kristin Grimone, Debra Herman, Ana Abrantes and Michael Stein. Fine-Grained Intoxicated Gait Classification using a Bi-Linear CNN, IEEE Sensors Journal, 2023.
Walter Gerych, Luke Buquicchio, Kavin Chandrasekaran, Hamid Mansoor, Emmanuel Agu, Elke Rundensteiner. Domain adaptation methods for Lab-to-field Human Context Recognition, Abdulaziz Alajaji, Sensors Journal, MDPI.
Hamid Mansoor, Walter Gerych, Abdulaziz Alajaji, Luke Buquicchio, Kavin Chandrakasekaran, Emmanuel Agu, Elke Rundensteiner, Angela Rodriguez, 2023. INPHOVIS: Interactive Visual Analytics for Smartphone-Based Digital Phenotyping, Elsevier Visual Informatics Journal.
David Clement, Emmanuel Agu, Muhammad Suleiman, John Obayemi, Steve Adeshina and Wole Soboyejo. Multi-Class Breast Cancer Histopathological Image Classification using Multi-Scale Pooled Image Feature Representation (MPIFR) and One-Versus-One Support Vector Machines, MDPI Applied Sciences.
Wafaa S. Almuhammadi, Emmanuel Agu, Jean King, Patricia Franklin. OA-Pain-Sense: Machine Learning Prediction of Hip and Knee Osteoarthritis Pain from IMU Data, In Informatics (Vol. 9, No. 4, p. 97). Multidisciplinary Digital Publishing Institute (MDPI).
Monica Ahluwalia, MD FACC, Anekwe Onwuanyi, MD, Emmanuel Agu, PhD, Jacques Kpodonu, MD FACC. Advocate for a Path to Increasing Diversity Enrollment in Cardiovascular Clinical Trials, JACC Advance.

Conference Papers:

Apiwat Dittrapron, Emmanuel Agu, Peder Pedersen, Clifford Lindsay, Bengisu Tulu, and Diane Strong. On Rejecting Low Quality Images to Improve Deep Smartphone Wound Assessment, IEEE International Conference on Machine Learning Applications (ICMLA), special session on health, 2024 (accepted, to appear).
Walter Gerych, Kevin Hickey, Luke Buquicchio, Kavin Chandrasekaran, Abdulaziz Alajaji, Elke Rundensteiner, and Emmanuel Agu. Debiasing Pretrained Generative Models by Uniformly Sampling Semantic Attributes, NeurIPS 2023 (poster) (accepted, to appear).
Apiwat Ditthapron, Adam Lammert, Emmanuel Agu, Masking Kernel for Learning Energy-Efficient Representations for Speaker Recognition and Mobile Health, Proc INTERSPEECH 2023.
Joshua Audibert, Elijah Gonzalez, Ryan Orlando, Nicholas Wong, Emmanuel Agu, and Mark Claypool. Machine Learning Prediction of Just Dance Enjoyment from Mobile Sensor Data, 2023 IEEE Conference on Games (CoG).
Abdulaziz Alajaji, Kavin Chandrasekaran, Luke Buquicchio, Walter Gerych, Emmanuel Agu, and Elke Rundensteiner. Adversarial Human Context Recognition: Evasion Attacks and Defenses, IEEE COMPSAC 2023.
Hamid Mansoor, Walter Gerych, Abdulaziz Alajaji, Luke Buquicchio, Kavin Chandrasekaran, Emmanuel Agu and Elke Rundensteiner. Population Level Analysis of Smartphone Detected Health and Wellness Using Community Phenotypes, IEEE ICHI 2023.
Wen Ge, Guanyi Mou, Emmanuel Agu and Kyumin Lee. Heterogeneous Hyper-Graph Neural Networks for Human Context Recognition, Work in Progress (WiP), Percom 2023.

Prof. Rose Bohrer’s Publications:

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Rose Bohrer. Centering Humans in the Programming Languages Classroom: Building a Text for the Next Generation. SPLASH-E Symposium, Cascais, Portugal, October 25, 2023.
Shano Liang, Michelle V. Cormier, Phoebe Toups Dugas, and Rose Bohrer.
ACM Symposium on Computer-Human Interaction in Play (CHI PLAY) 2023, Stratford, Canada, October 10–13, 2023.
Charlotte Clark and Rose Bohrer.
Homotopy Type Theory for Sewn Quilts,
ACM SIGPLAN International Workshop on Functional Art, Music, Modelling and Design (FARM) 2023, Seattle, USA, September 8, 2023.

Prof. Mark Claypool’s Publications:

Maryam Ataei Kachooei, Jae Chung, Feng Li, Benjamin Peters, and Mark Claypool. “SEARCH: Robust TCP Slow Start Performance over Satellite Networks”, in Proceedings of the 48th IEEE Conference on Local Computer Networks (LCN), Daytona Beach, FL, USA, October 1–5, 2023. Online at:  https://web.cs.wpi.edu/~claypool/papers/search-lcn-23/
Joshua Audibert, Elijah Gonzalez, Ryan Orlando, Nicholas Wong, Emmanuel Agu, and Mark Claypool. “Machine Learning Prediction of Just Dance Exergame Enjoyment from Mobile Sensor Data”, in Proceedings of the IEEE Conference on Games (COG), Boston, MA, USA, August 21–24, 2023. Online at:  https://web.cs.wpi.edu/~claypool/papers/cypress-23/
Shengmei Liu and Mark Claypool. “The Impact of Latency on Target Selection in First-Person Shooter Games,” in Proceedings of the ACM Multimedia Systems Conference (MMSys), Vancouver, Canada, June 7–10, 2023. Online at:  https://web.cs.wpi.edu/~claypool/papers/3d-selection-23/
Shengmei Liu, Atsuo Kuwahara, James Scovell, and Mark Claypool. “The Effects of Frame Rate Variation on Game Player Quality of Experience,” in Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI), Hamburg, Germany, USA, April 23–28, 2023. Online at:  https://web.cs.wpi.edu/~claypool/papers/frame-variation-chi-23/
Samantha Peznola, Lynne Gauthier, Mark Claypool, Benjamin Roop, and Adam Lammert. “Compliance with In-Home Self-Managed Rehabilitation Post-Stroke Is Largely Independent of Scheduling Approach”, In Archives of Physical Medicine and Rehabilitation, Volume 104, Number 4, Pages 554–561, DOI: 10.1016/j.apmr.2022.10.007 (Epub Nov 6, 2022), April 2023. Online at:  https://web.cs.wpi.edu/~claypool/papers/stroke-game-22/

Prof. Lane Harrison’s Publications:

