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Michael HuangView profile

Assistant Professor

• Operations Management
• Machine Learning
• Statistics
• +7 more

Michael Huang is an Assistant Professor at the Zicklin School of Business, Baruch College, where he serves in the N. Paul Loomba Department of Management. His academic journey includes a PhD in Data Sciences and Operations from the University of Southern California (2023), an MSc in Operations Research (2017), and a BSc in Operations Research (2016), both from Columbia University. PhD, Data Sciences and Operations, University of Southern California, 2023 MSc, Operations Research, Columbia University, 2017 BSc, Operations Research, Columbia University, 2016 Huang’s research bridges machine learning with operations management, focusing on data-driven optimization and decision-aware learning. He develops methodologies for denoising, debiasing policies, and integrating predictive models into optimization frameworks. His work has direct applications in healthcare operations, such as nurse staffing in emergency departments. The 2024 articles highlight his contributions to decision-focused learning, including Decision-Aware Denoising and Debiasing in-sample policy performance , while earlier work in Operations Research (2021) and algorithm design (2017) demonstrates his technical depth. His research spans machine learning, optimization theory, and statistical modeling. Scientific Awards Marshall PhD Teaching Award (2022) Marshall PhD Fellowship (2021) Marshall Outstanding Researcher Award (2021) The Robert Gartland Fellowship (2016) Huang actively participates in academic service as a reviewer and session chair for top-tier conferences and journals, including NeurIPS, INFORMS, and ICML.

• Machine Learning
• Large-Scale Data Analysis
• Bioinformatics
• +3 more

Inderjit Dhillon , Professor at the University of Texas at Austin , is a leading researcher in machine learning and big data analytics. As Director of the Center for Big Data Analytics, his work focuses on scalable algorithms for high-dimensional data, social network analysis, and gene-disease association prediction. ACM, IEEE, SIAM, and AAAS Fellow Gottesman Family Centennial Professor (2014) ICES Distinguished Research Award (2013) Research Interests: His contributions span machine learning , bioinformatics , and scientific computing , with recent work emphasizing deep learning for time series, adversarial robustness, and distributed optimization. Publications in top venues like NeurIPS and ICML highlight his expertise in inductive matrix completion and non-convex optimization . Scientific Awards: 2016: AAAS Fellow 2014: ACM, SIAM, IEEE Fellow 2013: ICES Distinguished Research Award 2011: SIAM Outstanding Paper Prize His students and collaborators frequently publish in areas like extreme classification , kernel methods , and multi-scale spectral decomposition . Labs and teams under his leadership have advanced nomadic computing and asynchronous optimization for massive datasets.

Dixin TangView profile

Assistant Professor

• Databases
• Systems and Networking
• Vector Databases
• +3 more

Dixin Tang is an Assistant Professor at the University of Texas at Austin Department of Computer Science. He previously served as a postdoc scholar at UC Berkeley under Prof. Aditya Parameswaran and earned his Ph.D. from the University of Chicago CS department under Prof. Aaron Elmore. Current research focuses on vector databases, large-scale unstructured data analysis, and data systems leveraging CXL memory technology. Key contributions include systems like Tigon (distributed CXL pod databases) and Pasha (scalable CXL pod architecture). His work emphasizes user-centered data systems, with projects like FormS (spreadsheet-to-SQL translation), Smash (string distance metrics), and Lux (visualization recommendations) demonstrating cross-decade innovation. Recent publications (2025) highlight trends in: CXL memory-optimized data architectures Homomorphism-based query frameworks Scalable unstructured data processing Scientific recognition includes: VLDB 2023 Best of Special Issue ICDE 2021 Short Paper Award Teaching roles include: CS 395T: Database Systems and LLMs (Fall 2025) CS 347: Data Management (Fall 2024) CS 395T: Advanced Query Optimizations (Spring 2024) Affiliated with the UT Data Systems research group, collaborating with Profs. Witchel and Chidambaram on CXL memory projects.

Claire WaltonView profile

Assistant Professor

• Control Theory
• Applied Mathematics
• Autonomous Systems
• +3 more

Claire Walton is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Texas at San Antonio , affiliated with the Klesse College of Engineering and Integrated Design . She works at the intersection of control theory, optimization, and autonomous systems, with a focus on applications like UAV trajectory planning and adversarial swarm modeling. Contact information: office at AET 2.340, email at claire.walton@utsa.edu. Research Interests: Control theory, optimization, and computational methods for autonomous systems, particularly addressing adversarial scenarios and hybrid dynamical systems. Publications: Recent work explores deep equilibria, Bernstein polynomial approximations, and optimal control for UAVs and large-scale swarms.

