|  | Huajie Shao
Assistant Professor of Computer Science
William & MaryControllable Variational Autoencoders [details] Dr. Huajie Shao is a tenure-track assistant professor of Computer Science at the College of William and Mary. Before that, he obtained his Ph.D. degree of Computer Science from the University of Illinois at Urbana Champaign (UIUC) in 2021. His research interests focus on deep representation learning, data mining, and physics guided machine learning. Thus far, he has published more than 30 papers in top-tier conferences and journals, such as ICML, ACL, TPAMI, VLDB, WWW, SIGIR, and INFOCOM. He also received SenSys™20 and ICCPS™17 Best Paper Award, FUSION™19 Student Paper Award, and UbiComp™19 Distinguished Paper Award. |
|  | Dan Roberts
Clemson UniversityIT-360: A Leisurely, Circular Tour of 50 Years of Information Technology [details] Dan Roberts began his career in Information Technology in the 1970s as a
student computer operator and programming tutor for Clemson University
Computer Center. He joined Monsanto Company after earning a BS in
Mathematics and Management Science from Clemson and advanced to
Director, Application Development – Fibers and Chemical Intermediates
Division while developing and managing the corporate portfolio of integrated
business applications, specializing in math-based forecasting, planning and
manufacturing scheduling systems.
In 1986, he founded and led Innovative Computing LLC, providing consulting
and application development for Planning/Scheduling Systems and Supply
Chain Optimization applications for such global enterprises as Monsanto,
Solutia, Fluor-Daniel, Shaw Industries/Berkshire Hathaway and DuPont.
In 1999, he joined Aspen Technology as Senior Consultant and Project
Manager where he led enterprise-wide Supply Chain Optimization application
development and SAP Implementation for the merger of BP and Amoco and
developed optimization model applications for many other global firms.
Concurrently, Dan also served as Lead Corporate Trainer for Aspen in
Forecasting, Production Planning and Scheduling tools, developing and
delivering curricula to clients across North America, Europe and Asia.
From 2005, Dan served in a series of Information Technology leadership
roles in Healthcare for UnitedHealth Group – Optum Technology Division,
developing applications and managing projects to implement leading-edge
middleware tools that permit hundreds of diverse health-information and
insurance applications for this Fortune-5 company to share a central core of
enterprise databases. Dan served on the corporate UHG Culture and
Communities team that defined and advanced the core principles, ethics and
best practices that guide the UHG enterprise. Dan’s IT work in the UHG
Eldercare/Healthcare segment motivated him to assist in the founding and
development of the Clemson Institute for Engaged Aging A |
|  | Xinyi Li
School of Mathematical and Statistical Sciences
Clemson UniversitySparse Learning of Imaging Genetics Studies in Alzheimer’s Disease [details] Dr. Xinyi Li is currently an assistant professor in the School of Mathematical and Statistical Sciences. Before joining Clemson, Dr. Li was a postdoctoral fellow at the Statistical and Applied Mathematical Sciences Institute (SAMSI), joint with the University of North Carolina at Chapel Hill. She obtained Ph.D. in statistics at Iowa State University in 2018. Dr. Li was awarded IMS New Researcher Travel Award in 2019. Her research interests include precision medicine, functional data analysis, non-/semi-parametric high-dimensional regression, spatio-temporal analysis, with application to statistical genetics, neuroimaging, and public health. |
|  | Dan Li
Department of Industrial Engineering
Clemson UniversityOnline Cyberattack Detection for Industrial Cyber-Physical Systems and Critical Infrastructures [details] Dan Li is an assistant professor in the Department of Industrial Engineering at Clemson University. She received her Ph.D. in Industrial Engineering and M.S. in Statistics from Georgia Institute of Technology in 2021 and 2020, respectively. She received her B.S. in Mechanical (Automotive) Engineering from Tsinghua University, Beijing, China, in 2015. Her research interests include cybersecurity for Cyber-Physical Systems (CPS) / Industrial Internet of Things (IIoT), machine learning applications, sensor-based anomaly detection, and complex system modeling. Specifically, she is interested in developing new data-driven algorithms that are tailored for securing CPSs. Dan is the recipient of the Best Track Paper Award in Data Analytics and Information Systems (DAIS) and have been recognized in Best Student Paper Competitions in Energy Systems, DAIS, and Quality Control and Reliability Engineering (QCRE) divisions at the IISE Annual Meetings. |
|  | Bikalpa Neupane
