| A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | |
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1 | Session | First Name | Last Name | Institution | Poster Title | Poster Station | ||||||||||||||||||||
2 | 1 | Sreejeet | Maity | North Carolina State University, Raleigh | Byzantine-Robust Federated Q-Learning | 1 | ||||||||||||||||||||
3 | 1 | Sourav | Ganguly | New Jersey Institute of Technology | Optimistic, yet prepared: Learning decisions that survive adversity. | 2 | ||||||||||||||||||||
4 | 1 | Kausar | Moshood | Oregon State University | RESTLocator: Fault Localization for Heterogeneous defects in REST API Systems | 3 | ||||||||||||||||||||
5 | 1 | Kartik | Pandit | New Jersey Institute of Technology | The Safety Knight: Certifiable Methods for Safer Language Models | 4 | ||||||||||||||||||||
6 | 1 | Bikash | Pal | University of Connecticut | Quantum Machine Learning for False Data Injection Attack Detection in Smart Grids | 5 | ||||||||||||||||||||
7 | 1 | Armita | Kazeminajafabadi | Northeastern University | Are Trajectory Classifiers Robust to Sequential Attacks? A Reinforcement Learning Approach | 6 | ||||||||||||||||||||
8 | 1 | Ali | Baheri | Rochester Institute of Technology | Federated Distributional Reinforcement Learning | 7 | ||||||||||||||||||||
9 | 1 | Mohamadamin | Rajabinezhad | University of Connecticut | Attack-Resilient CLF–CBF Framework for Nonlinear Systems Under Unbounded Cyber Attacks | 8 | ||||||||||||||||||||
10 | 1 | Nesa | Shams | University of Connecticut | Attack-Resilient CLF-Based Control of Quadrotors Under Unbounded Actuator-Channel Attacks | 9 | ||||||||||||||||||||
11 | 1 | Arman | Moradpour | University of Connecticut | Deep Reinforcement Learning for Security Constraint Unit Commitment optimization | 10 | ||||||||||||||||||||
12 | 1 | Leonardo Felipe | Toso | Columbia University | Physics-informed learning under mixing: How physical knowledge speeds up learning | 11 | ||||||||||||||||||||
13 | 1 | Hamza | Tariq | New Jersey Institute of Technology | Decentralized Safe Path Following for Multiple Quadrotors on Intersecting Paths | 12 | ||||||||||||||||||||
14 | 1 | Zhaofeng | Wang | Northwestern University | SENTINEL - Multi-Level Formal Framework for Safety Evaluation of Foundation Model-based Embodied Agents | 13 | ||||||||||||||||||||
15 | 1 | Rhythm | Chandak | University of Pennsylvania | Racing with Physics: Neuro-Symbolic Dynamics Learning for Model-Based RL | 14 | ||||||||||||||||||||
16 | 1 | Vaibhav | Thakkar | University of Pennsylvania | Model-Based RL with Symbolic Priors for Contact Locomotion | 15 | ||||||||||||||||||||
17 | 1 | Hamed | Ajorlou | University of Rochester | BUILD with precision: Bottom-Up Inference of Linear DAGs | 16 | ||||||||||||||||||||
18 | 1 | Liudong | Chen | Columbia University | A Prudent Framework for Understanding Risk-Awareness in Demand Response | 17 | ||||||||||||||||||||
19 | 1 | Arman | Ibrayeva | Cornell University | Interventional motion planning for navigation in crowded environments | 18 | ||||||||||||||||||||
20 | 1 | Houston | Claure | Yale University | Insights on Designing for Fairness and Safety in Human-Robot Interactions | 19 | ||||||||||||||||||||
21 | 1 | Shilpa | Mukhopadhyay | New Jersey Institute of Technology | Robust Peak-cost Constrained Reinforcement Learning | 20 | ||||||||||||||||||||
22 | 1 | Hyun Joe | Jeong | Carnegie Mellon University | Steering Vision-Language Action Models with Language Controllers | 21 | ||||||||||||||||||||
23 | 1 | Md Mizanur | Rahman | Auburn University | AI‑Powered Solar Power Forecasting: From Raw Data to Reliable Predictions | 22 | ||||||||||||||||||||
