DeepMath Conference on the Mathematical Theory of Deep Neural Networks
Recent advances in deep neural networks (DNNs), combined with open, easily-accessible implementations, have made DNNs a powerful, versatile method used widely in both machine learning and neuroscience. These advances in practical results, however, have far outpaced a formal understanding of these networks and their training. The dearth of rigorous analysis for these techniques limits their usefulness in addressing scientific questions and, more broadly, hinders systematic design of the next generation of networks. Recently, long-past-due theoretical results have begun to emerge from researchers in a number of fields. The purpose of this conference is to give visibility to these results, and those that will follow in their wake, to shed light on the properties of large, adaptive, distributed learning architectures, and to revolutionize our understanding of these systems.
The Future of Learning: Rethinking Practice in a Changing World
Thursday, March 26, 2026 (Workshops)
Friday, March 27, 2026 (Symposium)
Open to Stony Brook University Faculty, Staff, and Graduate Students. Hosted by the Center for Excellence in Learning and Teaching, Office of the Provost.
Thursday, March 26, 2026
Workshop: AI Tools and Techniques
Friday, March 27, 2026
Keynote: Teaching and Thinking with AI
Keynote Speaker: José Antonio Bowen
José Antonio Bowen has been leading innovation and change for over 40 years at Stanford, Georgetown and the University of Southampton (UK), as a dean at Miami University and SMU and as President of Goucher College. Bowen has worked as a musician with Stan Getz, Dave Brubeck, and many others and his symphony was nominated for the Pulitzer Prize in Music (1985).
Bowen holds four degrees from Stanford and has written over 100 scholarly articles and books, including the Cambridge Companion to Conducting (2003), Teaching Naked (2012 and the winner of the Ness Award for Best Book on Higher Education), Teaching Naked Techniques with C. Edward Watson (2017) and Teaching Change: How to Develop Independent Thinkers using Relationships, Resilience and Reflection (Johns Hopkins University Press, 2021).
Bowen has appeared in The New York Times, Forbes, The Wall Street Journal, and has three TED talks. Stanford honored him as a Distinguished Alumni Scholar (2010) and he has presented keynotes and workshops at more than 300 campuses and conferences 46 states and 17 countries around the world. In 2018, he was awarded the Ernest L. Boyer Award (for significant contributions to American higher education). He is a senior fellow for the American Association of Colleges and Universities.
Register here.
Thursday, March 26, 2026 (Workshops)
Friday, March 27, 2026 (Symposium)
Open to Stony Brook University Faculty, Staff, and Graduate Students. Hosted by the Center for Excellence in Learning and Teaching, Office of the Provost.
Thursday, March 26, 2026
Workshop: AI Tools and Techniques
- Open to all faculty & staff
- Hands-on, exploratory
- Registration only limited to the size of the room
- Location: In-person, TBD
- Time: 10 AM - 12 PM
- Registration required
Friday, March 27, 2026
Keynote: Teaching and Thinking with AI
- Faculty, TAs, postdocs, and academic staff
- In-person on-campus conference venue
- Location: SAC Balroom
- Time: 9 AM - 3 PM
- Registration required
Keynote Speaker: José Antonio Bowen
José Antonio Bowen has been leading innovation and change for over 40 years at Stanford, Georgetown and the University of Southampton (UK), as a dean at Miami University and SMU and as President of Goucher College. Bowen has worked as a musician with Stan Getz, Dave Brubeck, and many others and his symphony was nominated for the Pulitzer Prize in Music (1985).
Bowen holds four degrees from Stanford and has written over 100 scholarly articles and books, including the Cambridge Companion to Conducting (2003), Teaching Naked (2012 and the winner of the Ness Award for Best Book on Higher Education), Teaching Naked Techniques with C. Edward Watson (2017) and Teaching Change: How to Develop Independent Thinkers using Relationships, Resilience and Reflection (Johns Hopkins University Press, 2021).
Bowen has appeared in The New York Times, Forbes, The Wall Street Journal, and has three TED talks. Stanford honored him as a Distinguished Alumni Scholar (2010) and he has presented keynotes and workshops at more than 300 campuses and conferences 46 states and 17 countries around the world. In 2018, he was awarded the Ernest L. Boyer Award (for significant contributions to American higher education). He is a senior fellow for the American Association of Colleges and Universities.
Register here.