Ndlovu, Akim, Shrestha, Hilson, Harrison, Lane. “Taken By Surprise? Evaluating how Bayesian Weighting Influences Peoples’ Takeaways in Map Visualizations.” IEEE VIS Conference Short Paper (2023).
Shrestha, Hilson, Kathleen Cachel, Mallak Alkhathlan, Elke Rundensteiner, and Lane Harrison. “Help or Hinder? Evaluating the Impact of Fairness Metrics and Algorithms in Visualizations for Consensus Ranking.” ACM Conference on Fairness, Accountability, and Transparency (2023).
Ding, Yiren, Jack Wilburn, Hilson Shrestha, Akim Ndlovu, Kiran Gadhave, Carolina Nobre, Alexander Lex, and Lane Harrison. “reVISit: Supporting Scalable Evaluation of Interactive Visualizations.” IEEE VIS Conference Short Paper (2023).
Noëlle Rakotondravony, Priya Dhawka, and Melanie Bancilhon. “Beyond English: Centering Multilingualism in Data Visualization.” Visualization for Social Good Workshop at IEEE VIS (2023).
Russell Davis, Xiaoying Pu, Yiren Ding, Brian D. Hall, Karen Bonilla, Mi Feng, Matthew Kay, Lane Harrison. “The Risks of Ranking: Revisiting Graphical Perception to Model Individual Differences in Visualization Performance”. IEEE Transactions on Visualization and Computer Graphics (2022).
Yiren Ding and Lane Harrison. “Data in the Wind: Evaluating Multiple-Encoding Design for Particle Motion Visualizations”, IEEE VIS Conference Short Paper (2023).
Yuan Cui, Lily W. Ge, Yiren Ding, Fumeng Yang, Lane Harrison, and Matthew Kay. “Adaptive Assessment of Visualization Literacy”. IEEE VIS Conference (2023).
Yang, Fumeng, Yuxin Ma, Lane Harrison, James Tompkin, and David H. Laidlaw. “How Can Deep Neural Networks Aid Visualization Perception Research? Three Studies on Correlation Judgments in Scatterplots.” In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, pp. 1–17. 2023.
Singh, Akarsh, Michael Wan, Lane Harrison, Anne Breggia, Robert Christman, Raimond L. Winslow, and Saeed Amal. “Visualizing Decisions and Analytics of Artificial Intelligence based Cancer Diagnosis and Grading of Specimen Digitized Biopsy: Case Study for Prostate Cancer.” In Companion Proceedings of the 28th International Conference on Intelligent User Interfaces, pp. 166–170. 2023.

Prof. Neil Heffernan’s Publications:

Journal Articles:

Lu, X., Wang, W., Motz, B. A., Ye, W., and Heffernan, N. T. (2023). Immediate text-based feedback timing on foreign language online assignments: How immediate should immediate feedback be? Computers and Education Open, 5. https://doi.org/10.1016/j.caeo.2023.100148
Sales, A.C., Gagnon-Bartsch, J.A., Prihar, E.B., and Heffernan, N.T. (2023). Using Auxiliary Data to Boost Precision in the Analysis of A/B Tests on an Online Educational Platform: New Data and New Results. Journal of Educational Data Mining. Accepted. https://arxiv.org/abs/2306.06273
Gagnon-Bartsch, J. A., Sales, A.C., *, Wu, E., Botelho, A. F., Erickson, J. A., Miratrix, L. W., and Heffernan, N. T. (Accepted 2023). Precise unbiased estimation in randomized experiments using auxiliary observational data. Journal of Casual Inference. https://arxiv.org/abs/2105.03529

Book Chapters:

Mercedes, et al. (2023). The great challenges and opportunities of the next 20 years. In Benedict du Boulay, Antonija Mitrovic, and Kalina Yacef's (Eds.), The Handbook of Artificial Intelligence in Education.
Prihar, E. and Heffernan, N. (2023). Crowdsourcing Paves the Way for Personalized Learning. In B. du Boulay, A. Mitrovic, K. Yacef, (Eds.), Handbook of Artificial Intelligence in Education. (pp. 632–635). Edward Elgar Publishing.

Conference Papers:

Feng, M., Huang, C., and Collins, K. (2023, June). Promising Long Term Effects of ASSISTments Online Math Homework Support. In International Conference on Artificial Intelligence in Education, pp. 212–217. Cham: Springer Nature Switzerland.
Prihar, E., Lee, M., Hopman, M., Kalai, A., Vempala, S., Wang, A., Wickline, G., and Heffernan, N. (2023). Comparing Different Approaches to Generating Mathematics Explanations Using Large Language Models. In: Wang, N., Rebolledo-Mendez, G., Dimitrova, V., Matsuda, N., Santos, O.C. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium, and Blue Sky. AIED 2023. Communications in Computer and Information Science, vol 1831. Springer, Cham. https://doi.org/10.1007/978-3-031-36336-8_45
Wang, A., Prihar, E., and Heffernan, N. (2023). Assessing the Quality of Large Language Models in Generating Mathematics Explanations. Presented at the Fourth Annual Workshop on A/B Testing and Platform-Enabled Learning Research held at the Learning @ Scale Conference.
Baral, S., Botelho, A.F., Santhanam, A., Gurung, A., Erickson, J., and Heffernan, N. (2023). Investigating Patterns of Tone and Sentiment in Teacher Written Feedback Messages. Accepted to AIED 2023.
Baral, S., Santhanam, A., Botelho, A.F., Santhanam, A., Gurung, A., Cheng, L., and Heffernan, N. (2023). Auto-scoring Student Responses with Images in Mathematics. In The Proceedings of the 16th International Conference on Educational Data Mining.
Feng, M., Heffernan, N., Collins, K., Heffernan, C., and Murphy, R. (2023). Implementing and Evaluating ASSISTments Online Math Homework Support At Large Scale over Two Years: Findings and Lessons Learned. AIED2023.
Gurung, A., Baral, S., Vanacore, K. P., McReynolds, A. A., Kreisberg, H., Botelho, A. F., Shaw, S. T., and Heffernan, N. T. (2023). Identification, Exploration, and Remediation: Can Teachers Predict Common Wrong Answers? In LAK23: 13th International Learning Analytics and Knowledge Conference (LAK 2023), March 13–17, 2023, Arlington, TX, USA. ACM, New York, NY, USA, 16 pages. https://doi.org/10.1145/3576050.3576109
Gurung, A., Lee, M. P., Baral, S., Sales, A. C., Vanacore, K. P., McReynolds, A. A., Kreisberg, H., Heffernan, C., Haim, A, and Heffernan, N. T. (2023) How Common are Common Wrong Answers? Crowdsourcing Remediation at Scale. In Proceedings of the Tenth ACM Conference on Learning @ Scale (L@S ’23), July 20–22, 2023, Copenhagen, Denmark. ACM, New York, NY, USA, 11 pages. https://doi.org/10.1145/3573051.3593390
Haim, A., Gyurcssan, R., Baxter, C., Shaw, S., and Heffernan, N. (2023). How to Open Science: Developing and Testing Reproducibility Metrics at the Educational Data Mining Conference.
Haim, A., Gyurcsan, R., Baxter, C., Shaw, S., and Heffernan, N. (2023). How to Open Science: Analyzing the Open Science Statement Compliance of the Learning@Scale Conference. In Proceedings of the Tenth ACM Conference on Learning@Scale (L@S '23), July 20–22, 2023, Copenhagen, Denmark. ACM, New York, NY, USA, 8 pages.
Haim, A., Shaw, S., and Heffernan, N. (2023). How to Open Science: A Principle and Reproducibility Review of the Learning Analytics and Knowledge Conference. In LAK ’23: International Conference on Learning Analytics & Knowledge, March 13–17, 2023, Arlington, TX. ACM, New York, NY, USA. https://doi.org/10.1145/3576050.3576071
Lee, M.P., Croteau, E., Gurung, A., Botelho, A.F., and Heffernan, N. (2023). Knowledge Tracing Over Time: A Longitudinal Analysis. In The Proceedings of the 16th International Conference on Educational Data Mining.
Prihar, E., Haim, A., Shen, T., Sales, A., Lee, D., and Wu, X. (2023). Investigating the Impact of Skill-Related Videos on Online Learning. In Proceedings of the Tenth ACM Conference on Learning@Scale (L@S '23), July 20–22, 2023, Copenhagen, Denmark. ACM, New York, NY, USA, 10 pages.
Prihar, E., Sales, A., and Heffernan, N. (2023, June). A Bandit You Can Trust. In UMAP '23: Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization (UMAP '23), June 26–29, 2023, Limassol, Cyprus. ACM, New York, NY, USA, 10 Pages. https://doi.org/10.1145/3565472.3592955
Prihar, E., Vanacore, K., Sales, A., and Heffernan, N. (2023). Effective Evaluation of Online Learning Interventions with Surrogate Measures. In The Proceedings of the 16th International Conference on Educational Data Mining.
Vanacore, K.P., Gurung, A., McReynolds, A.A., Liu, A., Shaw, S.T., and Heffernan, N.T. (2023). Impact of Non-Cognitive Interventions on Student Learning Behaviors and Outcomes: An analysis of seven large-scale experimental inventions. In LAK ’23: Learning Analytics & Knowledge. ACM, New York, NY, USA. https://doi.org/10.1145/3576050.3576073

