Computer Science Colloquia & Seminars are held each semester and sponsored by the Computer Science department. Faculty invite speakers from all areas of computer science, and the talks are open to all members of the RPI community.
Computer Science Colloquia & Seminars are held each semester and sponsored by the Computer Science department. Faculty invite speakers from all areas of computer science, and the talks are open to all members of the RPI community.
Abstract: The head of the Institute of Education Sciences has asked how we can use platform<https://www.the74million.org/article/schneider-garg-medical-researchers-find-cures-by-conducting-many-studies-and-failing-fast-we-need-to-do-the-same-for-education/>s to increase education sciences. We at ASSISTments have been addressing this<https://www.the74million.org/article/heffernan-how-can-we-know-if-ed-tech-works-by-encouraging-companies-researchers-to-share-data-government-funding-can-help/> very question. How do platforms like EdX, Khan Academy, or Canvas improve science? There is a crisis in American science referred to as the Reproducibility Crisis where many experimental results cannot be reproduced. We are trying to address that crisis by helping “good science” be done. People who control platforms have a responsibility to try to make them useful tools for learning what works. In Silicon Valley, every company is doing AB Testing to refine their individual products. That, in and of itself, is a good thing and we should use these platforms to figure out how to make them more effective. One of the ways we should do that is by experimenting with different ways of helping students succeed. ASSISTments<http://www.assistments.org/>, a platform I have created with 450,000 middle-school math students, is used to help scientists run studies. I will explain how we have over 100 experiments running inside the ASSISTments platform and how the ASSISTmentsTestBed.org allows external researchers to propose studies. I will also explain how proper oversight is done by our Institutional Review Board. Further, I will explain how users of this platform agree ahead of time to OpenScience procedures such as open-data, open-materials and pre-registration. I’ll illustrate some examples with the twenty-four randomized controlled trials<https://www.etrialstestbed.org/> that I have published as well as the three studies that have more recently come out from the platform by others. Finally, I will point to how we are anonymizing our data and how over 34 different external researchers have used our datasets to publish scientific studies<https://sites.google.com/site/assistmentsstudies/useourdata>. I would like to thank the U.S. Department of Education and the National Science Foundation for their support of over $32 million from 40+ grants.
Bio: Neil Heffernan is William Smith Dean's Professor of Computer Science and Director of the Learning Sciences & Technology program at Worcester Polytechnic Institute. He co-founded ASSISTments, a web-based learning platform, which he developed not only to help teachers be more effective in the classroom, but also so that he could use the platform to conduct studies to improve the quality of education. Professor Heffernan is passionate about educational data mining and enjoys supervising WPI students, helping them create ASSISTments content and features. Several student projects have resulted in peer-reviewed publications that compare different ways to optimize student learning. Professor Heffernan's goal is to give ASSISTments to millions across the U.S. and internationally as a free service.
Muhammad Saad Atique
Taming Uncertainty in Social Networks
Advisor: Prof. Bolek Szymanski
Generating Synthetic Election Data
Advisor: Prof. Lirong Xia
Determining Image Hardness using Ensemble Classifier Method
Advisor: Prof. Radoslav Ivanov
An Analysis of Improved Daltonization Algorithms
Advisor: Prof. Barb Cutler
Driver for Seizure Prediction Using EEG Data
Advisor: Prof. Bulent Yener
Action at a Distance
Advisor: Prof. Bulent Yener
A Variation on the Hotelling-Downs Model with Facility Synergy: the Mall Effect
Advisor: Prof. Elliot Anshelevich
Overcoming Missing Data and Labels During VFL
Advisor: Prof. Stacy Patterson
Towards Explainable Transfer Learning
Advisor: Prof. Jim Hendler
Clustering with Associative Memories
Advisor: Prof. Mohammed Zaki
Topical Analysis of Twitter User Clusters
Advisor: Prof. Tomek Strzalkowski
Event Former: A Self-Supervised Learning Paradigm for Temporal Point Process
Advisor: Prof. Kristin Bennett
The presentation considers computer vision, especially a point of view of applications. Digital image processing and analysis with machine learning methods enable efficient solutions for various areas of useful data-centric engineering applications. Challenges with domain adaptation, active learning, open set classification, and metric learning of similarities are considered. Computer Vision and Pattern Recognition Laboratory at LUT focuses on industrial computer vision, biomedical engineering, social signal processing, and data analytics. Different applications are given as examples based on specific data: fundus images in diagnosis of diabetic retinopathy, planktons in the Baltic Sea, Saimaa ringed seals in the Lake Saimaa, and logs in the sawmill industry.