Please join University Libraries on March 29 at 1:00 via Zoom as we welcome Dr. Zhang, SUNY Empire Innovation Professor at SBU's Power Lab. This lab is pioneering the research of coordinated networked microgrids (NMs) that can possibly help to restore neighboring distribution grids after a major blackout. That these NMs hold promise to significantly enhance the day-to-day reliability of the power grids, we are proud to host Dr. Zhang as a member of our STEM Speaker Series. Registration required.
https://library.stonybrook.
Zoom Link: https://github.com/giorgianb/spdhackspring2021/blob/main/bit.ly/spdhack2021
ΣΦΔ Hack Spring 2021 is ΣΦΔ's first annual machine learning hackathon. ΣΦΔ Hack Spring 2021 aims to introduce Stony Brook students to the rich and challenging field of machine learning, and develop the skills necessary to build sophisticated machine learning models on their own.
More info here: https://github.com/giorgianb/spdhackspring2021/blob/main/README.md
ΣΦΔ Hack Spring 2021 is ΣΦΔ's first annual machine learning hackathon. ΣΦΔ Hack Spring 2021 aims to introduce Stony Brook students to the rich and challenging field of machine learning, and develop the skills necessary to build sophisticated machine learning models on their own.
More info here: https://github.com/giorgianb/spdhackspring2021/blob/main/README.md
Topic: Exotic Neural Networks
Time: Mar 10, 2021 08:00 PM Eastern Time (US and Canada)
Join Zoom Meeting
https://stonybrook.zoom.us/j/
Meeting ID: 913 8893 8500
Passcode: 501725
Title: AI-Driven Target Selection Methods for Touch and Gaze Input
Abstract: Accurately selecting targets is an essential aspect of Human-Computer Interaction. Erroneous selections can cause tedious undo and redo actions. Additionally, some selection errors are non-reversible and can lead to undesirable consequences. However, high-accuracy target selection remains a challenge on touchscreen devices due to the small target size and imprecise touch inputs, and in gaze interaction because of the gaze tracking noise and no easy-to-use selection action. We first propose ReLM, a Reinforcement Learning-based Method for touchscreen target selection. ReLM can automatically show suggestions and require a second touch if the input is ambiguous, and can directly select a target candidate when the input is certain. Our empirical evaluation shows that ReLM reduces the error rate from 6.92% to 1.63%, and the selection time from 2.23s to 1.59s over Shift, an existing suggestion-based method. Compared to BayesianCommand, a direct selection-based method, our ReLM reduces the error rate from 3.64% to 0.89%, while increasing the selection time by only 200 ms. Secondly, we investigate how to improve target selection performance for gaze interaction. We propose BayesGaze, an eye-gaze based target selection method. It accumulates the signal of each gaze point for selecting a target calculated by Bayes Theorem, and uses a threshold mechanism to determine the target selection. Our investigation shows that BayesGaze improves target selection accuracy and speed over a dwell-based selection method, and the Center of Gravity Mapping method.
All are welcome. Here is the zoom meeting link:
https://stonybrook.zoom.us/j/93130953411?pwd=Rm5IRlVPQ3M0cHJsTXpCVFljUlFGUT09 Meeting ID: 931 3095 3411Passcode: 999413
Abstract: Accurately selecting targets is an essential aspect of Human-Computer Interaction. Erroneous selections can cause tedious undo and redo actions. Additionally, some selection errors are non-reversible and can lead to undesirable consequences. However, high-accuracy target selection remains a challenge on touchscreen devices due to the small target size and imprecise touch inputs, and in gaze interaction because of the gaze tracking noise and no easy-to-use selection action. We first propose ReLM, a Reinforcement Learning-based Method for touchscreen target selection. ReLM can automatically show suggestions and require a second touch if the input is ambiguous, and can directly select a target candidate when the input is certain. Our empirical evaluation shows that ReLM reduces the error rate from 6.92% to 1.63%, and the selection time from 2.23s to 1.59s over Shift, an existing suggestion-based method. Compared to BayesianCommand, a direct selection-based method, our ReLM reduces the error rate from 3.64% to 0.89%, while increasing the selection time by only 200 ms. Secondly, we investigate how to improve target selection performance for gaze interaction. We propose BayesGaze, an eye-gaze based target selection method. It accumulates the signal of each gaze point for selecting a target calculated by Bayes Theorem, and uses a threshold mechanism to determine the target selection. Our investigation shows that BayesGaze improves target selection accuracy and speed over a dwell-based selection method, and the Center of Gravity Mapping method.