Prof. Fabricio Murai's Publications:

Samuel Guimarães, Gabriel Kakizaki, Philipe Melo, Márcio Silva, Fabricio Murai, Julio Reis, Fabricio Benevenuto. “Anatomy of Hate Speech Datasets: Composition Analysis and Cross-dataset Classification”. In ACM Conference on Hypertext and Social Media (HT’23).
Jackson de Faria, Renato Assunção, Fabricio Murai. “Fisher Scoring Method for Neural Networks Optimization”. In SIAM International Conference on Data Mining (SDM’23).
Libby Tiderman, Juan Sanchez Mercedes, Fiona Romanoschi, Fabricio Murai, “Towards Detecting Cascades of Biased Medical Claims on Twitter”. In IEEE MIT Undergraduate Research Technology Conference (URTC 2023).
Nan Ma, Qi Zhang, Fabricio Murai, William W. Braham, Holly W. Samuelson. “Learning building occupants’ indoor environmental quality complaints and dissatisfaction from text-mining Booking.com reviews in the United States”. Building and Environment, 2023.
Julio C. S. Reis, Philipe Melo, Fabiano Belém, Fabricio Murai, Jussara M. Almeida, Fabricio Benevenuto, “Helping Fact-Checkers Identify Fake News Stories Shared through Images on WhatsApp”. In Brazilian Symposium on Multimedia and the Web (WebMedia’23).
Pedro Henrique Barros, Heitor Ramos, Fabricio Murai. “Bayes and Laplace versus the world: A new label attack approach in federated environments based on Bayesian Neural Networks”. In Brazilian Conference on Intelligent Systems (BRACIS’23).

Prof. Xiangnan Kong’s Publications:

Jidapa Thadajarassiri, Thomas Hartvigsen, Walter Gerych, Xiangnan Kong, Elke A. Rundensteiner: Knowledge Amalgamation for Multi-Label Classification via Label Dependency Transfer. AAAI 2023.
Yao Su, Zhentian Qian, Lei Ma, Lifang He, Xiangnan Kong: One-shot Joint Extraction, Registration and Segmentation of Neuroimaging Data. KDD 2023.

Prof. Dmitry Korkin’s Publications:

Journal Papers:

Monopoli KR, Korkin D, Khvorova A. Asymmetric trichotomous partitioning overcomes dataset limitations in building machine learning models for predicting siRNA efficacy. Molecular Therapy-Nucleic Acids (IF: 10.183) 2023, Jun 14.
Nephew BC, Polcari JJ, Korkin D. Ideography insight from facial recognition and neuroimaging. The Behavioral and Brain Sciences. 2023 Oct 2;46: e249.
Pezeshkian W, Grünewald F, Narykov O, Lu S, Arkhipova V, Solodovnikov A, Wassenaar TA, Marrink SJ, Korkin D. Molecular architecture and dynamics of SARS-CoV-2 envelope by integrative modeling. Structure. (IF: 5.006) 2023 Apr 6;31(4):492–503.
Hariharan VN, Shin M, Chang C-W, O'Reilly D, Biscans A, Yamada K, Guo Z, Somasundaran M, Tang Q, Monopoli KR, ..., Korkin D, ..., Khvorova A. Divalent siRNAs are bioavailable in the lung and efficiently block SARS-CoV-2 infection. Proceedings of the National Academy of Sciences (PNAS, IF: 9.681) 2023 Mar 14;120(11): e2219523120.
Kitsak M, Ganin A, Elmokashfi A, Cui H, Eisenberg DA, Alderson DL, Korkin D, Linkov I. Finding shortest and nearly shortest path nodes in large substantially incomplete networks. Nature Communications. (IF: 17.694) 2023.
Srinivansan S, Harnett N, Zhang L, Dahlgren M, Jang J, Lu S, Nephew B, Palermo C, Pan X, Eltabakh M, Frederick B, Gruber S, Kaufman M, King J, Ressler K, Winternitz S, Korkin D*, Lebois L.* (co-corresponding authors). Unraveling psychiatric heterogeneity and predicting suicide attempts in women with trauma-related dissociation using artificial intelligence. Eur J Psychotraumatol. (IF: 4.071) 2022 Nov;13(2).

Conference Papers:

Hiscox C, Li J, Gao Z, Korkin D, Furlong C, Billiar K. Characterization of bioengineered tissues by digital holographic vibrometry and machine learning. In Summer Biomechanics, Bioengineering, and Biotransport Conference (SB3C2022) 2022 Jul.
Hiscox C, Li J, Gao Z, Korkin D, Furlong C, Billiar K. Characterization of Bioengineered Tissues by Digital Holographic Vibrometry and 3D Shape Deep Learning. In Society for Experimental Mechanics Annual Conference and Exposition 2022 Jun 13 (pp. 57–62). Cham: Springer International Publishing.

Prof. Kyumin Lee’s Publications:

Yichuan Li, Kyumin Lee, Nima Kordzadeh, and Ruocheng Guo. “What Boosts Fake News Dissemination on Social Media? A Causal Inference View.” In Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2023.
Wen Ge, Guanyi Mou, Emmanuel O. Agu, and Kyumin Lee. “Heterogeneous Hyper-Graph Neural Networks for Context-aware Human Activity Recognition.” In 2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), 2023.
Huma Varzgani, Nima Kordzadeh, and Kyumin Lee. “Toward Designing Effective Warning Labels for Health Misinformation on Social Media.” In the 56th Hawaii International Conference on System Sciences, 2023.