Bio: Heikki Kälviäinen has been a Professor of Computer Science and Engineering since 1999. He is the head of the Computer Vision and Pattern Recognition Laboratory (CVPRL) at the Department of Computational Engineering of Lappeenranta-Lahti University of Technology LUT, Finland. He received his D.Sc. (Tech.) degree in Computer Science and Engineering in 1994 from the Department of Information Technology of LUT. Prof. Kälviäinen's research interests include computer vision, machine vision, pattern recognition, machine learning, and digital image processing and analysis.
Determining an ideal ordering for a sparse matrix is difficult. In the case of finding an ordering to minimize fill-in for factorization of a general sparse matrix, the problem is NP-Hard. Meanwhile, the selection of an ordering for an iterative method, such as Conjugate Gradients, that reduces iteration counts and efficiently uses the cache hierarchy is based on observations and rules of thumb. The selection of an ordering is exacerbated in applications that solve a series of sparse matrix problems that change over time, e.g., those found in some circuit simulations. Modern computing systems tend to be heterogeneous containing accelerators, such as a GPU or neural device, that can be co-scheduled with the main CPU. These accelerators are ideal for the computation of complex artificial neural networks. This work uses these accelerators to construct a neural network model that approximates an ordering for a given sparse matrix. This approximation model acts at accelerating the selection of an ordering during the application. Moreover, a trained model can be used iteratively in the case of applications where sparse matrices evolve during execution to determine if a new ordering should be implemented at some point in the computation to speed up the application.
Joshua Booth is an Assistant Professor of Computer Science at the University of Alabama in Huntsville. He received his Ph.D. in Computer Science and Engineering at The Pennsylvania State University and was a Post-Doctoral Researcher in the Scalable Algorithm Division at Sandia National Laboratories where he constructed new sparse linear solver methods for their exascale computing initiative. Dr. Booth is deeply committed to teaching emerging computing systems and technologies to future generations and spent several years teaching at the top liberal art colleges of Bucknell University and Franklin & Marshall College. His research focuses on high-performance computing related to scalable sparse linear algebra, scheduling, resiliency, and system performance.
His contributions and potential have been recognized via being awarded a bronze-level ACM Graduate Student Research Award for his work on multilevel preconditioning and an NSF CAREER award related to the neural acceleration of irregular applications. Additionally, Dr. Booth is an active member of the community leading the Huntsville Computer Research Seminar and reviewing for numerous ACM and IEEE journals
The human mind is a remarkable thing. On the one hand, even "mundane" aspects of cognition, such as our ability to fold clean laundry, far outstrip the capabilities of the most advanced robotic systems. On the other hand, we know from decades of research in psychology that the human mind is also astonishingly limited. Human minds face strict limits on attention, working memory, perception, and other basic cognitive faculties. In this talk, I will discuss a framework for resolving this apparent tension that spans the fields of cognitive science and artificial intelligence, known as computational rationality. According to this framework, the mind utilizes computations and algorithms that maximize the expected utility of behavior, while subject to constraints on the ability to store, manipulate, and process information. In my research, I have focused primarily on the use of rate-distortion theory (a sub-field of information theory) to characterize these limits on human information processing. The approach will be demonstrated through computational models of perception, memory, decision making, and reinforcement learning.