24 | 1 | Amirhosein | Chahe | Drexel University | PiJEPA: Policy-Guided World Model Planning for Language-Conditioned Visual Navigation | 23 | ||||||||||||||||||||
25 | 1 | Jixian | Liu | Johns Hopkins University | Safety-Critical Control via Recurrent Tracking Functions | 24 | ||||||||||||||||||||
26 | 1 | Maryam | Soleymani | Louisiana State University | Minimizing the Total Cost of Data Center Cooling: A Robust Multi-Objective Deep Reinforcement Learning Approach with Domain Randomization | 25 | ||||||||||||||||||||
27 | 1 | Mahdi | Bonyani | Louisiana State University | Hierarchical Reinforcement Learning for Data Center Cooling Control: Decomposing Hybrid Action Spaces for Improved Energy Optimization | 26 | ||||||||||||||||||||
28 | 1 | Krishna | Suresh | Carnegie Mellon University | Learning Deformable Object Manipulation Using Task-Level Iterative Learning Control | 27 | ||||||||||||||||||||
29 | 1 | Nick-Marios | Kokolakis | University of Pennsylvania | Adversarial Physics-Informed Machine Learning for Robust Optimal Safe Predefined-Time Stabilization: A Game-Theoretic Approach | 28 | ||||||||||||||||||||
30 | 1 | Bhathiya | Rathnayake | Johns Hopkins University | Grid-Forming Control with Assignable Voltage Regulation Guarantees and Safety Critical Current Limiting | 29 | ||||||||||||||||||||
31 | 1 | Yahya | Sattar | Cornell University | Two-Layer Linear Auto-Regressive Models Estimate Latent States | 30 | ||||||||||||||||||||
32 | 1 | David | Snyder | University of Pennsylvania | Physics-Informed Learning of Feedback-Linearizing Representations | 31 | ||||||||||||||||||||
33 | 1 | Jin | Schofield | Princeton University | Don’t Explore What You Already Know: Pattern-Seeking Exploration Via Temporal Invariance | 32 | ||||||||||||||||||||
34 | 1 | Liza | Dahiya | Carnegie Mellon University | Uncertainty in Video Action Models | 33 | ||||||||||||||||||||
35 | 1 | Yuyang | Zhang | Harvard University | Diffusion Policies for Improved Exploration in Online Reinforcement Learning | 34 | ||||||||||||||||||||
36 | 1 | Junwon | Seo | Carnegie Mellon University | Steering Uncertain World Models using Noise Optimization Enables Robust Planning | 35 | ||||||||||||||||||||
37 | 1 | Haitong | Ma | Harvard University | SiMPO: Measure Matching for Online Diffusion Reinforcement Learning | 36 | ||||||||||||||||||||
38 | 1 | Jainik | Mehta | Rensselaer Polytechnic Institute | Adaptive Safety Bounds: RL-Tuned CBF constraints for Dynamic Obstacle Avoidance using NMPC | 37 | ||||||||||||||||||||
39 | 1 | Zirui | Zang | University of Pennsylvania | Lagrange model for learning model predictive control | 38 | ||||||||||||||||||||
40 | 1 | Luke | Snow | Cornell University | Efficient Neural SDE Training using Wiener Space Cubature | 39 | ||||||||||||||||||||
41 | 1 | Grant | Ruan | Massachusetts Institute of Technology | Flow Matching for Stochastic Policies in Reinforcement Learning | 40 | ||||||||||||||||||||
42 | 1 | Feras | Al Taha | Cornell University | Wasserstein Distributionally Robust Risk-Sensitive Estimation Using Conditional Value-at-Risk | 41 | ||||||||||||||||||||
43 | 1 | Tom | Dubnov | Princeton University | Detecting Cyber-Physical Telemetry Attacks with Domain-Constrained Cross-Signal Imputation | 42 | ||||||||||||||||||||
44 | 1 | Ian Xul | Belaustegui | Princeton University | A continuous flow relaxation of the linear threshold model | 43 | ||||||||||||||||||||
45 | 1 | Nicolas | Lizarralde | Rensselaer Polytechnic Institute | Heuristic-Guided Learning for Online Path Planning | 44 | ||||||||||||||||||||
46 | 1 | BuQing | Ou | Carnegie Mellon University | Can Tabular Foundation Models Guide Exploration in Robot Policy Learning? | 45 | ||||||||||||||||||||