Prof. Yanhua Li’s Publications:

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Mingzhi Hu, Xin Zhang, Yanhua Li, Xun Zhou, Jun Luo.
ST-iFGSM: Enhancing Robustness of Human Mobility Signature Identification Model via Spatial-Temporal Iterative FGSM.
The 29th SIGKDD conference on Knowledge Discovery and Data Mining (KDD'23), Long Beach, CA, USA, August 6–10, 2023.
Qiong Wu, Jian Li, Zhenming Liu, Yanhua Li, Mihai Cucuringu Symphony in the Latent Space: Provably Integrating High-dimensional Techniques with Non-linear Machine Learning Models. The Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI'23), Washington D.C., USA, Feb 7–14, 2023.
Mingzhi Hu, Zhuoyun Zhong, Xin Zhang, Yanhua Li, Yiqun Xie, Xiaowei Jia, Xun Zhou, and Jun Luo.
Self-supervised Pre-training for Robust and Generic Spatial-Temporal Representations. IEEE International Conference on Data Mining (ICDM'23), Shanghai, China, Dec. 1–4, 2023.
Xin Zhang, Yanhua Li, Ziming Zhang, Christopher Brinton, Zhenming Liu, Zhi-Li Zhang.
Distributional Cloning for Stabilized Imitation Learning via ADMM. IEEE International Conference on Data Mining (ICDM'23), Shanghai, China, Dec. 1–4, 2023.
Palawat Busaranuvong, Xin Zhang, Yanhua Li, Xun Zhou, Jun Luo.
CAC: Enabling Customer-Centered Passenger-Seeking for Self-Driving Ride Service with Conservative Actor-Critic. IEEE International Conference on Data Mining (ICDM'23), Shanghai, China, Dec. 1–4, 2023.
Yiqun Xie, Zhaonan Wang, Gengchen Mai, Yanhua Li, Xiaowei Jia, Song Gao and Shaowen Wang.
"Geo"-Foundation Models: Reality, Gaps and Opportunities (Vision Paper).
31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS'23), (Vision paper) Nov. 13–16, 2023, Hamburg, Germany.
Huimin Ren, Sijie Ruan, Ye Yuan, Yanhua Li, Jie Bao, Tianfu He, Huajun He, Chuishi Meng and Yu Zheng.
A Novel Approach for Company Real Workplace Identification via E-commercial Data (Industrial Paper). 31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS'23), (short paper) Nov. 13–16, 2023, Hamburg, Germany.
Guojun Wu, Xin Zhang, Ziming Zhang, Yanhua Li, Xun Zhou, Christopher Brinton, Zhenming Liu.
Learning Lightweight Neural Networks via Channel-Split Recurrent Convolution.
Winter Conference on Applications of Computer Vision 2023 (WACV'23), Waikoloa, Hawaii, Jan 3–7, 2023.
Xin Zhang, Yanhua Li, Ziming Zhang, Zhi-Li Zhang.
Domain Disentangled Meta-Learning. SIAM International Conference on Data Mining (SDM’23), Minneapolis, April 27–29, 2023.
Yingxue Zhang, Yanhua Li, Xun Zhou, Ziming Zhang, Jun Luo.
STM-GAIL: Spatial-Temporal Meta-GAIL for Learning Diverse Human Driving Strategies.
SIAM International Conference on Data Mining (SDM’23), Minneapolis, April 27–29, 2023.
Han Bao, Xun Zhou, Yiqun Xie, Yanhua Li, and Xiaowei Jia.
STORM-GAN+: Spatio-Temporal Meta-GAN for Cross-City Estimation of Heterogeneous Human Mobility Responses to COVID-19.
Knowledge and Information Systems (KAIS), Accepted, June 2023.

Prof. Xiaozhong Liu’s Publications:

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Jing Dong, Kangkang Yang, Dan Wu, Xi Niu, and Xiaozhong Liu. Human-Centered Design on Crowdsourcing Annotation toward Improving Active Learning Model Performance. Journal of Information Science, 2023.
Yunxue Cui, Yongzhen Wang, Xiaozhong Liu, Xianwen Wang, and Xuhong Zhang. Multidimensional Scholarly Citations: Characterizing and Understanding Citation Behaviors. Journal of the American Society for Information Science and Technology, 2022.
Kai Zhang, Kaisong Song, Yangyang Kang, and Xiaozhong Liu. Synthesizing Corpus Knowledge: Content- and Topology-Aware Representation Learning for Multi-Scientific Literature. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023.
Zirui Shao, Feiyu Gao, Zhongda QI, Hangdi Xing, Jiajun Bu, Zhi Yu, Qi Zheng, and Xiaozhong Liu. GEM: Gestalt Enhanced Markup Language Model for Web Understanding via Render Tree. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023.
Xiangnan Chen, Qian Xiao, Juncheng Li, Duo Dong, Jun Lin, Xiaozhong Liu, and Siliang Tang. Global Structure Knowledge- Guided Relation Extraction Method for Visually-Rich Document. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023.
Kaihang Pan, Juncheng Li, Hongye Song, Jun Lin, Xiaozhong Liu, and Siliang Tang. Self-supervised Meta-Prompt Learning with Meta-Gradient Regularization for Few-shot Generalization. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023.
Yiquan Wu, Siying Zhou, Yifei Liu, Weiming Lu, Xiaozhong Liu, Yating Zhang, Changlong Sun, Fei Wu, and Kun Kuang. Precedent-Enhanced Legal Judgment Prediction with LLM and Domain-Model Collaboration. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023.
Xurui Li, Yue Qin, Rui Zhu, Tianqianjin Lin, Yongming Fan, Yangyang Kang, Kaisong Song, Fubang Zhao, Chang-long Sun, Haixu Tang, and Xiaozhong Liu. STINMatch: Semi-Supervised Semantic-Topological Iteration Network for Financial Risk Detection via News Label Diffusion. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023.
Yang Wu, Xurui Li, Xuhong Zhang, Yangyang Kang, Changlong Sun, and Xiaozhong Liu. Community-Based Hierarchical Positive-Unlabeled (PU) Model Fusion for Chronic Disease Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. ACM, 2023.
Zilong Lin, Xiaojing Liao, Xiaofeng Wang, and Xiaozhong Liu. MAWSEO: Adversarial Wiki Search Poisoning for Illicit Online Promotion. In Proceedings of the 44nd IEEE Symposium on Security and Privacy. IEEE, 2023.
Kaisong Song, Kangkang Yang, Chang-long Sun, and Xiaozhong Liu. A Speaker Turn-Aware Multi-Task Adversarial Network for Joint User Satisfaction Estimation and Sentiment Analysis. In Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence. AAAI, 2023.
Yao Feng, Jingyuan Zhang, Yating Zhang, Xiaozhong Liu, Changlong Sun, Yun Liu, and Weixing Shen. Unsupervised Legal Evidence Retrieval via Contrastive Learning with Approximate Aggregated Positive. In Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence. AAAI, 2023.
Vincent Quirante Malic, Anamika Kumari and Xiaozhong Liu. Racial Skew in Fine-Tuned Legal AI Language Models. LegalAI Workshop at IEEE International Conference on Data Mining, 2023.
Lintian Jinqian, Qian Wang, Jiang Zhuoren, Patricia L Mabry, and Xiaozhong Liu. Investigating academic graph-based factors behind funding success in National Institutes of Health. Proceedings of the Association for Information Science and Technology, 60, 2023 (short paper).
Jacob Striebel, Rebecca Myers and Xiaozhong Liu. Career-Based Explainable Course Recommendation Proceedings of the iConference, 2023 (short paper).