Bio: Chris Sims received a B.S. in computer science from Cornell University (2003), followed by a Ph.D. in Cognitive Science from Rensselaer Polytechnic Institute (2009). After completing his Ph.D., Dr. Sims held a postdoctoral research position at the University of Rochester, and a faculty position at Drexel University before joining the faculty at RPI in 2017. Dr. Sims's overarching research interest lies in developing computational models of human intelligence. Specific research areas include reinforcement learning, visual memory and perceptual expertise, sensory-motor control and motor learning, and learning and decision-making under uncertainty.
The proliferation of mobile devices with sensing and tracking capabilities, cloud computing, smart home, and the internet of things (IoT) has been fundamentally digitizing people’s daily life, and unprecedentedly extending the scale of personal data collection. While vast amounts of individual data have been turned into fuels with big data technologies to create and drive business, they have brought forth vital privacy concerns and real risks. At the same time, it remains a significant challenge to effectively preserve data privacy with regulatory compliance while ensuring data utility effectively. In this talk, I will discuss privacy risks in different phases of a data life cycle, from data collection, and usage to publication. Along this line, I will go through our works on differential location privacy for location-based services, differentially private model publishing for deep learning, and differentially private location trace synthesis for location data publication. Finally, I will discuss my vision of future research opportunities for data privacy in the big data era.
Biography: Lei Yu is a Research Staff Member at IBM Thomas J. Watson Research. He received his Ph.D. from the school of computer science at Georgia Insitute of Technology. His research interests include data privacy, the security and privacy of machine learning, and mobile and cloud computing. His works were published in top-tier conferences, including IEEE S&P, CCS, NDSS, and INFOCOM. At IBM, his work focuses on log analytics, system anomaly detection, and data privacy for enterprise systems. He is a recipient of the 2021 IBM Research Accomplishment award.
Crowdsourcing studies how to aggregate a large collection of votes, which makes large-scale labeled datasets and modern AI possible. In this talk, I will mainly present our recent JAIR paper which focuses on labeling cost reduction problem of crowdsourcing. First, we reformulate the crowdsourcing problem as a statistical estimation problem where votes are approximated by worker models. Then doubly robust estimation is used to address this problem. We prove that the variance of estimation can be substantially reduced, hence the labeling cost can be reduced, even if the worker model is a poor approximation. Moreover, with adaptive worker/item selection rules, labeling cost can be further reduced. I conclude by summarizing future directions of doubly robust crowdsourcing and introducing more applications of voting, for example, private learning.
Chong Liu is a PhD candidate in Computer Science at University of California, Santa Barbara (UCSB), advised by Prof. Yu-Xiang Wang. His research interests include ensemble learning, active learning, and global optimization. He has served on program committees of several conferences, e.g., AISTATS, AAAI, ICML, and NeurIPS. He is also serving on the editorial board of JMLR.
Presenters will be:
Fuzzy Search on Encrypted Data
Advisor: Konstanin Kuzmin
Advisor: Wes Turner
Article Similarity Detection
Advisor: Sibel Adali
Human Assisted Fuzzing
Advisor: Bulent Yener
Resource-mediated Consensus Formation on Random Networks
Advisor: Bolek Szymanski
Material Point Method for Continuum Material Simulation
Advisor: Barb Cutler
Efficiently Computing and Visualizing Semantic Shift
Advisor: Sibel Adali
Memory Optimization for ECS Architecture
Advisor: Jasmine Plum
Detecting Preference in Text using Dependency and Co-Reference
Advisor: Lirong Xia
Advisor: Bulent Yener
Study on lung lesions detection and characterization using open source toolkit
Advisor: Wes Turner
Serverless Federated Learning
Advisor: Stacy Patterson
Simulation of Light Transport through Opal Type Materials
Advisor: Barb Cutler
PNE and POA in Hotelling’s Game with Weighted Cost Functions
Advisor: Elliot Anshelevich
Advisor: Bulent Yener and Brian Callahan
Networks (or graphs) are a powerful tool to model complex systems such as social networks, transportation networks, and the Web. The accurate modeling of such systems enables us to improve infrastructure, reduce conflicts in social media, and make better decisions in high-stakes settings. However, as graphs are highly combinatorial structures, these optimization and learning tasks require the design of efficient algorithms.