Jon CalhounView profile

Associate Professor

• Computer Science
• Electrical Engineering
• High-Performance Computing
• +4 more

Jon Calhoun is an Associate Professor in the Holcombe Department of Electrical and Computer Engineering at Clemson University, directing the Future Technologies in Heterogeneous and Parallel Computing (FTHPC) Laboratory. He holds a Ph.D. in Computer Science (2017) from the University of Illinois at Urbana-Champaign and dual B.S. degrees in Computer Science and Mathematics (2012) from Arkansas State University. Ph.D., Computer Science – University of Illinois at Urbana-Champaign B.S., Computer Science – Arkansas State University B.S., Mathematics – Arkansas State University His research focuses on fault tolerance and data compression in high-performance computing (HPC) systems. Key areas include silent data corruption (SDC) mitigation, error-bounded lossy compression for scientific workflows, and improving checkpoint-restart mechanisms. His work addresses critical HPC bottlenecks in data movement, storage, and reliability, with applications spanning exascale computing, medical imaging, and autonomous vehicle systems. Recent publications analyze compression trends in scientific data, including lossy techniques for DICOM files, federated learning communications, and real-time pedestrian detection systems. His group develops frameworks like SZ3 and LibPressio to optimize data management in HPC environments. NSF CAREER Award (2020) R&D 100 Award (2021) for SZ framework CECAS Junior Faculty Teaching Award (2022) Arkansas State Alumni Academy (2022) IEEE Senior Member (2022) The FTHPC Laboratory investigates heterogeneous computing systems, funded by the National Science Foundation, DOE, and the US Army. His research integrates fault injection analysis, data reduction techniques, and energy-efficient scalable solvers.

Wei WangView profile

Professor

• Big Data Analytics
• Machine Learning
• Data Mining
• +5 more

Wei Wang is the Leonard Kleinrock Chair Professor in Computer Science and Computational Medicine at University of California, Los Angeles (UCLA) , and the founding Director of the Scalable Analytics Institute (ScAI) . She is also a core faculty member of the UCLA Jonsson Comprehensive Cancer Center, Institute for Quantitative and Computational Biology, and Bioinformatics Interdepartmental Graduate Program. Education: Ph.D. in Computer Science, UCLA (1999) M.S. in Systems Science and Industrial Engineering, SUNY at Binghamton (1995) Research Interests: Dr. Wang’s research spans big data analytics , machine learning , data mining , natural language processing , bioinformatics , computational biology , and AI for science . Her work focuses on designing scalable algorithms for complex pattern discovery in large-scale, heterogeneous data, with applications in medicine, genomics, and scientific discovery. Scientific Awards & Honors: ACM Fellow (2020) IEEE Fellow (2022) NSF CAREER Award (2005) Microsoft Research New Faculty Fellow (2005) Phillip and Ruth Hettleman Prize (2007) Okawa Foundation Research Award (2013) ACM SIGKDD Service Award (2016) IEEE ICDM Outstanding Service Award (2012) IBM Invention Achievement Awards (2000, 2001) Multiple Best Paper Awards at SIGKDD, ICDE, and ACM BCB Advising & Research Team: Dr. Wang currently advises 15 PhD students and 1 postdoctoral fellow . Her lab focuses on AI-driven scientific discovery, scalable analytics, and interdisciplinary applications in biomedicine and materials science. Labs & Institutes: Director, Scalable Analytics Institute (ScAI) , UCLA Member, UCLA Jonsson Comprehensive Cancer Center Member, Institute for Quantitative and Computational Biology Member, Bioinformatics Interdepartmental Graduate Program

Gianluca StringhiniView profile

Associate Professor

• Cybersecurity
• Machine Learning
• Data-Driven Methods
• +4 more

Gianluca Stringhini is an Associate Professor in the Department of Electrical and Computer Engineering at Boston University. He co-directs the Security Lab (SeclaBU) and is affiliated with the Faculty of Computing & Data Sciences and the Computer Science Department. Specializes in data-driven cybersecurity research Focused on combating online fraud, malware, and disinformation Recipient of NSF CAREER Award and Early Career Excellence in Research Award His work integrates machine learning, systems security, and computational social science to address societal digital harms. Recent publications emphasize social media content moderation, malware analysis, and YouTube hate attack attribution. Over 150 peer-reviewed papers with media coverage in BBC, New York Times, and Wired. Google Academic Research Awards (GARA) Red Hat Collaboratory Research Incubation Awards NSF and OpenAI funding Stringhini leads interdisciplinary projects and mentors graduate students in cybersecurity and AI. He serves as a Distinguished Faculty Fellow at BU's College of Engineering.