Prof. Jennifer Mortensen’s Publications:

Jennifer C Mortensen, Jovan Damjanovic, Jiayan Miao, Tiffani Hu, and Yu-Shan Lin. “A backbone-dependent rotamer library with high (𝜙,𝜑) coverage using metadynamics simulations.” Protein Science. 2022; 31(12): e4491. https://doi.org/10.1002/pro.4491

Prof. Rodica Neamtu’s Publications:

Mansi Gera, Zijian Guan, Bryer Sousa, Danielle Cote, Rodica Neamtu, “Comparative Study of Clustering Methods for Additive Manufacturing” at the Material Science and Technology Conference (MS&T) in the Undergraduate Poster Competition, October 2022 (poster).
Ichwani R, Oyewole O, Neamtu R, Soboyejo W, “Using Machine Learning for Prediction of Spray Coated Perovskite Solar Cells Efficiency: From Experimental to Theoretical Models”. In Science Direct Materials and Design Journal, Materials & Design, 2023, 112161, ISSN 0264-1275, https://doi.org/10.1016/j.matdes.2023.112161.
Ashley Schuliger, Stephen Price, Bryer C. Sousa, Danielle L. Cote, Rodica Neamtu. “Multiple-instance Regression for Metallic Powder Hall Flow Rate Prediction using Augmented Particle Size and Shape Data”. In Proceedings of Powders Journal 2023, 2(1), 189–204; https://doi.org/10.3390/powders2010013
Stephen Price, Matthew Gleason, Bryer Sousa, Danielle Cote, Rodica Neamtu “Automated and Refined Application of Convolutional Neural Network Modeling to Metallic Powder Particle Satellite Detection,” In Proceedings of Journal Integrating Materials and Manufacturing Innovation by Springer 2022.
Alicia Howell-Munson, Christopher Micheck, Erin Solovey, Rodica Neamtu, “BrainEx: Interactive Visual Exploration and Discovery of Sequence Similarity in Brain Signals,” in Proceedings of ACM SIGCHI Symposium on Engineering Interactive Computer Systems (EICS PACM 2022). (doi.acm.org?doi=3534516).
Bryer Sousa, Christopher Vieira, Rodica Neamtu, Danielle Cote, “Clustering Algorithms for Nanomechanical Property Mapping and Resultant Microstructural Constituent and Phase Quantification,” In Proceedings of Algorithm Development in Materials Science and Engineering, 2022 TMS Annual Meeting & Exhibition.
Bryer Sousa, Richard Valente, Aaron Krueger, Eric Schmid, Danielle Cote, Rodica Neamtu, “Investigating the Suitability of Tableau Dashboards and Decision Trees for Particulate Materials Science and Engineering Data Analysis”, In Proceedings of AI\/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification, 2022 TMS Annual Meeting & Exhibition.
S Price, R Neamtu, “Identifying, Evaluating, and Addressing Nondeterminism in Mask R-CNNs,” ICPRAI 2022 (3rd International Conference on Pattern Recognition and Artificial Intelligence, June 2022. (https://link.springer.com/book/10.1007/978-3-031-09037-0)
S Price, R Ichwani, R Neamtu, W Soboyejo, “Predictability in Science in the age of AI” presented at the Nobel Symposium in South Africa in 2022.
Alicia Howell-Munson, Emily Doherty, Peter Gavriel, Claire Nicolas, Adam Norton, Rodica Neamtu, Holly Yanco, Yi-Ning Wu and Erin T. Solovey, “Towards Brain Metrics for Improving Multi-Agent Adaptive Human-Robot Collaboration: A Preliminary Study,” In Proceedings of CHIWORK'22 (Symposium on Human-Computer Interaction for Work).

Prof. Daniel Reichman’s Publications:

Mikulincer, D., and Reichman, D. (2022). Size and depth of monotone neural networks: interpolation and approximation. Advances in Neural Information Processing Systems, 35, 5522–5534.
Anderson, C. C., Balasubramanian, A., Poskanzer, H., Reichman, D., and Sárközy, G. N. (2023). New ordering methods to construct contagious sets and induced degenerate subgraphs. Involve, a Journal of Mathematics, 16(1), 59–68.
DiCicco, M., and Reichman, D. (2023, June). The learning and communication complexity of subsequence containment. In 2023 IEEE International Symposium on Information Theory (ISIT) (pp. 322–327). IEEE.
Reichman, D., Lieder, F., Bourgin, D. D., Talmon, N., and Griffiths, T. L. (2023). The Computational Challenges of Means Selection Problems: Network Structure of Goal Systems Predicts Human Performance. Cognitive science, 47(8), e13330.
Safran, I., Reichman, D., and Valiant, P. (2024). How many neurons does it take to approximate the maximum? In ACM-SIAM Symposium on Discrete Algorithms (SODA), 2024.

Prof. Elke Rundensteiner’s Publications:

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Journal Articles:

Hamid Mansoor, Walter Gerych, Abdulaziz Alajaji, Luke Buquicchio, Kavin Chandrasekaran, Angela Incollingo Rodriguez, Emmanuel Agu, and Elke Rundensteiner, “Exploratory Data Analysis of Population Level Smartphone-sensed Data”, Volume 1691, Communications in Computer, and Information Science series (CCIS). Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP Book), Springer, Jan 2023.
M. L. Tlachac, Avantika Shrestha, Mahum Shah, Benjamin Litterer, Elke A. Rundensteiner: Automated Construction of Lexicons to Improve Depression Screening with Text Messages. IEEE J. Biomed. Health Informatics 27(6): 2751-2759 (2023)
Dandan Tao, Ruofan Hu, Dongyu Zhang, Jasmine Laber, Anne Lapsley, Timothy Kwan, Liam Rathke, Elke Rundensteiner*, Hao Feng*, A Novel Foodborne Illness Detection and Web Application Tool Based on Social Media, Journal on Food Engineering and Technology Applications of Artificial Intelligence in Food Industry, MDPI, July 2023.
Lei Cao, Yizhou Yan, Yu Wang, Samuel Madden, Elke A. Rundensteiner: AutoOD: Automatic Outlier Detection. Proc. ACM Manag. Data 1(1): 20:1-20:27 (2023)
Alajaji, Abdulaziz, Walter Gerych, Luke Buquicchio, Kavin Chandrasekaran, Hamid Mansoor, Emmanuel Agu, and Elke Rundensteiner. "Domain Adaptation Methods for Lab-to-Field Human Context Recognition." Sensors 23, no. 6 (2023): 3081.
Tlachac, M. L., Avantika Shrestha, Mahum Shah, Benjamin Litterer, and Elke A. Rundensteiner. "Automated construction of lexicons to improve depression screening with text messages." IEEE Journal of Biomedical and Health Informatics (2022).
Mansoor, Hamid, Walter Gerych, Abdulaziz Alajaji, Luke Buquicchio, Kavin Chandrasekaran, Emmanuel Agu, Elke Rundensteiner, and Angela Incollingo Rodriguez. "INPHOVIS: Interactive visual analytics for smartphone-based digital phenotyping." Visual Informatics (2023).