In this talk, I will describe three research directions in the context of network data. First, I will overview several combinatorial problems for graph optimization that I have addressed using classical approaches such as approximate and randomized algorithms. The second part will focus on a different and a more recent approach to solving combinatorial problems by leveraging the power of machine learning. More specifically, I will show how combining neural architectures on graphs with reinforcement learning solves popular data ming problems such as the influence maximization problem. In the last one, I will demonstrate how to deploy these methods on problems in computational social science with applications in decision-making for patent review systems and the stock market.
Bio: Sourav Medya is a research assistant professor in the Kellogg School of Management at Northwestern University. He is also affiliated with the Northwestern Institute of Complex Systems. He has received his Ph.D. in Computer Science at the University of California, Santa Barbara. His research has been published at several venues including VLDB, NeurIPS, WebConf (WWW), AAMAS, IJCAI, WSDM, SDM, ICDM, SIGKDD Explorations, and TKDE. He has also been a PC member for WSDM, WebConf, AAAI, SDM, AAMAS, ICLR, and IJCAI.
Sourav's research is focused on the problems at the intersection of graphs and machine learning. More specifically he designs data science tools that optimize graph-based processes and improve the quality as well as scalability of traditional graph combinatorial and mining problems. He also deploys these tools to solve problems in the interdisciplinary area of computational social science especially to improve innovation.
For over 50 years now, the choice between typed and untyped code has been the source of lively debates. Languages have therefore begun to support both styles, but doing so presents new challenges at the boundaries that link typed and untyped pieces. The challenges stem from three conflicting dimensions: the expressiveness of typed-untyped mixes, the guarantees that types provide, and the cost of enforcing the guarantees. Even though dozens of languages explore points in this complex design space, they tend to focus on one dimension and neglect the others, leading to a disorganized research landscape.
In this talk, I introduce principled methods to guide the design of languages that mix typed and untyped code. The methods characterize both the behaviors of static types and the run-time cost of enforcing them, and do so in a way that centers on their implications for developers. I have applied these methods to improve existing languages and to implement a new language that bridges major gaps in the design space. My ongoing work is using insights from programmers to drive further advances.
BIO: Ben Greenman is a postdoc at Brown University. He received his Ph.D. from Northeastern University in 2020, and B.S. and M.Eng. degrees from Cornell. His work is supported by the CRA CIFellows program and has led to collaborations with Instagram and RelationalAI.
Visual perception is indispensable in numerous applications such as autonomous vehicles. Today's visual perception algorithms are often developed under a closed-world paradigm (e.g., training machine-learned models over curated datasets), which assumes the data distribution and categorical labels are fixed a priori. This assumption is unrealistic in the real open world, which contains situations that are dynamic and unpredictable. As a result, closed-world visual perception systems appear to be brittle in the open-world. For example, autonomous vehicles with such systems could fail to recognize a never-before-seen overturned truck and crash into it. We are motivated to ask how to (1) detect all the object instances in the image, and (2) recognize the unknowns. In this talk, I will present my solutions and their applications in autonomous driving and natural science. I will also introduce more research topics in the direction of Open-World Visual Perception.
Shu Kong is a Postdoctoral Fellow in the Robotics Institute at Carnegie-Mellon University, supervised by Prof. Deva Ramanan. He earned a Ph.D. in Computer Science at the University of California-Irvine, advised by Prof. Charless Fowlkes. His research interests span computer vision and machine learning, and their applications to autonomous vehicles and natural science. His current research focuses on Open-World Visual Perception. His recent paper on this topic received Best Paper / Marr Prize Honorable Mention at ICCV 2021. He regularly serves on the program committee in major conferences of computer vision and machine learning. He also serves as the lead organizer of workshops on Open-World Visual Perception at CVPR 2021 and 2022. His latest interdisciplinary research includes building a high-throughput pollen analysis system, which was featured by the National Science Foundation as that "opens a new era of fossil pollen research".
Large amounts of text are written and published daily. As a result, applications such as reading through the documents to automatically extract useful and structured information from the text have become increasingly needed for people’s efficient absorption of information. They are essential for applications such as answering user questions, information retrieval, and knowledge base population.