• Data Science
• Machine Learning
• Artificial Intelligence
• +2 more

Rajib Paul serves as a Professor in the School of Data Science, holding a core academic position focused on advancing data-intensive research methodologies. His research portfolio centers on foundational and applied aspects of data science, with particular emphasis on: Machine Learning algorithm development Artificial Intelligence systems design Large-scale Big Data Analytics frameworks Advanced Statistical Modeling techniques Cross-disciplinary data applications Professor Paul's scholarly contributions demonstrate consistent focus on computational approaches to complex data challenges across theoretical and practical domains.

John HildebrandView profile

Professor

• Marine Acoustics
• Bioacoustics
• Marine Mammal Behavior
• +4 more

John Hildebrand is a Distinguished Professor of Oceanography at Scripps Institution of Oceanography, University of California San Diego, and serves as an adjunct professor in the Department of Electrical and Computer Engineering. His research pioneers marine bioacoustics through innovative passive acoustic monitoring systems to study marine mammals and anthropogenic ocean noise impacts. Education Ph.D. in Applied Physics, Stanford University, 1983 B.S. in Physics and Electrical Engineering, UC San Diego, 1978 Research Focus : Hildebrand's work centers on developing acoustic technologies like the High-frequency Acoustic Recording Package (HARP) for long-term ocean monitoring. His lab deploys instruments globally to study whale communication, migration patterns, and the ecological consequences of rising ocean noise from shipping. Key discoveries include regional blue whale dialects and quantification of decades-long noise increases threatening marine mammals. His methodology combines engineering innovation with ecological fieldwork across the Arctic, Hawaii, California coast, and Gulf of Mexico. Publication Trends : Analysis of his 15 most recent publications reveals three dominant research thrusts: 1) Endangered species conservation (particularly Rice's whale monitoring in the Gulf of Mexico), 2) Climate-noise interactions in ocean soundscapes, and 3) Advanced acoustic tracking methodologies for deep-diving cetaceans. His work increasingly integrates multi-year datasets to assess anthropogenic impacts against natural variability. Awards Fellow of the Acoustical Society of America Professional Service : Hildebrand serves on the Marine Mammal Commission's board of scientific advisors and is an active member of the Society for Marine Mammalogy. His leadership in acoustic methodology development has established standardized protocols for global passive acoustic monitoring networks. Laboratory Infrastructure : He leads the Scripps Whale Acoustics Laboratory (SWAL) and collaborates with the Marine Physical Laboratory. His team maintains a global array of HARPs and develops open-source software like Where's Whaledo for acoustic localization, supporting international marine mammal research and conservation efforts.

• Distributed Control Systems
• Robust Control
• Networked Control Systems
• +7 more

Petros G. Voulgaris is a Professor in the Department of Aerospace Engineering at the University of Illinois at Urbana-Champaign (UIUC), with affiliate appointments in the Coordinated Science Laboratory (CSL) and the Department of Electrical and Computer Engineering (ECE). He is also an Adjunct Professor at UIUC. His research focuses on distributed control, robust control, networked systems, and optimization, with applications spanning UAVs, power systems, infrastructure, and robotics. Education: Ph.D. in Aeronautics and Astronautics, MIT, 1991 M.S. in Aeronautics and Astronautics, MIT, 1988 B.S. in Mechanical Engineering, National Technical University of Athens, 1986 Research Interests: His core research lies in the theory and application of distributed control and estimation , particularly in systems with communication constraints. He has contributed to the analysis and synthesis of time-varying and adaptive systems , and the coordination of multi-agent systems . His work also includes robust and multi-objective control methodologies. Application domains include UAV swarms , power grid control , traffic systems , infrastructure monitoring , and microscopy systems . Scientific Awards: IEEE Fellow (2011) ADGAS Visiting Chaired Professor, Petroleum Institute, UAE (2008–2010) UIUC Junior Xerox Award for Faculty Research (1996) ONR Young Investigator Award (1995) NSF Research Initiation Award (1993) Advising & Grants: While no specific students are listed, Voulgaris has been actively involved in collaborative research. He is a co-PI on an NSF grant titled "Distributed Quasi-Newton Methods for Nonsmooth Optimization" , working with Angelia Nedich and Nikolaos Freris. The project focuses on developing fast, distributed optimization methods for large-scale, structured systems. Labs & Teams: Voulgaris is affiliated with the Coordinated Science Laboratory (CSL) at UIUC, a hub for interdisciplinary research in control, computing, and communications. His work is also supported through collaborations with ECE and AAE departments, fostering cross-disciplinary research in cyber-physical systems.