Director of AI Companion Program at TakedaHuman-Computer Interaction and Artificial Intelligence Beyond the Labs [details] Dr. Bikalpa Neupane is the Director of AI Companion Program at Takeda, a multinational Pharmaceuticals where he is responsible to identify, manage, integrate and test end-to-end conversational AI and NLP components within the core Enterprise Digital Unit including robotic process automation use cases. Previously, Dr. Neupane led Natural Language Understanding product groups at IBM Watson where his team worked on natural language processing models as well as HITL (human-in-the-loop) approaches and pattern induction. Prior to IBM, Dr. Neupane worked in software engineering capacities at Microsoft and Overstock. Dr. Neupane holds a PhD in Informatics from the Penn State - main campus and undergraduate degrees from Brigham Young University, Utah. He was a visiting fellow with the CHILI lab at École polytechnique fédérale de Lausanne, and an Australian Endeavor Fellowship recipient with the Connected Intelligence Center at the University of Technology, Sydney. He is a recipient of 'Technology for Social Impact fellowship' at the Cornell University, 'United Nations Young Leader fellowship 2017', and 'Graduate Student Service Award - 2019', at the Penn State. Dr Neupane's dissertation explored the issues of AI justice and equity among underprivileged and marginalized communities. Dr. Neupane serves as a consultant to the organizations interested in developing and deploying data science and machine learning solutions at scale. He's published articles and journals in several artificial intelligence and human computer interaction outlets. |
|  | Xiaoqian Wang
School of Electrical and Computer Engineering
Purdue UniversityExplainable and Fair Machine Learning Algorithms with Applications in Biomedical Data Science [details] Xiaoqian Wang is an assistant professor of the School of Electrical and Computer Engineering at Purdue University. She received her Ph.D. degree from the University of Pittsburgh in 2019, and the B.S. degree in Bioinformatics from Zhejiang University in 2013. She focuses on designing new machine learning models with improved interpretability, fairness, and robustness, and addressing the emerging challenges in biomedical data science. She has published over 30 papers in top conferences and journals including NeurIPS, ICML, ICLR, AAAI, IJCAI, KDD, CVPR, etc. |
|  | Yuyuan “Lance” Ouyang
School of Mathematical and Statistical Sciences
Clemson UniversityGraph Topology Invariant Gradient And Sampling Complexity For Decentralized And Stochastic Optimization [details] Yuyuan “Lance” Ouyang is an Associate Professor at the School of Mathematical and Statistical Sciences, Clemson University. His research interest is on algorithm design and complexity analysis for solving large-scale nonlinear optimization problems. His research is supported by NSF and ONR. |
|  | Tom Williams
Associate Professor of Computer Science
Colorado School of MinesSecret Agents: The Real and Imagined Inner Lives of Interactive Robots [details] Tom Williams is a (newly promoted) Associate Professor of Computer Science at the Colorado School of Mines, where he directs the Mines Interactive Robotics Research Lab (MIRRORLab). Prior to joining Mines, Tom earned a joint PhD in Computer Science and Cognitive Science from Tufts University in 2017. Tom’s research focuses on enabling and understanding natural language based human-robot interaction that is sensitive to environmental, cognitive, social, and moral context. His work is funded by grants from NSF, ONR, and ARL, as well as by Early Career awards from NSF, NASA, and AFOSR. |
|  | Chris Crawford
Assistant Professor, Department of Computer Science
University of AlabamaBrain-Robot Interaction & Applying Block-Based Programming to Neurofeedback Application Development Dr. Chris S. Crawford is an Assistant Professor at the University of Alabama’s Department of Computer Science. He directs the Human-Technology Interaction Lab (HTIL). His research focuses on human-robot interaction and Brain-Computer Interfaces (BCIs). He has investigated systems that provide computer applications and robots with information about a user’s cognitive state. He previously developed a brain-drone racing system that was featured on over 800 news outlets including Discovery, USA Today, and the New York Times, and Forbes. Along with investigating brain-robot interaction applications, Dr. Crawford also developed Neuroblock, a tool designed to engage K-12 student in neurofeedback applications development. He recently won a NSF CAREER award for his research. |
|  | Ruy Ley-Wild
Software Engineer
Google ResearchRead, Learn, Write [details] Ruy Ley-Wild studied math and computing at CMU, has researched and developed in academic and industrial settings, and now is a Software Engineer in Google Research. |