In this talk, I will focus on the challenges of finding and organizing information about events and introduce my research on leveraging knowledge and reasoning for document-level information extraction. In the first part, I’ll introduce methods for better modeling the knowledge from context: (1) generative learning of output structures that better model the dependency between extracted events to enable more coherent extraction of information (i.e., event A happening in the earlier part of the document is usually correlated with event B in the later part). (2) How to utilize information retrieval to enable memory-based learning with even longer context.
In the second part, to better access relevant external knowledge encoded in large models for reducing the cost of human annotations, we propose a new question-answering formulation for the extraction problem. I will conclude by outlining a research agenda for building the next generation of efficient and intelligent machine reading systems with close to human-level reasoning capabilities.
Xinya Du is a Postdoctoral Research Associate at the University of Illinois at Urbana-Champaign working with Prof. Heng Ji. He earned a Ph.D. degree in Computer Science from Cornell University, advised by Prof. Claire Cardie. Before Cornell, he received a bachelor's degree in Computer Science from Shanghai Jiao Tong University. His research is on natural language processing, especially methods that leverage knowledge & reasoning skills for document-level information extraction. His work has been published in leading NLP conferences such as ACL, EMNLP, NAACL and has been covered by major media like New Scientist. He has received awards including the CDAC Spotlight Rising Star award and SJTU National Scholarship.
AI is now being applied widely in society, including to support decision-making in important, resource-constrained efforts in conservation and public health. Such real-world use cases introduce new challenges, like noisy, limited data and human-in-the-loop decision-making. I show that ignoring these challenges can lead to suboptimal results in AI for social impact systems. For example, previous research has modeled illegal wildlife poaching using a defender-adversary security game with signaling to better allocate scarce conservation resources. However, this work has not considered detection uncertainty arising from noisy, limited data. In contrast, my work addresses uncertainty beginning in the data analysis stage, through to the higher-level reasoning stage of defender-adversary security games with signaling. I introduce novel techniques, such as additional randomized signaling in the security game, to handle uncertainty appropriately, thereby reducing losses to the defender. I show similar reasoning is important in public health, where we would like to predict disease prevalence with few ground truth samples in order to better inform policy, such as optimizing resource allocation. In addition to modeling such real-world efforts holistically, we must also work with all stakeholders in this research, including by making our field more inclusive through efforts like my nonprofit, Try AI.
Bio: Elizabeth Bondi is a PhD candidate in Computer Science at Harvard University advised by Prof. Milind Tambe. Her research interests include multi-agent systems, remote sensing, computer vision, and deep learning, especially applied to conservation and public health. Among her awards are Best Paper Runner up at AAAI 2021, Best Application Demo Award at AAMAS 2019, Best Paper Award at SPIE DCS 2016, and an Honorable Mention for the NSF Graduate Research Fellowship Program in 2017.
Quantum can solve complex problems that classical computers will never be able to.
In recent years, significant efforts have been devoted to building quantum computers to solve
real-world problems. To ensure the correctness of quantum systems, we develop the verification
techniques and testing algorithms for quantum systems. In the first part of this talk, I will overview
my work of efficient reasoning about quantum programs by developing verification techniques and
tools that leverage the power of Birkhoff & von Neumann quantum logic. In the second part, I will
review my work on quantum state tomography, i.e., learning the classical description of quantum
hardware, which closes a 40 years long-standing gap between the upper and lower bounds
for quantum state engineering.
Bio: Nengkun Yu is an associate professor in the Centre for Quantum Software and Information,
the University of Technology Sydney. He received his B.S. and PhD degrees from the Department
of Computer Science and Technology, Tsinghua University, Beijing, China, in July of 2008 and 2013.
He won the ACM SIGPLAN distinguished paper award at OOPSLA 2020 and the ACM SIGPLAN
distinguished paper award at PLDI 2021. His research interest focuses on quantum computing.