47 | 1 | Louis | Nel | Carnegie Mellon University | SideKIC: Rapid, Expressive and Closed-Form Failure Prediction for Learned Control Policies | 46 | ||||||||||||||||||||
48 | 2 | Zizhe | Zhang | University of Pennsylvania | Viability-Preserving Passive Torque Control | 1 | ||||||||||||||||||||
49 | 2 | Ariana | Haghighi | Columbia University | Two-Layer Hierarchical Receding Horizon Optimal Power Flow with Storage Scheduling | 2 | ||||||||||||||||||||
50 | 2 | Kasra | Fallah | Columbia University | On the gradient domination of LQG problem | 3 | ||||||||||||||||||||
51 | 2 | Tesshu | Fujinami | University of Pennsylvania | History-dependent policy gradient for LQR with Domain Randomization | 4 | ||||||||||||||||||||
52 | 2 | Promise | Ekpo | Cornell University | AdaFair-MARL: Enforcing Adaptive Fairness Constraints in Multi-Agent Reinforcement Learning | 5 | ||||||||||||||||||||
53 | 2 | Viet-Anh | Le | University of Pennsylvania | Learning to Optimize and Its Applications in Control | 6 | ||||||||||||||||||||
54 | 2 | Alok | Shah | University of Pennsylvania | Optimization for Test-Time Performance, not Validation Loss | 7 | ||||||||||||||||||||
55 | 2 | Keshawn | Smith | University of Connecticut | Robust and Safe Multi-Agent Reinforcement Learning with Communication for Autonomous Vehicles: From Simulation to Hardware | 8 | ||||||||||||||||||||
56 | 2 | Jiguang | Yu | Boston University | Sparse Leader Control in Delayed Stochastic Multi-Agent Systems | 9 | ||||||||||||||||||||
57 | 2 | Ye | Liang | University of Iowa | Supervisory Control of Delay Disclosure in Partially Observed Tandem Service Systems | 10 | ||||||||||||||||||||
58 | 2 | Lakshitha | Ramanayake | Rutgers University | LSR Structured Ridge Regression for Matrix Covariates with Applications in Neuroimaging | 11 | ||||||||||||||||||||
59 | 2 | Guruprerana | Shabadi | University of Pennsylvania | Auction-based Online Policy Adaptation for Evolving Objectives | 12 | ||||||||||||||||||||
60 | 2 | Zhongqi | Wei | Carnegie Mellon University | Global Sampling-Based Trajectory Optimization for Contact-Rich Manipulation via KernelSOS | 13 | ||||||||||||||||||||
61 | 2 | Hassan | Iqbal | University of Texas at Austin | Zero-Shot Function Encoder-Based Differentiable Predictive Control | 14 | ||||||||||||||||||||
62 | 2 | Tianchonghui | Fang | University of Connecticut | Cooperative Multi-Agent Navigation in Unknown Environments via Incremental Tree Repair and Shared Wall Discovery | 15 | ||||||||||||||||||||
63 | 2 | Liang | Wu | Johns Hopkins University | piMPC: A Parallel-in-horizon and Construction-free NMPC Solver | 16 | ||||||||||||||||||||
64 | 2 | Pengbo | Zhu | Cornell University | Delivery with a mission: Cooperative Detour Planning for Dual-Task Drone Fleets | 17 | ||||||||||||||||||||
65 | 2 | Anne | Somalwar | University of Pennsylvania | Statistical Efficiency of Single- and Multi-step Models | 18 | ||||||||||||||||||||
66 | 2 | Chris | Verhoek | University of Pennsylvania | A Quantitative Framework for Navigating Controller Design Tradeoffs under Computational Constraints | 19 | ||||||||||||||||||||
67 | 2 | Ada | Yildirim | Dartmouth College | Objective Misspecification in Model Predictive Game Controllers: Stability and Sensitivity Analysis | 20 | ||||||||||||||||||||
68 | 2 | Gobinda Chandra | Sarker | University at Albany, SUNY | Risk-Aware Hosting Capacity Analysis for Flexible Load Interconnection in Distribution Networks | 21 | ||||||||||||||||||||
69 | 2 | Ana | Jain | Massachusetts Institute of Technology | Nature-Inspired Self-Organization in Autonomous Multi-Agent Systems | 22 | ||||||||||||||||||||