All are welcome. Here is the zoom meeting link:
https://stonybrook.zoom.us/j/
Abstract: Large Language Models (LLMs) have transitioned from standalone prediction interfaces into integrated systems that incorporate content protection, external knowledge retrieval, and multi-step reasoning. While these functional layers expand model capabilities, they also introduce complex, inter-component dependencies that create novel and systemic security risks. This research provides a systematic deconstruction of the structural vulnerabilities emerging across these functional layers.
In this proposal, we evaluate the security boundaries of LLM systems through three pivotal dimensions:
The Content Layer: We present Watermark under Fire, revealing the inherent fragility of content-based tracing mechanisms under adaptive perturbations and highlighting the limitations of surface-level safety measures.
The Retrieval Layer: We introduce GraphRAG under Fire to examine the security of topology-aware knowledge integration. We reveal how graph-based indexing can be exploited as a structural lever for high-success poisoning attacks.
The Reasoning Layer: We detail AutoRAN, the first framework demonstrating the hijacking of internal safety reasoning in Large Reasoning Models (LRMs). This work proves that the transparency of the reasoning process itself creates a critical and exploitable attack surface.
Collectively, these studies demonstrate a systemic failure of add-on safety mechanisms in securing the broader LLM ecosystem. By identifying recurring patterns of exploitation across different system layers, this research provides the necessary foundation for transitioning from reactive patching to a more unified and architecturally-grounded approach to AI trustworthiness.
Speaker: Jiacheng Liang
Zoom: https://stonybrook.zoom.us/j/
Meeting ID: 666 999 0420
Passcode: 075299
In this proposal, we evaluate the security boundaries of LLM systems through three pivotal dimensions:
The Content Layer: We present Watermark under Fire, revealing the inherent fragility of content-based tracing mechanisms under adaptive perturbations and highlighting the limitations of surface-level safety measures.
The Retrieval Layer: We introduce GraphRAG under Fire to examine the security of topology-aware knowledge integration. We reveal how graph-based indexing can be exploited as a structural lever for high-success poisoning attacks.
The Reasoning Layer: We detail AutoRAN, the first framework demonstrating the hijacking of internal safety reasoning in Large Reasoning Models (LRMs). This work proves that the transparency of the reasoning process itself creates a critical and exploitable attack surface.
Collectively, these studies demonstrate a systemic failure of add-on safety mechanisms in securing the broader LLM ecosystem. By identifying recurring patterns of exploitation across different system layers, this research provides the necessary foundation for transitioning from reactive patching to a more unified and architecturally-grounded approach to AI trustworthiness.
Speaker: Jiacheng Liang
Zoom: https://stonybrook.zoom.us/j/
Meeting ID: 666 999 0420
Passcode: 075299
The Hudson River Estuary (HRE) and New York Bight (NYB) are closely connected, with HRE acting as crucial areas where many NYB marine species spawn and grow. Understanding how these biotic and abiotic environments interact, especially with rapid climate change, is key to better managing fisheries and conserving ecosystems. To better understand the HRE-NYB ecosystem, we develop a comprehensive ecosystem model that links physical and biological processes. Using data from long-term monitoring programs, we analyze ecological patterns and identify key factors regulating the ecosystem. We use this information to develop a model that mimics the food web from tiny plankton to large predators in the ecosystem. This model can help us better understand how changes in the environment, like rising temperatures, and human activities such as fishing affect marine lives and ecosystem over time. The insights from this model can support smarter fisheries management and efforts to conserve marine ecosystems in the HRE-NYB region.
IACS Student Seminar Speaker: Xiangyan Yang, Dept. of Applied Math & Statistics
Location: IACS Seminar Room or Zoom
Join Zoom Meeting: https://stonybrook.zoom.us/j/91650247483?pwd=fvAGEwadplJh7jFC5RWcdvZ5NWPJth.1
Meeting ID: 916 5024 7483
Passcode: 631055
IACS Student Seminar Speaker: Xiangyan Yang, Dept. of Applied Math & Statistics
Location: IACS Seminar Room or Zoom
Join Zoom Meeting: https://stonybrook.zoom.us/j/91650247483?pwd=fvAGEwadplJh7jFC5RWcdvZ5NWPJth.1
Meeting ID: 916 5024 7483
Passcode: 631055
All are welcome to attend BMI grand rounds talk by Dr. Le Lu on 04/14.