Erik KomenderaView profile

Assistant Professor

• Autonomous Systems
• Space Robotics
• Field Robotics
• +3 more

Erik Komendera is an Assistant Professor in the Department of Mechanical Engineering at Virginia Tech. He leads the Field and Space Experimental Robotics (FASER) Laboratory, focusing on autonomous assembly, space robotics, and field robotics. His research integrates perception, machine learning, and multi-agent systems to enable precise robotic operations in large workspaces and off-nominal scenarios. Education: Ph.D. in Computer Science, University of Colorado (2014) M.S. in Computer Science, University of Colorado (2012) B.S.E. in Aerospace Engineering, University of Michigan (2007) Research Interests: Autonomous assembly and construction Space robotics and in-space assembly Field robotics for disaster response and environmental monitoring Perception and state estimation for large-scale operations Multi-agent motion planning and collaborative manipulation Machine learning for anomaly detection in robotics Key Projects: Built On-Orbit Robotically Assembled Gigatruss (BORG) system Development of lightweight manipulators for in-space operations Autonomous damage detection in constructed structures COVID-19 response efforts: PPE production via robotic prototyping Awards: NASA Early Career Achievement Medal (2018) Center Achievement Award (NASA Langley, 2017) First NASA Space Tech Fellow to earn a Ph.D. (2014) Outstanding Research Award (University of Colorado, 2014) Labs/Teams: Director of the FASER Lab, which advances robotics for space and terrestrial applications. Current projects include modular space systems, autonomous assembly algorithms, and robotic collaboration frameworks.

Qin LvView profile

Professor

• Data Mining
• Search Systems
• Mobile Systems
• +8 more

Qin Lv is a Professor in the Department of Computer Science at the University of Colorado Boulder. She earned her PhD in Computer Science from Princeton University, followed by a postdoctoral position there and an assistant professorship at Stony Brook University. Her research focuses on integrating system design with data analysis for managing large-scale datasets, spanning search systems, data mining, mobile systems, social networks, and interdisciplinary applications in geoscience, renewable energy, and environmental science. Her work has been cited over 2,000 times and earned notable awards such as the 2010 ISLEPED Best Paper nomination and the 2012 Computational Sustainability Award for personalized driving behavior analysis. Research Interests: Efficient system design for big data management Interdisciplinary applications in energy and environmental science Wearable technology and smart eyewear systems Social media analysis and public perception studies Fake news detection and misinformation mitigation Machine learning for geoscience and renewable energy systems Scientific Contributions: Dr. Lv has pioneered work in battery system modeling, mobile air quality sensing, and event analysis in social networks. Her recent research explores AI-driven wearable systems and cross-platform analysis of public responses to technological and environmental challenges. Over 30 peer-reviewed publications reflect her expertise in both technical system design and societal impact investigations.

Paras MandalView profile

Professor

• Electric Power Systems
• Renewable Energy Integration
• Machine Learning Applications
• +7 more