70 | 2 | Jasmine Jerry | Aloor | Massachusetts Institute of Technology | Decentralized Autonomous Traffic Management through Corridor Networks | 24 | ||||||||||||||||||||
71 | 2 | Geoffrey | Ding | Massachusetts Institute of Technology | Externalities of Prioritization in Routing Games | 25 | ||||||||||||||||||||
72 | 2 | Hamza | Mahmood | New Jersey Institute of Technology | A Geometric Solution of the Schrödinger Bridge Problem on SO(2) via Stochastic Optimal Control | 26 | ||||||||||||||||||||
73 | 2 | Burak | Aydin | Princeton University | Fare Game: A Mean Field Model of Stochastic Intensity Control in Dynamic Ticket Pricing | 27 | ||||||||||||||||||||
74 | 2 | Onur | Unlu | Cornell University | Fictitious Play in Markov Potential Games | 28 | ||||||||||||||||||||
75 | 2 | Yang | Hu | Harvard University | Solving Black-box Optimizations with Generative Models | 29 | ||||||||||||||||||||
76 | 2 | Apurva | Badithela | Princeton University | Reliable Evaluation for the Next Generation of Robotics | 30 | ||||||||||||||||||||
77 | 2 | Hongrui | Zheng | University of Pennsylvania | Stein Variational Fictitious Self Play | 31 | ||||||||||||||||||||
78 | 2 | Khai | Nguyen | Massachusetts Institute of Technology | Cheap Thrills: Effective Amortized Optimization Using Inexpensive Labels | 32 | ||||||||||||||||||||
79 | 2 | Yubo | Cai | Massachusetts Institute of Technology | Scalable Co-Design via Linear Design Problems: Compositional Theory and Algorithms | 33 | ||||||||||||||||||||
80 | 2 | Yanjun | Liu | Princeton University | Second-order Convex Interpolation with Applications to Algorithm analysis | 34 | ||||||||||||||||||||
81 | 2 | Yusuf | Jimoh | Princeton University | Nanosecond-latency Model Predictive Control using an Optical ASIC | 35 | ||||||||||||||||||||
82 | 2 | Sneha | Ramshanker | Princeton University | Strategic Sacrifice: Self-Organized Cost Amortization in Multi-Robot Systems | 36 | ||||||||||||||||||||
83 | 2 | Yixuan | Hua | Princeton University | Disjunctive Sum of Squares | 37 | ||||||||||||||||||||
84 | 2 | Giovanna | Amorim | Princeton University | Multi-Agent Opinion Dynamics on the Circle with Distributed Inputs | 38 | ||||||||||||||||||||
85 | 2 | Shashwat | Jain | Cornell University | Inferring Group Intent as a Cooperative Game. An NLP-based Framework for Trajectory Analysis using Graph Transformer Neural Network | 39 | ||||||||||||||||||||
86 | 2 | Kiran | Rokade | Cornell University | Asymmetric Network Games: $\alpha$-potential function and learning | 40 | ||||||||||||||||||||
87 | 2 | Yukai | Tang | Princeton University | Solving semidefinite programs using adaptive linear programming | 41 | ||||||||||||||||||||
88 | 2 | Zidi | Tao | Rensselaer Polytechnic Institute | Time-Optimal Control (Entrainment) of Circadian and Sleep Processes using Lighting and Photobiomodulation Inputs | 42 | ||||||||||||||||||||
89 | 2 | Shigeng | Wang | Johns Hopkins University | Decentralized Stability-Constrained Optimal Power Flow for Inverter-Based Power Systems | 43 | ||||||||||||||||||||
90 | 2 | Fardin | Ishtiaq | Rensselaer Polytechnic Institute | Coordinated Control and Active and Passive Elements for Energy-efficient Building Temperature Regulation | 44 | ||||||||||||||||||||
91 | 2 | Abigail | Rolen | Rensselaer Polytechnic Institute | Repetitive control for cancellation of Tollmien-Schlichting waves | 45 | ||||||||||||||||||||
92 | 2 | Riddhiman | Laha | Northeastern University | Whole Body Motion Discovery in Partially Observable Physical Worlds | 46 | ||||||||||||||||||||
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