Le Lu, Ph.D
Executive Director, PAII Inc
Johns Hopkins University
IEEE Fellow, MICCAI Board Member
Time: Wednesday, April 14, 2021 3:00 pm - 4:00 pm
Zoom Meeting
https://stonybrook.zoom.us/j/95617197636?pwd=KytzZ2pVRG9SZGpKZUtpNXJISjNjZz09
Meeting ID: 956 1719 7636 Passcode: 924293
Title:
In Search of Effective and Reproducible Clinical Imaging Biomarkers for Population Health and Oncology Applications of Screening, Diagnosis and Prognosis
Bio:
Le Lu received a PhD in 2007 from Johns Hopkins University. During his first six years at Siemens, he made significant contributions to the company's CT colonography and Lung CAD product lines. From 2013 to 2017, Dr. Lu served as a staff scientist in the Radiology and Imaging Sciences department of the National Institutes of Health Clinical Center. He then went on to found Nvidia's medical image analysis group and he held the position of senior research manager until June 2018. Since then, he has been the Executive Director at PAII Inc., Bethesda Research lab, Maryland, USA which has become one of the leading industrial research labs in medical imaging. He was the main technical leader for two of the most-impactful public radiology image dataset releases (NIH ChestXray14, NIH DeepLesion 2018). He won NIH Clinical Center Director Award in 2017, NIH Mentor of the year award in 2015, and won numerous best paper awards in MICCAI and RSNA from 2016 to 2020 (over 10000 citations). In 2021, He was elected into IEEE Fellow class cited for his contribution to machine learning for cancer detection and diagnosis, and MICCAI society board member (MICCAI-Industry Workgroup Chair). He is currently an Associate Editor for IEEE Trans. Pattern Analysis and Machine Intelligence and IEEE Signal Processing Letters. He has served as an Area Chair for recent MICCAI, AAAI, CVPR, WACV, ICIP and ICHI conferences for 14 times.
Abstract:
This talk will first give an overall on the work of employing deep learning to permit novel clinical workflows in two population health tasks, namely using conventional ultrasound for liver steatosis screening and quantitative reporting; osteoporosis screening via conventional X-ray imaging and AI readers. These two tasks were generally considered as infeasible tasks for human readers, but as proved by our scientific and clinical studies and peer-reviewed publications, they are suitable for AI readers. AI can be a supplementary and useful tool to assist physicians for cheaper and more convenient/precision patient management. Next, the main part of this talk describes a roadmap on three key problems in pancreatic cancer imaging solution: early screening, precision differential diagnosis, and deep prognosis on patient survival prediction. (1) Based on a new self- learning framework, we train the pancreatic ductal adenocarcinoma (PDAC) segmentation model using a larger quantity of patients (≈1,000, four institutions), with a mix of annotated/unannotated venous or multi-phase CT images. Pseudo annotations are generated by combining two teacher models with different PDAC segmentation specialties on unannotated images, and can be further refined by a teaching assistant model that identifies associated vessels around the pancreas. Our approach makes it technically feasible for robust large-scale PDAC screening from multi-institutional multi-phase partially-annotated CT scans. (2) We propose a holistic segmentation-mesh classification network (SMCN) to provide patient-level diagnosis, by fully utilizing the geometry and location information. SMCN learns the pancreas and mass segmentation task and builds an anatomical correspondence-aware organ mesh model by progressively deforming a pancreas prototype on the raw segmentation mask. Our results are comparable to a multimodality clinical test that combines clinical, imaging, and molecular testing for clinical management of patients with cysts. (3) Accurate preoperative prognosis of resectable PDACs for personalized treatment is highly desired in clinical practice. We present a novel deep neural network for the survival prediction of resectable PDAC patients, 3D Contrast-Enhanced Convolutional Long Short-Term Memory network (CE- ConvLSTM), to derive the tumor attenuation signatures from CE-CT imaging studies. Our framework can significantly improve the prediction performances upon existing state-of-the-art survival analysis methods. This deep tumor signature has evidently added values (as a predictive biomarker) to be combined with the existing clinical staging system.