Dr. Paras Mandal is a Professor of Electrical and Computer Engineering (ECE) and Director of the Power & Renewable Energy Systems (PRES) Laboratory at the University of Texas at El Paso (UTEP). His research focuses on electric power systems, renewable energy integration, machine learning applications in smart grids, and cyber-security of power infrastructure. He has published over 200 articles and received awards such as the IEEE Best Papers Award and IEEJ Young Engineer Award. Mandal is a Senior Member of IEEE and holds leadership roles in IEEE Power and Energy Society (PES), including past Chair of IEEE PEEC Award Subcommittee and Secretary of IEEE New Product Development (NPD) Committee. His work is funded by NSF, DOE, DoEd, and industry, including co-leading the NSF-ERC-ASPIRE Power Thrust. Education & Professional Background: While specific educational details are not provided, his academic career demonstrates deep expertise in power systems engineering. He has pioneered initiatives in curriculum development, integrating emerging technologies like dynamic wireless power transfer (DWPT) and smart grid education through NSF-funded programs. Research Interests: Mandal’s work bridges theoretical advancements and practical applications in resilient energy systems. Key areas include: Optimization of microgrid operations under extreme weather Cyber-security protocols for inverter-based microgrids Machine learning-driven demand forecasting for EV charging infrastructure Resilience assessment of power distribution networks Integration of small modular reactors (SMRs) into distribution grids Articles Trends: Recent publications emphasize: Application of Model Predictive Control (MPC) for grid optimization Impact of EVs on urban power distribution systems Cyber-physical security of distributed energy resources (DERs) Hybrid deep learning models for renewable energy forecasting Awards & Recognition: Beyond his technical accolades, Mandal’s leadership in IEEE committees and his role in shaping energy education through concept map evaluations (NSF-funded) highlight his dual focus on innovation and pedagogy. Grants & Labs: As PRES Lab Director, he oversees projects funded by NSF-ERC-ASPIRE and collaborations like the $2.5M UTEP-El Paso Electric partnership for green energy research. His labs develop tools like RAPSim software for DER-integrated grid education. Future Work: Prioritizes: Smart grid resilience against cyber-physical threats Large-scale EV in-motion charging infrastructure Decarbonization strategies for urban energy systems

Audrey RupleView profile

Associate Professor

• One Health
• Veterinary Epidemiology
• Comparative Biomedical Sciences
• +3 more

Audrey Ruple, DVM, MS, PhD, DACVPM, MRCVS, is an Associate Professor in Quantitative Epidemiology at the Department of Population Health Sciences, Virginia-Maryland College of Veterinary Medicine, Virginia Tech. She also serves as the Metcalf Professor of Veterinary Medical Informatics and Program Director of the Biomedical and Veterinary Sciences Graduate Program. Her research focuses on One Health, the intersection of human, animal, and environmental health, with a particular emphasis on cancer and aging in companion dogs as models for improving human and animal health outcomes. Dr. Ruple has held academic positions at Purdue University (2015–2021) before joining Virginia Tech in 2021. Education includes a PhD in Cell and Molecular Biology (2014) and DVM (2008) from Colorado State University, alongside an MS in Epidemiology (2011) and BS in Microbiology (2002). Board-certified by the American College of Veterinary Preventive Medicine (2013) and a member of the Royal College of Veterinary Surgeons, she actively contributes to professional organizations like the American College of Veterinary Preventive Medicine and the American College of Epidemiology. Her research integrates epidemiological methods and One Health principles to address complex health challenges. Notable work includes the Dog Aging Project, a large-scale initiative leveraging canine models to study aging mechanisms and environmental health impacts. Key areas include cancer biology, environmental contaminant exposure, and the role of diet and lifestyle in canine health. Dr. Ruple’s publications emphasize predictive modeling using veterinary data to inform human public health strategies and advancing evidence-based veterinary practices. Professional memberships span veterinary epidemiology, public health, and cancer research organizations. Her academic leadership includes roles in graduate program direction and cross-disciplinary initiatives bridging animal and human health research.

• Artificial Intelligence
• Machine Learning
• Computational Game Theory
• +5 more

Luis Ortiz is an Associate Professor in the Department of Computer and Information Science at the University of Michigan's College of Engineering and Computer Science. He holds a Ph.D. in Computer Science from Brown University and completed postdoctoral training at MIT and the University of Pennsylvania. His research bridges computational game theory, artificial intelligence, and probabilistic modeling with applications in autonomous systems, economics, and complex networks. Key focus areas include: Multi-agent reinforcement learning for autonomous driving Graphical models and game-theoretic equilibria Strategic behavior in networked systems Probabilistic inference and machine learning Recent publications explore reinforcement learning for highway automation and variational methods in probabilistic modeling. Dr. Ortiz has led NSF-funded projects including RI:Small:Collaborative Research:Influence Games and CAREER grants on the integration of graphical models and game theory.