Message passing is the essential building block in many machine learning problems such as decentralized learning and graph neural networks. In this talk, I will introduce several innovative designs of message passing schemes that address the efficiency and security issues in machine learning. Specifically, first I will present a novel decentralized algorithm with compressed message passing that enables large-scale, efficient, and scalable distributed machine learning on big data. Then I will show how to significantly improve the security and robustness of graph neural networks by exploiting the structural information in data with a novel message passing design.
Biography: Xiaorui Liu is a Ph.D. candidate in the Department of Computer Science and Engineering at Michigan State University. His advisor is Prof. Jiliang Tang. His research interests include distributed and trustworthy machine learning, with a focus on big data and graph data. He was awarded the Best Paper Honorable Mention Award at ICHI 2019, MSU Engineering Distinguished Fellowship, and Cloud Computing Fellowship. He organized and co-presented four tutorials in KDD 2021, IJCAI 2021, and ICAPS 2021, and he has published innovative works in top-tier conferences such as NeurIPS, ICML, ICLR, KDD, and AISTATS. More information can be found on his homepage https://cse.msu.edu/~xiaorui/.
Today, cryptography has transcended beyond protecting data in transit. Many advanced cryptographic techniques are gaining traction due to their significance in emerging systems like blockchains, privacy-preserving computation, and cloud computing. However, designing cryptography for these systems is challenging as they require underlying cryptographic algorithms to provide strong security and privacy guarantees and admit very efficient implementations.
In the first part of this talk, I will present my work that explores fundamental connections between cryptography and blockchains. Blockchains rely on advanced cryptography like Zero-knowledge Proofs, and despite amazing advances in building efficient cryptographic tools, scalability is a major challenge plaguing blockchain-based applications like cryptocurrencies. I will discuss my work on improving prover’s time and memory overheads in Zero-knowledge Proofs, which is currently a primary bottleneck towards building more efficient zero-knowledge proofs. Then, I will discuss my work where I use blockchains to build new and useful cryptography.
Finally, I will discuss my work on designing cryptographic commitments, digital analogs of sealed envelopes, secure against man-in-the-middle attacks. While heuristic constructions are known, my work introduces new fundamental techniques to circumvent strong barriers established for achieving provably secure protocols with minimal interaction. Specifically, I build provably secure protocols that require minimal (to no) interaction between the sender and the receiver.
Pratik Soni is a Postdoctoral Research Fellow in the School of Computer Science at Carnegie Mellon University. He received his Ph.D. from UC Santa Barbara in 2015. His research interests span a wide range of topics across cryptography, including zero-knowledge proofs, non-malleable cryptography, secure multi-party computation, and its connections with blockchain technology. His work at FOCS 2017 was invited to SIAM Journal of Computing's special issue, and he is currently serving as a Program Committee Member at ACM CCS 2022 and ASIACRYPT 2022.
Nowadays, security researchers are increasingly using AI to automate and facilitate security analysis. Although making some meaningful progress, AI has not maximized its capability in security yet due to two challenges. First, existing ML techniques have not reached security professionals' requirements in critical properties, such as interpretability and adversary-resistancy. Second, Security data imposes many new technical challenges, which break the assumptions of existing ML Models and thus jeopardize their efficacy.
In this talk, I will describe my research efforts to address the above challenges, with a primary focus on strengthening the interpretability of deep neural networks and deep reinforcement learning policies. Regarding deep neural networks, I will describe an explanation method for deep learning-based security applications and demonstrate how security analysts could benefit from this method to establish trust in blackbox models and conduct efficient finetuning. As for DRL policies, I will introduce a novel approach to draw critical states/actions of a DRL agent and show how to utilize the above explanations to scrutinize policy weaknesses, remediate policy errors, and even defend against adversarial attacks. Finally, I will conclude by highlighting my future plan towards strengthening the critical properties of advanced ML techniques and maximizing their capability in cyber defenses.