More information can be found at:
https://bmi.stonybrookmedicine.edu/sites/default/files/Lu_le_04_14.pdf
Le Lu, Ph.D
Executive Director, PAII Inc
Johns Hopkins University
IEEE Fellow, MICCAI Board Member
Time: Wednesday, April 14, 2021 3:00 pm - 4:00 pm
Zoom Meeting
https://stonybrook.zoom.us/j/
Meeting ID: 956 1719 7636 Passcode: 924293
Title:
In Search of Effective and Reproducible Clinical Imaging Biomarkers for Population Health and Oncology Applications of Screening, Diagnosis and Prognosis
Bio:
Le Lu received a PhD in 2007 from Johns Hopkins University. During his first six years at Siemens, he made significant contributions to the company's CT colonography and Lung CAD product lines. From 2013 to 2017, Dr. Lu served as a staff scientist in the Radiology and Imaging Sciences department of the National Institutes of Health Clinical Center. He then went on to found Nvidia's medical image analysis group and he held the position of senior research manager until June 2018. Since then, he has been the Executive Director at PAII Inc., Bethesda Research lab, Maryland, USA which has become one of the leading industrial research labs in medical imaging. He was the main technical leader for two of the most-impactful public radiology image dataset releases (NIH ChestXray14, NIH DeepLesion 2018). He won NIH Clinical Center Director Award in 2017, NIH Mentor of the year award in 2015, and won numerous best paper awards in MICCAI and RSNA from 2016 to 2020 (over 10000 citations). In 2021, He was elected into IEEE Fellow class cited for his contribution to machine learning for cancer detection and diagnosis, and MICCAI society board member (MICCAI-Industry Workgroup Chair). He is currently an Associate Editor for IEEE Trans. Pattern Analysis and Machine Intelligence and IEEE Signal Processing Letters. He has served as an Area Chair for recent MICCAI, AAAI, CVPR, WACV, ICIP and ICHI conferences for 14 times.
Abstract:
This talk will first give an overall on the work of employing deep learning to permit novel clinical workflows in two population health tasks, namely using conventional ultrasound for liver steatosis screening and quantitative reporting; osteoporosis screening via conventional X-ray imaging and AI readers. These two tasks were generally considered as infeasible tasks for human readers, but as proved by our scientific and clinical studies and peer-reviewed publications, they are suitable for AI readers. AI can be a supplementary and useful tool to assist physicians for cheaper and more convenient/precision patient management. Next, the main part of this talk describes a roadmap on three key problems in pancreatic cancer imaging solution: early screening, precision differential diagnosis, and deep prognosis on patient survival prediction. (1) Based on a new self- learning framework, we train the pancreatic ductal adenocarcinoma (PDAC) segmentation model using a larger quantity of patients (≈1,000, four institutions), with a mix of annotated/unannotated venous or multi-phase CT images. Pseudo annotations are generated by combining two teacher models with different PDAC segmentation specialties on unannotated images, and can be further refined by a teaching assistant model that identifies associated vessels around the pancreas. Our approach makes it technically feasible for robust large-scale PDAC screening from multi-institutional multi-phase partially-annotated CT scans. (2) We propose a holistic segmentation-mesh classification network (SMCN) to provide patient-level diagnosis, by fully utilizing the geometry and location information. SMCN learns the pancreas and mass segmentation task and builds an anatomical correspondence-aware organ mesh model by progressively deforming a pancreas prototype on the raw segmentation mask. Our results are comparable to a multimodality clinical test that combines clinical, imaging, and molecular testing for clinical management of patients with cysts. (3) Accurate preoperative prognosis of resectable PDACs for personalized treatment is highly desired in clinical practice. We present a novel deep neural network for the survival prediction of resectable PDAC patients, 3D Contrast-Enhanced Convolutional Long Short-Term Memory network (CE- ConvLSTM), to derive the tumor attenuation signatures from CE-CT imaging studies. Our framework can significantly improve the prediction performances upon existing state-of-the-art survival analysis methods. This deep tumor signature has evidently added values (as a predictive biomarker) to be combined with the existing clinical staging system.
More information can be found at:
https://bmi.
Join University Libraries for an engaging panel discussion where we delve in and learn about the impacts of artificial intelligence on the 2024 US elections! Panelists are Paige Lord, Tom Costello, and Musa al-Gharbi. The discussion will be moderated by Library Dean, Karim Boughida. Co-sponsored by the Office of Diversity, Inclusion, and Intercultural Initiatives.
Please RSVP for Democracy in the Digital Age: AI's Influence on 2024 Elections here.