Referred Conference Proceedings:

Walter Gerych, Kevin Hickey, Luke Buquicchio, Kavin Chandrasekaran, Abdulaziz Alajaji, Elke Rundensteiner, and Emmanuel Agu. Debiasing Pretrained Generative Models by Uniformly Sampling Semantic Attributes. NeurIPS 2023.
Peter VanNostrand, Dennis Hofmann, Huayi Zhang, and Elke Rundensteiner. FACET: Robust Counterfactual Explanation Analytics, ACM SIGMOD, (accepted Aug 2023)
Ricardo Flores, Avantika Shrestha, and Elke Rundensteiner, "DeepScreen: Boosting Depression Screening Performance with an Auxiliary Task" IEEE BigData 2023 Conference – 8th IEEE Special Session on Machine Learning on Big Data (MLBD 2023) 2023.
Ricardo Flores, Avantika Shrestha, ML Tlachac, and Elke Rundensteiner, Multi-Task Learning Using Facial Features for Mental Health Screening, IEEE BigData 2023 Conference – 6th Special Session on HealthCare Data 2023.
Ruofan Hu, Dongyu Zhang, Dandan Tao, Huayi Zhang, Hao Feng, and Elke Rundensteiner, "UCE-FID: Using Large Unlabeled, Medium Crowdsourced-Labeled, and Small Expert-Labeled Tweets for Foodborne Illness Detection IEEE BigData 2023 Conference – 8th IEEE Special Session on Machine Learning on Big Data (MLBD 2023) 2023.
Walter Gerych, Kevin Hickey, Thomas Hartvigsen, Luke Buquicchio, Abdulaziz Alajaji, Kavin Chandrasekaran, Hamid Mansoor, Emmanuel Agu, and Elke Rundensteiner, "Stabilizing Adversarial Training for Generative Networks" IEEE BigData 2023 Conference 8th IEEE Special Session on Machine Learning on Big Data (MLBD 2023) 2023.
Hamid Mansoor, Walter Gerych, Abdulaziz Alajaji, Luke Buquicchio, Kavin Chandrasekaran, Emmanuel Agu and Elke Rundensteiner, Population-Level Visual Analytics of Smartphone Sensed Health and Wellness Using Community Phenotypes, IEEE The 11th IEEE International Conference on Healthcare Informatics (ICHI), Houston, Texas, June 26-29, 2023.
Zhang, Liang, Noura Alghamdi, Huayi Zhang, Mohamed Y. Eltabakh, and Elke A. Rundensteiner. "PARROT: pattern-based correlation exploitation in big, partitioned data series." The VLDB Journal 32, no. 3 (2023): 665-688.
Thadajarassiri, Jidapa, Thomas Hartvigsen, Walter Gerych, Xiangnan Kong, and Elke Rundensteiner. "Knowledge amalgamation for multi-label classification via label dependency transfer." In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, no. 8, pp. 9980-9988. 2023.
Cachel, Kathleen, and Elke Rundensteiner. "Fair&Share: Fast and Fair Multi-Criteria Selections." In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, pp. 152-162. 2023.
Yin, Biao, Nicholas Josselyn, Ziming Zhang, Elke A. Rundensteiner, Thomas A. Considine, John V. Kelley, Berend C. Rinderspacher, Robert E. Jensen, and James F. Snyder. "MOSS: AI Platform for Discovery of Corrosion-Resistant Materials." In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, pp. 5128-5132. 2023.
Biao Yin, Yangyang Fan, Nicholas Josselyn, and Elke Rundensteiner, AlloyGAN: Domain-Promptable Generative Adversarial Network for Generating Aluminum Alloy Microstructures, 2023 Special Session on Machine Learning for Predictive Models in Engineering Applications (MLPMEA 2023), in 22nd IEEE International Conference on Machine Learning and Applications (ICMLA 2023).
Biao Yin, Nicholas Josselyn, Thomas Considine, John Kelley, Berend Rinderspacher, Robert Jensen, James Snyder, Ziming Zhang, and Elke Rundensteiner, DeepSC-Edge: Scientific Corrosion Segmentation with Edge-Guided and Class-Balanced Losses, 2023 Special Session on Machine Learning for Predictive Models in Engineering Applications (MLPMEA 2023), in 22nd IEEE International Conference on Machine Learning and Applications (ICMLA 2023).
Alajaji, Abdulaziz, Walter Gerych, Kavin Chandrasekaran, Luke Buquicchio, Emmanuel Agu, and Elke Rundensteiner. "Adversarial Human Context Recognition: Evasion Attacks and Defenses." In 2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC), pp. 223-232. IEEE, 2023.
Kathleen Cachel, Elke A. Rundensteiner: Fairer Together: Mitigating Disparate Exposure in Kemeny Rank Aggregation. FAccT 2023: 1347-1357
Shrestha, Hilson, Kathleen Cachel, Mallak Alkhathlan, Elke Rundensteiner, and Lane Harrison. "Help or Hinder? Evaluating the Impact of Fairness Metrics and Algorithms in Visualizations for Consensus Ranking." In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, pp. 1685-1698. 2023.
Mallak Alkhathlan, M. L. Tlachac, Elke A. Rundensteiner: Haptic Auditory Feedback for Enhanced Image Description: A Study of User Preferences and Performance. IFIP TC13 International Conference on Human-Computer Interaction (INTERACT'2023). INTERACT (1) 2023: 224-246
Hamid Mansoor, Walter Gerych, Abdulaziz Alajaji, Luke Buquicchio, Kavin Chandrasekaran, Emmanuel Agu and Elke Rundensteiner, Population-Level Visual Analytics of Smartphone Sensed Health and Wellness Using Community Phenotypes, IEEE The 11th IEEE International Conference on Healthcare Informatics (ICHI), Houston, Texas, June 26-29, 2023.