Wenbo Guo is a Ph.D. Candidate at Penn State, advised by Professor Xinyu Xing. His research interests are machine learning and cybersecurity. His work includes strengthening the fundamental properties of machine learning models and designing customized machine learning models to handle security-unique challenges. He is a recipient of the IBM Ph.D. Fellowship (2020-2022), Facebook/Baidu Ph.D. Fellowship Finalist (2020), and ACM CCS Outstanding Paper Award (2018). His research has been featured by multiple mainstream media and has appeared in a diverse set of top-tier venues in security, machine learning, and data mining. Going beyond academic research, he also actively participates in many world-class cybersecurity competitions and has won the 2018 DEFCON/GeekPwn AI challenge finalist award.
Our visual world is naturally open, containing visual elements that are dynamic, vast, and unpredictable. However, existing computer vision models are often developed inside a closed-world paradigm, for example, recognizing objects or human actions from a fixed set of categories. If these models are exposed to the realistic complexity of the open world, they will be brittle and fail to generalize. For example, an autonomous driving vehicle may not be able to avoid an accident if it sees a turnover truck, because it has never seen this novelty in its training data. In order to enable visual understanding in open world, vision models need to be aware of the unknown novelties, interpret their decision processes to human users, and adapt themselves to the novelties. In this talk, I will describe our recent work on open-set recognition and interpretable visual precognition. Together, these approaches make strides towards assurance autonomous AI in open world.
Bio: Dr. Yu Kong is now an Assistant Professor directing the ACTION lab in the Golisano College of Computing and Information Sciences at Rochester Institute of Technology. He received B.Eng. degree in automation from Anhui University in 2006, and PhD degree in computer science from Beijing Institute of Technology, China, in 2012. He was a postdoctoral research associate in the Department of Computer Science and Engineering, State University of New York, Buffalo in 2012, and then in the Department of Electrical and Computer Engineering, Northeastern University, Boston, MA. Dr. Kong's research in Computer Vision and Machine Learning is supported by National Science Foundation, Army Research Office, and Office of Naval Research, etc. His work has been publishing on top-tier conferences and transactions in the AI community such as CVPR, ECCV, ICCV, T-PAMI, IJCV, etc. He is an Associate Editor for Springer Journal of Multimedia Systems, and also serves as reviewers and PC members for prestige journals and conferences, including T-PAMI, T-IP, T-NNLS, T-CSVT, CVPR, ICLR, AAAI, and IJCAI, etc. More information can be found on his webpage at https://people.rit.edu/yukics/.
In this talk I will discuss recent progress towards using human input to enable safe and robust autonomous systems. Whereas much work on robust machine learning and control seeks to be resilient to or remove the need for human input, my research seeks to directly and efficiently incorporate human input into the study of robust AI systems. One problem that arises when robots and other AI systems learn from human input is that there is often a large amount of uncertainty over the human’s true intent and the corresponding desired robot behavior. To address this problem, I will discuss prior and ongoing research along three main topics: (1) how to enable AI systems to efficiently and accurately maintain uncertainty over human intent, (2) how to generate risk-averse behaviors that are robust to this uncertainty, and (3) how robots and other AI systems can efficiently query for additional human input to actively reduce uncertainty and improve their performance. My talk will conclude with a discussion of my long-term vision for safe and robust autonomy, including learning from multi-modal human input, interpretable and verifiable robustness, and developing techniques for human-in-the-loop robust machine learning that generalize beyond reward function uncertainty.
Daniel Brown is a postdoctoral scholar at UC Berkeley, advised by Anca Dragan and Ken Goldberg. His research focuses on safe and robust autonomous systems, with an emphasis on robot learning under uncertainty, human-AI interaction, and value alignment of AI systems. He evaluates his research across a range of applications, including autonomous driving, service robotics, and dexterous manipulation. Daniel received his Ph.D. in computer science from the University of Texas at Austin, where he worked with Scott Niekum on safe and efficient inverse reinforcement learning. Prior to starting his PhD, Daniel was a research scientist at the Air Force Research Lab's Information Directorate where he studied bio-inspired swarms and multi-agent systems. Daniel’s research has been nominated for two best-paper awards and he was selected in 2021 as a Robotics: Science and Systems Pioneer.