Other Publications:

Fobert Jensen, Kimberly Brady, Prathyush Parvatharaju, Biao Yin, Fatemeh Emdad, and Elke Rundensteiner, MIL-PRF-32662 Context-Aware Agile Platform DEVCOM Army Research Laboratory, Technical Report ARL-SR-0469, MAR 2023.
Nikola Grozdani, America Muñoz, Alexander Pietrick, Ricardo Flores, Avantika Shrestha, Xingtong Guo, Shichao Liu and Elke Rundensteiner. Wearable Wellness: Depression Screening via Fitbit Data Collected During COVID-19 Pandemic. IEEE MIT Undergraduate Research Technology Conference (URTC) 2023. (REU NSF Summer Site Project Paper).
IEEE COMPSAC 2023, Adversarial Human Context Recognition: Evasion Attacks and Defenses, Abdulaziz Alajaji, Kavin Chandrasekaran, Luke Buquicchio, Walter Gerych, Emmanuel Agu and Elke Rundensteiner (Worcester Polytechnic Institute, United States). Link: https://ieeecompsac.computer.org/2023/

Prof. Gabor Sarkozy’s Publications:

“A general approach for supporting time series matching using multiple-warped distances.” Transactions on Knowledge and Data Engineering 34 (4), 2022, pp. 1516–1529 (with Rodica Neamtu, Ramoza Ahsan, Cuong Nguyen, Charles Lovering, Elke Rundensteiner).
“Linear Turan numbers of acyclic triple systems.” European Journal of Combinatorics 99, 2022, 103435 (with András Gyárfás and Miklós Ruszinkó).
“Monochromatic square-cycle and square-path partitions.” Discrete Mathematics 345 (3), 2022, 112712.
“Improved monochromatic double stars in edge colorings.” Graphs and Combinatorics 38, 2022, 78.
“The linear Turan number of small triple systems or why is the wicket interesting?” Discrete Mathematics 345 (11), 2022, 113025 (with András Gyárfás).
“New ordering methods to construct contagious sets and degenerate subgraphs.” Involve, a Journal of Mathematics 16 (1), 2023, pp. 59–68 (with Connor Anderson, Akshaj Balasubramanian, Henry Poskanzer and Daniel Reichman).
“‘Less’ strong chromatic indices and the (7,4)-conjecture.” Accepted for publication in Studia Scientiarum Mathematicarum Hungarica (with András Gyárfás).
“Turan and Ramsey numbers in linear triple systems II.” Discrete Mathematics 346 (1), 2023, 113182.
“Proper edge colorings of Cartesian products with rainbow C_4-s.” Graphs and Combinatorics 39, 2023, Article number: 98 (with András Gyárfás).

Prof. Roee Shraga’s Publications:

Aamod Khatiwada, Grace Fan, Roee Shraga, Zixuan Chen, Wolfgang Gatterbauer, Renée J. Miller, and Mirek Riedewald. “SANTOS: Relationship-based Semantic Table Union Search.” Proceedings of the ACM on Management of Data 1, no. 1 (2023): 1–25.
Bar Genossar, Roee Shraga, and Avigdor Gal. “FlexER: Flexible Entity Resolution for Multiple Intents.” Proceedings of the ACM on Management of Data 1, no. 1 (2023): 1–27.
Roee Shraga, and Renée J. Miller. “Explaining Dataset Changes for Semantic Data Versioning with Explain-Da-V.” Proceedings of the VLDB Endowment 16, no. 6 (2023): 1587–1600.
Roee Shraga, and Avigdor Gal. “One Algorithm to Rule Them All: On the Changing Roles of Humans in Data Integration.” Computer 56, no. 4 (2023): 102–109.
Calvanese, Diego, Avigdor Gal, Davide Lanti, Marco Montali, Alessandro Mosca, and Roee Shraga. “Conceptually-grounded mapping patterns for Virtual Knowledge Graphs.” Data & Knowledge Engineering 145 (2023): 102157.
Aamod Khatiwada, Roee Shraga, and Renée J. Miller. “DIALITE: Discover, Align and Integrate Open Data Tables.” In Companion of the 2023 International Conference on Management of Data, pp. 187–190. 2023.

Prof. Craig Shue’s Publications:

Shuwen Liu, Yu Liu, Craig A. Shue, “Inspecting Traffic in Residential Networks with Opportunistically Outsourced Middleboxes,” IEEE/IFIP Network Operations and Management Symposium, May 2023.
Yunsen Lei, Julian P. Lanson, Craig A. Shue, Timothy W. Wood, “Attackers as Instructors: Using Container Isolation to Reduce Risk and Understand Vulnerabilities,” Detection of Intrusions and Malware and Vulnerability Assessment (DIMVA) conference, July 2023.
Shuwen Liu, Joseph P. Petitti, Yunsen Lei, Craig A. Shue, “By Your Command: Extracting the User Actions that Create Network Flows in Android,” IEEE Network of the Future Conference, October 2023.
Adam Beauchaine, Craig A. Shue, “Toward a (Secure) Path of Least Resistance: An Examination of Usability Challenges in Secure Sandbox Systems,” IEEE International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications, November 2023.

Prof. Erin Solovey’s Publications:

Rachel Boll, Shruti Mahajan, Tish Burke, Khulood Alkhudaidi, Brittany Henriques, Isabelle Cordova, Zoey Walker, Erin T. Solovey, Jeanne Reis. User Perceptions and Preferences for Online Surveys in American Sign Language: An Exploratory Study. In Proc. of ACM SIGACCESS Conference on Computers and Accessibility. (ASSETS'23). To Appear.
Sonmez Unal, D., Mowad, T. G., Howell-Munson, A., Walker, E., Solovey, E., & Arrington, C. (2023). Classification of Rule Learning Phases in Inductive Reasoning. Proceedings of the Annual Meeting of the Cognitive Science Society (poster).
Deniz Sonmez Unal, Catherine Arrington, Erin T. Solovey, Erin Walker. (2023). Eliciting Proactive and Reactive Control During Use of an Interactive Learning Environment. In: Wang, N., Rebolledo-Mendez, G., Matsuda, N., Santos, O.C., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2023. Lecture Notes in Computer Science, vol 13916. Springer, Cham.
Alicia Howell-Munson, Deniz Sonmez Unal, Theresa Mowad, Catherine Arrington, Erin Walker, Erin T. Solovey. (2023). Classification of Brain Signals Collected During a Rule Learning Paradigm. In: Wang, N., Rebolledo-Mendez, G., Dimitrova, V., Matsuda, N., Santos, O.C. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium, and Blue Sky. AIED 2023. Communications in Computer and Information Science, vol 1831. Springer, Cham.
Max Chen, Erin Solovey, Gillian Smith. 2023. Impact of BCI-Informed Visual Effect Adaptation in a Walking Simulator. In Foundations of Digital Games 2023 (FDG 2023). April 12–14, 2023, Lisbon, Portugal. ACM, New York, NY, USA, 8 pages.

Prof. Shubbhi Taneja’s Publications:

Foster, Brett, et al. “Evaluating Energy Efficiency of GPUs using Machine Learning Benchmarks.” 2023 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). IEEE, 2023.
N. Kiesler, J. Impagliazzo, K. Biernacka, A. Kapoor, Z. Kazmi, S.G. Ramagoni, A. Sane, K. Tran, S.Taneja, and Z. Wu. 2023. Where's the Data? Exploring Datasets in Computing Education. In Proceedings of the ACM Conference on Global Computing Education Vol 2 (CompEd 2023). Association for Computing Machinery, New York, NY, USA, 209–210. https://doi.org/10.1145/3617650.3624951

Professional Engagements and Leadership:

Prof. Emmanuel Agu was a panelist at the Critical Conversations: AI and Your Health panel, organized as part of Arts and Science Week, 2023.
In June 2023, WestEd published a report that showed Prof. Neil Heffernan’s ASSISTments program has a significant positive impact on math achievement and educational equity. The study was conducted in NC and replicated the outcomes of a previous randomized controlled trial, solidifying ASSISTments as one of the most rigorously researched and highly effective math learning solutions. The strongest impact was in schools where a majority of students are experiencing poverty.
In December 2022, Prof. Neil Heffernan and his team evaluated ChatGPT 3.5 for our task and found that it made 50% of math errors. They presented those findings at the AIED Meeting in Tokyo (in June) but very soon thereafter, they replicated the work with GPT4.0, finding only a 4% error rate for their task. Since GPT improved so quickly they have decided to start using LLM with the 100,000+ students who use their system. The first task is to provide feedback to students when they are asked to explain their work. Currently, students only receive feedback 2% of the time, when feedback has been shown to improve their understanding. Prof. Heffernan and his team have sought funding for this work and have two types of support so far. Together with Wanli Xing at the University of Florida, they received funding from Schmidt Futures to build a hub at the forefront of researching, developing, and evaluating LLMs within math education.
The second source of funding is led by Prof. Heffernan’s graduate student, Sami Baral, and Research Scientist, Li Cheng. Their team “Using Reinforcement Learning with Human in the Loop (RLHF) Feedback System to Make ChatBots” has advanced to the semi-finals (with a prize of $10,000). Final round participants are announced in November, and winners are announced in January 2024.
Prof. Xiaozhong Liu organized the LegalAI (Legal Intelligence & Fairness: Algorithmic, Data and Social Challenges) workshop at the IEEE International Conference on Data Mining (ICDM) 2023 conference.
Prof. Xiaozhong Liu organized the panel “From Data to Action: Leveraging Open Data to Drive Knowledge-Based Intelligent Governance” at the 86th Annual Meeting of the Association for Information Science and Technology (ASIST) 2023 conference.
Prof. Xiaozhong Liu gave an invited talk, “User-Oriented Natural Language Processing”, at the School of Information, The University of Texas at Austin.
Prof. Xiaozhong Liu gave an invited talk, “Misinformation, Can We Totally Remove It?”, at the Center for Russian, East European, and Eurasian Studies (CREEES), The University of Texas at Austin.
Prof. Xiaozhong Liu gave an invited talk, “Social Media Bubble Comparison - China vs. US”, at the Center for Russian, East European, and Eurasian Studies (CREEES), The University of Texas at Austin.
Prof. Xiaozhong Liu gave an invited talk, “Natural Language Processing (NLP) with User Feedback”, at the Department of Informatics and Networked Systems, University of Pittsburgh.
Prof. Xiaozhong Liu participated in the “ChatGPT, A Solution or A Problem?” (panel talk) at Worcester Polytechnic Institute.
Prof. Xiaozhong Liu gave an invited talk, “Large Language Models, Opportunities and Challenges from Information Science Viewpoint”, at the Department of Information Science, Zhejiang University.
Prof. Xiaozhong Liu gave an invited talk, “Large Language Models and Heterogeneous Data Mining”, at the Department of Information Science, Peking University.
Prof. Neamtu led activities to expand curricular offerings through the AI4ALL program through a series of classes on the ethics of AI, collaboration with industry partners, and gaining access to AI4ALL’s alumni network to pursue internships and jobs.
Prof. Neamtu led a teaching-oriented panel discussion on “Where Story-Telling Meets Project-Based Learning: Incorporating Projects into CS Curriculum”, SIGCSE 2023: Proceedings of the 54th ACM Technical Symposium on Computer Science Education, March 2023.
Prof. Erin Solovey was invited to a talk: Direct Brain Interface and Accessibility Panel, Future of Interface Workshop. February 16, 2023.   https://futureofinterface.org/auditorium_cpt/direct-brain-interfaces-accessibility/
Over the summer, Prof. Erin Solovey and Kathy Chen from the WPI STEM Education Center hosted the second summer session of an NSF-funded program called “Engineering for People and the Planet: WPI Research Experiences for Teaching Integrated STEM”. This three-year program supports authentic summer research experiences focused on the U.N. Sustainable Development Goals for ten middle and high school educators each year to enhance their scientific disciplinary knowledge in engineering and translate their research experiences into classroom activities and curricula to broaden their student’s awareness of and participation in computing and engineering pathways.
Prof. Reichman participated in the Meta-Complexity program at the Simons Institute for the Theory of Computing at UC Berkeley during Fall 2023.
Prof. Elke Rundensteiner was a panelist at the Critical Conversations: AI and Your Health panel, organized as part of Arts and Science Week in October 2023.
Prof. Elke Rundensteiner is a panelist at the Beyond These Towers: Data Science and AI in Our Lives, Alumni WPI Engagement event, hosted by President Grace Wang and WPI leadership in New York, New York on Thursday, November 30, 2023.
Over the summer, Prof. Elke Rundensteiner, Prof. Ngan and Dr. Kelsey Briggs hosted the 10-week summer REU site funded by NSF called “NSF 1852498 REU SITE: INTERDISCIPLINARY DATA SCIENCE RESEARCH FOR HEALTHY COMMUNITIES IN THE DIGITAL AGE”. This program offers a 10-week, all-expenses paid Research Experience in Data Science for undergraduate students. The WPI Data Science program has successfully operated this site since 2016, offering students from geographically diverse institutions around the country access to vibrant scientific research projects and undergraduate mentorship. The students working with WPI faculty, graduate students, and peer students, learn state-of-the-art data science techniques and technologies including machine learning, artificial intelligence, and big data technologies. They apply these new skills to research focused on solving societal challenges of high impact in our communities, including in digital health, sustainability, and mobility. Students present the results of their research at an Open Poster Session during the final week of the program, and, where appropriate, at scholarly venues and conferences.
Quoted in Diagnosing AI: Healthcare community excited, wary of artificial intelligence, Worcester Business Journal; April 17, 2023, By Isabel Tehan. Link: https://www.wbjournal.com/article/diagnosing-ai-healthcare-community-excited-wary-of-artificial-intelligence

Student and Post-Doctoral Scholar News:

Arthur Ames, Charlotte Clark, Evelyn Dube, and Declan Murphy received the WPI Provost’s MQP Award in Computer Science for “Creating the Pwnable Claw Machine.” The students were advised by Prof. Robert Walls and Prof. Loris Fichera.
Alex Friedman, a CS and Professional Writing double-major, received the WPI Provost’s MQP Award in Professional Writing for “Bismuth: A Programming Language for Distributed, Concurrent & Mobile Systems.” Alex was advised by Prof. Rose Bohrer and Prof. Yunus Telliel.
The WPI NSF CyberCorps Scholarship for Service program selected five new scholars: Adelynn Martin (BS/MS '26), Alton Miles (BS '25), Piper O'Connell (BS/MS '26), Jai Patel (BS/MS '25), and Christian Rijos (MS ’25).
The U.S. Department of Defense Cyber Scholarship Program (DoD CySP) selected Ella Dunne (BS/MS ’26) as one of 62 students nationwide to receive a CySP scholarship.